mutation profiling of colorectal cancer for kras,...

i

Mutation profiling of colorectal cancer for KRAS, BRAF, NRAS

and PIK3CA genes in Indian patient cohort

by

Harshali Anant Patil

(B.E. Biotechnology)

Submitted in fulfillment of the requirements for the degree of

Doctor of Philosophy

Deakin University

January 2017

sfol

Retracted Stamp

iv

Dedicated to the Almighty and to all patients suffering from Colorectal Cancer

v

Acknowledgements

It is a pleasant task to express my thanks to all those who contributed in many

ways to the success of this study and made it an unforgettable experience for me.

At this moment of accomplishment, I gratefully acknowledge Mr. K V

Subramaniam, President of Reliance Life Sciences Pvt Ltd (RLS), for giving me

this opportunity to pursue higher degree program while working in RLS. He has

always been very supportive and has always encouraged employees to further

their academic and professional aspirations.

I would like to express my sincere gratitude to my Principal Supervisor Prof. Colin

J. Barrow, Alfred Deakin Professor and Chair In Biotechnology, School of Life &

Environmental Sciences, for his invaluable guidance and mentorship.

I would like to thank my Co-Principal Supervisor Dr. Rupinder Kanwar, Senior

Research Fellow, School of Medicine, for all the support that she provided in these

years. I really admire her zeal for perfection. Her enthusiasm and drive for best

scientific research has always motivated me and has helped me grow in my

scientific career. I feel very fortunate to have you as my mentor in this PhD journey.

My sincere thanks to Prof. Jagat Kanwar, Professor In Nanomedicine, School of

Medicine, for giving me scientific advises and the feedback on the research work.

I would also like to thank Dr. Shailaja Gada Saxena, Head, Molecular Medicine

(MME). I have been associated with Molecular Medicine group for almost 10 years

and it has been a wonderful journey. Since the time she took up this responsibility

vi

of heading Molecular Medicine, she has always provided a constant support,

encouragement and valuable suggestions, which made my journey through

doctoral program a memorable one.

Dr. Arnab Kapat, my mentor and Director, Reliance Institute of Life Sciences, thank

you so very much for the belief you had in me and the encouragement you have

given me over the last few years will stay with me. Thank you for all the scientific

advises and prompt feedbacks given on the research work. I would like to specially

thank you for the care and guidance. My journey in these last four years under your

mentorship for PhD will be cherished for all the good times spent.

I am also thankful to Ms. Anuradha, Ms. Gayathri and Ms. Ruby from Deakin India

office for their support. I am also grateful to Ms. Helen Woodall, partnerships

coordinator of Deakin-India, for making me comfortable during my stay in Deakin.

Most of the results described in this thesis would not have been obtained without a

close collaboration with few hospitals. I owe a great deal of appreciation and

gratitude to Ruby Hall Clinic, Pune, and Hinduja Hospital for helping me with the

clinical details required.

I would like to thank all MMEiets, my colleagues, Kiran, Vrunda, Smita, Deepti and

team, Sandeep and Kundanben and team specially Mr. Ganesh. Dr. Rajesh Korde,

Dhanashree, Dr. Sonia, Dr. Harshal and Asha for the histopathology work. I would

also like to thank Rehana, Mitali, Tejas Mehta and team for helping me out in

completing my PhD.

vii

Also, a big thanks goes to all ex MMEites including Darshana and Akriti for their

constant support.

My heartfelt thanks to my friends who filled my life with love and laughter –

Sheena, Urvashi, Sanjukta, Dolly, Leena, Shivani, Pavani, Kavya and Moti. The

days spent with all of you would always be cherished in my life.

Above all, I would like to thank my husband, Anant, for his personal support and

great patience at all times. “Thank you for being there always!” My parents -

mummy and papa, my in-laws-aai and dada who supported me throughout these

years, my brother Dharmu and all my family members who have given me their

unequivocal support throughout, as always, for which my mere expression of

thanks likewise does not suffice. And finally my daughter Anushree, whose smiling

face has always encouraged me.

Harshali Anant Patil

02nd January 2017

viii

Table of Content

Acknowledgements ......................................................................................................... v

Table of Content ........................................................................................................... viii

List of Publications ........................................................................................................ xii

List of Abbreviations ...................................................................................................... xv

List of Tables ..................................................................................................................xx

List of Figures ............................................................................................................... xxii

Abstract ........................................................................................................................ xxv

Chapter 1 Introduction .......................................................................................... 1 1.1 Research Question ......................................................................................................... 3 1.2 Hypotheses .................................................................................................................... 3 1.3 Thesis Outline ................................................................................................................ 5

Chapter 2 Literature Review .................................................................................... 6 2.1 Introduction .................................................................................................................. 6 2.2 Epidemiology ............................................................................................................... 11

2.2.1 Colorectal cancer incidence varies globally ................................................................. 11 2.2.2 Mortality of colorectal cancer ..................................................................................... 13

2.3 Pathophysiology .......................................................................................................... 18 2.3.1 Hyperplastic polyps ...................................................................................................... 18 2.3.2 Adenomatous polyps .................................................................................................... 19 2.3.3 Molecular abnormalities in pathogenesis of CRC ........................................................ 21 2.3.4 WNT pathway ............................................................................................................... 25 2.3.5 Chromosomal Instability (CIN) ..................................................................................... 26 2.3.6 Microsatellite Instability .............................................................................................. 28 2.3.7 CpG island methylator phenotype (CIMP) pathway .................................................... 29 2.3.8 Clinical staging .............................................................................................................. 31

2.4 Risk factors .................................................................................................................. 34 2.4.1 Age ................................................................................................................................... 37 2.4.2 Hereditary Factors ........................................................................................................... 37 2.4.3 Inherited Syndrome ......................................................................................................... 37 2.4.4 Inflammatory bowel disease (IBD) ................................................................................... 39 2.4.5 Life Style Factors .............................................................................................................. 39 2.4.6 Dietary factors .................................................................................................................. 40 2.4.7 Race/ Ethinicity ................................................................................................................ 41

2.5 Diagnosis ..................................................................................................................... 42 2.6 Treatment strategies ................................................................................................... 44

2.6.1 Surgery ......................................................................................................................... 44 2.6.2 Conventional Chemotherapy ........................................................................................ 46

ix

2.6.1.1 5-Fluorouracil / Leucovorin .................................................................................................. 47 2.6.1.2 Oxaliplatin and Irinotican ..................................................................................................... 47 2.6.1.3 Capecitabine ........................................................................................................................ 50

2.6.2 Therapy options for colon cancer ................................................................................ 51 2.6.3 Therapy options for rectal cancer ................................................................................ 52 2.6.4 Treatment options for advanced disease i.e. mCRC .................................................... 54 2.6.5 Novel cytotoxic and targeted biologic therapeutics .................................................... 55

2.7 Targeting Vascular Endothelial Growth Factor (VEGF) .................................................. 57 2.7.1 Bevacizumab-Anti VEGF monoclonal antibody ............................................................ 60 2.7.2 Aflibercept- a novel antiangiogenic fusion protein ...................................................... 62 2.7.3 Regorafenib-small molecule inhibitor .......................................................................... 63 2.7.4 Identification of predictive biomarkers for anti-angiogenic agents: A priority for mCRC

management ............................................................................................................................. 64 2.8 Epidermal growth factor receptor targeting agents ...................................................... 66

2.8.1 Cetuximab ..................................................................................................................... 67 2.8.2 Panitumumab ............................................................................................................... 73 2.8.3 Predictive biomarkers for anti-EGFR agents ................................................................. 75

2.8.3.1 KRAS ..................................................................................................................................... 75 2.8.3.2 BRAF ..................................................................................................................................... 79 2.8.3.3 NRAS ..................................................................................................................................... 80 2.8.3.4 PIK3CA .................................................................................................................................. 81 2.8.3.5 PTEN ..................................................................................................................................... 82 2.8.3.6 TP53 ..................................................................................................................................... 84

2.9 Predictive and prognostic biomarkers in development ................................................ 85 2.9.1 Micro RNAs .................................................................................................................. 85 2.9.2 Cell free nucleic acid .................................................................................................... 85 2.9.3 Circulating tumor cells ................................................................................................. 86 2.9.4 Protein Biomarkers ...................................................................................................... 86 2.9.5 Cancer stem cells.......................................................................................................... 87

2.10 Molecular Pathology Epidemiology: Emerging discipline to help in optimizing disease

prevention and treatment strategies ..................................................................................... 93

Chapter 3 Materials and Methods ......................................................................... 98 3.1 Study Population ......................................................................................................... 98 3.2 Methods ...................................................................................................................... 98

3.2.1 Haematoxylin and eosin (H&E) Analysis ..................................................................... 102 3.2.1.1 Principle .............................................................................................................................. 102 3.2.1.2 Method ............................................................................................................................... 102

3.2.2 DNA extraction ............................................................................................................ 104 3.2.2.1 Principle .............................................................................................................................. 104 3.2.2.2 Methodology ...................................................................................................................... 105

3.2.3 Primer selection for Polymerase Chain Reaction (PCR) .............................................. 106 3.2.3.1 PCR Assay Optimization ..................................................................................................... 107

3.2.4 Detection of PCR products by agarose gel electrophoresis ........................................ 115 3.2.4.1 Method .............................................................................................................................. 115

3.2.5 Sequencing of PCR products for Detection of mutations ............................................ 115

x

3.2.5.1 Principle .............................................................................................................................. 115 3.2.5.2 Method ............................................................................................................................... 116

Chapter 4 Assay Validation .................................................................................. 119 4.1 Validation parameters: ............................................................................................... 121

4.1.1 Specificity .................................................................................................................... 121 4.1.2 Sensitivity .................................................................................................................... 122 4.1.3 Repeatability ............................................................................................................... 122 4.1.4 Reproducibility ............................................................................................................ 122

4.2 Validation of KRAS exon 2 and exon 3 (codons 12, 13 and 61) mutation detection assay

123 4.2.1 Specificity .................................................................................................................... 123 4.2.2 Sensitivity .................................................................................................................... 127 4.2.3 Reproducibility ............................................................................................................ 131 4.2.4 Repeatability ............................................................................................................... 134

4.3 Validation of BRAF exon 15 codon 600 mutation detection assay ............................... 146 4.3.1 Specificity .................................................................................................................... 146 4.3.2 Sensitivity ................................................................................................................... 147 4.3.3 Reproducibility ............................................................................................................ 149 4.3.4 Repeatability ............................................................................................................... 151

4.4 Validation of NRAS exon 2 and exon 3 (codons 12, 13 and 61) mutation detection assay

156 4.4.1 Specificity .................................................................................................................... 156 4.4.2 Sensitivity .................................................................................................................... 158 4.4.3 Reproducibility ............................................................................................................ 159 4.4.4 Repeatability ............................................................................................................... 163

4.5 Validation of PIK3CA exon 9 and exon 20 (codon 545 and codon 1047) mutation

detection assay .................................................................................................................... 171 4.5.1 Specificity .................................................................................................................... 171 4.5.2 Sensitivity .................................................................................................................... 171 4.5.3 Reproducibility ............................................................................................................ 172 4.5.4 Repeatability ............................................................................................................... 174

4.6 Laboratory methods used for detection of mutations ................................................. 177

Chapter 5 ..................................................................................................................... 180

Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological

features of CRC patients .............................................................................................. 180 5.1 Introduction ............................................................................................................... 180 5.2 Methodology .............................................................................................................. 182 5.3 Clinicopathological characteristics of CRC samples ...................................................... 184

5.3.1 KRAS mutations .......................................................................................................... 191 5.3.2 BRAF ............................................................................................................................ 203 5.3.3 NRAS ........................................................................................................................... 205 5.3.4 PIK3CA ......................................................................................................................... 207

5.4 Discussion ................................................................................................................... 210

xi

5.4.1 KRAS ........................................................................................................................... 211 5.4.2 BRAF ............................................................................................................................ 213 5.4.3 NRAS ........................................................................................................................... 215 5.4.4 PIK3CA ........................................................................................................................ 215

Chapter 6 Survival Analysis ................................................................................. 225 6.1 Introduction ............................................................................................................... 225 6.2 Patients ...................................................................................................................... 226 6.3 Statistics ..................................................................................................................... 227 6.4 Results: ....................................................................................................................... 228

6.4.1 Estimation of survival ................................................................................................. 232 6.4.2 Censored observations ................................................................................................ 235 6.4.3 Actuarial Life Table ..................................................................................................... 236 6.4.4 Kaplan – Meier Survival Curve .................................................................................... 238 6.4.4.1 Estimation of Progression free survival (PFS) and Overall Survival (OS) .................... 239

6.5 Discussion ................................................................................................................... 251

Chapter 7 Summary and Future Work .................................................................. 258 7.1 Mutation Studies in 203 CRC patients ......................................................................... 259 7.2 Correlation of mutations in KRAS, BRAF, NRAS and PIK3CA genes with clinico-

pathological data for a 203 Indian CRC patient cohort .......................................................... 261 7.3 Correlation of clinico-pathological data with survival in Indian patient cohort ............ 262

Appendix ..................................................................................................................... 268 List of Chemicals .................................................................................................................. 270 List of Instruments ............................................................................................................... 272 List of Software’s ................................................................................................................. 274

References .................................................................................................................. 275

xii

List of Publications

H.A. Patil, S. Gada Saxena, C. J. Barrow, J.R. Kanwar, A. Kapat, R.K. Kanwar

(2016). Chasing personalized medicine dream through biomarker validation in

colorectal cancer. Drug Discovery Today (2017) 22:111-119. Impact Factor – 6.12

Conference Presentations

Poster Presentation

H.A. Patil, S.Gada Saxena, C. J. Barrow, R.K. Kanwar, J.R. Kanwar, A. Kapat

(2016).

203P - Molecular analysis of predictive biomarkers –KRAS, BRAF, NRAS and

PIK3CA in colon cancer and correlation of clinicopathological features with

mutation profiling and therapeutic response in Indian patient cohort. ESMO ASIA

2016, Singapore, December 16-19, 2016

Citation:Annals of Oncology (2016) 27 (suppl 9): doi: 10.1093/annonc/mdw581.036

H.A. Patil, C. J. Barrow, R.K. Kanwar, J.R. Kanwar, A. Kapat (2015).

168P - Clinicopathological correlation with mutation profiling of colorectal cancer

for KRAS, BRAF, NRAS and PIK3CA genes in Indian patient cohort. ESMO ASIA

2015, Singapore, December 18-21, 2015

Citation: Annals of Oncology (2015) 26 (suppl_9): 42-70. 10.1093/annonc/mdv523.

xiii

H.A. Patil, C. J. Barrow, R.K. Kanwar, J.R. Kanwar, A. Kapat (2015).

Molecular analysis of predictive biomarkers-KRAS, BRAF, NRAS and PIK3CA in

colon cancer and correlation with clinicopathological features in Indian patient

cohort. DIRI International Symposium 2015, New Delhi, India, December 6-9, 2015

Conferences Attended

DIRI International Symposium organized by TERI-Deakin, New Delhi, India, Nov

29 -31, 2012.

DIRI Workshop on “Special higher degree by research”, New Delhi, India,

November 5-6, 2014.

DIRI International Symposium on Translational Research, TERI Deakin NanoBio

Centre, Gurgaon, India, December 6 – 9, 2015.

Manuscript in Preparation

H.A. Patil, S. Gada Saxena, C. J. Barrow, J.R. Kanwar, A. Kapat, R.K. Kanwar.

Mutation profiling of colorectal cancer for KRAS, BRAF, NRAS and PIK3CA genes

in Indian patient cohort.

Travel Grant Awards

1. ESMO ASIA 2015, Singapore, December 18-21, 2015 - Availed

2. EMBL PhD symposium, Life by Numbers, November 17-20, 2016 – Not

availed

3. ESMO ASIA 2016, Singapore, December 16-19, 2016 - Availed

xiv

Other publications

Patil, H., Korde, R. & Kapat, A. KRAS gene mutations in correlation with

clinicopathological features of colorectal carcinomas in Indian patient cohort.

Med. Oncol (2013) 30: 617. doi:10.1007/s12032-013-0617-5. Impact Factor-2.5

xv

List of Abbreviations

5-FU 5-fluorouracil

A Adenine

AJCC American Joint Committee on Cancer

APC Adenomatous polyposis coli

app. Approximately

ATP Adenosine-5'-triphosphate

BRAF V-raf murine sarcoma viral oncogene homolog B1

BSA Bovine serum albumine

CAPOX Capecitabine in combination with oxaliplatin

cDNA Complementary DNA

C Cytosine

CEA Carcinoembryonic antigen

CI Confidence interval

CIN Chromosomal instability

COSMIC Catalogue of somatic mutations in cancer

CRC Colorectal cancer

xvi

CR Complete response

DCC Deleted in colorectal carcinoma

DMSO Dimethyl sulfoxide

DNA Deoxyribonucleic acid

dsDNA Double-stranded deoxyribonucleic acid

DSF Disease-free survival

FAP Familial adenomatous polyposis

FFPE Formalin-fixed paraffin-embedded

FOBT Fecal occult blood test

FOLFIRI Leucovorin (folinic acid), 5-FU, and irinotecan

FOLFOX Leucovorin (folinic acid), 5-FU, and oxaliplatin

G Guanine

GAPDH Glyceraldehyde 3-phosphate dehydrogenase

gDNA Genomic DNA

GDP Guanosine diphosphate

GTP Guanosine triphosphate

GTPase Hydrolyze guanosine triphosphate (GTP)

xvii

H&E Haematoxylin and eosin

HER-2 Human epidermal growth factor receptor 2

HNPCC Hereditary non-polyposis colorectal cancer

IFL Irinotecan + 5-Fluorouracil/Leucovorin

IHC Immunohistochemistry

KRAS Kirsten rat sarcoma viral oncogene homolog

LNR Lymph node ratio

LOH Loss of heterozygosity

mAb Monoclonal antibody

MAPK Mitogen-activated protein kinase

mCRC Metastatic colorectal cancer patients

MEK Mitogen-activated protein kinase

MGB Minor groove binder

miR/miRNA microRNA

MLH1 MutL homolog 1

MMR Mismatch repair system

mRNA Messenger RNA

xviii

MSI Microsatellite instability

NR Non-responders

NRAS Neuroblastoma RAS viral (v-ras) oncogene homolog

Nt Nucleotide

OR Odds ratio

ORR Overall response rate

OS Overall survival

PCR Polymerase chain reaction

PFS Progression-free survival

PIK3CA Phosphoinositides-3-kinase catalytic alpha polypeptide

PR Partial response

PTEN Phosphatase and tensin homolog

R – RESICT Responder response evaluation criteria in solid tumors

RNA Ribonucleic acid

RT Room temperature

SNPs Single nucleotide polymorphisms

UBC Ubiquitin C

xix

T Thymine

TGF-ß Transforming growth factor beta

TGFBR2 Transforming growth factor beta receptor II

TP53 Tumour protein 53

VEGFA Vascular endothelial growth factor A

WT Wild-type

XELIRI A combination of capecitabine and irinotecan

xx

List of Tables

Table 2.1: Summary of risk factors associated with colorectal cancer. ............................................35

Table 2.2: Summary of clinical trials undertaken for bevacizumab. ..................................................61

Table 2.3: Summary of clinical trials undertaken for cetuximab. .......................................................69

Table 2.4: Summary of Biomarker based studies in CRC (Patil et al., 2016) ...................................88

Table 2.5: Biological agents in clinical trials ......................................................................................92

Table 3.1: Master mixture for first round of PCR for KRAS, BRAF, NRAS and PIK3CA. .............. 108

Table 3.2: Master mixture preparation for nested PCR for KRAS, BRAF, NRAS and PIK3CA. .... 109

Table 3.3: KRAS exons 2 and 3 mutation detection assay. ........................................................... 109

Table 3.4: BRAF exon 15 mutation detection assay- First round PCR. ......................................... 110

Table 3.5: BRAF exon 15 mutation detection assay- Nested PCR. ............................................... 110

Table 3.6 NRAS exon 2 mutation detection assay- First round and Nested PCR. ........................ 110

Table 3.7: NRAS exon 3 mutation detection assay- First round PCR. .......................................... 111

Table 3.8: NRAS exon 3 mutation detection assay- Nested PCR. ................................................ 111

Table 3.9: PIK3CA exon 9 mutation detection assay- First round and Nested PCR. .................... 111

Table 3.10: PIK3CA exon 20 mutation detection assay- First round PCR. .................................... 112

Table 3.11: PIK3CA exon 20 mutation detection assay- Nested PCR .......................................... 112

Table 4.1: Loading pattern for Figures 4.3A and 4.3B. .................................................................. 125

Table 4.2: Loading pattern on agarose gel electrophoresis for figures 4.5 and 4.6 ...................... 129

Table 4.3: Results of samples analysed by two different analysts for reproducibility. ................... 132

Table 4.4: Results of samples analysed by two different analysts for repeatability. ...................... 135

Table 4.5: Results of samples analyzed for sensitivity parameter for BRAF assay. ...................... 147

Table 4.6: Results of clinical samples analyzed by two different analysts for reproducibility. ....... 149

Table 4.7: Results of clinical samples analyzed by two different analysts for repeatability. .......... 151

Table 4.8: Results of clinical samples analysed by two different analysts for reproducibility for

NRAS codon 12/13 ................................................................................................................. 159


NRAS codon 61. ..................................................................................................................... 161

Table 4.10: Results of clinical samples analyzed by two different analysts for repeatability. ........ 164

Table 4.11: Results of clinical samples analysed by two different analysts for repeatability for NRAS

codon 61. ................................................................................................................................. 166


PIK3CA. ................................................................................................................................... 172

xxi

Table 4.13: Results of clinical samples analysed by two different analysts for repeatability for

PIK3CA. ................................................................................................................................... 174

Table 5.1: Frequency of mutations in Catalogue of Somatic Mutations In Cancer (COSMIC)

database. ................................................................................................................................. 181

Table 5.2: The clinicopathological characteristics of Indian colorectal cancer samples (n=203). . 184

Table 5.3: Spectrum of KRAS mutations in 203 CRC cases determined by Sanger sequencing. 192

Table 5.4: Correlation of mutation frequency in KRAS gene with clinicopathological factors of

colorectal cancer patients (n=203). ......................................................................................... 201

Table 5.5: Clinicopathological characteristics’ correlation with mutation frequency in BRAF gene in

colorectal cancer patients (n=203). ......................................................................................... 204

Table 5.6: Correlation of mutation frequency in NRAS gene with clinicopathological characteristics

in CRC cases (n=203). ............................................................................................................ 206

Table 5.7: Correlation of mutation frequency in PIK3CA gene with clinicopathological characteristics

in colorectal cancer cases (n=203). ........................................................................................ 208

Table 5.8: Multivariate Logistic Regression Analysis for the correlation between gene mutations and

clinicopathological features in Indian CRC patients (n=203) .................................................. 209

Table 5.9: KRAS, NRAS, BRAF and PIK3CA mutation frequencies in reported studies worldwide.

................................................................................................................................................ 217

Table 6.1a: Basic Data of CRC patients (n=30) ............................................................................. 228

Table 6.1b: Clinicopathological Data of CRC patients (n=30) ....................................................... 230

Table 6.2: Actuarial Life Table for patients (n=30) ......................................................................... 236

Table 6.3: Univariate and Multivariate analysis of PFS and OS .................................................... 245

Table 6.4: Patient, disease and treatment characteristics (n=30). ................................................ 247

xxii

List of Figures

Figure 1.1: Schematic outline of Aims and Objectives of the study. ...................................................4

Figure 2.1: Global cancer incidence and mortality according to Globocan 2012(Ferlay, 2012). ........7

Figure 2.2: Gender wise global cancer incidence and mortality according to Globocan 2012(Ferlay,

2012). ...........................................................................................................................................8

Figure 2.3: Increasing trend of cancers in India (Image source ICMR(Rath and Gandhi, 2014)). .....9

Figure 2.4: Cancer prevalence in metropolitan cities of India (Image source: Marimutthu et al, 2008

(Marimuthu, 2008)). ....................................................................................................................10

Figure 2.5: Age standardized Incidence and Mortality of colorectal cancer in men and women per

100,000 across geographical zones (Kupiers et al, Primer, 2015). ...........................................16

Figure 2.6A: Incidence rates of colorectal cancer. Data according to (Ferlay, 2012).......................16

Figure 2.6B: Incidence rates of colorectal cancer in US. Data according to (Jemal et al., 2010b). .17

Figure 2.7A: Mortality Rates of colorectal cancer. Data according to (Ferlay et al., 2010). .............17

Figure 2.7B: Mortality rates of colorectal cancer in US. Data according to (Jemal et al., 2010b). ...18

Figure 2.8: Diagrammatic representation of hyperplastic and adenomatous polyps. .......................20

Figure 2.9: Schematic overview of molecular abnormalities in pathogenesis of colorectal cancer

(Image source: (Patil et al., 2016). .............................................................................................21

Figure 2.10: Events in transformation of serrated polyps to adenocarcinomas. ...............................23

Figure 2.11: The WNT pathway: Image source: Reya T et al, Nature 2005(Reya and Clevers,

2005). .........................................................................................................................................24

Figure 2.12: TNM staging as maintained by American Joint Committee on Cancer (AJCC) and

Union for International Cancer Control (UICC) (Source-(Edge and Compton, 2010). ...............32

Figure 2.13: Schematic overview of incidence of colorectal cancer. ................................................33

Figure 2.14: Advantages and disadvantages of different screening modalities used for diagnosis of

CRC (Image source- Kuiper’s et al., 2013). ...............................................................................43

Figure 2.15: Schematic overview of advances made in treatment strategies for colorectal cancer. 44

Figure 2.16: Adjuvant therapy options for colon cancer patients with no metastasis i.e Mo as per

Indian Council for Medical Research (Sirohi et al., 2014). .........................................................52

Figure 2.17: Neo adjuvant and Adjuvant therapy options for rectal cancer with no metastasis i.e Mo

as per Indian Council for Medical Research (Sirohi et al., 2014). ..............................................53

Figure 2.18: EGFR and VEGF Signaling Pathways in CRC development and tumor survival. .......56

Figure 2.19: Activation of RAS pathway. ..........................................................................................76

Figure 2.20: Association of anti-EGFR therapy and KRAS mutations. .............................................77

Figure 2.21: Estimated Response Rate to EGFR inhibitors in Western population with activating

mutations in KRAS, BRAF, NRAS and PIK3CA Data according to -(Frattini et al., 2007). .......83

xxiii

Figure 2.22: Difference between traditional epidemiological studies and Molecular pathologic

epidemiology (MPE) (Image Source-Ogino et. al., Gut, 2011). .................................................94

Figure 2.23: Three approaches of MPE (Image source-Ogino et.al., Gut, 2011). ............................95

Figure 3.1: H&E stained tissue samples ........................................................................................ 104

Figure 4.1: Components of Assay Validation. ................................................................................ 119

Figure 4.2: Key assay considerations (Image Source-Steven Anderson, Expert Rev Mol Diagn,

2011). ...................................................................................................................................... 120

Figure 4.3A and 4.3B: Specificity experiment for KRAS mutation detection assay. ..................... 124

Figure 4.4: Clustal W for KRAS exon 2 codon 12 and 13 for specificity parameter. ..................... 126

Figure 4.5: KRAS Sensitivity 1 ....................................................................................................... 128

Figure 4.6: KRAS Sensitivity 2 ....................................................................................................... 128

Figure 4.7: Clustal W for KRAS exon 2, codons 12 and 13 for sensitivity parameter. .................. 130

Figure 4.8: Clustal W for KRAS for reproducibility parameter. ...................................................... 133

Figure 4.9: Clustal W for KRAS for repeatability parameter. ......................................................... 136

Figure 4.10: Agarose gel electrophoresis of exon2 and exon3 of KRAS gene after optimization of

the assay. ................................................................................................................................ 137

Figure 4.11: Electropherogram of KRAS exon 2 with no mutations detected. .............................. 138

Figure 4.12: Electropherogram of KRAS exon 2 with codon 12 mutation (GGT;GAT) Glycine to

Aspartic acid substitution. ....................................................................................................... 139

Figure 4.13: Electropherogram of KRAS exon 3 with no mutations detected. .............................. 140

Figure 4.13 A: KRAS-G12D Original-50% ..................................................................................... 142

Figure 4.13 B: KRAS-G12D 25% ................................................................................................... 143

Figure 4.13 C: KRAS-G12D 20% ................................................................................................... 144

Figure 4.13 D: KRAS-G12D 10% ................................................................................................... 145

Figure 4.14: Clustal W for BRAF for specificity parameter ............................................................ 146

Figure 4.15: Clustal W for BRAF for sensitivity parameter. ........................................................... 148

Figure 4.16: Clustal W for BRAF for reproducibility parameter. ..................................................... 150

Figure 4.17: Clustal W for BRAF for repeatability parameter. ....................................................... 152

Figure 4.18: Agarose gel electrophoresis of exon15 of BRAF gene after optimization of the BRAF

assay ....................................................................................................................................... 153

Figure 4.19: Electropherogram of BRAF exon 15 with no mutations detected. ............................. 154

Figure 4.20: ClustalW of exon15 of BRAF gene after sequencing. ............................................... 154

Figure 4.21: Representative Agarose gel electrophoresis of NRAS gene exon 2. ........................ 156

Figure 4.22: ClustalW of NRAS gene after sequencing for specificity. .......................................... 157

Figure 4.23: ClustalW of NRAS gene after sequencing for reproducibility. ................................... 160

Figure 4.24: ClustalW of nras exon 3 gene after sequencing for reproducibility. .......................... 162

Figure 4.25: ClustalW of NRAS gene after sequencing for repeatability ....................................... 165

xxiv

Figure 4.26: ClustalW of NRAS gene after sequencing for repeatability ....................................... 167

Figure 4.27: Agarose gel electrophoresis of exon2 and exon 3 of NRAS gene after optimization of

the NRAS assay. ................................................................................................................... 168

Figure 4.28: ClustalW of exon 2 and 3 of NRAS gene after sequencing. ...................................... 169

Figure 4.29: ClustalW of PIK3CA gene after sequencing for reproducibility. ................................ 173

Figure 4.30: ClustalW of PIK3CA gene after sequencing for repeatability .................................... 175

Figure 4.31: Agarose gel electrophoresis of exon 9 and exon 20 of PIK3CA gene after optimization

of the assay. ............................................................................................................................ 176

Figure 4.32: Key features of different methodologies used for mutation analysis (Image Source-

Steven Anderson, Expert Rev Mol Diagn, 2011). ................................................................... 178

Figure 4.33: Comparison of properties of different methodologies used for mutation analysis. .... 179

Figure 5.1: Clinicopathological and demographic characteristics of the CRC patient cohort

employed in this study (n=203). .............................................................................................. 186

Figure 5.2: Distribution of CRC tumor samples according to the anatomic location. .................... 188

Figure 5.3: Overall mutation frequency rate of four genes (KRAS, BRAF, NRAS and PIK3CA) in

Indian CRC patient samples (n=203). ..................................................................................... 189

Figure 5.4: Venn Diagram showing overall mutation distribution in CRC patient samples (n=203).

................................................................................................................................................ 190

Figure 5.5: Representative Sequencing electropherograms of KRAS showing KRAS codon 12 and

codon 13. ................................................................................................................................. 193

Figure 5.6 (a-f): Representative Sequencing electropherograms of NRAS, BRAF and PIK3CA

showing Wild type and mutant sequences. ............................................................................. 197

Figure 6.1: The data of 30 patients aligned in order of their survival time. Red bars indicate the

dead patients and yellow ones indicate the alive patients. ..................................................... 233

Figure 6.2: Tree diagram for CRC patients (n=30). ....................................................................... 234

Figure 6.3: Tree diagram showing 30 CRC cases of which 13 are dead, 6 have lost follow up and

11 are alive. ............................................................................................................................. 235

Figure 6.4: Actuarial survival curve (n=30) .................................................................................... 237

Figure 6.5: Kaplan-Meier survival curve for CRC patients (n=30). ................................................ 238

Figure 6.6: Kaplan Meier plots of PFS and OS for CRC patients (n=30). ..................................... 239

Figure 6.7: Kaplan Meier plots for comparison between patients with tumor mutation or wild type

tumors. .................................................................................................................................... 248

Figure 6.8: Kaplan Meier plots for comparison between different therapy options. ....................... 249

xxv

Abstract

Colorectal cancer (CRC) is one of the leading causes of cancer mortality

worldwide. It is the most common of all gastrointestinal malignancies.

Unfortunately, most CRC tumors are benign, grow slowly and do not show

symptoms until they grow larger. Wide geographical, racial and ethnic differences

in incidence are observed for this type of cancer. In rapidly developing nations as

India, the incidence of CRC is 4.2 and 3.2 per 100,000 in males and females,

respectively, which is comparatively much lower compared with rates in developed

nations. Despite many advances in early diagnosis, surgical techniques, molecular

and therapeutic characterization made over the past decade, CRC still remains a

major health burden with high unmet medical, diagnostic and clinical needs.

The past decade has uncovered various new pathways in the development of

CRC. Several studies have been performed to identify the prognostic impact of

various clinico-pathological factors. The survival rates of CRC in Asian countries

are lower as compared to European and other Western developed countries. This

can be attributed mainly to late diagnosis and less awareness about the disease in

low socioeconomic populations.

Early detection and proper treatment are some of the key strategies for improving

overall survival. In this direction some improvement in the CRC patient survival rate

has been observed due to the combinatorial use of chemotherapeutics alone/and

or with targeted therapy. 5-Fluoro Uracil (5-FU) was the only drug used for

xxvi

decades for CRC treatment. With the use of combination drugs in chemotherapy

for example, 5-FU plus Leucovorin (LV), either with irinotican (FOLFIRI) and

oxaliplatin (FOLFOX), has led to an improvement in survival. Two FDA approved

monoclonal antibodies (MAb) targeted at epidermal growth factor receptor (EGFR),

the chimeric IgG1 MAb cetuximab and the fully humanized IgG2 panitumumab,

and a VEGF inhibitor bevacizumab have proven to be effective in combination with

chemotherapy or as single agent for the treatment of metastatic colorectal cancer

(mCRC). However, clinicians require accurate outcome prediction to adopt

appropriate therapeutic regime.

The efficacy of MAb is not consistent for every patient; some patients experience a

dramatic response to MAb, whereas others show no response. Accurate

biomarkers are not yet available for differentiating between responders and non-

responders, i.e. resistant population. V-Ki-ras2 Kirsten rat sarcoma viral oncogene

homolog (KRAS) has been a well-established predictive biomarker and it is

mandatory to test for KRAS gene mutations before giving cetuximab or

panitumumab to CRC patient. However, recent studies have demonstrated that not

all KRAS wild type tumors are responders of targeted therapy. Therefore, further

molecular characterization of the EGFR signaling pathway is required. Various

signaling molecules downstream of KRAS, such as v-Raf murine sarcoma viral

oncogene homolog B (BRAF) and Neuroblastoma RAS viral oncogene homolog

(NRAS), have been extensively studied to evaluate the effect of molecular

alterations in these genes on the response of therapy. The majority of these

studies have been carried in Western developed nations like USA, UK. Only three

xxvii

studies have been published on the Indian population, which have tried to evaluate

the rate of mutations in RAS-RAF or PIK3 signaling pathway in CRC.

Therefore, the current study is taken up to better understand the molecular

mechanism that may predict the efficacy of EGFR targeted mAb therapy in Indian

population. In this study the frequency of KRAS, BRAF, NRAS and PIK3CA was

studied by analysis of a larger cohort of CRC patients (n=203). To study the

mutation profiling in Indian CRC patients, these four molecular markers were

assessed in 203 histologically confirmed CRC patients and the correlation of

different clinicopathological factors was evaluated.

Formalin-fixed paraffin-embedded colorectal cancer tissues (n = 203) were

prospectively collected from Indian CRC patients from all over India between the

period from January 2013 to July 2016. Genomic DNA was isolated from tissue

sections using Invitrogen DNA extraction kit and screened for mutations in KRAS

(exon 2, exon 3), BRAF (exon 15), NRAS (exon 2, exon 3) and PIK3CA (exon 9,

exon 20) genes using automated DNA sequencing. Treatment data were also

studied on the basis of different clinico-pathological features of the tumor.

Correlation between these molecular signatures and clinico-pathological

characteristics was further studied so as to meet the unmet medical need for

personalized medicine and to help in improvisation of survival rates in the Indian

population.

In 36% of CRC cases, at least one mutation in the analyzed hot spot region was

observed. The prevalence of KRAS, BRAF, NRAS and PIK3CA mutations in the

Indian population was found to be 24%, 6%, 2% and 4%, respectively. In KRAS

xxviii

wild type (WT) population, approximately 12% of CRC patients have mutations in

NRAS, BRAF or PIK3CA. BRAF mutations were found to be mutually exclusive

with KRAS mutations (n=12/203). Coexistence of PIK3CA and KRAS mutations

was observed. The statistical analysis of clinico-pathological characteristics and

mutation showed significant association between KRAS mutations with age and

tumor differentiation (p<0.05).

In the case of BRAF, a more statistically significant correlation was observed in

moderately differentiating and poorly differentiating adenocarcinomas than in well-

differentiated adenocarcinomas. No significant association was observed between

any of the clinico-pathological features with NRAS or PIK3CA mutations. To study

the effect of clinicopathological features on survival 30 patients were

retrospectively analyzed. Overall survival of 37% with median survival of 25

months was observed for the Indian population, which is much lower than that

observed in developed nations, where the overall survival rates are 64%.

Significant association was observed in the survival rate of patients and grade of

tumor i.e. poorly differentiated adenocarcinoma (PDA). Significantly lower survival

rates were observed in PDA in comparison to moderately differentiated

adenocarcinoma (MDA).

The current experimental evidence of survival rates of CRC patients in relation to

different clinico-pathological features in the Indian population indicates that there

are factors which influence prognosis of colorectal cancer patients. However, life

expectancy has not increased drastically in recent years. This study further

indicates that KRAS, BRAF, NRAS and PIK3CA mutations in Indian CRC patients

xxix

occur at lower levels compared to those of Western developed nations. Our

findings are consistent with the published literature that differences in patients'

origins and related genetic backgrounds contribute to and even determine the

incidence rate of somatic mutations in candidate cancer genes. This study

supports the existing data that clinical and pathological characteristics of a tumour

are important determinants of prognosis in CRC patients worldwide.

Chapter 1. Introduction

1

Chapter 1 Introduction

Colorectal cancer (CRC) is a major health burden worldwide. It is one of the

leading causes of mortality from cancer, affecting both males and females. The

optimal approach to the diagnosis, management and treatment of CRC involves

multidisciplinary and integrated management practices. The field is rapidly

changing because of recent advancements in molecular characterization of CRC

and introduction of targeted therapy and diverse patient response, better biological

drugs and the effective combination regimes being employed for treatment (Patil et

al., 2016).

Two monoclonal antibodies (MAb) targeted at epidermal growth factor receptor

(EGFR), the chimeric IgG1 MAb cetuximab and the fully humanized IgG2

panitumumab, have proven to be effective in combination with chemotherapy or as

single agent for treatment of metastatic colorectal cancer (mCRC) (Cunningham et

al., 2004). However, the efficacy of MAb is not consistent for every patient; some

patients experience dramatic response to MAb, whereas others show no response.

In order to facilitate selection of mCRC patients who may benefit from anti-EGFR

MAbs treatments, a clear need for identifying predictive biomarkers that indicate

likelihood of response amongst potential recipients is currently widely appreciated

(Patil et al., 2016).

It has been reported that oncogenic activations of intracellular signaling pathways

downstream of EGFR, including the RAS-RAF- MAPK plays an important role in


2

generating resistance to anti-EGFR MAbs. To date, KRAS mutations have been

identified as a predictive marker of resistance to anti-EGFR MAbs in patients with

mCRC, and the use of anti-EGFR MAbs is now restricted to CRC patients with

wild-type KRAS (Karapetis et al., 2008).

In patients with KRAS wild type tumors, it remains unclear why a large number of

patients are still not responsive to the treatment. Other oncogenic mutations, such

as BRAF, NRAS and PIK3CA are found likely to be promising predictors for the

resistance in mCRC patients with wild-type KRAS (Moroni et al., 2005, Karapetis et

al., 2008).

Most of the studies that investigated the predictive value of KRAS, BRAF, NRAS,

and PIK3CA mutations were performed in western developed countries (De Roock

et al., 2011, Bozzao et al., 2011, Kawazoe et al., 2015). Little is known about the

relation of these biomarkers with the clinical outcomes of MAb treatment in Indian

patients with CRC.

Hence, this study was undertaken to investigate the status of KRAS, BRAF,

NRAS and PIK3CA mutations in Indian CRC patients, in order to clarify the

rate of mutations and to detect the correlation between mutations and

clinico-pathological factors.


3

1.1 Research Question

In the case of the Indian patient cohort, how different will the mutation profiling of

KRAS, BRAF, NRAS and PIK3CA genes involved in CRC, and how the correlation

with clinico-pathological data, impact the clinical outcome and survival?

1.2 Hypotheses

This project investigated the hypothesis that rate of mutations in critical CRC genes

involved in the tumor growth and survival i.e. KRAS, BRAF, NRAS and PIK3CA

differ according to racial differences.

It also investigated the hypothesis that different clinicopathological factors would

have impact on clinical outcome of the patient in the context of Indian patient

cohort. This hypothesis was addressed by pursuing the following aims and

objectives.

I. IA. Performing immunohistochemistry on CRC patient tumor samples

(n=203) to investigate the tumor percentage and also to grade and

categorize the tumors according to World Health Organization (WHO)

criteria.

IB. Screening all these colorectal cancer patient samples for

i) KRAS mutations in exon 1 (codons 12 and 13) and exon 2 (codon 61)

of KRAS gene.

ii) BRAF mutations in exon 15 (codon 600)


4

iii) NRAS gene mutations exon 1 (codons 12 and 13) and exon 2 (codon

61) and

iv) PIK3CA gene mutations exon 9 (codons 542 and 545) and exon 20

(codon 1047)

IC. Analysing the frequency of mutations in these four gene in Indian

CRC patients.

II. Investigate the possible correlation between the mutations observed and clinico-

pathological factors.

III. Examine the correlation between clinicopathological characteristics and survival

outcome in the Indian CRC patient cohort (n=30).

Figure 1.1: Schematic outline of Aims and Objectives of the study.


5

1.3 Thesis Outline

Chapter two discusses the literature review related to the thesis topic with main

focus on the diagnosis, biomarker analysis and treatment of metastatic CRC

(mCRC). Also, reviewed here is the biomarker validation for personalized medicine

and new candidate predictive and prognostic biomarkers requiring further

investigations in prospective trials.

Chapter three lists the materials and methodologies used in the experiments.

Chapter four describes the standardization and validation of the assays for KRAS,

BRAF, NRAS and PIK3CA mutation detection in CRC patient samples.

Chapter five investigates the correlation between the mutations observed in

KRAS, BRAF, NRAS and PIK3CA genes with the clinico-pathological data of the

patients.

Chapter six examines the correlation between clinicopathological characteristics

and survival outcome in the Indian CRC patient cohort.

Chapter seven provides the conclusion of the thesis and discusses future

perspectives.

Chapter 2. Literature Review

6

Chapter 2 Literature Review

2.1 Introduction

Cancer is one of the major cause of death worldwide. According to the World

Cancer report, cancer rates will increase by 50% from 2015 to 2020. There will

be 24 million people with cancer by 2035 (Stewart and Wild, 2016). According

to the World Health Organization (WHO), one in five men and one in six women

develop cancer before the age of 75 and one in eight men and one in twelve

women die of this disease. Hence, WHO has labelled it as global ‘tidal wave’ of

cancer (Siegel et al., 2015). Cancer is also a leading cause of economic loss.

The annual economic cost of cancer is around 1.16 trillion USD (Siegel et al.,

2015).

Cancer is mostly associated to Western developed countries. However, it has

been observed recently that less developed or developing countries have

shown a dramatically rise in new cancer cases. According to Globocan 2012,

the highest incidence of new cancer cases and highest mortality is observed in

Asian countries with 48% and 54.9% respectively (Ferlay, 2012) (Ferlay et al.,

2015). Figure 2.1 describes the global cancer incidence and mortality rate and

Figure 2.2 describes the prevalence of different types of cancer in males and

females.


7

Figure 2.1: Global cancer incidence and mortality according to Globocan

2012(Ferlay, 2012).

Figure Legend: According to the estimates of International agency for Research on

Cancer (IARC) 2012, global cancer incidence was estimated as 14.1 million cases with

highest percentage observed in Asian countries (48%) followed by European (24.4%),

American (20.5%), African (6%) and Oceania (1.1%) countries. Similarly, overall

mortality was estimated to be 8.2 million cases with highest percentage observed in

again Asian countries (54.9%) and lowest in Oceania (0.7%).


8

Figure 2.2: Gender wise global cancer incidence and mortality according to

Globocan 2012(Ferlay, 2012).

Figure Legend: According to the estimates of International agency for Research on

Cancer (IARC) 2012, in females, highest incidence of breast cancer (25.2%) is

observed followedy colorectum (9.2%) and lung (8.8%) whereas in males, lung cancer

(16.7%) cases are the highest followed by prostate (15%) and colorectum (10%).

Similarly, in case of mortality, breast cancer (14.7%) is leading cause of mortality in

females followed by lung (13.8%) and colorectum (9%) whereas in males lung cancer

(23.6%) is leading cause of mortality.

The prevalence of cancer in India is estimated to be around 2.5 million

with 800,000 new cases and 500,000 deaths due to cancer per year (Rath

and Gandhi, 2014). The cancer numbers in the Indian subcontinent are

increasing due to inadequate medical facilities, less patient awareness or

education and late detection. An increasing trend has been observed in cancer

incidence in the past few years as seen in the data compiled by the Indian

Council of Medical Research (ICMR) (Rath and Gandhi, 2014) (Figure 2.3)

Incidence and mortality number in thousands


9

Figure 2.3: Increasing trend of cancers in India (Image source ICMR(Rath and

Gandhi, 2014)).

Figure Legend: According to Indian Council of Medical Research (ICMR), increasing

trend is observed in cancer incidence in India. By 2020, it has been estimated that the

cancer incidence would reach to around 1100 thousand cases with increasing

incidence observed in females in comparison to males.

According to the study published by Marimutthu et al in 2008, the highest Indian

incidence of cancer is seen in Delhi (Figure 2.4) with the most frequently

observed cancers being lung, breast, stomach, oral, colon and rectum

(Marimuthu, 2008).


10

Figure 2.4: Cancer prevalence in metropolitan cities of India (Image source:

Marimutthu et al, 2008 (Marimuthu, 2008)).

Figure Legend: Amongst the five metropolitan cities highest incidence of cancer is

recorded in Delhi with around 14000 cases followed by Mumbai, Chennai, Bangalore

and Bhopal. Higher cancer incidence is observed in females in comparison to males

except in case of Bhopal.

Colorectal cancer is one of the leading causes of cancer mortality worldwide. It

is the most common of all gastrointestinal malignancies. It is the third most

common cancer in men and the second in women with 60% of cases occurring

in developed regions (Ferlay et al., 2010). Colorectal cancer develops as a

result of stepwise progression through several genetic alterations (Migliore et

al., 2011). The past decade has uncovered various new pathways in the

development of colorectal cancer. Despite this, an integrated view of the

genetic and genomic changes and their implication on colorectal tumorigenesis

remains to be obtained.


11

2.2 Epidemiology

Colorectal cancer is an important public health problem. In 2012, there were an

estimated total of 1,234,000 incident cases of colorectal cancer diagnosed

worldwide (Ferlay, 2012). It is the third most common cancer in men with

746,000 cases diagnosed and second in women with 614,000 cases diagnosed

worldwide (Ferlay, 2012). This cancer affects men and women almost equally

and accounts for 10% of total cancer burden in men and 9.2% in women (M.P

Curado, 2008). Age standardized rates of colorectal cancer incidence are

higher in men than in women (overall sex ratio of the ASR’s 1.4:1) (Ferlay,

2012). About 41% of new cases of colorectal cancer occur outside

industrialized countries, indicating that colorectal cancer is not just a disease of

the developed world. Rapidly increasing populations, changes in lifestyle

associated with economic development, together with the epidemiologic

transition of less developed countries is leading to increasing numbers of

colorectal cancer cases.

2.2.1 Colorectal cancer incidence varies globally

There is a large geographic variation in the global distribution of colorectal

cancer (World Cancer Research Fund et al., 2007). Countries with highest

incidence rates include Australia, New Zealand, Canada, the United States and

parts of Europe. The countries with the lowest risk include China, India and

parts of Africa and South America (Boyle and Ferlay, 2005). The incidence

rates vary up to 10-fold between countries with the highest rates and those with

the lowest rates (Center et al., 2009b). The incidence of colorectal cancer


12

ranges from more than 40 per 100,000 people in US, Australia, New Zealand,

and Western Europe to less than 5 per 100,000 in Africa and some parts of

Asia (World Cancer Research Fund et al., 2007). Slovakia had the highest

incidence rate in 2012 of colorectal cancer in men followed by Hungary and the

Czech Republic, with age standardized rates per 100,000 as 61, 56 and 54,

respectively. New Zealand, Israel, Denmark and Norway had the highest

incidence rates with 38, 36 and 34, respectively (Figure. 2.5 and 2.6A). The

incidence shows considerable variation among racially or ethnically defined

populations in multiracial or ethnic countries. For example, in 2009, 136,717

people in the US were diagnosed with colorectal cancer, including 70,223 men

and 66,494 women. African Americans have the highest incidence of colorectal

cancer, followed by White, Hispanic, Asian Pacific Islander and American

Indian/Inuits. Incidence rates reported (per 100,000) for all the races, Whites,

African American, Asia/Pacific Islander, American Indian/Inuit, Hispanic are

42.5, 41.3, 50.8, 33.7, 30.6, 36.4, (per 100,000), respectively (Figure. 2.6B)

(Jemal et al., 2010a). Although the majority of colorectal cancer incidence rates

in men are observed in Europe and North America, select registries in Asia also

have recorded two to four fold increases in the incidence of colorectal cancer in

men (Sung et al., 2005, Shin et al., 2012). However, among women colorectal

cancer incidence rates have declined in New Zealand and Australia but have

continued to increase in Israel (Center et al., 2009a). Lower rates of colorectal

cancer are observed in women when compared to men. This may be due to

behavioral differences, such as smoking rates and differing effects of obesity in

men and women (McCormack and Boffetta, 2011, Frezza et al., 2006).


13

Asia has greatest diversity with regards to incidence of colorectal cancer High

incidence rates are observed in China, Japan, South Korea, Singapore and

Israel (Shin et al., 2012, Yiu et al., 2004) comparatively much lower are

observed in Nepal, Bhutan and India (3.2, 3.5 and 6.1 per 100,000

respectively). These variations are due to different socioeconomic levels.

However, increased incidence rates are observed in the developing countries

which can be attributed to westernization, including consumption of high calorie

food and physical inactivity. Among ethnic groups in Asia, the incidence of

colorectal cancer is higher in Chinese (Lu et al., 2003, Yang et al., 2004). A

rapid increase in incidence has also been observed in Taiwan and Iran in recent

years (Moghimi-Dehkordi et al., 2008, Safaee et al., 2012, Su et al., 2012).

Iranian data suggests a younger age distribution compared to Western

countries, probably related to lifestyle changes (Mahmodlou et al., 2012,

Khayamzadeh et al., 2011, Malekzadeh et al., 2009)

In India, the annual incidence rate of colon cancer is 4.9 and that of rectal

cancer is 4.1 in men per 100,000 and in women it is 3.9 per 100,000. The

annual incidence rate in men, as recorded in 2013, is highest in

Thiruvananthapuram (state of Kerala) (4.9) followed by Bengaluru (state of

Karnataka) (3.9) and Mumbai (state of Maharashtra) (3.7) whereas in women, it

is highest in the state of Nagaland (5.1) (Rath and Gandhi, 2014).

2.2.2 Mortality of colorectal cancer

In 2013, around 771,000 people died globally of CRC. The age standardized

mortality rate is higher in men (10 per 100,000) as compared to women (6.9 per

100,000). The mortality rate is higher in developed countries (11.6 per 100,000)


14

than in less developed countries (6.6 per 100,000) which could be due to the

stage distribution at diagnosis (early diagnosis facility), awareness and

availability of population screening programs and level of health care in each

country. Western Africa has the lowest mortality rate (3.0 per 100,000) in the

world and highest is seen in Central and Eastern Europe (11.7 per 100,000)

(Figure 2.5) (Kuipers et al., 2015).

However, recently, mortality trend analysis has shown that the colorectal cancer

mortality has reduced in both men and women over the last several years.

Along with the US, Australia, New Zealand and Western Europe, colorectal

cancer mortality rates have also decreased in a few Asian (Japan and

Singapore) and Eastern European countries. The decreasing mortality rates

may be due to improvements in colorectal cancer treatments or to early

detection i.e by introduction of colonoscopy. Increased mortality rates of

colorectal cancer have been observed in South American countries, Russia,

Romania, China, Croatia, Latvia and Spain (Figure 2.7A). The increasing

mortality trends in these countries may be a reflection of increasing colorectal

cancer trends observed in economically transitioning countries. This increase

may also reflect lack of screening programs and changes in lifestyle, dietary

factors and urbanization (Kuipers et al., 2015).


15

Mortality rates for colorectal cancer also vary with race and ethnicity. There is a

six-fold variation in male mortality rates among different regions of the world

and a five-fold variation in female versus male rates. In the US in 2009 the

mortality rates reported (per 100,000, age adjusted to 2000 US standard

population) for all races, whites, African Americans, Asia/Pacific Islanders,

American Indian/Inuit and Hispanic are 15.7, 15.3, 22.1, 10.4, 12.6 and 12.1,

respectively (Figure 2.7B) (Jemal et al., 2010a). Among men, African Americans

are more likely to die of colorectal cancer followed by white, Hispanic,

American, Indian/Inuit and Asia/Pacific Islander. In women, African Americans

are more likely to die of colorectal cancer, followed by white, American

Indian/Inuit, Hispanic and Asia/Pacific Islander. In Singapore, where different

ethnic groups live in a similar environment, the incidence of colorectal cancer is

lower in Indian and Malay population, in comparison to Chinese (Yang et al.,

2003).

In 2012, mortality rates were the highest in Central and Eastern Europe (20 and

12 per 100,000 in males and females, respectively) and the lowest in middle

Africa and South central Asia (3-4 per 100,000) in both the sexes (Ferlay,

2012).


16

Figure 2.5: Age standardized Incidence and Mortality of colorectal cancer in

men and women per 100,000 across geographical zones (Kupiers et al, Primer,

2015).

Figure Legend: Highest incidence and mortality rates of colorectal cancer are

observed in Australia and New Zealand, Europe and Northern America with males

showing the higher rates in comparison to females.

Figure 2.6A: Incidence rates of colorectal cancer. Data according to (Ferlay,

2012).

Figure Legend: In males the highest incidence of CRC is observed in Slovakia where

as in females its observed in New Zealand

0

10

20

30

40

50

60

70

Inci

de

nce

Rat

e p

er

10

0 0

00

Males

Females


17

Figure 2.6B: Incidence rates of colorectal cancer in US. Data according to

(Jemal et al., 2010b).

Figure Legend: In U.S highest incidence of CRC is observed in African Americans and

lowest is observed in American Indians/ Alaska Native.

Figure 2.7A: Mortality Rates of colorectal cancer. Data according to (Ferlay et

al., 2010).

Figure Legend: The highest mortality rate is observed in males compared to females.

In Hungary and Slovakia the mortality rate is highest amongst males whereas in case

of females the mortality rate is highest in Hungary, New Zealand and Uruguay.

0 20 40 60 80

All

White

Black

Asia/Pacific Islander

American Indian/Alaska Native

Hispanic

Incidence Rate per 100 000

Races

Females

Males

Both

0

5

10

15

20

25

30

35

Mo

rtal

ity

Rat

e p

er

10

0 0

00

Males

Females


18

Figure 2.7B: Mortality rates of colorectal cancer in US. Data according to

(Jemal et al., 2010b).

Figure Legend: In U.S highest mortality rate of CRC is observed in African Americans

and lowest is observed in Asia/Pacific Islanders.

2.3 Pathophysiology

Colorectal cancer usually develops over a period of 10-15 years where most of

colorectal cancers are silent tumors, which grow slowly and do not produce

symptoms until they grow to a large size. Colorectal cancer genesis is a

multistep process involving accumulation of genetic and epigenetic changes

that transform normal glandular epithelial cells into invasive adenocarcinoma.

Colorectal cancer arises from mucosal colonic polyp. Some individuals are

more prone to develop polyp, especially those with personal or family history of

polyp and or colorectal cancer and those who carry specific genes for hereditary

forms of colorectal cancer (Lanza et al., 2011). The two most common

histologic types of polyps found in colon and rectum are described below.

2.3.1 Hyperplastic polyps

0 10 20 30

All

White

Black

Asia/Pacific Islander

American Indian/Alaska Native

Hispanic

Mortality Rate per 100 000

Races

Females

Males

Both


19

These polyps are non-dysplastic and have little potential for malignant

transformation (Figure 2.8). Histologically, these polyps contain an increased

number of glandular cells with decreased cytoplasmic mucous, but lack nuclear

hyperchromatism, stratification or atypia (Lanza et al., 2011). Recent evidence

shows that serrated variants similar in morphology to hyperplastic polyps, but

with malignant potential. These exhibit hyper methylation and arise primarily in

proximal colon and may account for one third of all colorectal cancers

(Torlakovic et al., 2003) .

2.3.2 Adenomatous polyps

These polyps most likely turn into colon cancer. These are considered pre-

cancerous. Adenomatous nuclei are mostly hyper chromatic, enlarged and

crowded together in a palisade manner as seen in Figure 2.8 (Lanza et al.,

2011). Adenomas are common; an estimated one-third to one-half of all

individuals will eventually develop one or more adenomas (Bond and Practice

Parameters Comm Amer, 2000). Adenomas are further classified as tubular,

villous or tubulovillious (Flejou, 2011). Adenomas usually grow on stalk and

resemble mushrooms (Figure 2.8). They tend to grow slowly over a decade or

more. The risk of adenoma developing into cancer increases with the size of

adenoma and the time for which it is actually growing. In the early stages the

abnormal cells are contained in the polyp, which can be removed. However, as

cancer cells grow and divide inside the polyp, they can eventually invade

nearby colon tissue and can grow into and beyond the walls of colon and

rectum (Arnold et al., 2005)


20

Figure 2.8: Diagrammatic representation of hyperplastic and adenomatous

polyps.

Figure Legend: Polyps are finger like projections growing from the lining of colon.

There are two types of polyps-Hyperplastic and adenomatous. Hyperplastic polyps are

pale, curved elevations and may rarely develop into cancer. However, adenomatous

polyps are hyper chromatic, large, cigar shaped, crowded and resemble mushroom.

These polyps turn into colon cancer.


21

2.3.3 Molecular abnormalities in pathogenesis of CRC

Dr. Bert Vogelstein first described that colorectal cancer and associated polyps

develop as a result of genetic mutations or other chemical modifications causing

inactivation or promotion of specific genes known as tumor suppressing genes

and tumor promoter genes (Fearon and Vogelstein, 1990) (Bosman, 2013). The

most common molecular abnormalities that have been identified in the

pathogenesis of colorectal cancer (Figure 2.9) (Patil et al., 2016).

Figure 2.9: Schematic overview of molecular abnormalities in pathogenesis of

colorectal cancer (Image source: (Patil et al., 2016).

Figure Legend: This figure depicts the stepwise genetic events that lead to

transformation of normal colonic epithelium to CRC. This progression requires

accumulation of stepwise genetic changes that drive metastasis. Abbreviations: APC

Adenomatous polyposis coli; COX, Cyclooxegenase; KRAS, v-ki-ras2 Kristen rat

sarcoma viral oncogene homologue; MLH1, MutL homolog 1; MSH2, MutS protein

homolog 2; DCC, Deleted in Colorectal Carcinoma; MGMT, O6-methylguanine DNA

methyltransferase; p16 INK4A, Inhibitors of Cyclin Dependent Kinase 4; SMAD,

Mothers against decapentaplegic homologue.

According to this classical model, majority of colorectal cancers arise from

aberrant crypt polyp which then converts into early adenoma with tubular or

tubulovillious histology. The early adenoma then progresses to advanced


22

adenoma with/without villous histology and finally becomes a colorectal cancer.

This process takes around 10-15 years except in patients with Lynch syndrome,

where it can progress more rapidly (Jones et al., 2008). However, the

understanding of molecular pathology has advanced in this past decade and

has led to several revisions of this theory. The original theory proposed that only

tubular and tubulovillious adenomas had the potential to progress towards

invasive adenocarcinomas, however, it has been observed that hyperplastic

polyps and sessile serrated polyps, accounting to around 5-10%, also have a

potential of malignant transformation. Serrated polyps arise from histological

and molecular events different from tubular polyps. These are classified into

three categories- hyperplastic polyps, sessile serrated polyps and traditional

serrated adenomas (Rex et al., 2012) (Figure 2.10). Serrated polyps that arise

in right colon (caecum, ascending colon and transverse colon) commonly show

Microsatellite Instability (MSI) and CpG island methylator phenotype (CIMP).

Polyps arising in left colon (descending colon, sigmoid and rectum) show MSS

phenotype with KRAS mutations and have attenuated form of CIMP (Bettington

et al., 2013).


23

Figure 2.10: Events in transformation of serrated polyps to adenocarcinomas.

Figure Legend: Hyperplastic polyps and sessile serrated polyps have potential of

malignant transformation. Serrated polyps arise from histological and molecular events

different from tubular polyps and are classified into three categories- hyperplastic

polyps, sessile serrated polyps and traditional serrated adenomas. Polyps in left colon

show presence of KRAS mutations, MGMT methylation and MSS/MSI-L status

whereas polyps in right colon show BRAF mutations along with presence of MLH-1

methylation and MSI-H state.


24

Figure 2.11: The WNT pathway: Image source: Reya T et al, Nature

2005(Reya and Clevers, 2005).

Figure Legend: WNT (Wingless plus int) pathway signaling pathway plays a vital role

in embryonic development, proliferation and differentiation. Beta-catenin is the main

component of WNT pathway. In normal cell, the level of beta-catenin is regulated by a

complex of proteins - actin and tumor suppressor Adenomatous polyposis coli (APC).

Mutation in APC leads to accumulation of APC, which travels to the nucleus and binds

to TCF wherein it activates the genes. This process is considered as trigger for

transforming event in colon cancer. Colorectal cancer is initiated by mutations in WNT

signaling pathway and then progress upon deregulation of other signaling pathways

including-RAS-RAF-MAPK, TGFB and PIK3-AKT pathway (Grady and Pritchard,

2013).


25

2.3.4 WNT pathway

The most common initiator of colorectal cancer is aberrant activation of WNT

(Wingless plus int) pathway. Beta-Catenin, a major mediator of WNT pathway,

is a membrane associated protein with function of regulation of cellular

adhesion (Figure 2.11). In the absence of Wnt ligand, cytoplasmic beta-catenin

forms a multiprotein complex with two other cellular proteins; axin and APC

(Adenomatous polyposis coli). Beta-catenin then is phosphorylated by GSK3β

(glycogen synthase kinase 3β), leading to destruction of beta-catenin by

proteolysis. Hence, leading to low and steady state concentrations of beta-

catenin in the cytoplasm. On the other hand, when the WNT signaling is

activated by the Wnt ligand binding to Frz receptor (Frizzled family of

transmembrane proteins), GSK3β is blocked and beta-catenin is saved from

rapid destruction, leading to accumulation of unphosphorylated beta-catenin in

the cytoplasm. This accumulation leads to translocation into the nucleus where

beta-catenin binds to transcription factors, and activates transcription of target

genes, including those involved in cell proliferation, for example cyclin D1,

contributing to tumour progression. Constitutive WNT signaling leads to an

expansion of the proliferative compartment of the crypt by mutation of the

tumour suppressor gene APC, there by destroying the equilibrium between

proliferation and differentiation, leading to the development of precancerous

lesions (Reya and Clevers, 2005).

In addition to genetic mutations, epigenetic alterations seem to cooperate with

genetic mutations to drive polyp to carcinoma transformation. There are three

major pathways for colorectal carcinogenesis- Chromosomal instability (CIN),

Microsatellite instability (MSI) and CpG island methylator phenotype (CIMP).


26

2.3.5 Chromosomal Instability (CIN)

CIN is the most common and well-characterized pathway of carcinogenesis. It is

characterized by high grade of differentiation, distal location and intermediate

prognosis. Around 70% of colorectal cancer arise from CIN pathway (Markowitz

and Bertagnolli, 2009). CIN pathway is associated with mutations in

Adenomatous polyposis coli (APC) and/loss of chromosome 5q which harbors

APC gene, loss of chromosome 18q, deletion of chromosome 17p, which

contains tumor suppressor gene TP53, and mutations in KRAS oncogene. Only

a small minority of CRC having CIN, have a full complement of these

abnormalities (Grady and Carethers, 2008).

APC is an important tumor suppressor gene. Pathogenic mutations in APC

truncate the APC protein and hence interrupts the binding of APC to beta

catenin involved in Wnt –signaling pathway (Cadigan and Liu, 2006). It is also

seen that loss of functional APC may interfere with regulation of mitosis

contributing to CIN. Frequency of APC mutations is observed in around 80% of

early adenomas. Mutations of APC are observed in around 60% colonic

cancers and 82% rectal cancers (Jass et al., 2002).

DCC, SMAD2 and SMAD4 are located on chromosome 18q21.1. Allelic loss of

18q is observed in around 60% of colorectal cancers. SMAD2 and SMAD4 are

involved in TGF-β signaling pathway, which plays an important role in regulating

growth and apoptosis. Mutations of SMAD4 cause juvenile polyposis syndrome

associated with CRC (Worthley and Leggett, 2010b).


27

KRAS, a proto oncogene, plays an important role in CIN pathway. It is located

on chromosome 12p12 and encodes a GTP binding protein. When mutated

results in constitutive signaling through RAS-RAF-MAPK signaling pathway and

thus permits cell to evade apoptosis and acquire growth advantage. Activating

KRAS mutations are observed in 35-42% of colorectal cancer cases (Shen et

al., 2007).

p53 gene, is located on chromosome 17p13. This gene is also known as

‘guardian of genome’. Impairment of TP53 is often a late event in colorectal

carcinogenesis and usually occurs through allelic loss of 17p. Mutations or loss

of heterogeneity in TP53 is observed in around 4-26% of adenomas, 50% of

adenomas with invasive foci and 50-75%of CRC (Worthley and Leggett,

2010a). TP53 abnormalities increase relative to advancing histological state of

lesion. In normal functioning, p53 plays an important role in cell recovery form

genetic damage. It increases the expression of cell cycle genes, slows down the

cell cycle and hence provides time for DNA repair. Further, whenever there is

great extent of DNA damage, p53 induces pro apoptotic gene, thus promoting

early programmed cell death (Mills, 2005).

These alterations of CIN pathway are the followed by subsequent events that

promote and facilitate the progression towards malignant state.


28

2.3.6 Microsatellite Instability

Microsatellite instability (MSI) is another important genomic instability in

colorectal cancers. Microsatellites are nucleotides repeat DNA sequences

distributed throughout the genome and MSI refers to the instability in these

repeat sequences in germline DNA versus tumor DNA. These replication errors

occur due to DNA polymerase activity while copying and inserting the repeat

sequences during DNA replication. One of the methods to repair such

replication errors is DNA Mismatch repair (MMR) system. Hence, dysfunction of

MMR results in MSI. MSI is involved in development of about 15% of sporadic

CRC cases and in about 95% of HNPCC syndrome associated tumors. CRC

which develops through MSI pathway has distinct features like location in

proximal colon, poor differentiation and/or mucinous histology and increased

number of tumor infiltrating lymphocytes (Jass et al., 2002).

MMR system consists of seven proteins, mlh1, mlh3, msh2, msh3, msh6, pms1

and pms2, which associate with specific partners to form heterodimers which

are then responsible for surveillance and correction of replication errors.

Mutations in MLH1, MSH2, MSH6 and PMS2 are associated with HNPCC

(Boland and Goel, 2010).

To detect the frameshift mutations at the microsatellite regions in CRC, a

standardized panel of microsatellites was created to provide uniformity in

research and practice. This panel consists of two mono- nucleotide (BAT25,

BAT26) and three dinucleotide microsatellites (D5S346, D2S123, and


29

D17S250). MSI-High (MSI-H) is defined as instability in more than 2 (40%) sites

out of the five sites mentioned and MIS-Low (MSI-L) is instability at one site.

And MSS i.e. microsatellite stable CRC has no instability in the mentioned five

markers (Boland et al., 1998). HNPCC cause pure form of MSI, however,

majority of MSI positive tumors i.e. MSI-H tumors occur sporadically due to

methylation of MLH-1 promoter. Such cancers exhibit both MSI and CIMP.MSI-

H tumors share similar biology irrespective of been sporadic or inherited (Zhang

and Li, 2013).

MSI-H CRC patients show good prognosis and survival and MSI is relatively

uncommon in metastatic CRC (Kawakami et al., 2015). MSI is frequently

observed in women especially older women in comparison to men (Kim et al.,

2015). It has also been observed that MSI-H CRC are less responsive to 5-

flurouracil based chemotherapy (Kurzawski et al., 2004).

2.3.7 CpG island methylator phenotype (CIMP) pathway

CIMP pathway is second most common pathway for sporadic CRC. It is

observed in around 15% of sporadic cases and in 70-80% of all dysplastic

serrated lesions of right colon (Issa, 2008). It is observed that CIMP is closely

associated with older age, female gender, mucinous histology, smoking, MSI,

KRAS and BRAF mutations (Issa, 2008).

CpG islands in DNA, are the regions which are proximally located in the

transcription start site of genes. These islands contain high frequency of CG


30

dinucleotide. In various tumor suppressor genes in tumor cells, these CpG

islands are frequently methylated resulting in repression of transcription.

Subgroups of CRC show a wide range of hypermethylation of MLH1 gene, a

mismatch repair gene, which is known as CpG island methylator phenotype.

Tumors are classified as CIMP-high (CIMP-1) or CIMP-low (CIMP-2) depending

on the extent of methylation. This methylation can be detected by a panel of

CpG markers assessed by PCR (Shen et al., 2007).

CIMP positive CRC, which show presence of MSI-H exhibit MSI-H

characteristics like good prognosis. However, CIMP positive CRC which show

absence of MSI-H are characterized by advanced pathology, poor clinical

outcome and absence of tumor infiltrating lymphocytes (Jass, 2007). CRCs

which develop via CIN pathway, including HNPCC, originate from adenomatous

polyp. However, in CIMP pathway sessile serrated adenomas are primary

precursors of CRC (Jass, 2007).

Hence, all these three pathways of carcinogenesis have distinct clinical,

pathological and genetic features, all being potentially useful for molecular

characterization of CRC for improved diagnostic, prognostic and treatment

prediction.


31

2.3.8 Clinical staging

In the advanced stage of cancer the tumor metastasizes, shedding cells in the

circulatory system and spreading the cancer to other organs like liver and lungs.

The extent to which the cancer has spread is described as its stage. Staging is

essential to determine the choice of treatment and to assess prognosis. At early

stages, that is Stage I and II, a curative treatment is achieved by surgical

resection or by chemotherapy, which is often applied in an adjuvant manner.

The metastatic stage IV disease indicates a more advanced cancer and is

usually incurable. The Stage I and II have moderate risk of relapse after surgical

resection, whereas patients with stage III and IV have higher chance of

recurrence (Libutti et al., 2008).

In past, Dukes and Astler-Coller classification systems was most commonly

used for staging (Dukes and Bussey, 1958, Astler and Coller, 1954). However,

this system was not considered to be elaborate enough, hence, recently TNM

staging system is used which is maintained by American Joint Committee on

Cancer (AJCC). This system codes the extent of the primary tumor (T), regional

lymph nodes (N), and distant metastases (M) and provides a stage grouping

based on T, N, and M (Edge and Compton, 2010) (Figure 2.12).


32

Figure 2.12: TNM staging as maintained by American Joint Committee on

Cancer (AJCC) and Union for International Cancer Control (UICC) (Source-

(Edge and Compton, 2010).

Figure Legend: TNM Staging system is the most widely used staging system. It is

based on extent of tumor (T), the extent of spread of lymph nodes (N) and the

presence of metastasis (M).The status of T, N and M together decide the stage of

cancer. The stages of cancer are further subdivided as II a or b, III a,b ,c etc.


33

Colorectal cancer may also present histologically with poorer prognostic cell

types, such as adenosquamous or signet ring cells, and may have

undifferentiated cells (Secco et al., 2009). Carcinoid tumors and mucosa-

associated lymphoid tumor (MALT) lymphomas may also present as tumors in

the colon but are not usually considered types of CRC (Oberhuber and Stolte,

2000). Colorectal cancers can be characterized according to their location. They

have a better prognosis rate when detected before bowel obstruction or

perforation occurs. Incidence rates of colorectal cancer differ by sub-sites

(Figure 2.13). Previously, tumors of the rectum were common but now the

highest frequency of tumors are present in right ascending colon (CO and IN,

2011). This change in incidence is attributed to improved screening and

detection techniques to discover early polyps.

Figure 2.13: Schematic overview of incidence of colorectal cancer.

Figure Legend: According to anatomy the prevalence of CRC is predominantly higher

in the left side of the bowel as compared to the right side (Macrae et al., 2015).


34

2.4 Risk factors

There are many known factors that increase or decrease the risk of colorectal

cancer. Summary of risk factors associated with colorectal cancer is provided in

Table 2.1. Incidence and death rate for colorectal cancer increase with age.

Further as reviewed earlier in this chapter (Section 2.2), incidence and mortality

rates of colorectal cancer are about 35-40% higher in men than in women. As

suggested by Taunk and Audrey, the reason for this is not understood, but is

likely related to the “complex interactions between gender related differences in

exposure to hormones and risk factors” (Murphy et al., 2011). Further as

detailed in Table 2.1, factors that increase risk also include a personal or family

history of chronic inflammatory bowel disease (Crohn’s disease or ulcerative

colitis), genetic mutations, high consumption of red or processed meat,

smoking, physical inactivity, obesity and moderate to heavy alcohol

consumption. Additional risk factors include exposure to asbestos, radiation,

synthetic fibers, halogens (anesthetics), printing supplies and fuel (Moradi et al.,

2008).


35

Table 2.1: Summary of risk factors associated with colorectal cancer.

Risk Factors References

Genetics-Hereditary Colorectal Cancers (Ghazi, 2012, Dunlop and Farrington,

2009)

Familial adenomatous polyposis

MUTYH associated polyposis

Lynch Syndrome

Gardner’s Syndrome

Peutz-Jeghers Syndrome

Hyperplastic polyposis

Family history of non-syndromic colon

cancer

(Lynch et al., 2008, Jass, 2000)

Cowden syndrome

Bannayan Zonana Syndrome

Inflammatory bowel disease (Bewtra et al., 2013)

Ulcerative colitis

Crohn’s Disease

History of neoplasia (Niell et al., 2004)

Prior colon cancer

Breast cancer

Other (Sun and Yu, 2012, Abdulamir et al., 2011,

Sonnenberg and Genta, 2013, Dutta et al.,

2012, Zhao et al., 2012)

Diabetes mellitus and insulin resistance

Strepotococcus bacteremia


36

Helicobacter pylori

Acromegaly

Prior cholecystectomy

Use of androgen deprivation therapy

Additional Factors

(Dong et al., 2010, Hendifar et al., 2009,

Bardou et al., 2013)

Age

Gender

Race/ethnicity

Obesity

Physical inactivity

Diet

Smoking

Alcohol


37

Recent studies found that about one quarter of colorectal cancer cases could

be avoided by following a healthy lifestyle (Anderson et al., 2013). Also, non-

steroidal anti-inflammatory drugs (Walker et al., 2012), estrogen and calcium

(Lappe et al., 2007) have been found to protect against colorectal cancer.

2.4.1 Age

Age is one of the most important factors strongly related to CRC. The peak

incidence of colorectal cancer occurs in the sixth and seventh decade of life.

Incidence and mortality rate of CRC specifically increases after the age of 50

years. Around 75% of cases of CRC occur in people above 65 years of age

(Ferlay, 2012). However, recently it has been observed that the incidence of

CRC is increasing in younger population. This could be due to lack of

screening, behavioural factors such as alcohol consumption, smoking and

lifestyle factor like obesity. It has also been observed that in the young patients

CRC is more aggressive and has poor pathological features (Chou et al., 2011).

2.4.2 Hereditary Factors

Majority of CRC are sporadic, however about 20-30% of cases have a negative

family history. People with their first degree relative having colorectal cancer or

adenomatous polyps, diagnosed at the age of <50 years are at higher risk. Risk

increases in individuals having two or more family members affected by

colorectal cancer. The reason for increased risk is still unclear, however it may

be due to genetic or environmental factors or due to combination of both

(Boardman et al., 2007).

2.4.3 Inherited Syndrome

Around 5% of CRC cases are due to hereditary conditions. The most common

inherited conditions include Hereditary nonpolyposis colorectal cancer (HNPCC)


38

or Lynch syndrome and Familial adenomatous polyposis (FAP) (Lynch et al.,

2008). HNPCC is caused by mutations DNA mismatch repair genes-MLH1,

MSH2, MSH6, PMS2 or EPCAM (Lynch et al., 2008). Inheritance is in

autosomal dominant pattern accounting for 2-5% of CRC and carriers of the

mutations have around 50-80% lifetime risk of developing CRC.HNPCC display

specific characteristics like mucinous or signet ring histology, poor

differentiation, lymphoid infiltration and predominance of right side tumors. One

of the most common clinical feature of HNPCC is that multiple generations get

affected by CRC at an early age around 45 years (Jass et al., 2002). To identify

individuals at risk, personal and family cancer history analysis, CRC molecular

testing for MSI and MMR gene mutation analysis should be performed.

Familial adenomatous polyposis (FAP), second most common hereditary

colorectal cancer syndrome accounting for 1% of all colorectal cancer cases, is

caused by mutations in Adenomatous polyposis coli (APC) and is inherited in

autosomal dominant manner (Jasperson et al., 2010). Patients with FAP

develop large number of adenomatous colorectal polyps that develop after first

decade of life, unlike individuals with HNPCC who develop only few adenomas

(Half et al., 2009). Carriers of APC gene mutations or individuals with known

family history of FAP should start colorectal cancer screening for polyps at the

age of 10-12 years with flexible sigmoidoscopy and should undergo annual

colonoscopy once polyps are detected (Kastrinos and Syngal, 2011). If there

are numerous polyps which cannot be managed by endoscopy then

prophylactic colectomy is recommended (Galiatsatos and Foulkes, 2006).

Other hereditary colorectal cancer syndrome include polyposis associated with

mutations in the mutY DNA glycosylase (MUTYH) gene, Gardner Syndrome, a


39

type of FAP, Peutz–Jeghers syndrome, caused by mutations in STK1 gene,

serrated polyposis and juvenile polyposis.

2.4.4 Inflammatory bowel disease (IBD)

Chronic colitis and Crohn’s disease are significant risk factors of CRC. The risk

increases with longer duration of IBD and is highest in patients with early onset

and widespread manifestation (Jess et al., 2012). However, recently it has been

observed that the incidence of CRC in IBD patients is reducing which may be

attributed to effective anti-inflammatory treatment and improved surveillance

(Castaño‐ Milla et al., 2014).

2.4.5 Life Style Factors

Life style related risk factors for CRC include lack of exercise, smoking, alcohol

consumption and obesity. Intensity of physical activity is inversely proportional

to risk of CRC. Increased risk is seen more in case physical inactivity and colon

cancer than in comparison to rectal cancer. The mechanisms responsible for

the association of reduced physical activity and increased risk to CRC are still

not fully understood. However, there are few studies, which indicate reduction in

insulin resistance, effects on endogenous steroid hormone metabolism and

reduced gut transit time as possible biological mechanism responsible for

increasing the risk of CRC (Samad et al., 2005).

Carcinogens from cigarette smoking cause irreversible damage in normal

colorectal mucosa, however, many years are required for completion of all

carcinogenic events from the time of initiation. The association between

smoking and CRC is stronger in case of rectal cancer in comparison to colon


40

cancer. Smoking initiates formation of adenomatous polyps which are one of

the precursor lesion of CRC (Giovannucci, 2001).

Studies have found increased risk of CRC with regular high alcohol intake

(>45g/day) in comparison to nondrinkers (Cho et al., 2004). Studies have

shown that obesity is associated with increased risk of CRC. Insulin and insulin

like growth factors, leptin, adipose tissue induced changes of estrogen and

androgen and inflammatory molecules are proposed to be the putative link

between obesity and CRC. High insulin and IGF levels seen in obese people

activate certain signaling pathways favoring pro-carcinogenic processes

(Khandekar et al., 2011). Elevated levels of leptin in obese have shown to

suppress apoptosis and stimulate proliferation of colonic epithelial cells (Stattin

et al., 2004).

2.4.6 Dietary factors

Dietary factors potentially increase risk of CRC. Diet high in fats is associated

with increased risk of CRC. Diet rich in fats seem to favor development of

bacterial flora that degrade bile salts to potentially carcinogenic nitrogen

compounds (Larsson and Wolk, 2006). Consumption of red and processed

meat is also associated with increased risk of CRC. In The ERBITUX Plus

Irinotecan for Metastatic Colorectal Cancer (EPIC) study, positive association

between consumption of red meat and CRC was observed and also an inverse

association between consumption of fish and CRC was seen (Norat et al.,

2005). Similarly, diet rich in fibers is inversely associated with CRC. Possible

anti-carcinogenic effects of dietary fibers include reduction of secondary bile


41

acid production, reduction in intestinal transit time, increased fecal bulk,

formation of short chain fatty acids from formation by colonic bacteria and

reduction in insulin resistance (Trock et al., 1990).

2.4.7 Race/ Ethinicity

As reviewed earlier in Epidemiology section in this chapter, African Americans

have the highest incidence and mortality rate of CRC in all racial groups. Also,

the Eastern European Jewish population i.e. Ashkenazi Jews has one of the

highest CRC risk (Besterman-Dahan, 2008). Several mutations leading to

increased risk of CRC in this ethnic group have been observed. The most

common mutation is observed in APC gene, I1307K, observed in around 6% of

American Jewish population. Individual preferences, social or cultural biases

contribute to racial and ethic disparities, cancer prevalence in few groups is also

linked to socio-economic status (Mitchell, 2013).


42

2.5 Diagnosis

Colorectal cancer is associated with classical symptoms like blood in stool, pain

in abdomen and altered bowel habits. Other symptoms include fatigue,

shortness of breath, weight loss and anemia related symptoms. Diagnosis of

CRC results from assessment of patient showing above mentioned symptoms

or as a result of screening.

Colonoscopy is a preferred method of investigation in symptomatic individuals,

however, endoscopic methods are also available like-high definition white-light

endoscopy, chromo-endoscopy, magnification endoscopy, narrow band

imaging, intelligent colour enhancement and iScan imaging, autofluorescence

endoscopy and microendoscopy. Colonoscopy is a gold standard for CRC

diagnosis. This method has high accuracy, can enable simultaneous biopsy

sampling and can access location of tumor. Over the past decade, colonoscopy

has efficiently helped in reduction of CRC incidence and mortality. Past 20 year

follow up data from UN National Polyp study has showed 53% reduction in CRC

related mortality (Zauber et al., 2012).This method provides both diagnostic and

therapeutic effect. The quality of colonoscopy has improved drastically over the

past decade. The current standards utilize high power endoscopes with high

resolution video screens to yield high definition endoscopy to improve the

diagnosis of polyps and CRC. The invasive nature of this methodology possess

a burden to the patients and might affect the participation in screening

programs. However, recently various diagnostic methods and screening

biomarkers have been introduced, such as capsule endoscopy and biomarker

tests as mentioned in Figure 2.14 (Kuipers et al., 2015).


43

Figure 2.14: Advantages and disadvantages of different screening modalities

used for diagnosis of CRC (Image source- Kuiper’s et al., 2013).

FIT, fecal immunochemical test; gFOBT, guaiac faecal occult blood test. *Less

problematic with newer-generation tests.

There are few serological markers that allow early detection and diagnosis of

CRC. The most widely studied marker is carcinoembryonic antigen (CEA).

Serial CEA measurements can detect recurrent CRC and liver metastasis

(Harrison et al., 1997). High preoperative levels of CEA are associated with

poor prognosis.


44

2.6 Treatment strategies

Although CRC is highly treatable if diagnosed in the early stages and surgically

removed, recurrence after surgery and adjuvant therapy and metastatic disease

are still major problems with a median overall survival of approximately 24

months (Howlader et al., 2011). Nevertheless, there has been improvement in

survival due to the introduction of new cytotoxic and targeted agents (Figure

2.15). Systematic therapeutic efficacy is central to determining the outcome for

patients.

Figure 2.15: Schematic overview of advances made in treatment strategies for

colorectal cancer.

Figure Legend: CRC management has evolved evidently over the last few decades.

Incorporation of new therapeutic concepts has led to improved survival [Image Source-

(Patil et al., 2016)].

2.6.1 Surgery

Surgery is the mainstay curative treatment for non-metastatic colorectal cancer

patients. The extent of surgery is determined by blood supply and distribution of

regional lymph nodes and the choice of surgical method depends on


45

preoperative TNM staging. Tumors located at caecum and right colon are

removed by right hemicolectomy followed by ileocolic anastomosis. Similarly,

tumors located at hepatic flexure and transverse colon are removed by

extended right hemicolectomy. Tumors of descending or sigmoid region are

treated with left hemicoloectomy. Recently, complete mesocolic excision (CME)

technique, similar to TME (Total mesorectal excision), has been introduced

which has shown to improve overall survival and has also reduced the

recurrence rate. To minimize surgical complications, perioperative protocols like

fast track protocols and enhanced recovery after surgery have been designed.

These protocols describe the list of requirements for taking care of patients at

various steps in perioperative process (Willemsen et al., 1999).

In case of operable disease primary surgery is carried out with or without

adjuvant chemotherapy. For locally advanced disease, primary curative

resection is unlikely hence, preoperative chemotherapy is considered (Kuipers

et al., 2015).

In case of isolated metastatic disease, resection of primary disease is carried

out followed by metastasectomy with or without neoadjuvant and/or adjuvant

chemotherapy. For widespread metastatic disease palliative chemotherapy

along with supportive care is considered (Kuipers et al., 2015).


46

2.6.2 Conventional Chemotherapy

It is unfortunate that surgical resection is not suitable for majority of metastatic

cases, and the only option to prolong survival is systemic therapy directed at

metastatic colonies. The conventional chemotherapy drugs used to treat

metastatic colorectal cancer include:

5-Fluorouracil (abbreviated FU)

Capecitabine (Xeloda®)

Oxaliplatin (Eloxatin®)

Irinotecan (Camptosar®)

These drugs work by interfering with the ability of rapidly growing cells (like

cancer cells) to divide or reproduce themselves. Because most of an adult's

normal cells are not actively growing, they are less affected by chemotherapy,

with the exception of bone marrow (where the blood cells are produced), the

hair, and the lining of the gastrointestinal tract. Effects of chemotherapy on

these and other normal tissues cause side effects during treatment. The

introduction of combination regimes of oxaliplatin or irinotican and 5-FU/LV

have improved response rates, progression free survival and overall survival by

15-20%, 5-6 and 10-12 months to 30-40%, 8 and 20-24 months, respectively

(Colucci et al., 2005).


47

2.6.1.1 5-Fluorouracil / Leucovorin

5-Fluorouracil (5-FU) has been the first choice of treatment options for

colorectal cancer patients for over 40 years. It is used in combination with

leucovorin, a vitamin, which makes 5-FU more effective. 5-FU is given

intravenously, although recently a pill form of 5-FU, capecitabine (Xeloda®) has

been developed, which is used in metastatic colorectal cancer patients. 5-

FU/LU became the standard of care for patients with stage III and selected

stage II colon cancer in the early 1990’s. Many clinical trials have shown that

these regimens improve overall survival, but how they affect the risk of

recurrence over time is not clear. Results of multiple randomized trials that have

enrolled more than 4,000 patients comparing adjuvant chemotherapy with 5-FU-

leucovorin (5FU/LV) to surgery or 5-FU-semustine-vincristine demonstrate a

relative reduction in mortality of between 22% and 33% (3-year OS of 71%–

78% increased to 75%–84%). Later studies have refined the use of 5-FU-

leucovorin in adjuvant settings (Gill et al., 2004).

2.6.1.2 Oxaliplatin and Irinotican

In early 2000’s, the introduction of oxaliplatin and irinotican resulted in

meaningful improvement in the management of colorectal cancer patients.

Irinotecan, a topoisomerase I inhibitor, was initially introduced as monotherapy

for patients with metastatic colorectal cancer refractory to 5-FU. irinotican has

demonstrated clinical efficacy and tolerability in multiple non randomized and

randomized Phase II and Phase III studies both as single agents and in

combinations with bolus or infusional 5 FU/LV or in concomitant versus

alternative schedules of administration (de Gramont et al., 2000).


48

In two of the second line trials of irinotican, 2.3 month (p>0.03) improvement in

survival and 2.7 month (p>0/001) median survival improvements were observed

(Rougier et al., 1998, Cunningham et al., 1998).

In four Phase III trials, therapy with single agent irinotican resulted in longer

survival time than BSC or FU/LV therapy in FU refractory patients(Cunningham

et al., 1998, Fuchs et al., 2007, Rougier et al., 1998). A large trial involving 683

patients compared 5-FU/LV with irinotican/bolus 5-FU/LV or single-agent

irinotican (125 mg/m2) in weekly X 4, every-6-weeks schedules. Irinotecan/5-

FU/LV resulted in a higher response rate, a longer median time to disease

progression, and longer median survival compared with 5-FU/LV, which showed

efficacy comparable to that of irinotican. Grade 3 or 4 diarrhea occurred in 31%

of patients who received irinotican alone versus 23% and 13% of patients who

received IFL and 5-FU/LV, respectively. However, significantly greater

incidence rates for Grade 4 neutropenia, fever, and severe mucositis were

associated with 5-FU/LV. Combining irinotecan with 5-FU/LV did not affect

quality-of-life scores compared with 5-FU/LV alone (Fuchs et al., 2007). Based

on data from these trials, the United States Food and Drug Administration

(USFDA) and European regulatory authorities approved irinotecan in

combination with both bolus and infusional FU/LV as first-line therapy for

mCRC, replacing FU/LV as the standard of care. Interim data from a pair of

North American randomized trials have raised concerns regarding the safety of

irinotecan containing regimens, with gastro-intestinal and thromboembolic

syndromes accounting for the unexpectedly high rates of treatment-induced or

treatment-exacerbated death (2.5% and 3.5%, respectively)(Rougier et al.,


49

1998, Cunningham et al., 1998). However, European oncologists continued to

use infusional FU/LV.

To define the optimal first-line irinotecan regimen, the BICC-C trial compared

5FU infusion plus irinotecan (FOLFIRI) to IFL and capecitabine plus irinotecan

(CapeIri) (Fuchs et al., 2007). FOLFIRI was associated with improved

progression-free survival (PFS) as compared with IFL (7.6 versus 5.9 months;

p=0.004) and trended toward improved overall survival (23.1 versus 17.6

months; p=0.09). In addition, FOLFIRI was associated with the most favorable

toxicity profile of the three regimens, thereby establishing it as a reference

standard for treatment of naive patients with colorectal cancer (Fuchs et al.,

2007).

Similarly for oxaliplatin, a second generation platinum analogue, in an initial

Phase III study oxaliplatin plus infusional 5FU (FOLFOX) was compared to

infusional 5FU in the first-line setting (de Gramont et al., 2000). In the 2,246

patients with resected stage II or stage III colon cancer in the completed

Multicenter International Study of Oxaliplatin/5-Fluorouracil/Leucovorin in the

Adjuvant Treatment of Colon Cancer (MOSAIC [NCT00275210]) study, the toxic

effects and efficacy of FOLFOX4 were compared with the same 5-FU-

leucovorin (5FU/LV) regimen without oxaliplatin administered for 6 months

(André et al., 2004). Based on results from the MOSAIC trial, adjuvant

FOLFOX4 demonstrated prolonged OS for patients with stage III colon cancer

compared with patients receiving 5-FU-leucovorin without oxaliplatin (André et


50

al., 2009). Finally, the NCCTG-intergroup established 5FU/LV and oxaliplatin

(FOLFOX4) as the new first-line standard regimen compared with the previously

used irinotecan and bolus 5FU/LV (IFL) regimen (Alberts et al., 2005). Hence,

FOLFOX has become the reference standard for the next generation of clinical

trials for patients with stage III colon cancer.

2.6.1.3 Capecitabine

Later in the year of 2000, capecitabine was introduced and was found that for

stage III colon cancer it provides equivalent outcome to intravenous 5-FU-

leucovorin (Twelves et al., 2005). Several Phase II and III randomized trials also

investigated the substitution of 5FU/LV by capecitabine (XELOX) and showed

similar PFS and OS, but lower ORR (odds ratio = 0.85; 95% CI: 0.74–0.97; p =

0.02) for XELOX compared with FOLFOX (Ducreux et al., 2011) .

Grade 3 hand- foot syndrome was reported more frequently with capecitabine,

although the condition was tolerated with a reduced dose (Van Cutsem et al.,

2001). Based on these data, capecitabine was approved in the U.S. as first-line

therapy for patients with mCRC for whom combination therapy is not warranted.

Oxaliplatin can be combined with synergistic efficacy with fluropyrimidines,

irinotecan, bevacizumab and EGFR antibodies to further enhance treatment

efficacy in metastatic colorectal cancer (Assenat et al., 2011, Tveit et al., 2010).

In recent data from the Adjuvant Colon Cancer Endpoints (ACCENT) group,

individual patient data from 18 phase III clinical trials of adjuvant 5-FU based

chemotherapy for colon cancer was reviewed to show that the regimes provide


51

the beneficiary effect primarily by reducing the high risk of reoccurrence within

the first two years of surgery. By five years after treatment with 5-FU based

adjuvant therapy, the risk of recurrence dropped to 1.5% and dropped again to

0.5% after eight years of treatment (Sargent et al., 2009).

As sequential therapies cannot be predefined in treatment protocols, overall

survival may no longer be regarded as the most sensitive end point for

assessing the efficacy of first-line therapy; other factors, such as PFS and TTP,

should be considered.

2.6.2 Therapy options for colon cancer

When the patients have undergone potentially curative resection with no

residual disease then the Five year survival rate without adjuvant chemotherapy

for Stage I is >90%, for Stage II is 70-80% and for Stage III is 50-60% (Mitry et

al., 2008). In most cases adjuvant chemotherapy is well tolerated however, it

may cause potential morbidity. Before administrating adjuvant treatment few

patient selection criteria should be considered. A minimum of 8 lymph nodes

and ideally >12 should be examined to determine the metastatic spread

(Eisenhauer et al., 2009). Other factors like poorly differentiated tumors,

presence of extramural vascular invasion or perineural invasion and T4

classification are reportedly associated with relatively high risk of recurrence

(Tsai et al., 2008). In addition to this MMR/MSI status should be evaluated in

patients before administration of adjuvant therapy. 5-FU is not effective in MSI-

positive and deficient MMR patients (Sinicrope, 2010) .For patients with high

risk Stage II tumors, capecitabine monotherapy is appropriate therapy.

Colonoscopy should be performed after 1 year of surgery and every three years


52

thereafter (Sirohi et al., 2014). Figure 2.16 describes various adjuvant therapy

options available to colon cancer.

Figure 2.16: Adjuvant therapy options for colon cancer patients with no

metastasis i.e Mo as per Indian Council for Medical Research (Sirohi et al.,

2014).

5-FU-5-Fluorouracil, FOLFOX-5-FU plus oxaliplatin, CAPEOX-Capecitabine plus

oxaliplatin.

2.6.3 Therapy options for rectal cancer

In case of rectal cancers, rate of local recurrence is higher in comparison to

colon cancer due to limited availability to obtain wide radial resection margins at

the time of surgery due to presence of pelvic bone (Sagar and Pemberton,

1996). Surgery is the first option for tumors with low of positive or uninvolved

mesorectal fascia. Short course of preoperative ratio therapy may reduce the

local recurrence rate. Surgery is performed soon after completion of

radiotherapy. In case of tumors associated with poor prognosis as assessed by

MRI, T3 and T4 tumors with 4 or more lymph node involvement neoadjuvant

chemotherapy along with radiotherapy is recommended. For Stage II and III

rectal cancers, adjuvant chemotherapy is recommended following neoadjuvant


53

therapy (Sirohi et al., 2014). Adjuvant chemotherapy should be initiated soon

after surgery so as to improve survival rate (Sirohi et al., 2014) (Figure 2.17).

Figure 2.17: Neo adjuvant and Adjuvant therapy options for rectal cancer with

no metastasis i.e Mo as per Indian Council for Medical Research (Sirohi et al.,

2014).

5-FU-5-FluoroUracil, LV- Leucovorin, FOLFOX-5-FU plus oxaliplatin, RT- Radiotherapy


54

2.6.4 Treatment options for advanced disease i.e. mCRC

In case of patients with resectable metastatic disease, surgery is the first option

alternative approach been neoadjuvant chemotherapy. Adjuvant chemotherapy

is usually recommended to reduce the rate of recurrence. The therapy options

include FOLFOX, FOLFIRI, CAPOX or FOLFIRI or FOLFIRINOX with or without

bevacizumab and cetuximab (in case of wild type RAS).

For patients with unresectable mCRC, single agent chemotherapy is

recommended. In first line treatment various options are recommended like

capecitabine alone or 5-FU/LV alone or CAPOX with or without bevacizumab,

FOLFOX with or without bevacizumab, FOLFIRI with or without bevacizumab or

cetuximab, CAPIRI with or without bevacizumab, FOLFOX with or without

Panitumumab. Before administration of Cetuximab and Panitumumab RAS

testing is recommended (Sirohi et al., 2014) (Rossi et al., 2013).

In case of second line treatment option single agent irinotecan or FOLFIRI,

oxaliplatin + 5-FU, cetuximab or panitumumab with irinotecan, 5-FU with or

without bevacizumab are the options which are taken into consideration.

Aflibercept is also used in few cases (Sirohi et al., 2014, Carethers, 2008).

In third line treatment option for mCRC, cetuximab with or without irinotecan has

demonstrated to improve survival. In certain cases treatment may be offered ‘off

study’ i.e. retreatment with previously successful regime after long disease free

interval. Other options include reference of patients to clinical trials, treatment

with regorafenib and best supportive care alone (Sirohi et al., 2014) (Carethers,

2008).


55

2.6.5 Novel cytotoxic and targeted biologic therapeutics

Recently cytotoxic drugs showed promising activity in the first-line treatment of

patients with advanced CRC. Studies have demonstrated that the efficacy and

safety of S-1, a novel oral fluropyrimidines, are comparable with those of 5-FU

and capecitabine for metastatic colorectal cancer patients (Kusaba et al., 2010).

However S-1 has mainly been studied among Asian population. Thus, at least

for now, S-1 cannot be recommended in global populations with metastatic

colorectal cancer as observed for many chemotherapeutic agents that the dose

recommendation and safety vary significantly with ethnicity. A novel antifolate,

Pemetrexed, that inhibits TS as well as folate dependent enzymes involved in

purine synthesis, showed modest efficacy in a pair of frontline Phase II studies,

with response rates of 15–17% (Braun et al., 2004).

Advances in the understanding of tumor cell biology have fostered the

development of novel biologic modifiers and molecular targeted therapeutics

that interfere with specific tumor cell propagation mechanisms. These range

from monoclonal antibodies to fusion proteins and small molecule inhibitors. As

depicted in Figure 2.15, the USFDA since 2004 has approved targeted agents

for example an antivascular endothelial growth factor (anti-VEGF) monoclonal

antibody (mAb), bevacizumab (Avastin®; Genentech, Inc., South San

Francisco, CA, http://www.gene.com) and a human epidermal growth factor

receptor (HER-1/EGFR)-targeted mAb, cetuximab (Erbitux®; Imclone Systems,

Inc., New York, NY, http://www.imclone.com), as first- and second-line mCRC

therapy, respectively (Figure 2.18).


56

Figure 2.18: EGFR and VEGF Signaling Pathways in CRC development and

tumor survival.

Figure Legend: EGFR and VEGF signaling pathway play a pivotal role in tumor

initiation, progression and survival, including CRC. EGFR gene is located on

chromosome 7 and encodes a 170 kDa transmembrane receptor EGFR. EGFR

belongs to ErBb family of receptor tyrosine kinases and is activated by several ligands,

including EGF, transforming growth factor-α, amphiregulin (AREG), heparin-binding

EGF, epiregulin (EREG), and betacellulin. Consequently, two pathways are activated

by EGFR namely RAS–RAF–MAP kinase pathway and the PI3K–PTEN–Akt pathway

which in turn leads to tumor initiation and progression. Activating mutations in RAS,

RAF and PIK3CA can affect patients response to EGFR inhibitors. VEGF belongs to

family of angiogenic growth factors and comprises of 5 VEGF glycoproteins. VEGFA is

well characterized VEGF family member. Its receptor is VEGFR2. Binding of ligand

leads to further activation of MAPK through a cascade of events leading to

angiogenesis, proliferation, migration and survival of cells.


57

2.7 Targeting Vascular Endothelial Growth Factor (VEGF)

VEGF is a critical regulator of angiogenesis. Since the growth and spread of

tumors require angiogenesis, inhibiting this process makes an interesting

strategy for the treatment of cancer (Folkman, 1971). The VEGF-A –targeting

mAb bevacizumab (Avastin®) and VEGF targeting Ziv-aflibercept (Zaltrap®),

and regorafenib (Stivarga®) have all been FDA approved for use in mCRC and

studied in combination with chemotherapy.

To date, there are no clinically validated biomarkers which are in routine use,

despite the widespread use of antiangiogenic therapy for mCRC (Duda et al.,

2013) (Mousa et al., 2015). The exact mechanism of benefit of anti-VEGF drugs

is unclear, which has resulted in lack of biomarker based selection of patients

for antiangiogenic therapy (Lambrechts et al., 2013).

A retrospective analysis of baseline plasma/serum sample data for 88–97% of

patients/study (>2000 patients), from two randomized Phase III studies

HORIZON II and III investigating cediranib (an oral VEGFR TKI) in mCRC,

reveals that baseline VEGF levels were treatment-independent prognostic

biomarker for PFS and OS in both the studies (Jürgensmeier et al., 2013).

Similarly, another retrospective analysis of the AVF2107 study data from phase

III trial of bevacizumab in mCRC, reports the prognostic value of total circulating

VEGF-A levels (pretreatment), but not predictive for bevacizumab based

therapeutic benefit (Jannuzzi et al., 2015).

In one of the phase II trials it was observed that when CRC patients were

treated with FOLFIRI in combination with bevacizumab, significant increases in

fibroblast growth factor 2 (FGF2), phosphatidylinositol-glycan biosynthesis class


58

F protein (PIGF), stromal cell-derived factor 1 (SDF-1) and macrophage chemo

attractant protein 3 were observed, which may represent the mechanism of

resistance. Also, increases in the baseline interleukin -8, (a promoter of

angiogenesis), was observed and this was associated with decreased

progression free survival (Kopetz et al., 2010).

Furthermore, in the direction of achieving goal of personalized medicine, for

host-specific variability single-nucleotide polymorphisms (SNPs) are also

studied as potential biomarkers for response to anti angiogenic agents. Single

nucleotide polymorphisms like VEGF1154G>A and VEGF405C>1 are found to

be associated with improved overall survival and PFS (Formica et al., 2011).

Also, in another study it was observed that low gene expression levels of

VEGFA,VEGFR1 and VEGFR2 in colon cancer patients is associated with

longer mean disease free survival (Zhang et al., 2015b). Other biomarkers

such as angiopoietin-2 (a key regulator of vascular remodeling along with

VEGF) and CD133, identified as a potential biomarker of bevacizumab therapy

outcome, need future validation for their true predictive rather than prognostic

value reviewed in Patil et al, 2016.

Recently, mathematical models have also been developed to address this issue

(Finley and Popel, 2013), which could give new insight into the use of VEGF

isoforms as predictive biomarkers. These mathematical models might shed

some light on resistance mechanisms to anti-VEGF therapy and also be useful

in the analysis of future large prospective studies to address these predictive

biomarkers.


59

Hence, inhibiting the VEGF pathway has become an important strategy in the

treatment of metastatic malignancies including metastatic colorectal cancer.

Studies reveal that VEGF expression is elevated in a wide variety of tumor

types including colorectal cancer. Hyper expression of VEGF is also seen to be

associated with progression, invasion and metastasis of colorectal

cancer(Martins et al., 2011). In case of metastatic colorectal cancer, along with

VEGF, growth factors such as prostaglandin E2, EGF as well as molecular

mediators of the epithelial-mesenchymal transition have been identified as

potentiators of metastatic spread.

Shaked et al 2008, have identified several hypothesis to explain the chemo

sensitization action of anti-VEGF drugs. Anti-VEGF drugs work via several

mechanisms, including increasing the delivery of cytotoxic drug via vessel

normalization. Another possible mechanism is control of the repopulation of

tumor cells during the chemotherapy-free intervals in between treatment cycles.

A third hypothesis is inhibiting the mobilization of marrow derived circulating

endothelial cells or their progenitors. This slows down the tumor growth and

makes chemotherapy more effective (Shaked et al., 2008).


60

2.7.1 Bevacizumab-Anti VEGF monoclonal antibody

Bevacizumab (Avastin) is a humanized monoclonal antibody that inhibits VEGF-

A through inhibition of blood vessel formation, normalization of vasculature and

by reducing the intratumoral hydrostatic pressure (Krämer and Lipp, 2007).

Bevacizumab binds directly to VEGF to form a protein complex which is

incapable of further binding to VEGF receptor sites (which would initiate vessel

growth) effectively reducing available VEGF. The Bevacizumab/VEGF complex

is both metabolized and excreted directly.

It received its first approval in 2004 by the US FDA for combination use with

standard chemotherapy (as a first line treatment) and with 5-fluorouracil-based

therapy for second line treatment for metastatic colorectal cancer. This

recommendation was based on the E3200 trial which examined the addition of

bevacizumab to oxaliplatin/5-FU/leucovorin (FOLFOX4) in therapy. The addition

of bevacizumab was associated with improved progression free survival and a

4.7 month survival advantage (20.3 versus 15.6 months, Harzard

Ratio=0.66,p<0/001)(Kabbinavar et al., 2003). Other studies have also shown

that bevacizumab can be safely combined with capecitabine plus oxaliplatin

(XELOX, CAPOX), or irinotecan based regimens (Saltz et al., 2008, Hurwitz et

al., 2004). Summary of clinical trials undertaken for bevacizumab is shown in

Table 2.2. This strategy provides an extra option for patients with metastatic

colorectal cancer. The challenge remains to determine which patients benefit

most from continuing bevacizumab beyond progression. Hence, further

investigations are required to clarify how this strategy can be used in present

clinical scenarios, such as in v-ki-ras2 Kristen rat sarcoma viral oncogene

homologue (KRAS) wild type tumors.


61

Table 2.2: Summary of clinical trials undertaken for bevacizumab.

Study No. of

patients

Chemotherapy Overall

Survival (in

months)

E3200- FOLFOX4

with or without

bevacizumab as

second-line

therapy in patients

who have

progressed on

irinotecan-based

therapy(Giantonio

et al., 2007)

829 Bevacizumab+FOLFOX4(n=286)

versus FOLFOX4 alone (n=291)

and bevacizumab alone (n=252)

12.9 vs 10.8

and 10.2

BRiTE-

Bevacizumab

Regimens:

Investigation of

Treatment Effects

and

Safety(Grothey et

al., 2008)

1953 Bevacizumab as first and

second line(n=642) versus

bevacizumab as first line and

chemotherapy as second

line(n=531)

31.8 versus

19.9


62

ARIES-

Avastin in

Combination With

Chemotherapy for

Treatment of

Colorectal Cancer

and Non-Small Cell

Lung Cancer(Cohn

et al., 2010)

1550 FOLFOX+ Bevacizumab

(n=968)versus FOLFIRI

+Bevacizumab (n=243)

23.7 versus

25.5

TML18147(Helwick,

2012)

820 Second line therapy with or

without concomitant

bevacizumab

11.2 versus

9.8

FOLFOX4-Oxaliplatin/5-Fluorouralcil/Leucovorin, FOLFOX- oxaliplatin +5Flurouracil/Leucovorin, FOLFIRI-

5Fluorouracil/Leucovorin + Irinotican

2.7.2 Aflibercept- a novel antiangiogenic fusion protein

Aflibercept (Zaltrap, VEGF trap) is a fully human recombinant fusion protein

composed of both VEGFR-1 and VEGFR-2 ligand binding components fused to

the Fc portion of human IgG1 (W Stewart, 2011). It functions as a decoy VEGF

receptor, binds VEGF-A, VEGF-B, and placental growth factors 1 and 2 with

high affinity, prevents their binding to native VEGF receptors, and therefore

inhibits angiogenesis through downstream signaling. In 2012, the USFDA

approved Zaltrap for use in combination with 5-fluorouracil, leucovorin and

irinotecan to treat adults with metastatic colorectal cancer that are resistant to or

has progressed following an oxaliplatin containing regimen. This approval was

based on a recent randomized Phase III study VELOUR, in which a total of

1226 patients who had previously received oxaliplatin-based chemotherapy


63

were randomized to either FOLFIRI plus aflibercept or FOLFIRI plus placebo

(Van Cutsem et al., 2012b). The study showed a significant increase in OS

(13.5 vs. 12.1 months; HR 0.81) and PFS (6.9 vs. 4.7 months). There was a

significant improvement in overall response rate in the aflibercept group when

compared to FOLFIRI group (19.8% vs. 11.1%, p=0.0001). However, the side

effects include hemorrhage, GI perforation and compromised wound healing.

This agent is being evaluated in the first-line setting in combination with

modified FOLFOX6 (mFOLFOX6) in the phase 2 Aflibercept And Modified

FOLFOX6 As First-Line Treatment In Patients With Metastatic Colorectal

Cancer (AFFIRM) trial (Wang and Lockhart, 2012). Hence, aflibercept is an

important new option in oxaliplatin failing metastatic colorectal cancer patients

in combination with FOLFIRI.

2.7.3 Regorafenib-small molecule inhibitor

Regorafenib (Stivarga) is an oral multi-kinase inhibitor which targets

angiogenic, stromal and oncogenic receptor tyrosine kinase. Regorafenib can

also inhibit c-KIT, RET, and BRAF. In September 2012, the USFDA approved

regorafenib for the treatment of chemorefactory metastatic colorectal cancer

patients. This approval was based on the CORRECT study (an international,

multicentre, randomised, placebo-controlled, phase III trial) which investigated

the use of regorafenib (160 mg orally daily for 3 out of 4 weeks) or placebo in

760 patients (Van Cutsem et al., 2012a). The study revealed a significant

improvement in overall survival by 29% (6.4 versus 5 months, Hazard

Ratio:0.77). Regorafenib also significantly prolonged median progression-free

survival (PFS) from 1.7 months to 1.9 months when added to best supportive

care (p < 0.000001, 1-sided). The 0.2-month difference in PFS belies the 51%


64

reduction in the risk of disease progression with regorafenib. The most common

side effects grade 3 or higher reported in patients treated with Stivarga

included weakness or fatigue (9.6%), hand-foot syndrome (also called palmar-

plantar erythrodysesthesia) (16.6%), diarrhea (7.2%), hypertension (7.2%) ,

rash or desquamation (5.8%). Other side effects included loss of appetite,

mouth sores (mucositis), weight loss, high blood pressure, and changes in voice

volume or quality (dysphonia). Approximately 8% of patients assigned to

regorafenib permanently discontinued treatment due to adverse events,

compared with 1% in the placebo arm. CORRECT study provides evidence that

regorafenib is the first small-molecule multikinase inhibitor with survival benefits

in metastatic colorectal cancer which has progressed after all standard

therapies. It also therefore, highlights for a continuing role of targeted treatment

after disease progression, with regorafenib offering a potential new line of

therapy in this treatment-refractory population. Unlike bevacizumab and

alfibercept, regorafinib is given by itself and not with other chemotherapy

agents. The precise mechanism of action of regorafenib in mCRC remains

unclear, and the predictive biomarkers are also not yet available for optimal

patient selection.

2.7.4 Identification of predictive biomarkers for anti-angiogenic agents: A

priority for mCRC management

Introduction of anti-angiogenic drugs over the last decade has established anti-

angiogenic therapy as a novel therapeutic modality, but their implementation

has raised several important questions on whether we can find biomarkers to

identify the patients who would benefit from these drugs. Validated biomarkers

are not yet available for routine clinical use (Duda et al., 2013). The exact


65

mechanism of benefit of anti-VEGF drugs is unclear, which has resulted in lack

of biomarker based selection of patients for antiangiogenic therapy(Lambrechts

et al., 2013). This makes the identification of mechanistic biomarkers of

response to anti-angiogenic therapy a priority. Researchers are currently

studying tissue markers, blood derived markers, imaging parameters,

genotypes and systemic measurements (Jain et al., 2009). There are limited

and inconsistent clinical data for VEGF expression levels as the natural

candidate for biomarker for anti-VEGF drugs. Some studies suggest that

circulating VEGF may predict response to anti-VEGF therapy in hepatocellular

carcinoma (Zhu et al., 2009).

Some phase III studies have found no association between circulation VEGF

and response to bevacizumab (Hegde et al., 2013). In one of the phase II trials

it was observed that when colorectal cancer patients were treated with FOLFIRI

in combination with bevacizumab, significant increases in Fibroblast Growth

Factor 2 (FGF2) , Phosphatidylinositol-glycan biosynthesis class F protein

(PIGF), stromal cell-derived factor 1 (SDF-1) and macrophage chemoattractant

protein 3 were observed, which may represent the mechanism of resistance.

Also, increases in base line interleukin 8, which is the promoter of angiogenesis,

was observed and this was associated with decreased progression free survival

(Kopetz et al., 2010). Furthermore, in the direction of achieving goal of

personalized medicine, for host-specific variability single-nucleotide

polymorphisms (SNPs) are also studied as potential biomarkers for response to

these anti angiogenic agents, and several SNPs have been studied as

candidates for predictive markers for bevacizumab in non-colorectal cancers

(Lambrechts et al., 2012). However, these have not been validated as good


66

markers to response to anti angiogenic agents in metastatic colorectal cancer

primarily due to conflicting reports, disparity in detection/measurement of genes

and enzyme activity, and variation in data analysis and interpretation. Recent

mathematical models have also been developed to address this issue (Finley

and Popel, 2013), which could give new insight into the use of VEGF isoforms

as predictive biomarkers. These mathematical models might shed some light on

mechanisms of resistance to anti-angiogenic therapy and also be useful in the

analysis of future large prospective studies to address these predictive

biomarkers.

2.8 Epidermal growth factor receptor targeting agents

The epidermal growth factor receptor (EGFR) is a transmembrane glycoprotein

which belongs to the human epidermal growth factor receptor (Her)-erbB family

of receptor tyrosine kinases. It is composed of an extracellular ligand-binding

domain, a hydrophobic trans-membrane region and an intracellular domain with

tyrosine kinase activity. EGFR is activated by ligands belonging to EGF family

of peptide growth factors which include TGF-α, EGF, amphiregulin, betacellulin

or epiregulin. Binding of these ligands to its extracellular domain leads to

formation of both homo or heterodimers with its family members ErbB2/Neu,

Erbb3/Her3and Erbb4/Her4, which in turn leads to auto phosphorylation of

intracellular tyrosine kinase domains and subsequent activation of downstream

signaling (Ciardiello and Tortora, 2008). The most commonly activated

downstream signaling pathways are the RAS-RAF-MAPK, the PI3K/AKT and

the Jak2/Stat3 pathways, which in turn stimulate cancer cell proliferation,


67

survival, invasion, metastasis and neoangiogenesis (Ciardiello and Tortora,

2008). Over-activation has been shown to induce tumorigenesis. Such

overexpression is observed in 25% to 77% of colorectal cancers and has shown

to be associated with tumor aggressiveness, poor prognosis and

chemoresistance (Capdevila et al., 2009). Several mechanisms have been

reported to contribute to this phenomenon, including mutations in the kinase

domain of EGFR, overexpression of EGFR, and its ligands, and gene copy

number changes. Hence, these findings led to a rational to target EGFR as a

therapeutic strategy for colorectal cancer. EGFR inhibitors act by preventing

ligand binding and subsequent downstream signaling of the oncogenic

pathway.

Cetuximab and panitumumab are two anti-EGFR mAbs that by targeting the

extracellular domain of the receptor inhibit its dimerization and subsequent

phosphorylation and signal transduction. These mAbs have improved patient

outcomes and hence have been incorporated into routine clinical practice with

the finding that the KRAS oncogene is a predictive biomarker for anti-EGFR

therapy (Chee and Sinicrope, 2010). The therapeutic benefit of anti-EGFR

treatment is restricted to tumors with wild-type KRAS. The use of molecular

targeted agents has fewer yet more specific toxicities compared with

conventional cytotoxic drugs and enables a more personalized approach to

cancer therapy.

2.8.1 Cetuximab

Cetuximab (Erbitux) is a chimeric human/mouse recombinant immunoglobin (Ig)

G1 that specifically binds to extracellular domain II of EGFR and blocks the

ligand binding induced receptor dimerization and its further tyrosine kinase


68

activation. It also elicits antibody dependent cellular cytotoxicity against cancer

cells (Kimura et al., 2007). In 2004, the FDA granted approval for cetuximab for

use in combination with irinotecan for first line treatment of irinotecan refractory

advanced colorectal cancer in patients. In 2012, FDA granted approval for

cetuximab in combination with FOLFIRI (irinotecan, 5-FU and leucovorin) for

first line treatment of patients with KRAS mutation negative mCRC. This

approval was based on retrospective analyses of tumor samples from patients

enrolled in the CRYSTAL trail and in two supportive studies, CA225025 and

EMR 62 202 -047(OPUS). These studies led to the American and European

health authorities restricting the use of cetuximab to patients with KRAS wild-

type tumors. The most common side effects of this medication are acne like

rash and hypomagnesaemia (Van Cutsem et al., 2012c). A summary of clinical

trials is shown in Table 2.3.


69

Table 2.3: Summary of clinical trials undertaken for cetuximab.

Study No. of patients Chemotherapy Overall Survival

(in months)

CRYSTAL-

Cetuximab

Combined with

Irinotecan in First-

Line Therapy for

Metastatic

Colorectal

Cancer(KGaA,

2005)

1198

63%-wild type

KRAS

37%-mutant

KRAS

Cetuximab +

FOLFIRI (n=599)

Versus FOLFIRI

(n=599)

23.5 versus 19.5-

wild type KRAS

16.0 versus 16.7 –

mutant KRAS

CA225025(Huang

et al., 2013)

572 Cetuximab

+BSC(n=287)

versus BSC(n=285)

8.6 versus 5-wild

type KRAS

No improvement

for mutant KRAS

EMR62 202-047

OPUS-

Oxaliplatin and

Cetuximab in

First-Line

Treatment of

Metastatic

Colorectal

Cancer(Chang et

al., 2013)

337

57%-wild type

KRAS

43%-mutant

KRAS

Cetuximab +

FOLFOX-4

(n=169)versus

FOLFOX-4 (n=168)

22.8 versus 18.5-

wild type KRAS

No improvement

for mutant KRAS


70

BOND-

Bowel Oncology

with Cetuximab

Antibody(Moroni

et al., 2005)

329 Cetuximab +

Irinotecan (n=218)

versus Cetuximab

(n=111)

4.1 versus 1.5

EPIC-

ERBITUX Plus

Irinotecan for

Metastatic

Colorectal

Cancer(Martinelli

et al., 2009)

1298 Cetuximab +

Irinotecan(n=628)

versus

Irinotican(n=650)

10.7 versus 10

COIN-

Continuous

Chemotherapy

Plus Cetuximab,

or Intermittent

Chemotherapy

With Standard

Continuous

Palliative

Combination

Chemotherapy

With Oxaliplatin

1630

57%-wild type

KRAS

43%-mutant

KRAS

Fluorouracil

+Oxaliplatin

+Cetuximab (n=815)

versus Fluorouracil

+Oxaliplatin (n=815)

17.9 versus 17-

KRAS wild type

14.4 versus 20.1-

KRAS mutant


71

and a

Fluoropyrimidine

in First-Line

Treatment of

Metastatic

Colorectal

Cancer(Tejpar et

al., 2012)

NORDIC VII-

Cetuximab with

Continuous or

Intermittent

Fluorouracil,

Leucovorin and

Oxaloplatin

(NORDIC FLOX)

versus FLOX

Alone in First-Line

Treatment of

metastatic

colorectal cancer

(Tveit et al., 2012)

571

39%-mutant

KRAS

12%-mutant

BRAF

Cetuximab+

Oxaloplatin+

Fluorouracil+

Leucouracil versus

Oxaliplatin+

Fluorouracil+

Leucouracil

9.5 versus 22-

BRAF mutant –

strong prognostic

marker 20.5

versus 21-KRAS

mutant – no

significant

difference


72

NORDIC VII-

Cetuximab With

Continuous or

Intermittent

Fluorouracil,

Leucovorin, and

Oxaliplatin

(Nordic FLOX)

Versus FLOX

Alone in First-Line

Treatment

of Metastatic

Colorectal

Cancer(Tveit et al.,

2012)

571-

39%-mutant

KRAS

12%-mutant

BRAF

Cetuximab +

Oxaliplatin+

Fluorouracil+

Leucouracil versus

Oxaliplatin+

Fluorouracil+

Leucouracil

9.5 versus 22-

BRAF mutant-

strong prognostic

marker

20.5 versus 21-

KRAS mutant-no

significant

difference

FOLFIRI-5Fluorouracil/Leucovorin + Irinotican, BSC-Best Supportive Care, FOLFOX4-Oxaliplatin/5-

Fluorouralcil/Leucovorin,


73

2.8.2 Panitumumab

Panitumumab (Vectibix) is a human IgG2 monoclonal antibody targeting the

extracellular domain of EGFR with high affinity and preventing its activation.

Panitumumab is responsible for inhibition the of proliferation, angiogenisis and

downregulation of EGFR expression (Foon et al., 2004). The most common

side effects are skin rash and hypomagnesaemia, which are similar to those for

cetuximab. Panitumumab was approved by the USFDA in 2006 for the

treatment of EGFR-expressing metastatic colorectal cancer for cases where

disease progression continues despite prior treatment. It was also approved by

the European Medicine Agency (EMA) in 2007 and by Health Canada in 2008

for the treatment of refractory EGFR-expressing metastatic colorectal cancer in

patients with non-mutated (wild-type) KRAS. The approval was based on the

result of single randomized multinational study which had 463 metastatic

colorectal cancer patients. Patients were randomly assigned to either best

supportive care (BSC) alone or BSC plus panitumumab (Van Cutsem et al.,

2007). The mean progression free survival was 96 days for patients receiving

panitumumab and 60 days for patients receiving BSC alone. The median times

of progression were similar (~8 weeks). The objective response rate for

panitumumab monotherapy was 10 %, comparable with cetuximab

monotherapy. A longer progression free survival with panitumumab was

observed in wild type KRAS group (12.3 weeks) as compared to KRAS mutant

group (7.3 weeks).

Furthermore, a phase III randomized trial, called the PRIME study, evaluated

the role of panitumumab in combination with oxaliplatin in first line chemo-naïve


74

metastatic colorectal cancer patients (Douillard et al., 2010). In this study 1183

patients were enrolled, of which 40% had mutated KRAS. The treatment

outcome was analyzed according to KRAS mutational status. The proportion of

wild type and mutated patients was preserved in both of the study groups.

There was a significant increase in progression free survival in KRAS wild-type

patients who received panitumumab plus FOLFOX-4, as compared with the

FOLFOX alone group (9.6 vs. 8 months; Hazard Ratio: 0.80, p=0.02). Similarly

to cetuximab, KRAS status was predictive of panitumumab resistance.

Another phase III randomized trial the 20050181 study, evaluated the role of

panitumumab in combination with FOLFIRI (Peeters et al., 2008). A total of

1186 fluropyrimidine refractory patients were randomized to receive either

panitumumab in combination with FOLFIRI or FOLFIRI alone. The KRAS status

was analyzed for all patients. A significant prolonged progression free survival

was observed in KRAS wild-type patients who received panitumumab in

combination with FOLFIRI (5.9 months), as compared to patients who received

FOLFIRI alone (3.9 months). There was no significant improvement in overall

survival (14.5 vs 12.5 months; Hazard ratio: 0.85; p=0.12).


75

2.8.3 Predictive biomarkers for anti-EGFR agents

The studies described above emphasize the role of anti-EGFR inhibitors for the

treatment of metastatic colorectal cancer. From the results of these studies it is

clear that primary resistance probably plays a pivotal role, and that only a

specific subset of metastatic colorectal cancer patients might benefit from these

anti-EGFR monoclonal antibodies. The discovery of biomarkers has led to

improvements in the therapeutic index for these anti- EGFR antibodies. There

are both positive and negative predictors of response which have been

identified.

2.8.3.1 KRAS

v-ki-ras2 Kristen rat sarcoma viral oncogene homologue (KRAS), a proto

oncogene, is a signal transducer modulated by the EGFR signaling pathway. It

is the most frequently mutated gene in colorectal cancer (20%-40%)(Karapetis

et al., 2008). KRAS activation induces activation of downstream components of

the RAF-MAPK signaling pathway. KRAS is a cytoplasmic GTP-binding protein

with low inherent GTPase activity. When the KRAS protein is bound to GTP, it

relays signals of cellular proliferation and inhibition of apoptosis, acting as a

typical oncogene as described in Figure 2.19 and 2.20. KRAS mutations were

observed mainly in gene exon 2, resulting in abrogated GTPase activity and

locking the KRAS protein in the active KRAS-GTP conformation. By activating

the RAS/RAF/MAPK axis downstream of EGFR, these mutations render

therapeutic modulation of EGFR irrelevant. The most frequent mutations are

observed in codons 12 and 13 (Vaughn et al., 2011). Mutations in codons 61

and 146 are under investigation.


76

Figure 2.19: Activation of RAS pathway.

Figure Legend: The ligand EGF (Epidermal growth factor) binds to EGFR (Epidermal

growth factor receptor) and leads to phosphorylation of tyrosine kinase domain of the

receptor. Upon stimulation of EGF receptor, Grb2 an adaptor protein binds to the

tyrosine kinase domain through its SH2 domain and simultaneously binds to another

protein SOS. This process catalyzes removal of GDP from RAS .RAS then binds to

GTP acquiring an active conformation leading to further downstream RAF and MEK

activation through phosphorylation.

Binding of growth factors to receptor tyrosine kinases stimulates the

autophosphorylation of specific tyrosines on the receptors. The phosphorylated

receptor then binds to an adaptor protein called GRB2 which, in turn, recruits

SOS to the plasma membrane. SOS is a guanine nucleotide exchange factor

which displaces GDP from Ras, subsequently allowing the binding of GTP and

consequently activating RAS.


77

Figure 2.20: Association of anti-EGFR therapy and KRAS mutations.

Figure Legend: Anti EGFR drugs block receptor signals thus preventing downstream

events. In case of Wild type KRAS, when EGFR receptor is blocked, it stops signaling

and tumor cells do not proliferate. Whereas in case of mutant KRAS, it is permanently

turned on allowing the tumor to continue to proliferate.

Many retrospective studies and trials have shown the impact of KRAS

mutational status on treatment efficacy with anti-EGFR monoclonal antibodies

in metastatic colorectal cancer patients (Qiu et al., 2010). The studies have

suggested that treatment with anti –EGFR produces better outcomes only in

patients with wild type KRAS, whereas these drugs had no effect on mutant

KRAS patients. Analysis of data from CRYSTAL and OPUS trials showed that

addition of cetuximab with chemotherapy provided significant improvement in

progression free survival and overall survival, in comparison to chemotherapy

alone. Addition of cetuximab significantly reduced the risk of disease


78

progression by 34% in KRAS wild type patients and increased the likelihood of

achieving a response by greater than 2 fold (Odds ratio:2.16, p<0.0001). In the

re-analysis of CRYSTAL study done in 2015 additional RAS mutations were

examined at KRAS exon 3 (codons 59 and 61), KRAS exon 4 (codons 117 and

146), RAS status was evaluable in 430 of 666 patients (64.6%) (Van Cutsem et

al., 2015) (Allegra et al., 2015). Recently, reports have suggested that different

KRAS mutations may have different biological characteristics with respect to

treatment sensitivity. Tumors having mutations of KRAS codon 13, glycine to

aspartate (G13D), have been suggested to retain cetuximab sensitivity and has

improved outcomes in some patients during cetuximab therapy (Tejpar et al.,

2012). In the reported studies from different population backgrounds the KRAS

mutations frequency varies from 14%-40% (Ozen et al., 2013, Mao et al.,

2012a, D. Lambrechts, 2009). These variations in patterns of KRAS mutations

may be due to the racial differences and etiological factors. In view of the

results of several clinical trials, KRAS mutation screening in codons 12 and 13

for metastatic colorectal cancer treatment has been recommended (Allegra et

al., 2009). The use of cetuximab and bevacizumab has been approved only for

patients with KRAS wild type tumors.


79

2.8.3.2 BRAF

V-raf murine sarcoma viral oncogene homologue B1 (BRAF) is a member of the

RAF family acting downstream of KRAS in the MAPK cascade. The BRAF gene

is another potential predictive factor. BRAF and KRAS mutations are mutually

exclusive events in tumor (Fransen et al., 2004). The most frequently reported

BRAF tumor mutation is a valine-to-glutamic acid amino acid (V600E)

substitution that leads to the aberrant activation of the MEK–ERK pathway

(Ikenoue et al., 2003). This mutation leads to a 500-fold increase in BRAF

activity compared to the wild type form. BRAF mutations are used as exclusion

criteria in the diagnosis of hereditary nonpolyposis colorectal cancer syndrome.

Also, BRAF mutation is closely associated with MSI-H phenotype, MLH1

hypermethylation and CIMP high status (Zlobec et al., 2010). The predictive

value of BRAF mutations in KRAS wild-type patients treated with anti-EGFR

therapy has been demonstrated by several groups. In the study carried out by

Di Nicanlotonio et al., it was seen that among 79 patients with wild type KRAS,

86% had wild type BRAF. No patient with a mutated BRAF had objective tumor

response compared to 32% in patients with wild type BRAF(Di Nicolantonio et

al., 2008). In the PETACC-3 study, BRAF mutations occurred in 7.9% of tumors

in stage II and stage III colon cancer patients. These mutations were found to

be prognostic for overall survival (Hazard ratio=2.2, p=0.003)(Roth et al., 2010).

An analysis of 724 patients treated with irinotecan plus cetuximab showed

mutated BRAF was present in 5% of patients and that it was associated with

reduced responses compared with wild-type BRAF (6% versus 24%) (Tejpar

and De Roock, 2009). In the CAIRO-2 study, the predictive and prognostic


80

value of BRAF was analyzed in 516 patients. 8% of patients had BRAF

mutations and had decreased median free survival compared to those without

mutation (5.9 versus 12.2 months, P = .003 without cetuximab; and 6.6 vs 10.4

months, P = .010 with cetuximab, respectively) (Tol et al., 2009). This finding

suggests that BRAF can be a prognostic factor and not a predictive factor of

cetuximab efficacy. Pooled analysis from CRYSTAL and OPUS data confirms

that patients with mutated BRAF have worse prognosis than those with wild

type BRAF. Based on these findings, BRAF genetic screening has been

recommended in patients negative for KRAS mutations before treatment with

anti- EGFR drugs.

2.8.3.3 NRAS

Neuroblastoma Ras viral oncogene homolog (NRAS) is a member of RAS

family. Along with KRAS and BRAF, NRAS has also been evaluated recently as

a potential predictive marker in metastatic colorectal cancer. The most

frequently reported NRAS mutations are observed in codons 12, 13, 61, 117

and 146 (D. Lambrechts, 2009). Recently in a retrospective study the predictive

value of NRAS was evaluated in KRAS wild type metastatic colorectal cancer

patients. As opposed to KRAS mutations, the NRAS mutation frequency was

low (3-5%). In the pooled retrospective analysis led by the European

Consortium, the rates of response to cetuximab in a large cohort of patients

were lower in patients with KRAS wild-type tumors bearing NRAS mutations

(De Roock et al., 2010). The only randomized dataset available demonstrates a

numeric lack of benefit from panitumumab, another EGFR-targeted monoclonal

antibody, for the NRAS mutant population, but trying to extract statistical


81

significance from findings obtained in a 14-patient population is not feasible. In

one recent study, it was observed that in metastatic colorectal cancer the Q61K

NRAS mutation had a favorable response to bevacizumab (Janku et al., 2013).

These results suggest that NRAS mutations merits further investigation as a

potential biomarker predicting the efficacy of bevacizumab-based treatment.

2.8.3.4 PIK3CA

Phosphatidylinositide-3-kinases (PIK3) are lipid kinases that are divided in three

classes, I, II and III. Only the a-type isoform of the catalytic subunit, PI3KCA,

harbors oncogenic mutations that are present in 15–20% of all colorectal

cancers(Jehan et al., 2009). PIK3CA mutations occurring in the “hotspots”

located in exon 9 (E542K, E545K) and exon 20 (H1047R) (Samuels et al.,

2005). It has been demonstrated that PIK3CA mutations confer resistance to

apoptosis, whilst enhancing invasion capacity and metastatic potential. Several

studies have demonstrated that PIK3CA mutations do not respond to anti-

EGFR therapy and these mutant colorectal cancer patients have shorter

progression free survival than wild type patients (Lièvre et al., 2010). The

findings of a European consortium suggest that response to EGFR treatment

can be predicted only if specific PIK3CA mutation status is co-evaluated with

KRAS status (De Roock et al., 2010). These investigations suggest that

combining mutational analysis for KRAS and PIK3CA could identify up to 70%

patients with metastatic colorectal cancer who are unlikely to respond to

treatment with an EGFR targeted monoclonal antibody (Sartore-Bianchi et al.,

2009). In one of the recent studies a contradictory evidence was reported in

which it was found that there was no strong rationale for using PIK3CA


82

mutations as a single marker for sensitivity to cetuximab in chemotherapy

refractory metastatic colorectal cancer (Prenen et al., 2009). Since tumors with

oncogenic PIK3CA are likely to be driven by PI3K as the primary source of

growth, proliferation and survival, the use of selective PI3K inhibitors is being

tested in ongoing trials. Several PI3K inhibitors are progressing from pre-clinical

studies to phase I trials. These include XL147,GDC-0941,BGT226, XL765 and

NVP-BEZ235 (Yuan and Cantley, 2008). This data from various trials needs to

be validated in clinical applications in larger study groups due to occurrence of

low frequency of PIK3CA mutations.

2.8.3.5 PTEN

Phosphatase and tensin homolog (PTEN) acts as a tumor suppressor gene

through the action of its phosphatase protein product. This phosphatase is

involved in the regulation of the cell cycle, preventing cells from growing and

dividing too rapidly (Chu and Tarnawski, 2004). It negatively regulates

intracellular levels of phosphatidylinositol-3,4,5-trisphosphate in cells and

functions as a tumor suppressor by negatively regulating Akt/PKB signaling

pathway. PTEN loss or inactivation leads to hyperactivation of the PI3K

signaling pathway. Loss of PTEN expression occurs in 30% of sporadic CRCs

(Thomas and Grandis, 2004). There are only few studies which have

demonstrated that loss of PTEN expression may be useful in predicting

response to cetuximab. Frattini et al. reported that none of 11 patients with

tumor PTEN loss responded to cetuximab-based treatment, whereas 10 (63%)

of 16 patients with intact PTEN protein expression were partial responders

(Frattini et al., 2007, Sartore-Bianchi et al., 2009). Further studies are required

to confirm these findings.


83

Figure 2.21: Estimated Response Rate to EGFR inhibitors in Western

population with activating mutations in KRAS, BRAF, NRAS and PIK3CA Data

according to -(Frattini et al., 2007).


84

2.8.3.6 TP53

The TP53 gene encodes a tumor suppressor protein p53 which is one of the

most frequently mutated genes in human cancer. Activated p53 binds to the

regulatory sequences of a number of target genes to initiate a program of cell

cycle arrest, DNA repair, apoptosis, and angiogenesis (Vogelstein et al., 2000).

Loss of function of TP53 is critical in tumorigenesis, and mutations which result

in overexpression of the protein are frequent events in colorectal cancer. p53

alterations are more frequent in tumors that are aneuploid, non-mucinous, and

do not show any MSI or CIMP molecular phenotypes (Westra et al., 2005).

Associations of TP53 tumor alterations with patient prognosis and response to

adjuvant chemotherapy have been widely studied. The majority of translational

studies carried out which aimed at determining whether TP53 mutation and

overexpression of p53 have prognostic value in colorectal cancer (Popat et al.,

2006). Few studies in colon cancer patients failed to demonstrate correlations

between TP53 alterations and benefit from adjuvant therapy (Allegra et al.,

2003). Similarly, a subset of functionally inactive mutations in TP53 predict poor

survival in late stage colorectal cancer (Iacopetta et al., 2006). Oden- Gangloff

et al. suggests that TP53 mutations may be predictive of increased likelihood of

response to cetuximab treatment, particularly in patients with wild-type KRAS

status (Slevin et al., 2008).


85

2.9 Predictive and prognostic biomarkers in development

2.9.1 Micro RNAs

MicroRNAs (miRNAs) are small non-coding RNA molecules involved in the

post-transcriptional and translational regulation of gene expression. miRNAs are

emerging as ideal disease biomarkers for diagnosis as well as therapeutics in

CRC patients. Several studies have demonstrated that the expression of

various miRNAs in plasma may be indicative of presence of CRC. High plasma

miR-29a and miR-92a expression are useful non-invasive biomarkers to

distinguish CRC from healthy controls. Studies have shown a total of 362

differentially expressed miRNAs in colorectal cancer of which 242 are

upregulated and 120 are down regulated (Ma et al., 2012). miRNAs have also

been evaluated as therapeutic targets. Two general strategies for miRNA-based

therapeutics are seen: blocking oncogenic miRNAs and restoration of tumor-

suppressor miRNAs. Blocking of oncogenic miRNAs by anti-miRNAs has

suppressed cell proliferation and has enhanced chemo sensitivity (Akao et al.,

2011). Similarly, studies have shown that restoration of miR-143 using miR-143

precursor has reduced tumor growth (Ng et al., 2009).

2.9.2 Cell free nucleic acid

Circulating cell free nucleic acids has been reported in blood, stool and urine of

colorectal cancer patients in higher levels in comparison to healthy individuals.

Hence, presence of circulating DNA or RNA expression can provide valuable

molecular information about the tumor. It has been shown that cfDNA levels

decreased after tumor resection and increased in patients with recurrence and

metastasis (Frattini et al., 2008). More studies are required to evaluate cfNA


86

levels in same cohort and different sample types to select the best possible

panel for diagnosis.

2.9.3 Circulating tumor cells

Circulating tumor cells (CTC) levels in peripheral blood have shown a significant

correlation with more advanced disease. CTC detection is significantly

associated with depth of tumor invasion, venous invasion, lymph node

metastasis, liver metastasis and stage (Iinuma et al., 2006).Metastatic

colorectal cancer patients with liver metastasis and poorer performance had

higher baseline CTC levels. Baseline CTC is an independent prognostic factor

in metastatic colorectal cancer. Patients with unfavorable levels of CTCs at

baseline had significantly shorter median disease-free and overall survival than

patients with fewer CTCs (Aggarwal et al., 2012). Also, patients with low

baseline and post-treatment CTC counts had longer progression-free and

overall survival than patients with an initially high baseline CTC count which

decreased after chemotherapy. Hence, in multiple studies CTCs have shown to

be prognostic and predictive biomarker.

2.9.4 Protein Biomarkers

Due to limited clinical applicability of CEA and CA19-9, additional proteins have

been proposed as colorectal cancer protein markers. These include TIMP-1,

MAPKAPK3, ACVR2B, CCSA-2, CCSA-3, CCSA-4 and matrix

metalloproteinase 9, S100A8, and S100A9. These proteins show very

promising results as CRC diagnostic biomarkers. However, these proposed

biomarkers need to be validated individually or in panel so as to make it

clinically relevant diagnostic tool (García-Bilbao et al., 2012).


87

2.9.5 Cancer stem cells

Recently, compelling evidence has emerged in support of the cancer stem cell

(CSC) hypothesis in several solid organ epithelial malignancies including CRC.

CSC's are responsible for tumor initiation, metastases and resistance to

treatment leading to disease relapse following surgery and/or chemo

radiotherapy. As CSC have a potential to self-renew, capacity to differentiate

and initiate tumor and also allow asymmetric cell division via non-random

chromosomal co-segregation researchers have used these properties to isolate

colorectal cancer stem cells (Langan et al., 2013). Till date various putative

CRC stem cell markers have been identified- CD133, CD24, CD29, CD44,

CD166 (ALCAM), EpCAM, Lgr5, ALDH1A1 and ALDH1B1. These CSC markers

have shown to predict disease progression, and identify patients at risk for

recurrence. However, their prognostic significance as not been effectively

evaluated(Lugli et al., 2010). Further as reviewed by Lagan et al, 2013 in order

to translate the CSC based findings into clinical practice, comprehensive

analysis of a panel of CSC expression in large groups of colorectal cancer

patients is crucial.


88

Table 2.4: Summary of Biomarker based studies in CRC (Patil et al., 2016)

Type Biomarker Biological Role Clinical use References C

he

mo

the

rap

y

TS : Thymidylate

Synthetase

Response to 5-

Fluorouracil

Predictive (Choueiri et

al., 2015)

Nuclear excision repair

pathway - ERCC1

expression

Absence or low

expression, prolonged

disease free survival with

Cisplatin based adjuvant

chemotherapy

Prognostic (Choueiri et

al., 2015)

DPD : Dihydropyrimidine

dehydrogenase

5-FU Prognostic (Falvella et

al., 2015)

TP : Thymidine

Phosphorylase

High expression, poor

prognosis with

Capecitabine

Prognostic (Bai et al.,

2015)

MTHFR :

Methylenetetrahydrofola

te Reductase

Genetic polymorphisms;

low risk to CRC

Prognostic (Zhao et

al., 2013)

UGT1A1 : Uridine

diphosphate

glucuronosyl transferase

1A1

Genetic polymorphisms ;

predicting toxicity to

Irinotecan

Prognostic (Lu et al.,

2015,

Hirose et

al., 2012)

GSTP1 : Glutathione S-

transferase P1

Genetic polymorphism

Ile105Val ,increased risk

to CRC

Prognostic (Song et

al., 2014)

Ta

rgete

d t

hera

py

KRAS : v-ki-ras2 Kristen

rat sarcoma viral

oncogene homologue

Genetic mutations in

exon2, 3 and 4. Predicting

resistance to anti-EGFR

moAB

Predictive (Allegra et

al., 2015)

BRAF : V-raf murine Genetic mutations in exon Prognostic (Fransen et


89

sarcoma viral oncogene

homologue B1

15 V600 E, poor

prognosis

al., 2004)

NRAS : Neuroblastoma

Ras viral oncogene

homolog

Genetic mutations in

exon2, 3 and 4. Predicting

resistance to anti-EGFR

moAB

Predictive (De Roock

et al., 2010)

PI3K:

Phosphatidylinositide-3-

kinases

Genetic mutations.

Predicting resistance to

anti-EGFR moAB

Predictive (Jehan et

al., 2009)

PTEN : Phosphatase

and tension homolog

Loss of expression.

Predicting response to

anti-EGFR moAB

Predictive (Thomas

and

Grandis,

2004)

TP53 Genetic mutations.

Predicting response to

anti-EGFR moAB

Predictive (Slevin et

al., 2008)

Ep

ige

ne

tic

ma

rke

rs

MSI : Microsatellite

Instability

Lynch Syndrome Prognostic (Lech et al.,

2016)

COX2 : Cyclooxegenase

-COX2

COX2 inhibitors

associated with worst

outcome to treatment and

low risk to CRC

Prognostic (Rahman et

al., 2012)

CIMP : CpG island

methylator phenotype

Methylation of CpG

islands. Indicator of poor

prognosis

Prognostic (Bae et al.,

2013)

18q LOH : 18q loss of

heterogeneity

Allelic loss of 18 q, poor

prognosis

Prognostic (Colussi et

al., 2013)

CIN : Chromosomal

instability

Abnormal chromosome

number, poor prognosis

Prognostic (Reimers et

al., 2013)


90

Pro

tein

ma

rke

rs

CEA : Carcinoembryonic

antigen

Monitoring therapy and

prognosis

Prognostic (García-

Bilbao et

al., 2012)

CA19-9 : Cancer

antigen 19-9


prognosis


Bilbao et

al., 2012)

TIMP : Tissue Inhibitors

of Metalloproteinases


prognosis


Bilbao et

al., 2012)

Bio

ma

rkers

In

de

ve

lop

men

t

Micro RNA (miRNA) Regulating gene

expression, poor

prognosis

Predictive and

prognostic

(Akao et

al., 2011)

Cell free nucleic acid High levels of cfNA, poor

outcome

Predictive and

prognostic

(Frattini et

al., 2008)

Circulating tumor cells High CTC, poor outcome Predictive and

prognostic

(Aggarwal

et al., 2012)

Cancer stem cells Predict disease

progression and risk of

reoccurrence

Predictive and

prognostic

(Lugli et al.,

2010)


91

In summary as reviewed in this chapter, over the past decade significant

advances have been made in the management of colorectal cancer. Various

genes and pathways have been identified in colorectal cancer and extensive

knowledge has been gained about initiation and progression of the disease.

These recent advancements have attributed to an increase in overall survival of

metastatic colorectal cancer patients, with current overall survival averaging at

approximately 2 years. Advances in the development of chemotherapy and

biological agents to treat colorectal cancer have resulted in incremental gains

for patients survival (Table 2.5).

Recently several dynamic predictive markers have been identified which partly

solve the challenge of selecting patients who will respond to the high cost

targeted therapy (Table 2.4). However, currently there are only a few predictive

tools which are available to select patients who would best respond to specific

tumor treatments. Hence, there is a strong need to develop and validate more

biomarkers to assist with clinical decision making.

Population based studies are required to assess the most recent benefits of

clinical trials and also to determine the meaningful survival improvements in

colorectal cancer. With the recent genomic profiling of colorectal cancer and the

development of new proteomic and modeling studies, selecting and stratifying

colorectal cancer patients based on their molecular profile will be improved,

resulting in better patient management and individualized and personalized

health care.


92

Table 2.5: Biological agents in clinical trials

Biological Agent Clinical Trial Stage Biological Agent Clinical Trial Stage

Edrecolomab Phase III-Completed MEHD7945A Phase I-Recruiting

Adecatumumab Phase II-Completed R05083945 Phase II

Cixutumumab Phase II-Completed OMP-21M18 Phase I-Active not

recruiting Conatumumab Phase II-Recruiting MGA271 Phase I-Recruiting

Figitumumab Phase II-Completed Dalotuzumab Phase II-Completed

Ganitumab Phase II Drozitumab Phase Ib

Necitumumab Phase II-Completed Ensituximab Phase I-Recruiting

Nimotuzumab Phase II-Terminated Etaracizumab Phase I-Completed

Trastuzumab Phase II-Completed Ramucirumab Phase II-Completed

Tremelimumab Phase II-Completed Tigatuzimab Phase II

Zalutumumab Phase II-Terminated CDX-1127 Phase I-Recruiting

AMG386 Phase I-Completed CEP37250/KHK280

4

Phase I-Active not

recruiting

CNTO328 Phase I/II-Completed Hu3S193 Phase I-Completed

CT011 Phase II-Completed ING-1 Phase I-Completed

GS-6624 Phase II-Recruiting KRN330 Phase I/II-Completed

IMC18F1 Phase II-Active not

recruiting

MDX-1105 Phase I-Active not

recruiting

IVIG Phase II-Unknown MDX-1106 Phase I-Completed

L19 Phase II-Completed MGA-271 Phase I-Recruiting


93

2.10 Molecular Pathology Epidemiology: Emerging discipline to help in

optimizing disease prevention and treatment strategies

Molecular Pathological Epidemiology (MPE) or Molecular Pathologic

Epidemiology is a promising interdisciplinary research field that deals with the

integration of molecular signatures with epidemiological studies to elucidate

disease aetiologies. Its concept was consolidated in the year 2010 by Ogino

and Stampfer (Ogino and Stampfer, 2010), and has been designed to reveal

how various lifestyle exposures affect initiation, transformation and progression

of neoplasia (Ogino and Stampfer, 2010, Ogino et al., 2016).

CRC comprises of heterogeneous group of diseases with varied genetic and

epigenetic alterations. With the introduction of MPE, interactive effect of

molecular features of the tumor and lifestyle or other exposure factors on

prognosis and clinical outcome can be examined. In traditional epidemiological

studies, the risk of developing CRC in accordance with the different genetic,

environmental or lifestyle related factors was examined. However, MPE

investigates the genetic or molecular variation in population, its interaction with

lifestyle or environmental factors to evaluate possible causative links by

encompassing Genome wide association studies (GWAS) (Figure 2.22).


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Figure 2.22: Difference between traditional epidemiological studies and

Molecular pathologic epidemiology (MPE) (Image Source-Ogino et. al., Gut,

2011).

As mentioned by Ogino and co-workers, there are three approaches to

investigate exposure and molecular change- Case-Case approach, Case-

Control Study and Prospective Cohort Study (Ogino et al., 2011). The same has

been demonstrated in Figure 2.23. There is one more approach as mentioned

by Ogino et. al. which is known as Interventional Cohort study. This approach is

considered as a ‘gold standard’, however, no data has been published yet.


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Figure 2.23: Three approaches of MPE (Image source-Ogino et.al., Gut, 2011).

In ‘case- case’ approach, the tumor is classified according to the subtypes and

the effect of the variable of interest is compared amongst the different subtypes.

The limitation of this approach is that the direction of any association between

the exposure variable and the molecular subtype cannot be ascertained. Like

for example as provided by Ogino et al, if smoking an exposure variable is

studied along with KRAS mutations which acts as molecular subtype, then in

case- case approach it cannot be ascertained if smoking protects form KRAS

mutations or causes KRAS mutations.


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The second approach is ‘Case-Control’ approach. In this approach the

distribution of the exposure variable is studied in the cases with specific genetic

or epigenetic alterations as well as in the control cases without those

alterations.The third approach of Prospective cohort study combines case-case

and case-control approach. This approach requires substantial number of

participants, frequent follow up time, large funding and substantial efforts by

researchers.

Hence, MPE with its distinctive strengths can provide insights into the

pathogenic process of a disease and help optimize personalized therapy and

prevention.

However, there are few challenges of MPE such as sample size limitations,

validations of assays and study findings, paucity of interdisciplinary experts and

standardized guidelines (Ogino et al., 2015). To overcome these challenges,

Professor Shuji Ogino has taken an initiative and has introduced International

MPE meeting series since April 2013.He has also started many projects like-

Strengthening the Reporting of Observational Studies in Epidemiology-MPE

guideline project and ongoing efforts for multidisciplinary consortium projects.

These approaches are similar to other existing initiatives like Big Data to

Knowledge (BD2K), Genetic Associations and Mechanisms in Oncology

(GAME-ON), and Precision Medicine initiatives. Hence, in oncology, MPE, a

different approach from traditional epidemiological studies, has led to address

the interactive effect of exposure factors and molecular changes on the tumor

aggressiveness.MPE will in future help in providing substantial insights in


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carcinogenic processes and will also help in optimizing prevention and

treatment strategies.

Chapter 3. Materials and Methods

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Chapter 3 Materials and Methods

3.1 Study Population

A total of 203 formalin fixed paraffin embedded (FFPE) colorectal cancer tissue

samples of patients from Indian origin were analyzed for mutations in KRAS,

BRAF, NRAS and PIK3CA genes.

The age range for males was 21-90 years and of females was 27-76 years and

median age was 54 years. These samples were resected at various hospitals all

over India and were sent to us for testing during January 2013 till June 2016.

Demographic and clinicopathological features were obtained along with the test

requisition form. Each sample has been designated with a unique accession

number. The project has been approved by scientific committee of Reliance Life

Sciences. The samples were processed according to guidelines of College of

American Pathologists (CAP) and National Accreditation Board for testing and

calibration Laboratory (NABL) and the samples were collected from patients

with informed consent.

3.2 Methods

The detection of mutations in KRAS exons 2 and 3, BRAF exon 15, NRAS

exons 2 and 3 and PIK3CA exons 9 and 20 was developed and standardized

in-house for PCR amplification and direct nucleotide sequencing of PCR

products. The assay was validated in order to comply with the guidelines of


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National Accreditation Board for testing and calibration Laboratory (NABL),

India and College of American Pathologists (CAP), USA. The validation

parameters included sensitivity, specificity, repeatability and reproducibility as

defined below. The performance characteristics were determined at Molecular

Medicine Group, Reliance Life Sciences Pvt. Ltd., Navi Mumbai, India, after the

completion of validation of the assay.

Sensitivity: Analytical sensitivity represents ability of the assay to consistently

detect presence or absence of mutations in minimum percentage of tumor

present in the sample and also to detect the mutant allele burden in normal

allele.

Specificity: Analytical specificity represents ability of the assay to consistently

amplify only specific exons or regions of the mentioned genes and absence of

non-specific additional fragments.

Repeatability: The agreement in the results of an assay performed by the same

analyst on two different occasions in independent assay is defined as

repeatability of the assay.

Reproducibility: The agreement in results of an assay performed by different

analysts on different occasions in independent assay is defined as

reproducibility of the assay.


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The mutation detection assays for KRAS, BRAF, NRAS and PIK3CA were

based on six major processes outlined below.

1. Haematoxylin and eosin (H&E) analysis for histological assessment of

tumor in FFPE tissue samples.

2. Specimen preparation: DNA extraction from FFPE tissues, quality check

and quantification of the DNA.

3. PCR amplification of exon 2 and exon 3 for KRAS, exon 15 for BRAF,

exon 2 and exon3 for NRAS and exon 9 and exon 20 for PIK3CA genes.

4. Detection of amplified products by agarose gel electrophoresis.

5. Direct sequencing of the amplified products.

6. In-silico data analysis for mutation screening.


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Flow chart of Methodology

Formalin Fixed Paraffin Embedded Tissue (FFPE) specimen collection (n=203)

(Demographic and clinico-pathological details)

Immunohistochemistry (Tumor % + Tumor grading)

DNA isolation from FFPE samples

PCR assay standardization, validation and set up for all four genes (KRAS,

BRAF, NRAS and PIK3CA genes)

Sequencing and analysis for mutation profiling of four genes

Correlation between mutations and clinico-pathological characteristics

Survival Analysis


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3.2.1 Haematoxylin and eosin (H&E) Analysis

H & E staining is an essential first step to confirm the presence of

representative tumor tissue before further analysis can be started. It is the gold

standard and allows the typing and grading of tumors as well as gauging the

extent of tissue involvement by tumor.

3.2.1.1 Principle

Tissue processing leaves the tissue structurally enabled to undergo

downstream processes of sectioning and staining for diagnostic purpose. The

H&E is the most widely used stain for histopathology in which nuclei is stained

by oxidized haematoxylin (haematin) through mordant bonds such as aluminum

followed by counter-staining by xanthene dye eosin, which colors in various

shades the different tissue fibers and cytoplasm. Haematoxylin is basic in

nature and binds to basophilic substances i.e. DNA or RNA, whereas eosin is

acidic in nature and binds to acidophilic substances like amino acid side chains.

3.2.1.2 Method

Pre-cooled FFPE block was immobilized on a microtome. The angle of high

profile microtome blade was adjusted. Trimming of the paraffin blocks was done

on 10 µM. Then microtome setting was adjusted for section thickness of 3-4

µM. Ribbons of tissue sections taken and were layered on a floatation water

bath (temperature = 50° C) from where they were mounted on egg albumin

coated ordinary double frosted slide for H & E analysis.

Slides were then placed in oven at 60°C overnight and then transferred into

xylene bath and three serial changes were performed at an interval of 5 min.

Rehydration of slides was done using 100 % ethanol in two changes at 5 min


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interval and then the slides were placed in freshly prepared 90 % ethanol for 5

min followed by 70% and 50%. Slides were gently rinsed under tap water for 5

min and were kept in Harris haematoxylin solution for 10 min followed by gentle

wash under tap water for 5 min. The slides were then dipped in 1% acid alcohol

for differentiation and then gently rinsed under tap water followed by treatment

with 1% ammonia solution with single change followed by gentle wash under

tap water. These slides were then placed in 70% ethanol followed by absolute

ethanol for 30 s each and were then dipped in Eosin stain for one min. Slides

were dehydrated in two changes of fresh 100% ethanol for 5 min each followed

by blot drying and were kept in xylene overnight and mounted in DPX. These

were then reviewed by the pathologist for tumor content and grading.


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Figure 3.1: H&E stained tissue samples

A

B

Figure Legend:

A Representative H&E photomicrograph of colorectal cancer tissue sample with well

differentiated adenocarcinoma, WHO grade I;

B H&E photomicrographs of normal tissue.

3.2.2 DNA extraction

3.2.2.1 Principle

The PureLink Genomic DNA kits (Invitrogen, Cat No. K1820-02, Carlsbad, CA,

USA) are based on the selective binding of DNA to silica-based membrane in

the presence of chaotropic salts. The lysate was prepared from a variety of

starting material such as tissues. The cells were digested with Proteinase K at

55C using an optimized digestion buffer formulation that aids in protein

denaturation and enhances Proteinase K activity. Any residual RNA was

removed by digestion with RNase A prior to binding samples to the silica

membrane. The lysate was mixed with ethanol and Purelink Genomic Binding

Buffer that allows high DNA binding Purelink Spin Column (Mini kit). The DNA


105

binds to the silica based membrane in the column and impurities were removed

by thorough washing with Wash Buffers. The genomic DNA was then eluted in

low salt Elution Buffer.

3.2.2.2 Methodology

The protocol was followed as per the manufactures instructions (Invitrogen, Cat

No. K1820-02, Carlsbad, CA, USA)

1. Specimen preparation

Four sections each of 8 µM of FFPE tissue specimen were taken in 1.5 ml

microfuge tube. Tumor sections having 20% tumor content, as assessed by the

pathologist, were processed further for macrodissection. Sections on the slide

containing tumor were marked by the pathologist which were further scraped

and taken for DNA extraction. One ml xylene was added followed by vortex for

10 s and centrifugation at 13000 rpm for 3 min. The supernatant was removed

and the xylene wash was repeated again to remove paraffin from the tissue.

Then 1 ml of absolute ethanol was added to the tube followed by vortexing for

10 s and centrifugation at 13000 rpm for 3 min. The supernatant was removed

and the ethanol wash step was repeated again. Supernatant was removed and

the tube with open lid was placed at 37 °C dry block until all residual ethanol

was evaporated. Pellet was re-suspended in 180 µl digestion buffer and 20 µl

Proteinase K followed by incubation at 50° C overnight.


106

2. DNA extraction

After overnight incubation, the lysate was centrifuged at 13000 rpm for 3 min.

The supernatant was transferred to 1.5 ml micro centrifuge tube. 20 ul of RNase

A solution was added followed by one min incubation at RT.200 µl of

Lysis/Binding buffer and 200 µl of ethanol was added to the lysate, vortexed to

yield a homogenous solution and spun briefly. The 620 µl solution was then

layered on Purelink spin column and centrifuged at 10000 rpm for 1 min. The

spin column was then transferred to fresh collection tube. Wash Buffer WB1

500 µl was added to the spin column and centrifuged at 10000 rpm for 1 min.

The column was then placed in fresh collection tube and 500 µl of Wash Buffer

WB2 was added and centrifuged at 13000 rpm for 3 min. The column was then

placed in 1.5 ml microfuge tube and 50 µl of elution buffer was added to the

center of the column, incubated for 1 min at RT followed by centrifugation at

10000 rpm for 1 min. The spin column was discarded and the DNA was

collected in 1.5 ml tube.

The extracted DNA was quantified by Nanodrop spectrophotometer using the

Nanodrop 26 ND-1000 spectrophotometer (Nanodrop Technologies Inc.

Wilmington, NC, USA). Purified DNA with the A260/A280 ratio of 1.7-1.9 and

absorbance scans with symmetric peak at 260 nm confirmed the purity of the

DNA.

The DNA was further processed for PCR amplification followed by nucleotide

sequencing.

3.2.3 Primer selection for Polymerase Chain Reaction (PCR)

The primers were selected from literature survey for amplification of KRAS exon

2 and exon 3, BRAF exon 15, NRAS exon 2 and exon 3 and PIK3CA exon 9


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and exon 20. The described primers were modified , when necessary using

Primer Express software (Applied Bio systems, Foster City, USA) to check the

secondary structure and critical parameters like primer length, melting

temperature (Tm), specificity, complimentary primer sequences, G/C content

polypyrimidine (T,C) or polypurine (A,G) stretches, 3’- end sequence, primer-

dimer formation.

3.2.3.1 PCR Assay Optimization

PCR assay optimization for KRAS, BRAF, NRAS and PIK3CA mutation

detection included optimization of DNA concentration, tumor percentage, and

Taq polymerase, MgCl2, DMSO and BSA. The PCR conditions including

temperature and time for denaturation, annealing and extension were

standardized and were finalized on observation of the PCR products on

agarose gel electrophoresis.

The nested PCR was standardized for all four genes. Table 3.1 and 3.2

summarize the thermal cycling conditions:


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Table 3.1: Master mixture for first round of PCR for KRAS, BRAF, NRAS and

PIK3CA.

Stock Solution 1 reaction (µl)

Sterile MilliQ water 11.7

5 X Buffer (Promega) 10

2mM d NTPs (Takara) 5

25mM MgCl2 (Promega) 3

DMSO (Sigma) 3

0.1% BSA (Sigma) 5

Forward Primer (Sigma) 1

Reverse Primer (Sigma) 1

Taq DNA Polymerase (5U/µl) Promega 0.3

Total Volume 40

10 µl of extracted DNA was added to the reaction mix. Each assay had an

amplification control, and reaction control to check the assay validity.


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Table 3.2: Master mixture preparation for nested PCR for KRAS, BRAF, NRAS

and PIK3CA.

Stock Solution 1 reaction (µl)

Sterile MilliQ water 7.7

5 X Buffer (Promega) 5

2mM d NTPs (Takara) 2.5

25mM MgCl2 (Promega) 1.5

DMSO (Sigma) 1.5

0.1% BSA (Sigma) 2.5

Forward Primer (Sigma) 1

Reverse Primer (Sigma) 1

Taq DNA Polymerase (5U/µl) Promega 0.3

Total Volume 23

About 2 µl of the first round product was added to the reaction mix

Thermal cycling conditions are given in Table 3.3 - Table 3.11.

Table 3.3: KRAS exons 2 and 3 mutation detection assay.

Initial denaturation 94°C- 5 min

Denaturation 94°C – 30sec

Annealing 52°C – 30sec

Extension 72°C – 30sec

Number of cycles 35

Final extension 72°C – 7min

Hold 15°C - ∞

First round and Nested PCR for KRAS exons 2 and 3 have same conditions.


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Table 3.4: BRAF exon 15 mutation detection assay- First round PCR.





Number of cycles 22


Hold 15°C - ∞

Table 3.5: BRAF exon 15 mutation detection assay- Nested PCR.





Number of cycles 35


Hold 15°C - ∞

Table 3.6 NRAS exon 2 mutation detection assay- First round and Nested PCR.





Number of cycles 35


Hold 15°C - ∞

First round and Nested PCR for NRAS exons 2 and 3 have same conditions.


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Table 3.7: NRAS exon 3 mutation detection assay- First round PCR.





Number of cycles 35


Hold 15°C - ∞

Table 3.8: NRAS exon 3 mutation detection assay- Nested PCR.





Number of cycles 35


Hold 15°C - ∞

Table 3.9: PIK3CA exon 9 mutation detection assay- First round and Nested

PCR.




Extension 72°C – 1min

Number of cycles 35


Hold 15°C - ∞

First round and Nested PCR for PIK3CA exon 9 have same conditions.


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Table 3.10: PIK3CA exon 20 mutation detection assay- First round PCR.




Extension 72°C – 1min

Number of cycles 35


Hold 15°C - ∞

Table 3.11: PIK3CA exon 20 mutation detection assay- Nested PCR





Number of cycles 35


Hold 15°C - ∞


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Primer Details:

Gene Primer Name

Primer sequence 5'-3' Base

pairs

KRAS KRAS E2-FP TACTGGTGGAGTATTTGATAGTG 23

KRAS E2-RP TGTATCAAAGAATGGTCCTG 20

KRAS-E2IF TAGTGTATTAACCTTATGTGTGAC 24

KRAS-E2IR ACCTCTATTGTTGGATCATATTC 23

KRAS-E3 F AAGGTGCACTGTAATAATCCA 21

KRAS-E3 R CATGGCATTAGCAAAGACTC 20

KRAS-E3IF AATCCAGACTGTGTTTCTCC 20

KRAS-E3IR TTTAAACCCACCTATAATGG 20

(Patil et al., 2013)

BRAF BRAFEX15F1 CATAATGCTTGCTCTGATAGG 21

BRAFEX15R1 GGCCAAAAATTTAATCAGTGGA 22

BRAFEX15F2 CATAATGCTTGCTCTGATAGG 21

BRAFEX15R2 TAGCCTCAATTCTTACCATC 20

(Houben et al., 2004)

NRAS NRAS2F1 TAATCCGGTGTTTTTGCGTTCTC 23

NRAS2R1 GCTACCACTGGGCCTCACCTCTA 23

NRAS2F2 AGTACTGTAGATGTGGCTCGC 21

NRAS2R2 ACTGGGCCTCACCTCTATG 19

NRAS3F1 CCCCCTTACCCTCCACACC 19

NRAS3R1 GAGGTTAATATCCGCAAATGACTT 24

NRAS3F2 GATTCTTACAGAAAACAAGTG 21

NRAS3R2 ATGACTTGCTATTATTGATGG 21

(Houben et al., 2004)

PIK3CA PIKEX9 P1 TTGCTTTTCTGTAAATCATCTGTG 24


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PIKEX9 P2 GGGAAAAATATGACAAAGAAAGC 23

PIKEX9 P3 GAATCTCCATTTTAGCACTTACCTG

TGACT

30

PIKEX20 P1 TTTTCTCAATGATGCTTGGC 20

PIKEX20 P2 GGATTGTGCAATTCCTATGC 20

PIKEX20 P3 AATCTTTTGATGACATTGCATACAT

TCG

28

PIKEX20 P4

(Bisht et al., 2014)

TCAGTTATCTTTTCAGTTCAATGCA

TG

27


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3.2.4 Detection of PCR products by agarose gel electrophoresis

3.2.4.1 Method

The agarose was made in 1X TBE buffer after initial homogenization in

microwave. After cooling to 55°C, Ethidium bromide (0.5µg/ml) was added to

agarose, mixed thoroughly and poured in Bio-Rad gel casting tray (Bio-Rad

Laboratories Inc., Hercules, USA) and the gel was allowed to solidify.

About 10 µl of PCR product is loaded into the wells of the gel along with 4 µl of

DNA ladder. The gel was run at 200 volts with 30-35 min run time and

visualized under Gel Documentation system (Amersham Pharmacia Biotech,

Sweden). Image was captured and saved as *tif file and documented.

3.2.5 Sequencing of PCR products for Detection of mutations

Direct Nucleotide Sequencing, the gold standard for detection of mutations was

used to determine the mutations in KRAS, BRAF, NRAS and PIK3CA genes.

3.2.5.1 Principle

Sanger dideoxy sequencing method, developed by Nobel Laureate Fredrick

Sanger, is an enzymatic procedure in which DNA synthesis is carried out by

DNA polymerase. DNA polymerase requires both a primer, to which nucleotides

are added and a template strand to guide selection of each nucleotide. The

primer binds to the 3’hydroxyl group of the DNA to be synthesized. The 3’ end

of the primer reacts with the incoming deoxynucleoside phosphates (dNTPs). In

Sanger sequencing procedure dideoxynucleoside triphosphate (ddNTP)

analogues are used to interrupt DNA synthesis as these lack 3’-hydroxyl group

needed for addition of subsequent nucleotide. Thus, these ddNTPS prematurely

terminate the reaction. This process synthesizes DNA strands of varying length.


116

Each of the resulting fragment is analyzed by electrophoresis and a base

sequence is elucidated from the results.

3.2.5.2 Method

Five major processes are involved in nucleotide sequencing

1. Pre sequencing clean up

2. Sequencing clean up

3. Post sequencing clean up

4. Capillary electrophoresis

5. In-Silico Data Analysis

1. Pre sequencing clean up:

After the PCR amplification was completed, unincorporated primers, excess

dNTPs interfere with subsequent sequencing reaction. For removing these

contaminants pre sequencing clean-up was carried out before setting up

sequencing reaction. The volume of PCR products to be sequenced was

adjusted to 100 µl by MilliQ and was layered on Millipore Montage µPCR 96

cleanup plate. These plates incorporate Millipore’s size exclusion technology for

sample clean-up. The plate having PCR product was then placed on a vacuum

manifold and vacuum of 20 inches of Hg was applied for 5 min. The products

were subsequently washed with 50 µl MilliQ water at same vacuum pressure for

5 min. In the dried well the 25 µl of MilliQ was added and the purified PCR

products were retrieved from each well by pipetting.

2. Sequencing PCR:

The purified PCR products were subjected to cycle sequencing using Big Dye

Terminator v3.1 Cycle sequencing kit. The PCR included initial denaturation at


117

96°C for 10 sec, 50° C for 5 sec and 60° C for 4 min. The reaction was carried

out in 10 µl volume, containing 1 µl of BigDye Terminator, 3 µl of 2.5X

sequencing buffer, 4 µl of MilliQ water, 1 µl each of primer and template.

3. Post sequencing clean-up:

Unincorporated fluorescence dye terminators in the sequencing reaction

interfere with the quality of sequence by forming unwanted dye blobs in the

sequence read. Hence, it is important to remove these from the reaction before

loading it ABI Prism 3100 Genetic Analyzer.

Sequencing reaction cleanup was carried using Montage SEQ 96 sequencing

reaction clean up kit. Each cycle sequencing product was mixed with 30 ul of

injection solution provided in the kit and transferred to Montage SEQ 96 plate.

Vacuum pressure of 20 inches of Hg was applied until wells become dry. The

wash was given again with 30 µl of injection solution at same vacuum pressure.

The purified sequencing products were re-suspended n 25 µl of injection

solution and were transferred to ABI 96 well Optical plates.

4. Capillary electrophoresis:

The 96 well ABI Optical plate having purified products was loaded on the BI

3100 Genetic analyzer. Electro kinetic injection was done at 1kv for 22 sec,

voltage of 12.2 kV was applied and 50°C constant temperature was maintained

in the oven throughout the run time of 2 hrs. 22 min. For correct data collection,

analysis and extraction, following settings were made in the software. Run

module: StdSeq50_POP6_1 Default Module, Dye set Z-BigDyeV3, Analysis


118

Protocol: 3100POP6_BDTv-3-kb-dEnOvO_V5.1, Base Caller KB.bcp, Mobility

File: KB_3100_POP6_BDTv3.mob.

After the run was completed, the data was extracted and analyzed

automatically by the software provided with the instrument.

5. In-silico Sequence Analysis:

The sequences of KRAS, BRAF, NRAS and PIK3CA genes obtained were

aligned with respective exonic sequences obtained from NCBI to screen for

mutations using BioEdit software.

6. Statistical analysis:

Statistical analysis was performed using GraphPad and GraphPad Prism

software. The comparative distribution of mutations in all four genes was

studied and its correlation with clinico-pathological parameters was analyzed.

Chi square test was employed and a finding of p<0.05 was considered as

statistically significant.

Chapter 4. Assay Validation

119

Chapter 4 Assay Validation

According to National Accreditation Board for testing and calibration Laboratory

(NABL), India and College of American Pathologists (CAP), USA. Guidelines,

assay validation involves three critical components- Assay development, Assay

validation and Validation retention system.

Figure 4.1: Components of Assay Validation.

Assay development phase involves performing a thorough background search,

developing a rationale, aims and objectives. The key assay requirements which

should be taken in consideration include-Pre analytical, Analytical and Post

analytical components. The Pre-analytical component includes- Specimen Type

and tumor content which includes parameters like Frozen tissue or FFPE tissue,

primary or metastatic tissue, necrotic or inflammatory and if there is any need

for macro dissection. Analytical phase includes methodology i.e in-house

developed or kit based, assay validation includes sensitivity, robustness and

reproducibility of the assay and spectrum of mutations covered. The post


120

analytical parameter includes the cost of testing i.e. the reagent and labor cost

and the report content. The assay requirements are summarized in Figure 4.2.

Figure 4.2: Key assay considerations (Image Source-Steven Anderson, Expert

Rev Mol Diagn, 2011).

Further, assay validation involves four parameters – Specificity, Sensitivity,

Reproducibility and Repeatability. Then comes the assay validation retention,

which comprises of monitoring and maintenance of the assay.


121

4.1 Validation parameters:

4.1.1 Specificity

Specificity is primarily a function of primer selection in PCR assays. Twenty

samples and ten plasmid DNA samples were tested to establish that the assay

did not generate false-positive reactions. Cross contamination concerns were

addressed by testing the panel containing alternating panel of the plasmid DNA

to be tested after every two human DNA samples. The PCR products were

loaded on 2% agarose gel along with a 100 bp molecular weight marker and

reaction controls. The results were observed, and recorded. All the positive

samples were subjected to nucleotide sequencing for the critical point mutations

at exons 2 and 3 for KRAS and NRAS, exon 15 for BRAF and exons 9 and 20

for PIK3CA and the sequences were analyzed.


122

4.1.2 Sensitivity

The detection limit of the qualitative assay must account for the detection of

gene mutation in excess of normal DNA. The positive cut-off point was the

minimum percentage of malignant cells in the tissue as visualized in H&E

staining of the tissue sample, for detection of mutation in the tissue sample. The

test was performed on 20 samples with malignant cell percentages ranging from

80% to 10%. The PCR products were observed visually on gel documentation

system and gel images recorded. The positive PCR products were sequenced

on ABI 3100 Genetic Analyzer, and analyzed with Sequence Analysis Software

version 5.1.1. The allelic sensitivity was also established using commercially

available standards each of 50% allele burden from Horizon Diagnostics. The

nucleotide sequences obtained were screened for mutations. The sequences

were aligned using BioEdit tool against respective exonic sequences of

reference sequence.

4.1.3 Repeatability

Twenty samples comprising wild-type and mutated were analyzed for specific

exons of all four genes, on two different days in independent assays, by two

different analysts. The results were recorded.

4.1.4 Reproducibility

Twenty samples comprising wild-type and mutated were analyzed for specific

exons of all four genes, twice on different days in independent assays, by the

same analyst. The results were recorded.


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4.2 Validation of KRAS exon 2 and exon 3 (codons 12, 13 and 61)

mutation detection assay

4.2.1 Specificity

Twenty samples comprising wild-type and mutated used for specificity assay

showed specific amplimers for exons 2 and 3 of K-ras gene. The ten plasmid

DNA samples did not show amplification in exons 2 or 3. The nucleotide

sequences showed >99% homology with the reference sequence of K-ras

(Accession No: NC_000012) indicating 100% specificity of the assay. The

variations obtained were due to mutations. The results are shown in Figures

4.3A and 4.3B and the description for the same is mentioned in Table

4.1.Figure 4.4 is the ClustalW of KRAS exon 2, codons 12 and 13.


124

Figure 4.3A and 4.3B: Specificity experiment for KRAS mutation detection

assay.

Figure 4.3A

Figure 4.3B

Figure Legend (4.3A and 4.3B): Twenty samples were analysed using PCR followed

by Sanger sequencing along with ten plasmid DNA samples having insert other than

KRAS gene. The samples showed amplification whereas the plasmid DNA samples did

not show any amplification. Two bands were observed in few sample lanes like lane

nos. 7, 14, 16, 17 of Figure 4.3A and lane nos.2, 7, 11, 13, 14, 16 in Figure 4.3B which

could be due to non specific amplification. This non specific amplification was

overcome by increasing the annealing temperature to 52°C.The loading pattern is

mentioned in Table 4.1.


125

Table 4.1: Loading pattern for Figures 4.3A and 4.3B.

Figure 4.3A: KRAS Spec 1 Figure 4.3B: KRAS Spec 2

Lane No.

Sample Lane No. Sample

1 100 bp Ladder 1 100 bp Ladder

2 Positive Control 2 Positive Control

3 Reaction Control 3 Reaction Control

4 7C40344 4 7C51922

5 7C49157 5 7C47808

6 Plasmid 1 6 Plasmid 6

7 7C49142 7 7C47583

8 7C42607 8 7C50956


10 7C49133 10 7C47333

11 7C49132 11 7B35583


13 7C50984 13 7C47933

14 7C51169 14 7C43426


16 7C47888 16 7C49168

17 7C43404 17 7C43424



126

Figure 4.4: Clustal W for KRAS exon 2 codon 12 and 13 for specificity

parameter.

Codon 12/13

KRAS_E2 GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 85

7C40344_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 100

7C49157_K2F GTTGGAGCTG RTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 92











7C47583_K2F GTTGGAGCTK GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 98

7C50956_K2F GTTGGAGCTK GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 92


7B35583_K2F GTTGGAGCTG GTGGCGTAGG CAAGAGTGCC TTGACGATAC AGCTAATTCA 89





Clustal Consensus ********* ********* ********** ********** ********** 77

Figure Legend: The PCR amplified samples were sequenced and aligned with the

reference sequence using BioEdit software. The highlighted nucleotides in the box

correspond to codon 12 (GGT) and codon 13 (GGC) and the heterozygous G/A is

depicted by R and heterozygous G/T is depicted by K.


127

4.2.2 Sensitivity

The test was performed on 20 samples with malignant cell percentages ranging

from 80% to 20%, for exon 2, codons 12/13 and exon3, codon 61. The PCR

products are observed visually on gel documentation system and gel images

recorded. The KRAS positive PCR products are sequenced on ABI 3100

Genetic Analyzer, and analyzed with Sequence Analysis Software version

5.1.1. The nucleotide sequences obtained were screened for mutations at

exon2, codons12/13 and/or exon3, codon 61. The sequences are aligned using

BioEdit tool against respective exonic sequences of reference sequence of

KRAS gene (Accession No: NC_000012).

The minimum percentage of malignant cells in the tissue as visualized in H&E

staining, for detection of KRAS mutation in the tissue sample was found to be

20%. The results are shown in Figures 4.5 and 4.6 and the description for the

same is mentioned in Table 4.2. Clustal W for KRAS is shown in Figure 4.7 for

sensitivity parameter.


128

Figure 4.5: KRAS Sensitivity 1

Figure 4.6: KRAS Sensitivity 2

Figure Legend: Figure 4.5 and 4.6: Sensitivity experiment for KRAS mutation

detection assay. Twenty samples with malignant cell percentage from 20 to 80% were

analysed using PCR. The amplified products were further sequenced. The loading

pattern is mentioned in Table 4.2.


129

Table 4.2: Loading pattern on agarose gel electrophoresis for figures 4.5 and

4.6

Figure 4.5: KRAS Sensitivity 1 Figure 4.6: KRAS Sensitivity 2

Lane No.

Sample % Tumor

Lane No.

Sample % Tumor

1 100 bp Ladder 1 100 bp Ladder

2 Positive Control 2 Positive Control

3 Reaction Control 3 Reaction Control

4 7C55300 80 4 7C43458 60

5 7C61089 70 5 7C59701 50

6 7C58028 70 6 7C60972 50

7 7C58014 70 7 7C46780 50 8 7C49157 70 8 7B35569 50

9 7C49110 70 9 7C61075 40 10 7C42621 70 10 7C58194 40 11 7C60966 60 11 7C58062 30 12 7C52618 60 12 7C61886 20

13 7C52160 20


130

Figure 4.7: Clustal W for KRAS exon 2, codons 12 and 13 for sensitivity

parameter.

KRAS_E2 AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 84

7C55300_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 100

7C61089_k2f AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 99


7C58014_k2f AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 61






7C43458_K2F AGTTGGAGCT GSTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 91

7C59701_k2f AGTTGGAGCT GSTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 78


7C46780_K2F AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 79

7B35569_K2F AGTTGGAGCT GGTGRCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 63

7C61075_K2F AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 64

7C58194_k2f AGTTGGAGCT RGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 100

7C58062_K2F AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 60

7C61886_K2F AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 62


Clustal Consensus ********** ** ***** ********** ********** ********** 57



correspond to codon 12 (GGT) and codon 13 (GGC). The mutation was observed in

the sample with 20% malignant cells (Sample accession no.7C52160) indicating the

sensitivity as 20%.The heterozygous G/A is depicted by R, heterozygous G/T is

depicted by K and heterozygous G/C is depicted by S.


131


Twenty samples comprising wild-type and mutated were analysed for codons

12/13 and 61 mutations at exons 2 and 3 of KRAS gene, on two different days

in independent assays, by two different analysts, using KRAS gene mutation at

codons 12/13 and 61 testing protocol. Detection of KRAS gene mutation at

codons 12/13 and 61 was observed to be 100% reproducible assay as, all the

samples showed same nucleotide sequence when performed by two different

analysts (Table 4.3).


132

Table 4.3: Results of samples analysed by two different analysts for

reproducibility.

Accession No Test Result Mutation Detected

Test Result Mutation Detected

Analyst 1 Analyst 2

7C42621 Detected GGT ; GTT Detected GGT ; GTT


7C49823 Detected GGT ; GAT Detected GGT ; GAT

7C50956 Detected GGT ; TGT Detected GGT ; TGT

7C58194 Detected GGT ; AGT Detected GGT ; AGT

7C52484 Detected GGT : GAT Detected GGT : GAT


7C43458 Detected GGT ; GCT Detected GGT ; GCT

7A12168 Not Detected - Not Detected -

7C46456 Not Detected - Not Detected -


7B35559 Not Detected - Not Detected -





133

Figure 4.8: Clustal W for KRAS for reproducibility parameter.


7c42621_k2f AGTTGGAGCT GKTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 77


7c47823_k2f AGTTGGAGCT GRTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 83


7c58194_k2f AGTTGGAGCT RGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 82



7C43458_K2F AGTTGGAGCT GSTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 61

7A12168_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 99

7c46456_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 61


7B35559_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 91

7c35575_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 64

7B35583_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 79



Clustal Consensus ********** ******** ********** ********** ********** 59

Figure Legend: The samples were processed independently by two analyst to validate

the reproducibility parameter and the results were concordant. The highlighted

nucleotides in the box correspond to codon 12 (GGT) and codon 13 (GGC). R depicts

the heterozygous G/A, K depicts heterozygous G/T and S depicts heterozygous G/C.


134

4.2.4 Repeatability

Twenty samples comprising wild-type and mutated were analyzed for codons

12/13 and 61 mutations at exons 2 and 3 of KRAS gene, twice on different days

in independent assays, by the same analyst, using KRAS gene mutation at

codons 12/13 and 61 testing protocol. The samples showed identical nucleotide

sequences in two independent assays, by the same analyst, indicating 100%

repeatability (Table 4.4).


135

Table 4.4: Results of samples analysed by two different analysts for

repeatability.



Analyst 1 Analyst 1



7C49823 Detected GGT ; GAT Detected GGT ; GAT


7C58194 Detected GGT ; AGT Detected GGT ; AGT

7C52484 Detected GGT : GAT Detected GGT : GAT


7C43458 Detected GGT ; GCT Detected GGT ; GCT

7A12168 Not Detected - Not Detected -








136

Figure 4.9: Clustal W for KRAS for repeatability parameter.





7c50956_k2f AGTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 91

7c58194_k2f AGTTGGAGCT RGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 92


7c52618_k2f -GTTGGAGCT KGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 49

7c43458_k2f AGTTGGAGCT GSTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 92

7a12168_k2f AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 100



7b35559_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 63



7C47087_K2F AGTTGGAGCT GGTGGCGTAG GCAAGAGTGC CTTGACGATA CAGCTAATTC 79


Clustal Consensus ********* ******** ********** ********** ********** 47

Figure Legend: To validate the repeatability parameter of the KRAS assay, the same

analyst processed the samples used in reproducibility assay on two different days and

concordant results were observed. The highlighted nucleotides in the box correspond

to codon 12 (GGT) and codon 13 (GGC). R depicts the heterozygous G/A, K depicts

heterozygous G/T and S depicts heterozygous G/C.

Thus, a nested PCR protocol was used to amplify exons 2 and 3 of KRAS gene

with specific primers. Annealing temperature used for both the rounds of PCR

was 52° C. Base pair size for KRAS exon 2 is 189 bp and for exon 3 is 218 bp.

The agarose gel images after optimization of the assay for each exon fragments

is given in Fig. 4.10. A representative electropherogram obtained after

sequencing of the amplified product is shown in Figure 4.11, 4.12 and 4.13.

Hence, the data suggests the KRAS gene mutation at codons 12/13 and 61

testing protocol showed 100% specificity, 100% reproducibility and 100%

repeatability and lower detection limit of 20% tumor involvement, in the samples

studied.


137

Figure 4.10: Agarose gel electrophoresis of exon2 and exon3 of KRAS gene

after optimization of the assay.

Figure Legend: KRAS mutation detection assay was optimised at 52°C annealing

temperature with a nested PCR protocol complying all the validation parameter

requirements of specificity, sensitivity, reproducibility and repeatability. The base pair

size of exon 2 amplified product was 189 bp and for exon 3 -218 bp. Lane 1-100 bp

molecular weight ladder, lane 2-amplification control for KRAS exon 2, lane 3- reaction

control, lane 4 till lane 10- KRAS exon 2 PCR amplified samples, lane 11-blank well,

lane 12-amplification control for KRAS exon 3, lane 13- reaction control, lane14till lane

18-KRAS exon 3 PCR amplified samples.

100 bp ladder Exon 2(189 bp) Exon 3(218bp)


138

Figure 4.11: Electropherogram of KRAS exon 2 with no mutations detected.

Figure Legend: Highlighted region is codon 12 –GGT at position 84, 85 and 86 and

codon 13- GGC at position 87, 88 and 89 on the electropherogram.


139

Figure 4.12: Electropherogram of KRAS exon 2 with codon 12 mutation

(GGT;GAT) Glycine to Aspartic acid substitution.

Figure Legend: Highlighted region is codon 12 –GGT at position 83, 84 and 85 and

codon 13- GGC at position 86, 87 and 88 on the electropherogram. R depicts the

heterozygous G/A change in the system as seen at the position number 84. This

change results in Glycine (GGT) to Aspartic acid (GAT) substitution.


140

Figure 4.13: Electropherogram of KRAS exon 3 with no mutations detected.

Figure Legend: Highlighted region is codon 61 –CAA at position 96, 95 and 94 on the

electropherogram. The sample is sequenced using reverse primer.


141

Mutations of clinical significance for KRAS:

According to published findings critical KRAS mutations at codon 12/13, and

one at codon 61 have been associated with resistance to anti-EGFR therapy,

as tabulated:

Exon Codon Nucleotide Change Amino acid change

2 12 GGT - GAT Gly - Asp

2 12 GGT - GTT Gly - Val

2 12 GGT - CGT Gly - Arg

2 12 GGT - GCT Gly - Ala

2 12 GGT - AGT Gly - Ser

2 12 GGT - TGT Gly - Cys

2 13 GGC - GAC Gly - Asp

2 13 GGC-TGC Gly - Cys

3 61 CAA - CAC Gln - His

3 61 CAA- AAA Gln - Lys

3 61 CAA- GAA Gln - Glu

3 61 CAA- CTA Gln - Leu

3 61 CAA- CCA Gln - Pro

3 61 CAA- CGA Gln - Arg

3 61 CAA-CAT Gln - His

Presence of the above mentioned mutations indicate resistance to anti-EGFR

therapy. Absence of the mutations indicates wild-type allele indicating

responsiveness to anti-EGFR therapy.

Establishment of allelic sensitivity on Sanger sequencer

Commercially available standards for KRAS G12D and NRAS G12V each of

50% allele burden from Horizon Diagnostics’ were mixed with wild type genomic

DNA to obtain 50%, 25%, 20%, 10% and 5% tumor DNA content. PCR was set

up followed by Sanger sequencing. Samples were processed in triplicates.

Sanger sequencing yielded an optimum sensitivity of 20%. Representative

electropherograms of KRAS G12D allelic sensitivity establishment experiment

are shown below.


142

Figure 4.13 A: KRAS-G12D Original-50%

Figure Legend: An electropherogram of the original sample received from Horizon

Diagnostics’ having 50% allele burden for the mutation KRAS G12D having Glycine

(GGT) to Aspartic acid (GAC) substitution. The G/A substitution is marked with R,

highlighted in red, on the electropherograms at position 84.


143

Figure 4.13 B: KRAS-G12D 25%

Figure Legend: An electropherogram obtained after titration of KRAS G12D reference

sample received from Horizon Diagnostics’ having 50% allele burden with wild type

genomic DNA to obtain 25% allele burden. The G/A substitution is marked with R,



144

Figure 4.13 C: KRAS-G12D 20%

Figure Legend: An Representative electropherogram obtained after titration of KRAS

G12D reference sample received from Horizon Diagnostics’ having 50% allele burden

with wild type genomic DNA to obtain 20% allele burden. The G/A substitution is

marked with R, highlighted in red, on the electropherograms at position 86.


145

Figure 4.13 D: KRAS-G12D 10%

Figure Legend: An electropherogram obtained after titration of KRAS G12D reference

sample received from Horizon Diagnostics’ having 50% allele burden with wild type

genomic DNA to obtain 10% allele burden. The G/A substitution is marked with R,



146

4.3 Validation of BRAF exon 15 codon 600 mutation detection assay

4.3.1 Specificity

Twenty samples and ten plasmid DNA samples with no BRAF insert were used

to test BRAF specificity. All the 20 samples showed specific amplimers for exon

15 of BRAF gene. Ten plasmid DNA samples did not show amplification in exon

15. The nucleotide sequences showed >99% homology with the reference

sequence of BRAF (Accession No: NG_007378) indicating 100% specificity of

the assay. The variations obtained were due to mutations.

Figure 4.14: Clustal W for BRAF for specificity parameter

BRAF_EX15 AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 179

7D67172_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 64

7D86257_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 65





7E00488_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 68



7E01934_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 66

7C82482_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 72


7D67181_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 82







Clustal Consens ********** ********** ******** * ********** ********** ********** 82



correspond to codon 600 of exon 15 and the K depicts the heterozygous G/T on the

sequence.


147

4.3.2 Sensitivity

The minimum percentage of malignant cells in the tissue as visualized in HNE

staining, for detection of BRAF mutation in the tissue sample was found to be

20%.

Table 4.5: Results of samples analyzed for sensitivity parameter for BRAF

assay.

Sample % Tumor Sample % Tumor

7D83666 80 7D28772 60

7D92670 70 7D28773 50

7E01745 70 7D87169 50

7D87179 70 7A15581 50

7E01761 70 7E01840 50

7D92584 70 7D83668 40

7E04378 70 7D52242 40

7D52160 60 7A15506 30

7D97202 60 7D71912 20

7D97108 10


148

Figure 4.15: Clustal W for BRAF for sensitivity parameter.














7A15581_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 75




7A15506_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 80



Clustal Consens ********** ********** ******** * ********** ********** ********** 76



correspond to codon 600 in exon 15. The mutation was observed in the sample with

20% malignant cells (Sample accession no.7D71912) indicating the sensitivity as 20%.

K depicts the heterozygous G/T.


149


The reproducibility of the assay, as observed in 15 samples was 100%

concordance in two independent assays by two analysts. The results in both the

assays were identical, indicating 100% reproducible.

Table 4.6: Results of clinical samples analyzed by two different analysts for

reproducibility.

Accession No



Analyst 1 Analyst 2

7D83668 Detected GTG ; GAG Detected GTG ; GAG

7D83666 Not Detected - Not Detected -


7E01702 Not Detected - Not Detected -

7E01703 Detected GTG ; GAG Detected GTG ; GAG












150

Figure 4.16: Clustal W for BRAF for reproducibility parameter.


7D83668_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 65

7D83666_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGRKCAGTT 69

7D96917_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC GATCAGTTTT 75

7E01702_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGATCAGTT 78



7D87169_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGRTCAGTT 86

7D52242_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGRTCAGTT 83




7D52160_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGRKCAGTT 75




Clustal Consens ********** ********** ******** * ********** ********** ********** 80


the reproducibility parameter and the results were concordant. The PCR amplified

samples were sequenced and aligned with the reference sequence using BioEdit

software. The highlighted nucleotides in the box correspond to codon 600 (GTG) in

exon 15. K depicts the heterozygous G/T.


151

4.3.4 Repeatability

The repeatability of the assay, as observed in 15 samples was 100%

concordance in the independent assays by the same analyst. The results in

both the assays were identical, indicating 100% repeatability.


repeatability.



Analyst 1: HP Analyst 1: HP





7E01703 Detected GTG ; GAG Detected GTG ; GAG












152

Figure 4.17: Clustal W for BRAF for repeatability parameter.


7D83668_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGATCAGTT 57


7D96917_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC GATCAGTTTT 75




7D87169_F AAAATAGGTG ATTTTGGTCT AGCTACAGKG AAATCTCGAT GGAGTGGGTC CCGRTCAGTT 57

7D52242_F AAAATAGGTG ATTTTGGTCT AGCTACAGTG AAATCTCGAT GGAGGGGGTC CCGRTCAGTT 65








Clustal Consens ********** ********** ******** * ********** ********** ********** 71

Figure Legend: To validate the repeatability parameter of the BRAF assay, the same


concordant results were observed. The PCR amplified samples were sequenced and

aligned with the reference sequence using BioEdit software. The highlighted

nucleotides in the box correspond to codon 600 (GTG) in exon 15. K depicts the

heterozygous G/T.

Thus, a nested PCR protocol is used to amplify exon 15 of BRAF gene with

specific primers. Annealing temperature used for both the rounds of PCR is 55°

C. Base pair size for BRAF exon 15 is 228 bp. The agarose gel image after

optimization of the assay for exon 15 fragment is given in Fig. 4.18. A

representative electropherogram obtained after sequencing of the amplified

product is shown in Fig.4.19 ClustalW of the sequenced product is shown in Fig

4.20.


153

Figure 4.18: Agarose gel electrophoresis of exon15 of BRAF gene after

optimization of the BRAF assay

Figure Legend: BRAF mutation detection assay was optimised using nested PCR

protocol complying all the validation parameter requirements of specificity, sensitivity,

reproducibility and repeatability. The base pair size of exon 15 amplified product was

228 bp. Lane 1-100 bp molecular weight ladder, lane 2-amplification control for BRAF

exon 15, lane 3- reaction control, lane 4 till lane 8- BRAF exon 15 PCR amplified

samples.

100 base pair (bp) ladder

228 bp


154

Figure 4.19: Electropherogram of BRAF exon 15 with no mutations detected.

Figure Legend: Highlighted region is codon 600(GTG) of exon 15 at position 90, 91

and 92 on the electropherograms.

Figure 4.20: ClustalW of exon15 of BRAF gene after sequencing.

Figure Legend: The highlighted nucleotides in the box correspond to codon 600

(GTG) in exon 15.


155

Hence, the data suggests the BRAF gene mutation at codons 12/13 and 61



studied.

Mutation of clinical significance

V600E, GTG - GAG (Valine to Glutamic Acid)


therapy. Absence of the mutations indicates wild-type allele indicating

responsiveness to anti-EGFR therapy (Di Nicolantonio et al., 2008).


156

4.4 Validation of NRAS exon 2 and exon 3 (codons 12, 13 and 61)

mutation detection assay

4.4.1 Specificity

Twenty samples used for specificity assay showed specific amplimers for exons 2 and

3 of NRAS gene. The ten plasmid DNA samples did not show amplification in exons 2

or 3. The nucleotide sequences showed >99% homology with the reference sequence

of NRAS (Accession No: NG_007572.1) indicating 100% specificity of the assay. The

variations obtained were due to mutations.

Figure 4.21: Representative Agarose gel electrophoresis of NRAS gene exon

2.

Figure Legend: NRAS

specific amplification was

observed in genomic DNA

samples (lanes 4-8) however

plasmid DNA samples did not

show any amplification (lanes

9-13) showing specificity of

primers used for NRAS

amplification.

Lane No. Sample

1 100 bp Ladder

2 Amplification control

3 Reaction control

4 7G23701(60%tumor)

5 7G23702(70%tumor)

6 7G23703(30%tumor)

7 7G23704(50%tumor)

8 7G23705(70%tumor)

9 Plasmid 1

10 Plasmid 2

11 Plasmid 3

12 Plasmid 4

13 Plasmid 5


157

Figure 4.22: ClustalW of NRAS gene after sequencing for specificity.



correspond to codons 12 (GGT) and 13 (GGT) of exon 2.


158

4.4.2 Sensitivity

The detection limit of the qualitative assay must account for the detection of

NRAS gene mutation in excess of normal DNA. The positive cut-off point is the

minimum percentage of malignant cells in the tissue as visualized in HNE

staining of the tissue sample, for detection of NRAS mutation in the tissue

sample. The test was performed on 20 samples with malignant cell percentages

ranging from 80% to 10%, for exon 2, codons 12/13 and exon3, codon 61. The

PCR products are observed visually on gel documentation system and gel

images recorded. The NRAS positive PCR products are sequenced on ABI

3100 Genetic Analyzer, and analyzed with Sequence Analysis Software version


exon2, codons12/13 and/or exon3, codon 61. The sequences are aligned using

BioEdit tool against respective exonic sequences of reference sequence of

NRAS gene (Accession No: NG_007572.1).

The minimum percentage of malignant cells in the tissue as visualized in HNE

staining, for detection of NRAS mutation in the tissue sample was found to be

20%.


159


Twenty samples were analyzed for codons 12/13 and 61 mutations at exons 2

and 3 of NRAS gene, on two different days in independent assays, by two

different analysts, using NRAS gene mutation at codons 12/13 and 61 testing

protocol. Detection of NRAS gene mutation at codons 12/13 and 61 is 100%

reproducible assay as, all the samples showed same nucleotide sequence

when performed by two different analysts.

Table 4.8: Results of clinical samples analysed by two different analysts for

reproducibility for NRAS codon 12/13

Sample Codon Test Result (Analyst 1)

Test Result (Analyst 2)

7G15477 12/13 Not Detected Not Detected
















160

Figure 4.23: ClustalW of NRAS gene after sequencing for reproducibility.



nucleotides in the box correspond to codon 12 (GGT) and codon 13 (GGT).


161

NRAS EXON 3


reproducibility for NRAS codon 61.



7G15477 61 Not Detected Not Detected
















162

Figure 4.24: ClustalW of nras exon 3 gene after sequencing for reproducibility.



nucleotides in the box correspond to codon 61 (CAA).


163

4.4.4 Repeatability


12/13 and 61 mutations at exons 2 and 3 of NRAS gene, twice on different days

in independent assays, by the same analyst, using NRAS gene mutation at

codons 12/13 and 61 testing protocol. The samples showed identical nucleotide

sequences in two independent assays, by the same analyst, indicating 100%

repeatability.


164

NRAS EXON 2


repeatability.



















165

Figure 4.25: ClustalW of NRAS gene after sequencing for repeatability

Figure Legend: To validate the repeatability parameter of the NRAS assay, the same




nucleotides in the box correspond to codon 12 (GGT) and codon 13 (GGT) in exon 2.


166

NRAS EXON 3


repeatability for NRAS codon 61.



















167

Figure 4.26: ClustalW of NRAS gene after sequencing for repeatability

Figure Legend: To validate the repeatability parameter of the NRAS assay, the same




nucleotides in the box correspond to codon 61 (CAA) in exon 3.

Thus, a nested PCR protocol is used to amplify exon 2 and 3 of NRAS gene

with specific primers. Annealing temperature used for both the rounds of PCR is

58 ° C. Base pair size for NRAS exon 2 is 192 bp and for exon 3 is 157 bp. The

agarose gel images after optimization of the assay for each exon fragments is

given in Figure 4.27. ClustalW of the sequenced product is shown in Figure

4.28.


168

Hence, the data suggests the NRAS gene mutation at codons 12/13 and 61



studied.

Figure 4.27: Agarose gel electrophoresis of exon2 and exon 3 of NRAS gene

after optimization of the NRAS assay.

Figure Legend: NRAS mutation detection assay was optimised at 58°C annealing




molecular weight ladder, lane 2-amplification control for NRAS exon 2, lane 3- reaction

control, lane 4 till lane 9- NRAS exon 2 PCR amplified samples, lane 10-amplification

control for NRAS exon 3, lane 11- reaction control, lane12 till lane 17-NRAS exon 3

PCR amplified samples.

100 bp ladder

NRAS exon2 (192bp) NRAS exon3 (157 bp)


169

Figure 4.28: ClustalW of exon 2 and 3 of NRAS gene after sequencing.



correspond to codon 12 (GGT), codon 13 (GGT) and codon 61 (CAA).


170

Mutations of clinical significance

According to published findings critical NRAS mutations at codon 12/13, and

one at codon 61 have been associated with resistance to anti-EGFR therapy,

as tabulated below.

Exon Codon Nucleotide Change Amino acid change













3 61 CAA - CAC Gln - His

3 61 CAA- AAA Gln - Lys

3 61 CAA- GAA Gln - Glu

3 61 CAA- CTA Gln - Leu

3 61 CAA- CCA Gln - Pro

3 61 CAA- CGA Gln - Arg

3 61 CAA-CAT Gln - His


therapy. Absence of the mutations indicates Wild-type allele indicating



171

4.5 Validation of PIK3CA exon 9 and exon 20 (codon 545 and codon

1047) mutation detection assay

4.5.1 Specificity

Twenty samples used for specificity assay showed specific amplimers for exons

9 and 20 of PIK3CA gene. The ten plasmid DNA samples did not show

amplification in exon 2 or 3. The nucleotide sequences showed >99% homology

with the reference sequence of PIK3CA (Accession No: NG_012113.2)

indicating 100% specificity of the assay. The variations obtained were due to

mutations.

4.5.2 Sensitivity

The test was performed on 20 samples with malignant cell percentages ranging

from 80% to 10%, for exon 9, codon 545 and exon 20, codon 1047. The PCR

products are observed visually on gel documentation system and gel images

recorded. The PIK3CA positive PCR products are sequenced on ABI 3100

Genetic Analyzer, and analyzed with Sequence Analysis Software version


exon9, codon 545 and/or exon20, codon 1047. The sequences are aligned

using BioEdit tool against respective exonic sequences of reference sequence

of PIK3CA gene (Accession No: NG_012113.2).

The minimum percentage of malignant cells in the tissue as visualized in H&E

staining, for detection of PIK3CA mutation in the tissue sample was found to be

20%.


172


Twenty samples were analysed for codons 12/13 and 61 mutations at exons 9

and 20 of PIK3CA gene, on two different days in independent assays, by two

different analysts, using PIK3CA gene mutation at codons 545 and 1047 testing

protocol. Detection of PIK3CA gene mutation at codons 545 and 1047 is 100%

reproducible assay as, all the samples showed same nucleotide sequence

when performed by two different analysts.


reproducibility for PIK3CA.

Accession No



Analyst 1 Analyst 2

7D83668 Detected GGT ; GAT Detected GGT ; GAT




7E01703 Detected CAA - CAG Detected CAA - CAG












173

Figure 4.29: ClustalW of PIK3CA gene after sequencing for reproducibility.

PIK3_EX09 AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 179

7D83668_F AAAATAGGTG ATTTTGGTCT AGCTACAGRT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 64

7D83666_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 65



7D95188_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 75


7E00488_F AAAATAGGTG ATTTTGGTCT AGCTACAGRT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 68



7E01934_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 66

7C82482_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 72








7E00505_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 71

Clustal Consens ********** ********** ******** * ********** ********** ********** 82


the reproducibility parameter and the results were concordant. The PCR amplified

samples were sequenced and aligned with the reference sequence using BioEdit

software. The highlighted nucleotides in the box correspond to codon 545 (GGT) in

exon 15. R depicts the heterozygous G/A.


174

4.5.4 Repeatability


545 and 1047 mutations at exons 9 and 20 of PIK3CA gene, twice on different

days in independent assays, by the same analyst, using PIK3CA gene mutation

at codons 545 and 1047 testing protocol. The samples showed identical

nucleotide sequences in two independent assays, by the same analyst,

indicating 100% repeatability.


repeatability for PIK3CA.

Accession No



Analyst 1 Analyst 2





7E01703 Detected CAA - CAG Detected CAA - CAG












175

Figure 4.30: ClustalW of PIK3CA gene after sequencing for repeatability

PIK3_EX09 AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 179







7E00488_F AAAATAGGTG ATTTTGGTCT AGCTACAGRT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 68



7E01934_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 66

7C82482_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGTGGGTC CCGATCAGTT 72








7E00505_F AAAATAGGTG ATTTTGGTCT AGCTACAGGT AAATCTCGAT GGAGGGGGTC CCGATCAGTT 71

Clustal Consens ********** ********** ******** * ********** ********** ********** 82

Figure Legend: To validate the repeatability parameter of the PIK3CA assay, the

same analyst processed the samples used in reproducibility assay on two different

days and concordant results were observed. The PCR amplified samples were

sequenced and aligned with the reference sequence using BioEdit software. The

highlighted nucleotides in the box correspond to codon 545 (GGT) in exon 15. R

depicts the heterozygous G/A.

Thus, a nested PCR protocol was used to amplify exon 9 and 20 of PIK3CA

gene with specific primers. Annealing temperature used for both the rounds of

PCR is 56 ° C. Base pair size for PIK3CA exon 9 was 197 bp and for exon 20

was 225 bp. The agarose gel images after optimization of the assay for each

exon fragments is given in Figure 4.31. A representative electropherogram

obtained after sequencing of the amplified product is shown in Figure 4.32 and

Figure 4.33. ClustalW of the sequenced product is shown in Figure 4.34.


176

Figure 4.31: Agarose gel electrophoresis of exon 9 and exon 20 of PIK3CA

gene after optimization of the assay.

Figure Legend: PIK3CA mutation detection assay was optimised at 56°C annealing




molecular weight ladder, lane 2-amplification control for PIK3CA exon 9, lane 3-

reaction control, lane 4 till lane 10- PIK3CA exon 9 PCR amplified samples, lane 11-

blank well, lane 12-amplification control for PIK3CA exon 20, lane 13- reaction control,

lane14till lane 18- PIK3CA exon 20 PCR amplified samples.

Hence, the data suggests the PIK3CA gene mutation at codons 545 and 1047



studied.

100 bp ladder Exon 9(197 bp) Exon 20(225bp)


177

Mutations of clinical significance

According to published findings critical PIK3CA mutations at codon 545 and one

at codon 1047 have been associated with resistance to anti-EGFR therapy, as

tabulated below. :

Exon Codon Nucleotide Change

Amino acid change


20 1047 CAA - CAG His – Arg

20 1047 CAA - CAT His - Leu


therapy. Absence of the mutations indicates Wild-type allele indicating


4.6 Laboratory methods used for detection of mutations

World wide a variety of laboratory methods have been used for detection of

mutations in KRAS, BRAF, NRAS and PIK3CA genes like, Allele specific PCR,

ARMS PCR, Realtime PCR, Sanger Sequencing, Pyrosequencing, Multiplex

PCR etc. each having specific characteristics and sensitivity as mentioned in

Figure 4.32.


178

Figure 4.32: Key features of different methodologies used for mutation analysis

(Image Source-Steven Anderson, Expert Rev Mol Diagn, 2011).

To provide a cost effective solution to CRC patients Sanger sequencing was

used in this study. The sensitivity of Sanger sequencing was established as

20% with a broad spectrum of mutations being detected in comparison to kit

based assays wherein the sensitivity is higher than Sanger sequencing

however the spectrum of mutation detection is limited. Also the reagent and

labor cost is minimal in case of Sanger Sequencing. The comparison of different

methodologies used recently is shown in Figure 4.33. More recently, with the

advent of Next generation sequencing (NGS) platform the mutation analysis

would be more cost efficient and time efficient however, larger studies are

required to assess its use in routine clinical setting.


179

Figure 4.33: Comparison of properties of different methodologies used for

mutation analysis.

Chapter 5. Correlation of KRAS, BRAF, NRAS and PIK3CA mutation profiling with clinicopathological features of CRC patients

180

Chapter 5

Correlation of KRAS, BRAF, NRAS and PIK3CA mutation

profiling with clinicopathological features of CRC patients

5.1 Introduction

As reviewed in Chapter 2, genetic alterations in KRAS, NRAS, BRAF and PIK3CA

have an important role in colorectal cancer evolution. Mutations in KRAS exons 2

and 3, BRAF exon 15, NRAS exons 2 and 3 and PIK3CA exon 9 and 20 are the

most frequently mutated hotspots. These alterations lead to tumorigenesis and

cause resistance to the targeted therapy.

The aim of this chapter was to screen for mutations in these four oncogenes

in Indian CRC patients (n=203) and to study the correlation with different

clinicopathological features.

As per the COSMIC database, the hotspot mutations analysed for KRAS in the

current study account for 99% of all KRAS mutation found in CRC. Similarly V600E

mutations of BRAF accounts for 98%, 9 mutations of NRAS account for 99% and 3

hotspot mutations of PIK3CA, two in exon 9 and one in exon 20 accounts for 77%

of all mutations observed in CRC. This information is summarised in the table

given below (Table 5.1).


181

Table 5.1: Frequency of mutations in Catalogue of Somatic Mutations In Cancer

(COSMIC) database.

Gene Mutation COSMIC Database Frequency

KRAS

G12D 12.5%

G12V 8.0%

G12S 2.2%

G12C 3.0%

G12R 0.5%

G12A 2.3%

G13D 7.1%

G13C 0.2%

NRAS G12D 3.0%

G13D 0.9%

BRAF V600E 10.1%

PIK3CA E545K 4.1%

E542K 2.2%

H1047R 3.2%


182

5.2 Methodology

In summary, a total of 203 Indian CRC patient samples were analysed for KRAS,

BRAF, NRAS and PIK3CA mutation status.

Formalin-fixed, paraffin-embedded tissue blocks were cut at 4μm thicknesses and

stained with haematoxylin and eosin (HE) for histopathological examination as

mentioned in Chapter 3, section 3.2.1. Histological examination was carried out to

ensure the tumor percentage was greater than 20% in the sections analysed. All

tissue samples were analyzed for their tumor content by NABL certified internal

pathologists. The tumor was further classified in poorly differentiated, well

differentiated and moderately differentiated tumor according to the WHO

classification (Jass and SOBIN, 2012). Mucinous and signet ring tumors were

classified separately. Subsequently for DNA extraction, 10μm section of tumor

tissue was used. DNA was extracted by using Purelink DNA extraction kit (Life

Technologies, Carlsbad, California, USA) according to manufacturer’s protocol as

described in Chapter 3, section 3.2.2.

In total, 25–50 ng of DNA was added to a volume of 2 mM deoxynucleotide

triphosphates (Takara Bio.Inc, Shinga, Japan), 10 pmol of each primer, 5U of DNA

polymerase (Promega, Madison, WI, USA), 0.1 % BSA (Sigma Aldrich, Missouri,

USA) , 6 % dimethylsulfoxide, 25 mM MgCl2 and 5X polymerase chain reaction

(PCR) buffer (Promega, Madison, WI, USA). The cycling conditions and primer

details for KRAS (exon 2 and 3), BRAF (exon 15), NRAS (exon 2 and 3) and

PIK3CA (exon 9 and 20) are mentioned in Chapter 3, section 3.2.3. The PCR

products were electrophoresed on 2 % agarose gels, visualized in Gel-


183

Documentation system (Amersham Pharmacia Biotech, Uppsala, Sweden) and

recorded as described in Chapter 3, section 3.2.4. The PCR product obtained after

amplification was subjected to nucleotide sequencing. The sequencing reactions

used 5 pmol of forward and reverse primers, 10l volume and Big Dye Terminator

v.3.3 cycle sequencing kit and analysed after sequencing using ABI 3100

Genetic Analyzer (Life Technologies, Carlsbad, CA, USA) refer Chapter 3, section

3.2.5.

The chi-square test using GraphPad Software was performed to examine statistical

differences between mutation distribution and clinicopathological data. It was also

used to compare the mutation frequency found in other studies to that in our

present study. P<0.05 was considered significant.


184

5.3 Clinicopathological characteristics of CRC samples

The clinicopathological characteristics of collected 203 CRC patient samples from

Indian population are detailed in the Table 5.2.

Table 5.2: The clinicopathological characteristics of Indian colorectal cancer

samples (n=203).

Characteristics Total Samples Percentage

Age ≤50 64 31.5

>50 139 68.5

Gender Males 138 68.0

Females 65 32.0

Geographical location East 8 3.9

West 119 58.6

North 38 18.7

South 38 18.7

Primary or Metastatic Primary 129 63.5

Metastatic 74 36.5

Type of tumor Adenocarcinoma 174 85.7

Mucinous 19 9.4

Signet ring 10 4.9

Tumor Differentiation MDA 111 54.7

WDA 41 20.2

PDA 51 25.1

pT T1 3 1.5

T2 12 5.9

T3 136 67.0

T4 52 25.6

Lymph Node Metastasis Yes 152 74.9

No 51 25.1

Anatomic Site Colon 147 72.4

Rectum 56 27.6

MDA-Moderately differentiated adenocarcinoma, WDA-Well differentiated adenocarcinoma

and PDA-Poorly differentiated adenocarcinoma, pT-extent of primary tumor.


185

In these CRC samples analysed, it was observed that the frequency of CRC

tumors was higher in patients with age above 50 years (68.5%). The frequency

was more in males (68%) as compared to females (32%) (Table 5.2). Figures 5.1

and 5.2 further show the distribution of samples as per the clinicopathological

features.

According to geographical location of India, 58.6% cases were from West followed

by 18.7% cases from North and South and 3.9% cases from East. With regards to

the malignancy status 63.5% cases were of primary tumor and 36.5% were

metastatic cases. When these samples were subtyped histologically, the tumor

samples were distributed as – 85.7% adenocarcinoma, 9.4% mucinous

adenocarcinoma and 4.9% signet ring carcinoma. The tumor samples were also

classified as moderately differentiated adenocarcinomas (MDA) (n=111, 54.7%),

well differentiated adenocarcinoma (WDA) (n=41, 20.2%) and poorly differentiated

adenocarcinoma (PDA) (n=51, 25.1%).

According to AJCC/UICC TNM staging system, these tumors were classified as T1

(1.5%), T2 (5.9%), T3 (67%) and T4 (25.6%). Lymph node metastasis was also

investigated in this study and 74.9% cases had lymph node metastasis and 25.1%

cases did not show any lymph node metastasis (Table 5.2 and Figure 5.1). On the

basis of anatomic location of tumors (colon or rectum) 72.4% cases were from

colon and 27.6% cases from rectum. The distribution of cases as per the anatomic

location is shown in Figure 5.2.


186

Figure 5.1: Clinicopathological and demographic characteristics of the CRC

patient cohort employed in this study (n=203).


187

Figure Legend: The CRC cohort included in this study was investigated according to age,

gender, geographical location, tumour differentiation, lymph node metastasis and tumour

stage. MDA-Moderately differentiated adenocarcinoma, WDA-Well differentiated

adenocarcinoma and PDA-Poorly differentiated adenocarcinoma


188

Figure 5.2: Distribution of CRC tumor samples according to the anatomic location.

Figure Legend: In this study 35% of cases were from colon followed by 28% cases from

rectum and 20% from sigmoid. Rest of the cases belonged to caecum, transverse colon,

ascending colon, descending colon, appendix and jejunum.

Figure 5.3 shows the overall mutation frequency of KRAS, BRAF, NRAS and

PIK3CA genes in the CRC samples was found to be 36%. KRAS showed the

highest mutation rate (24%), followed by BRAF (6%) and PIK3CA (4%) while

NRAS gene had the lowest frequency rate (2%).

Colon35%

Rectum28%

Sigmoid20%

Transverse colon

4%

Appendix2%

Caecum5%

Descending colon 2%

Jejunum1%

Ascending Colon 3%

Anatomic Sitewise distribution of samples

Colon

Rectum

Sigmoid

Transverse colon

Appendix

Caecum

Descending colon

Jejunum

Ascending Colon


189

Figure 5.3: Overall mutation frequency rate of four genes (KRAS, BRAF, NRAS

and PIK3CA) in Indian CRC patient samples (n=203).

Figure Legend: Out of the 203 CRC samples, 36% of cases had mutation in either of the

four genes studied. KRAS mutation frequency was highest with 24% of cases having

KRAS mutation followed by BRAF, PIK3CA and NRAS.

The overall mutation rate was further represented in detail as a Venn diagram

(Figure 5.4)

KRAS, 24%

BRAF, 6%

NRAS, 2%

PIK3CA, 4%

Overall mutation frequency


190

Figure 5.4: Venn Diagram showing overall mutation distribution in CRC patient

samples (n=203).

Figure Legend: Out of the 203 CRC samples, 49 cases (24%) harboured KRAS mutation,

12 cases (6%) harboured BRAF mutation, PIK3CA mutations were observed in 8 cases

(4%) and 4 cases (2%) had NRAS mutations. BRAF, NRAS and KRAS mutations were

mutually exclusive. 3 cases harboured both KRAS and PIK3CA mutations.

KRAS Wild Type

n=130(64%)

PIK3CA Mutant

n=8 (4%)

NRAS Mutant

n= 4 (2%)

KRAS Mutant

n=49 (24%) n=5 (2.5%)

n=3 (1.5%)

BRAF Mutant

n=12 (6%)


191

5.3.1 KRAS mutations

KRAS mutational status tested by Sanger Sequencing showed that 49 cases out of

a total of 203 (24.1%) harboured KRAS mutation and the spectrum of these

mutations in KRAS gene is further analysed and summarised in Table 5.3. In these

49 samples, KRAS codon 12 mutations were most frequent (20.2%) followed by

codon 13 (3.9%). The most frequent mutation was Glycine to Aspartic acid

substitution G12D (6.9%) followed by Glycine to Valine substitution G12V (6.4%).

Other mutations observed in codon 12 were Glycine to Alanine G12A (3.94%),

Glycine to Cysteine G12C (2.46%) and Glycine to Serine substitution G12S

(0.49%). At codon 13, only one substitution i.e. Glycine to Aspartic acid G13D

(3.9%) was observed. Thus, in KRAS predominantly G>A transition mutations were

observed followed by G>T transversions. All mutations observed were in

heterozygous state. None of the concomitant mutations in codon 12 and 13 were

observed. Representative sequencing electropherograms are further shown below

in Figure 5.5.


192

Table 5.3: Spectrum of KRAS mutations in 203 CRC cases determined by Sanger

sequencing.

Codons

Type of mutation

No. of patients (%)

Wild Type codon (amino

acid)

Mutated codon

(amino acid)

KRAS cd12 G>A 14 (6.9) GGT (Gly) (G)

GAT (Asp) (D)

1 (0.49) AGT (Ser) (S)

G>T 13 (6.4) GTT (Val) (V)

5 (2.46) TGT (Cys) ( C)

G>C 8 (3.94) GCT (Ala) (A)

KRAS cd13 G>A 8 (3.94) GGC (Gly) (G) GAC (Asp) (D)

Gly glycine, asp aspartic acid, ser serine, val valine, cys cysteine, ala alanine, arg arginine, his histidine,


193

Figure 5.5: Representative Sequencing electropherograms of KRAS showing

KRAS codon 12 and codon 13.

a-KRAS Wild type

b-KRAS G12V


194

c-KRAS G12D

d-KRAS G12A


195

e-KRAS G12C

f-KRAS G12S


196

g- KRAS G13D

Figure Legend: Figure a- KRAS Wild type with no mutation, Figure b-KRAS Glycine

(GGT) to Valine (GTT) substitution G12V, Figure c-KRAS Glycine (GGT) to Aspartic acid

(GAT) substitution G12D, Figure d-KRAS Glycine (GGT) to Alanine (GCT) substitution

G12A, Figure e-KRAS Glycine (GGT) to Cysteine (TGT) substitution G12C and Figure f-

KRAS Glycine (GGT) to Serine (AGT) substitution G12SD and Figure g-KRAS Glycine

(GGC) to Aspartic acid (GAC) substitution at codon 13 G13D. Highlighted regions on the

electropherograms correspond to codon 12 (GGT) and codon 13(GGC).R depicts the

heterozygous G/A substitution, K depicts the heterozygous G/T substitution, S depicts

heterozygous G/C substitution on the electropherograms.


197

Figure 5.6 (a-f): Representative Sequencing electropherograms of NRAS,

BRAF and PIK3CA showing Wild type and mutant sequences.

a-NRAS Wild Type

b-NRAS G12V


198

c-BRAF Wild Type

d-BRAF V600E


199

e- PIK3CA exon 9 – Wild Type

f- PIK3CA exon 20 – Wild Type


200

Figure Legend: a- Representative electropherogram of NRAS Wild type with no mutation,

b-NRAS Glycine (GGT) to Valine (GTT) substitution G12V, c-BRAF Wild type with no

mutation, d-BRAF Valine (GTG) to Glutamic acid (GAG) substitution V600E, e-PIK3CA

exon 9 wild type with no mutation for codon 545- (GAG) and f- PIK3CA exon 20 wild type

with no mutation for codon 1047-Histidine (CAT). Highlighted regions on the

electropherograms correspond to respective codon positions of NRAS, BRAF and

PIK3CA. R depicts the heterozygous G/A substitution, K depicts the heterozygous G/T

substitution, W depicts heterozygous A/T substitution on the electropherograms.


201

Table 5.4: Correlation of mutation frequency in KRAS gene with clinicopathological factors of colorectal cancer patients (n=203).

Characteristics Total Samples

% Total Detected

% KRAS Detected Codon12

KRAS Detected Codon13

Total KRAS

Detected

% Not detec

ted

% p value

Age ≤50 64 31.5 20 9.9 9 2 9 4.4 55 27.1 0.02*

>50 139 68.5 53 26.1 32 6 40 19.7 99 48.8

Gender Males 138 68.0 53 26.1 30 6 36 17.7 102 50.2 0.38

Females 65 32.0 20 9.9 11 2 13 6.4 52 25.6

Geographical

location

East 8 3.9 2 1.0 0 2 2 1.0 6 3.0 0.84

West 119 58.6 47 23.2 27 3 30 14.8 89 43.8

North 38 18.7 11 5.4 6 1 7 3.4 31 15.3

South 38 18.7 13 6.4 8 2 10 4.9 28 13.8

Primary or Metastatic tumor

Primary 129 63.5 45 22.2 26 5 31 15.3 98 48.3 0.99

Metastatic 74 36.5 28 13.8 15 3 18 8.9 56 27.6

Mucinous or Adeno or Signet

Adeno 174 85.7 66 32.5 38 7 45 22.2 129 63.5 0.35

Mucinous 19 9.4 5 2.5 2 1 3 1.5 16 7.9

Signet ring 10 4.9 2 1.0 1 0 1 0.5 9 4.4

Differentiation status MDA 111 54.7 43 21.2 18 6 33 16.3 78 38.4 0.04*

WDA 41 20.2 19 9.4 14 1 10 4.9 31 15.3

PDA 51 25.1 11 5.4 9 1 6 3.0 45 22.2

Stage T1 3 1.5 1 0.5 1 0 1 0.5 2 1.0 0.98

T2 12 5.9 3 1.5 2 1 3 1.5 9 4.4

T3 136 67.0 49 24.1 27 6 33 16.3 103 50.7

T4 52 25.6 20 9.9 9 1 12 5.9 40 19.7

Lymph Node Metastasis

Yes 152 74.9 61 30.0 32 7 39 19.2 113 55.7 0.45

No 51 25.1 12 5.9 9 1 10 4.9 41 20.2

Site Colon 147 72.4 51 25.1 28 7 35 17.2 112 55.2 0.86

Rectum 56 27.6 22 10.8 13 1 14 6.9 42 20.7 * statistically significant


202

Statistical analysis of KRAS gene mutational status with clinico-pathological data

revealed that KRAS mutations were significantly higher in patients above 50 years

of age (p<0.05) (Table 5.4). KRAS mutations were more frequent in males (17.7%)

as compared to females (6.4%), however, this association was not statistically

significant (p=0.38). Furthermore there was no significant correlation seen between

KRAS mutations and geographical location of India.

In correlation with tumor differentiation, KRAS gene mutations were significantly

higher in moderately differentiated and poorly differentiated tumors (p<0.05). Also,

KRAS mutations were seen to be more frequent in Adenocarcinoma (22.17%), T3

stage (16.26%) and colon as the anatomic site (17.24%), however, no this

association was not statistically significant.


203

5.3.2 BRAF

In this study, 5.9% (12/203) cases showed presence of BRAF gene mutations. All

these 12 samples showed presence of codon 600 GTG to GAG substitution

resulting in Valine to Glutamic acid substitution V600E. None of the mutations in

BRAF gene were overlap with KRAS, NRAS or PIK3CA mutations. All these BRAF

mutations were present in heterozygous state. Representative electropherograms

are shown in Figure 5.6.

In regards with clinicopathological status BRAF mutation frequency was similar in

patients above and below 50 years of age (3% each). Frequency of BRAF

mutations was higher in Western region of India (55.2%); however, there was no

significant association between the geographical location and BRAF mutations. In

correlation with tumor differentiation status, BRAF mutations were significantly

associated with moderately differentiated and poorly differentiated

adenocarcinomas (p>0.05). No significant association was observed in BRAF and

other clinico-pathological features like type of tumor, T stage, lymph node

metastasis and site of tumor (Table 5.5).


204

Table 5.5: Clinicopathological characteristics’ correlation with mutation frequency in BRAF gene in colorectal cancer

patients (n=203).

Characteristics Total Samples % Total Detected % Detected % Not detected

% p value

Age ≤50 64 31.5 20 9.9 6 3.0 58 28.6 0.2

>50 139 68.5 53 26.1 6 3.0 133 65.5

Gender Males 138 68.0 53 26.1 8 3.9 130 64.0 0.99

Females 65 32.0 20 9.9 4 2.0 61 30.0

Geographical location East 8 3.9 2 1.0 0 0.0 8 3.9 0.44

West 119 58.6 47 23.2 7 3.4 112 55.2

North 38 18.7 11 5.4 4 2.0 34 16.7

South 38 18.7 13 6.4 1 0.5 37 18.2


Primary 129 63.5 45 22.2 8 3.9 121 59.6 0.99

Metastatic 74 36.5 28 13.8 4 2.0 70 34.5


Adeno 174 85.7 66 32.5 9 4.4 165 81.3 0.55

Mucinous 19 9.4 5 2.5 2 1.0 17 8.4

Signet ring 10 4.9 2 1.0 1 0.5 9 4.4

Differentiation status MDA 111 54.7 43 21.2 4 2.0 107 52.7 0.02*

WDA 41 20.2 19 9.4 6 3.0 35 17.2

PDA 51 25.1 11 5.4 2 1.0 49 24.1

Stage T1 3 1.5 1 0.5 0 0.0 3 1.5 0.74

T2 12 5.9 3 1.5 0 0.0 12 5.9

T3 136 67.0 49 24.1 8 3.9 128 63.1

T4 52 25.6 20 9.9 4 2.0 48 23.6


Yes 152 74.9 61 30.0 10 4.9 142 70.0 0.73

No 51 25.1 12 5.9 2 1.0 49 24.1

Site Colon 147 72.4 51 25.1 8 3.9 139 68.5 0.74

Rectum 56 27.6 22 10.8 4 2.0 52 25.6

* Statistically significant


205

5.3.3 NRAS

Out of 203 cases, NRAS mutations were observed in only 4 patients accounting to

1.9%. Three of the NRAS mutations were observed in codon 12 and one was seen

in codon 13. All of the codon 12 mutations were G12V substitutions and in codon

13 was G13D substitution. Representative electropherograms for these changes in

the gene are shown in Figure 5.6. All these NRAS mutations were observed in

adenocarcinomas, metastatic lymph nodes and colon; however, the presence was

not statistically significant. There was no statistically significant correlation between

other clinicopathological and demographic features (Table 5.6) ; such as primary

or metastatic tumor, tumor staging, distant metastasis or anatomical site.


206

Table 5.6: Correlation of mutation frequency in NRAS gene with clinicopathological characteristics in CRC cases (n=203).


% Total Detected

% Detected Codon 12

Detected Codon 61

Total Detected

% Not detected

% p value

Age ≤50 64 31.5 20 9.9 1 1 2 1.0 62 30.5 0.59

>50 139 68.5 53 26.1 2 0 2 1.0 137 67.5

Gender Males 138 68.0 53 26.1 2 0 2 1.0 136 67.0 0.59

Females 65 32.0 20 9.9 1 1 2 1.0 63 31.0

Geographical location

East 8 3.9 2 1.0 0 0 0 0.0 8 3.9 0.75

West 119 58.6 47 23.2 3 0 3 1.5 116 57.1

North 38 18.7 11 5.4 0 0 0 0.0 38 18.7

South 38 18.7 13 6.4 0 1 1 0.5 37 18.2


Primary 129 63.5 45 22.2 2 0 2 1.0 127 62.6 0.62

Metastatic 74 36.5 28 13.8 1 1 2 1.0 72 35.5


Adeno 174 85.7 66 32.5 3 1 4 2.0 170 83.7 0.71

Mucinous 19 9.4 5 2.5 0 0 0 0.0 19 9.4

Signet ring 10 4.9 2 1.0 0 0 0 0.0 10 4.9

Differentiation status

MDA 111 54.7 43 21.2 1 0 1 0.5 110 54.2 0.43

WDA 41 20.2 19 9.4 0 1 1 0.5 40 19.7

PDA 51 25.1 11 5.4 2 0 2 1.0 49 24.1

Stage T1 3 1.5 1 0.5 0 0 0 0.0 3 1.5 0.95

T2 12 5.9 3 1.5 0 0 0 0.0 12 5.9

T3 136 67.0 49 24.1 3 0 3 1.5 133 65.5

T4 52 25.6 20 9.9 0 1 1 0.5 51 25.1


Yes 152 74.9 61 30.0 3 1 4 2.0 148 72.9 0.57

No 51 25.1 12 5.9 0 0 0 0.0 51 25.1

Site Colon 147 72.4 51 25.1 3 1 4 2.0 143 70.4 0.57

Rectum 56 27.6 22 10.8 0 0 0 0 56 27.6


207

5.3.4 PIK3CA

PIK3CA mutations were seen in 3.9% cases (8/203) in the current study. Seven of

the PIK3CA mutations were observed in exon 9 and all were GAG to AAG

substitution i.e. E545K mutation. One mutation was observed in exon 20 namely

H1047R resulting in CAT to CGT substitution. It was also observed that 3

mutations of exon 9 were overlap with KRAS mutant cases. This suggests that

KRAS and PIK3CA mutations can occur in the overlapping form in colon cancer.

No such concomitant mutations were observed in PIK3CA and NRAS and BRAF

suggesting that these mutations occur in exclusive manner. Interestingly, no

significant correlation was observed in PIK3CA mutations and any of the

clinicopathological characteristics (Table 5.7).


208

Table 5.7: Correlation of mutation frequency in PIK3CA gene with clinicopathological characteristics in colorectal cancer

cases (n=203).


% Total Detected

% Detected exon 9

Detected exon 20

Total Detected

% Not detected

% p value

Age ≤50 64 31.5 20 9.9 2 0 3 1.5 61 30.0 0.71 >50 139 68.5 53 26.1 5 1 5 2.5 134 66.0

Gender Males 138 68.0 53 26.1 5 1 7 3.4 131 64.5 0.44 Females 65 32.0 20 9.9 2 0 1 0.5 64 31.5

Geographical location

East 8 3.9 2 1.0 0 0 0 0.0 8 3.9 0.36 West 119 58.6 47 23.2 6 1 7 3.4 112 55.2 North 38 18.7 11 5.4 0 0 0 0.0 38 18.7 South 38 18.7 13 6.4 1 0 1 0.5 37 18.2


Primary 129 63.5 45 22.2 4 0 4 2.0 125 61.6 0.46 Metastatic 74 36.5 28 13.8 3 1 4 2.0 70 34.5


Adeno 174 85.7 66 32.5 7 1 8 3.9 166 81.8 0.49 Mucinous 19 9.4 5 2.5 0 0 0 0.0 19 9.4

Signet ring 10 4.9 2 1.0 0 0 0 0.0 10 4.9

Differentiation status

MDA 111 54.7 43 21.2 5 0 5 2.5 106 52.2 0.69 WDA 41 20.2 19 9.4 1 1 2 1.0 39 19.2 PDA 51 25.1 11 5.4 1 0 1 0.5 50 24.6

Stage T1 3 1.5 1 0.5 0 0 0 0.0 3 1.5 0.78 T2 12 5.9 3 1.5 0 0 0 0.0 12 5.9 T3 136 67.0 49 24.1 4 1 5 2.5 131 64.5 T4 52 25.6 20 9.9 3 0 3 1.5 49 24.1


Yes 152 74.9 61 30.0 7 1 8 3.9 144 70.9 0.21 No 51 25.1 12 5.9 0 0 0 0.0 51 25.1

Site Colon 147 72.4 51 25.1 4 0 4 2.0 143 70.4 0.22 Rectum 56 27.6 22 10.8 3 1 4 2.0 52 25.6


209

Table 5.8: Multivariate Logistic Regression Analysis for the correlation between gene mutations and clinicopathological

features in Indian CRC patients (n=203)

Clinico-pathological features

KRAS BRAF NRAS PIK3CA

OR 95%CI P OR 95%CI P OR 95%CI P OR 95%CI P

Age 2.4691 1.1154 - 5.4660

0.018* 0.4361 0.1350 - 1.4091

0.17 0.4526 0.0623 - 3.2869

0.439 0.7587 0.1757 - 3.2768

0.715

Gender 0.7083 0.3459 - 1.4505

0.339 1.0656 0.3089 - 3.6754

0.92 2.1587 0.2973, 15.6757

0.452 0.2924 0.0352 - 2.4276

0.191

Geographic location

0.9777 0.6654 - 1.4366

0.909 0.9667 0.4791 - 1.9506

0.925 0.968 0.2940 - 3.1870

0.957 0.6283 0.2346 - 1.6826

0.327

Primary or metastatic

1.0161 0.5214 - 1.9802

0.963 0.8643 0.2512 - 2.9741

0.816 1.7639 0.2433 - 12.7904

0.577 1.7857 0.4332 - 7.3616

0.425


0.5534 0.2409 - 1.2712

0.123 1.5807 0.6332 - 3.9458

0.36 The model could not be fit.# The model could not be fit.#

Differentiation 0.5921 0.3852 - 0.9102

0.012* 1.2045 0.6163 - 2.3539

0.59 2.077 0.6516 - 6.6208

0.206 0.7196 0.2835 - 1.8268

0.471

Stage 0.911 0.5293 - 1.5681

0.737 1.7155 0.6019 - 4.8890

0.304 1.284 0.2274 - 7.2593

0.775 1.9522 0.5421 - 7.0306

0.297

Lymph Node Mets

0.7067 0.3236 - 1.5435

0.375 0.5796 0.1227 - 2.7376

0.468 The model could not be fit.# The model could not be fit.#

Site 1.0667 0.5223 - 2.1785

0.86 1.3365 0.3861 - 4.6269

0.652 The model could not be fit.# 2.75 0.6635 - 11.3973

0.171

#Maximum likelihood estimates of parameters may not exist due to quasi-complete separation of data points. *-Statistically significant p<0.05 OR-Odds ratio, 95%CI-95% Confidence interval

In the multivariate logistic regression analysis it was observed that mutant KRAS was directly associated with increased

age i.e above 50 years (p=0.018) and greater differentiation (p=0.012) as seen in Table 5.8.


210

5.4 Discussion

In this phase of the study, the mutation frequencies in KRAS, BRAF, NRAS and

PIK3CA genes in 203 Indian colorectal cancer patients was examined and also the

correlation between clinicopathological features of CRC patients with these

mutations was further investigated. To the best of my knowledge, the present study

is the first study to determine collectively the mutation status of KRAS, BRAF,

NRAS and PIK3CA genes along with clinicopathological and geographical

incidence in a cohort of 203 Indian CRC patients.

The estimated incidence of CRC worldwide is 1.3 million (Ferlay et al., 2015).

Incidence of CRC in India has been estimated as 4.2 and 3.2 per 100,000 in males

and females, respectively. Population based time trend studies show a rising trend

in incidence of CRC in India (Ferlay et al., 2015).

Significant developments have been made in the recent past in the field of treating

CRC with the use of monoclonal antibodies targeting Epidermal growth factor

receptor (EGFR) such as cetuximab and panitumumab (Velho et al., 2009). EGFR

pathway plays a very critical role in tumorigenesis and progression of CRC. EGFR

initiates cascade of downstream signalling pathways such as RAS-RAF-MAPK and

PIK3-AKT pathways, which are responsible for, cell proliferation, differentiation and

survival (Patil et al., 2016). However, these anti-EGFR monoclonal antibodies are

effective against a small subset of CRC patients. This is due to the presence of

activating oncogenic mutations downstream of EGFR like KRAS, BRAF, NRAS

and PIK3CA, which negatively predict the response to anti-EGFR therapy. Studies


211

have identified KRAS, BRAF, NRAS and PIK3CA gene mutations as predictive

biomarkers in response to anti-EGFR antibody therapy (D. Lambrechts, 2009, De

Roock et al., 2010).

The overall frequency of mutations in current study from 203 Indian CRC cases,

revealed the presence of one of at least gene mutation in 36% cases and

remaining 64% of cases did not have any mutations in the four genes tested. The

prevalence of KRAS, BRAF, NRAS and PIK3CA mutations in this Indian cohort

cases was 24%, 6%, 2% and 4%, respectively. Hence, it can be seen further that

12% of KRAS wild type CRC patients had mutations in NRAS, BRAF or PIK3CA

genes.

5.4.1 KRAS

KRAS mutation frequency varies from 14% to 67% worldwide as seen in Table 5.8.

In the present study, the KRAS mutation frequency was observed to be 24%. In

Western countries such as USA, UK, France, Italy, Lithuania, Germany, Russia

and Australia, KRAS frequency ranges from 13%-67% where as in Asian countries

such as China, Korea, India, Japan and Taiwan it varies from 20%-66% as

mentioned in Table 5.8. The KRAS mutation frequency of 24% seen in the present

study is thus similar to those reported from our group as well as from Bagadi et.al.

and Bhist et.al. (20%-24%) (Patil et al., 2013, Bagadi et al., 2012, Bisht et al.,

2014) (Table 5.9). The variations seen in KRAS mutation frequency could be


212

attributed to ethnicity, geographical location, sample size, and techniques used and

other etiological factors.

In the current study, mutations in KRAS codons 12, 13 and 61 were evaluated.

From the past studies, it has been observed that point mutations in KRAS codon

12 are most common mutations in CRC (Vaughn et al., 2011). KRAS G12D is the

most frequent change observed which is trailed by G12V, G12C, G12S and G12A

(Vaughn et al., 2011, Neumann et al., 2009). In agreement with this, in present

study, codon 12 mutations were observed in 20.2% cases followed by codon 3.9%

in codon 13. No mutation was observed in codon 61.

The glycine residue at codon 12 has a very critical role in normal functioning of ras

protein. Hence, the single base substitutions that occur at this position cause

GTPase formation, which further gets locked at the activating site (Arrington et al.,

2012). In the current study, G12D substitution was the most frequent followed by

G12V, G12A, G12C and G12S. Similarly, G13D is the most frequent mutation

observed in codon 13, which was also seen as the only mutation observed in this

study in codon 13. (Vaughn et al., 2011)

Correlation of clinico-pathological characteristics was further investigated with

respect to KRAS mutations. It was observed that there was a significant positive

association between KRAS mutations and age of a CRC patient (p<0.05). Mutation

rate in patients with above 50 years of age was higher as compared to the patients


213

younger than 50 years. Furthermore, KRAS mutations were significantly

associated with tumor differentiation status and significantly associated

(moderately and poorly differentiated adenocarcinoma as compared to well-

differentiated adenocarcinoma (p<0.05) (Table 5.4). This observation is in

agreement with the previous reports (Zhang et al., 2015a). In present study, it was

further seen that KRAS mutations were more frequent in adenocarcinomas than in

mucinous or signet ring carcinoma, which is also interestingly observed in an

earlier study, however, statistically significant association was not observed in the

current study (Li et al., 2011). Other clinico-pathological characteristics showed no

significant association with KRAS mutations (p>0.05) which is in concordance with

the recent reports of Indian population study based data (Bisht et al., 2014).

5.4.2 BRAF

BRAF is a member of the serine-threonine protein kinase family i.e. RAF family. It

plays a very crucial role downstream of EGFR signalling pathway. The codon 600

Valine to Glutamic acid substitution is the most frequent alteration observed in

many human cancers including CRC (Di Nicolantonio et al., 2008). The BRAF

mutation frequency ranges from 0.2% to 25% worldwide. In the present study the

BRAF frequency is seen to be 5.9% which is in concordance with Asian studies

(Table 5.9). As mentioned earlier geographical location, etiological factors, genetic

makeup have an important role in these variations.

BRAF codon 600 V600E was the only mutation observed in our study. Other

mutations of BRAF i.e. V600K, V600Q or V600L were not observed which have


214

been reported in Western population (Mao et al., 2012b). Also, there was no

presence of concomitant BRAF mutation with KRAS mutant cases. This lies in

concordance with previous studies which show that BRAF and KRAS mutations

are mutually exclusive (Di Nicolantonio et al., 2008).

However, till today there is insufficient data to justify the predictive role of BRAF for

benefit from anti-EGFR monoclonal antibody therapy in KRAS wild type cases.

Few studies in past have shown worst outcome in case of BRAF mutant cases(Di

Fiore et al., 2010). In the recent past few studies have evaluated anti-BRAF/EGFR

combination regimes to elucidate the best treatment outcome (Connolly et al.,

2013, Yaeger et al., 2015). This combinatorial approach would emerge as a

potential strategy for future cancer treatment.

In the present study, it was seen that BRAF mutations were significantly observed

in moderately differentiating and poorly differentiating adenocarcinomas than in

well-differentiated adenocarcinomas. Presence of BRAF mutations in poorly

differentiated adenocarcinomas is similar to findings of previously reported studies

(Shen et al., 2013). No significant association was observed in BRAF mutations

and other clinico-pathological features which supports the reported studies (Li et

al., 2011, Mao et al., 2012b, Bisht et al., 2014).


215

5.4.3 NRAS

In human cancers RAS proto-oncogenes, KRAS and NRAS, are found mostly in

mutant oncogenic forms. In case of CRC unlike KRAS, which are most frequently

mutated, NRAS mutations are rare (Irahara et al., 2010). The mutation frequency

for NRAS in CRC varies from 2%-10% (Table 5.9). In the present study, NRAS

mutations were observed in 1.9% which lies in concordance with previously

reported studies from Asian countries (Bagadi et al., 2012, Zhang et al., 2015a).

There was no coexistence of KRAS , BRAF and NRAS mutations in the study. No

significant association was observed between NRAS mutations and patient

demographics.

5.4.4 PIK3CA

Alterations in phosphoinositide-3-kinase catalytic alpha; a catalytic domain in PIK3,

is seen in many cancers. In CRC these mutations range from 1%-37% and majority

of mutations are observed in exon 9 and exon 20 (Table 5.9). There are two hot

spot regions in exon 9 –codon 542 and codon 545 and one in exon 20-codon 1047.

Recently, PIK3CA has been observed as a potential predictive marker for targeted

therapy in CRC. A low response rate has been observed in patients having

PIK3CA mutations (De Roock et al., 2011). In the present study, the frequency of

PIK3CA mutations was found to be 3.9% similar to other Asian studies (Zhang et

al., 2015a, Bisht et al., 2014, Hsieh et al., 2012). Higher percentage of mutations

were observed in exon 9 which is similar to Western population (Palomba et al.,

2012). Further current study data shows that mutations were seen in exon 9 only


216

E545K and in exon 20 H1047R while other mutations of exon 9 namely E542K and

H1047L in exon 20 were absent. Mutations in exon 9 and 20 may affect differently

the response to anti-EGFR therapy. Mutations in exon 20 are associated with lower

response rates as seen in the study done by Mao et.al (Mao et al., 2012b, De

Roock et al., 2010).

It was also seen in the present study results that 3 cases showed overlapping

mutations in KRAS and PIK3CA. Such coexistence of KRAS and PIK3CA

mutations has been reported earlier in few studies (Mao et al., 2012b). However,

no significant correlation was seen in clinico-pathological characteristics and

PIK3CA mutations which corresponds to previous studies of Asian countries (Mao

et al., 2012b, Bisht et al., 2014) .


217

Table 5.9: KRAS, NRAS, BRAF and PIK3CA mutation frequencies in reported studies worldwide. Study Sample

size

Method Target Population KRAS

%

NRAS

%

BRAF

%

PIK3C

A %

(Baltruškevičienė et al.,

2016)

55 Sanger sequencing KRAS exons 2,3 and 4

NRAS exons 2,3 and 4

BRAF exon 15

PIK3CA exons 9 and 20

Lithuania 67.3 0 1.8 5.5

(Zhang et al., 2015a) 1110 RT PCR and Sanger sequencing KRAS exons 2,3 and 4


BRAF exon 15

PIK3CA exon 9

China 45.4 3.9 3.1 3.5

(Foltran et al., 2015) 194 Pyro sequencing KRAS exons 2 and 3

BRAF exon 15

NRAS exons 2 and 3


Italy 47.4 3.6 5.2 16.5

(Kawazoe et al., 2015) 264 Luminex assay KRAS exons 2,3 and 4

NRAS exons 2 and 3

BRAF exon 15


Japan 34.1 4.2 5.4 6.4

(Normanno et al., 2015) 182 Next Generation Sequencing KRAS exons 2,3 and 4


BRAF exon 15


Italy 24.7 7.1 8.2 13.2

(Kriegsmann et al., 2015) 93 Mass spectrometry KRAS exons 2,3 and 4


BRAF exon 15

Germany 49 2 1 Not

done

(Negru et al., 2014) 2071 Sanger sequencing KRAS exons 2,3 and 4


Greek 46.56 9 14.4 Not

done


218

BRAF exon 15,

(Negru et al., 2014) 2071 Sanger sequencing KRAS exons 2,3 and 4


BRAF exon 15

Romania 46.3 10.3 10.2 Not

done

(Bisht et al., 2014) 204 DNA sequencing KRAS exons 2 and 3

NRAS exons 2 and 3

BRAF exon 15


India 23.5 Not

done

9.8 5.9

(Guedes et al., 2013) 212 High resolution melting analysis KRAS exons 2,3 and 4

BRAF exon 15


Portugal 44.1 Not

done

18.3 37.3

(Rosty et al., 2013) 757 HRM and Sanger sequencing KRAS exons 2 and 3

BRAF exon 15


Australia 28.4 Not

done

15.9 14

(Patil et al., 2013) 1323 DNA sequencing KRAS exons 2 and 3 India 20.5 Not

done

Not

done

Not

done

(Sinha et al., 2013) 62 DNA sequencing KRAS exons 2 and 3 India 62.1 Not

done

Not

done

Not

done

(Malhotra et al., 2013) 30 PCR Restriction digestion KRAS exons 2 and 3 India 26.7 Not

done

Not

done

Not

done

(Smith et al., 2013) 1976 Mass spectrometry and pyro

sequencing

KRAS exons 2 and 3

BRAF exon 15


UK 42.3 Not

done

9 12.7

(Yanus et al., 2013) 195 HRM/COLDPCR/Allele Specific

PCR and Sequencing

KRAS exons 2 and 3

NRAS exons 2 and 3

BRAF exon 15


Russia 35.9 4.1 4.1 12.3


219

(Derbel et al., 2013) 98 DNA sequencing KRAS exons 2 and 3

BRAF exon 15

PIK3CA exon 9 and 20

France 23.5 Not

done

2 4

(Neumann et al., 2013) 171 Pyro sequencing KRAS exon 2,

BRAF exon 15


Germany 40.9 Not

done

11.1 18.7

(Yip et al., 2013) 44 KRAS-DNA sequencing, BRAF-

Real Time

KRAS exons 2 and 3

BRAF exon 15

Malaysia 25 Not

done

2.3 Not

done

(Nakanishi et al., 2013) 254 DNA sequencing KRAS exon 2,

BRAF exon 15

Japan 33.5 Not

done

6.7 Not

done

(Soeda et al., 2013) 43 DNA sequencing KRAS exons 2 and 3,

BRAF exon 15


Japan 27.9 Not

done

4.7 4.7

(Mao et al., 2012a) 69 Sanger sequencing KRAS codons 12,13,14

BRAF codon 600


China 43.9 Not

done

25.4 8.2

(Bagadi et al., 2012) 100 DNA sequencing KRAS exons 2 and 3

NRAS exons 2 and 3

BRAF exon 15

India 23 2 17 Not

done

(Liao et al., 2012) 964 Pyro sequencing KRAS exons 2 and 3

BRAF exon 15


USA 35 Not

done

13.7 16.7

(Bozzao et al., 2011) 209 HRM and Sanger sequencing KRAS exon 2

BRAF exon 15

PIK3CA exon 20

Italy 43.5 Not

done

0 2.3

(Palomba et al., 2012) 478 DNA sequencing KRAS exons 2 and 3

BRAF exon 15

Sardinia 30.3 Not

done

0.26 17.4


220


(Hsieh et al., 2012) 182 HRM KRAS exon 2

BRAF exon 15


Taiwan 33.5 Not

done

1.1 7.1

(Ling et al., 2012) 331 DNA sequencing KRAS exon 2

BRAF exon 11 and 15


China 44.1 Not

done

5.8 2.6

(Balschun et al., 2011) 21 Sanger sequencing and Pyro

sequencing

KRAS exons 2 and 3

NRAS exons 2 and 3

BRAF exon 15

PIK3CA exon 20

Germany 31.6 3.5 12.3 0

(Wong et al., 2011) 29 Real Time PCR KRAS exon 2

BRAF exon 15


USA 34.9 Not

done

10.3 10.3

(Saridaki et al., 2011) 112 KRAS and PIK3CA-DNA

sequencing, BRAF - Real Time

PCR

KRAS exon 2

BRAF exon 15


Greece 33 Not

done

7.2 9.8

(Janku et al., 2011) 504 DNA sequencing KRAS exons 2 and 3

NRAS exons 2 and 3

BRAF exon 15


USA 19 8 9 11

(Baba et al., 2011) 717 Pyro sequencing KRAS exon 2

BRAF exon 15


USA 37.7 Not

done

15.4 16.8

(Kwon et al., 2011) 92 DNA sequencing KRAS exons 2 and 3

BRAF exon 15


Korea 20.7 Not

done

3.3 1.1


221

(Aoyagi et al., 2011) 134 DNA sequencing KRAS exon 2

BRAF exon 15


Japan 30.6 Not

done

0.75 13.4

(De Roock et al., 2010) 1022 Mass spectrometry KRAS exons 2 and 3

NRAS exons 2 and 3

BRAF exon 15


Belgium 40 2.6 4.7 14.5

(Di Nicolantonio et al.,

2010)

43 DNA sequencing KRAS exon 2

BRAF exon 15


Italy 43.5 Not

done

0 2.3

(Lurkin et al., 2010) 294 Multiplex PCR and sequencing KRAS exons 2 and 3

NRAS exons 2 and 3

BRAF exon 15


Germany 48.6 2 5.3 13.1

(Perkins et al., 2010) 42 DNA sequencing KRAS exon 2

BRAF exon 15

PIK3CA exon 9

France 45.2 Not

done

2.4 14.3

(Baldus et al., 2010) 100 Pyro sequencing KRAS exon 2

BRAF exon 15


Germany 41 Not

done

7 21

(Berg et al., 2010) 181 DNA sequencing KRAS exons 2 and 3

BRAF exon 15


Chinese 32 Not

done

16 3

(Irahara et al., 2010) 225 Pyro sequencing KRAS exons 2 and 3

NRAS exons 2 and 3

BRAF exon 15


USA 41 2.2 14 11

(Roth et al., 2010) 1404 Real Time PCR KRAS exon 2 Swiss 37 Not 7.9 Not


222

BRAF exon 15 done done

(Souglakos et al., 2009) 168 DNA sequencing KRAS exon 2

BRAF exon 15


UK 36.9 Not

done

7.7 15.5

(Ogino et al., 2009) 450 Pyro sequencing KRAS exon 2

BRAF exon 15


USA 35.7 Not

done

15.8 18.2

(Perrone et al., 2009) 32 DNA sequencing KRAS exon 2

BRAF exon 11,15


Italy 24.1 Not

done

9.7 12.9

(D. Lambrechts, 2009) 153 Squenome MALDI TOF

MassArray

KRAS exons 2,3 and 4

NRAS exons 2 and 3

BRAF exon 15


Belgium 42 5.4 9.8 12

(Velho et al., 2008) 150 PCR and sequencing KRAS exon 2

BRAF exon 15

PIK3CA exon 20

Portugal 31 Not

done

18 14

(Simi et al., 2008) 116 HRM KRAS exon 2

BRAF exon 15


Italy 43 Not

done

9.5 17.2

(Freeman et al., 2008) 62 DNA sequencing KRAS exon 2

BRAF exon 15


USA 38.7 Not

done

5.6 3.2

(Cappuzzo et al., 2008) 80 PCR and Suveyor digestion KRAS exon 2

BRAF exon 11,15


Italy 52.5 Not

done

5.06 17.7

(Barault et al., 2008) 586 DNA sequencing KRAS exon 2

BRAF exon 15

France 33.8 Not

done

13.3 16.7


223

PIK3CA exons 1, 2,9 and

20

(Velho et al., 2005) 150 PCR-SSCP-Sequencing KRAS exon 2

BRAF exon 15


Portugal 20.7 Not

done

12 9.3

(Fransen et al., 2004) 130 PCR-SSCP-Sequencing KRAS exons 2 and 3

BRAF exon 11,15

Sweden 40 Not

done

10 Not

done

(K Servomaa, 2000) 118 PCR-SSCP-Sequencing KRAS exons 2 and 3 Finland 14 Not

done

Not

done

Not

done

Current Study 203 DNA sequencing KRAS exons 2 and 3

NRAS exons 2 and 3

BRAF exon 15


India 24 2 6 4


224

These variations in the mutation patterns could be due to racial differences,

geographical differences, environmental factors, and lifestyle factors, which include

obesity or physical inactivity, or other etiological factors. Nevertheless, future

studies on large cohorts are required for in depth investigations on the genetic and

epigenetic markers involved in colorectal cancer to aid in identification of new

targets for personalised medicine.

Chapter 6. Survival Analysis

225

Chapter 6 Survival Analysis

6.1 Introduction

Several studies have been performed to identify prognostic effects of various

clinico-pathological factors in colorectal cancer (Desolneux et al., 2010, Gharbi et

al., 2010, Laohavinij et al., 2010, Moghimi-Dehkordi et al., 2008, Rath and Gandhi,

2014). In Asia, the 5-year survival rate is around 42% as compared to the USA,

where it is around 60% (Jemal et al., 2010b). Early stage detection of disease

increases the patient survival rate to around 90%. However, in developing

countries early detection is possible only in 35% of CRC patients due to lack of

screening programs. Relative survival rates of CRC patients in Asian countries

ranges from 28%-42%, with the highest being in China and lowest in India (Siegel

et al., 2015). As reviewed in Chapter 2, the survival rates in Asian countries are

lower as compared to European and Western developed countries. This has been

attributed mainly to late diagnosis. Aggressive treatment besides early detection is

another key strategy for improving overall survival.

Further as summarized in Figure 2. 15, and reviewed in Treatment strategies

section 2.6, 5-FU was the only drug used for decades for the treatment of CRC.

With the use of combination of drugs in chemotherapy, the oncologists have

achieved improvement in patient’s overall survival. Use of 5-FU plus Leucovorin

(LV) either with irinotican (FOLFIRI) or oxaliplatin (FOLFOX) has led to

improvement in survival. Further, the addition of monoclonal antibodies to standard


226

chemotherapy regime has improved the survival rates. The clinicians require

accurate outcome prediction to adopt appropriate therapeutic regime.

The aim of this phase of study was to examine the correlation between

clinicopathological characteristics and survival outcome in the Indian CRC patient

cohort (n=30). This may help in disease understanding and better patient

management for the Indian patients. Hence, evaluation of progression free and

overall survival of Indian CRC patients was carried out by exploring relevant

clinicopathological factors affecting prognosis like age, sex, site, stage etc.

6.2 Patients

This study is a retrospective observational study comprising of a total of 30 Indian

CRC patients studied between January 2013 till August 2016. All clinical and follow

up data were collected from medical records. The data included age, sex, tumor

differentiation, location of tumor, lymph node involvement, depth of invasion, date

of onset, cause of recurrence if any, date of death and treatment given. The data

was collected after every 3 months. Patients were treated with either FOLFOX

(Folinic acid, 5-FU and Oxaliplatin) or CAPOX (Oxaliplatin and Capecitabine) or

CAPIRI (Capecitabine and Irinotecan). Three patients were given bevacizumab

(Avastin) and three were given cetuximab (Erbutix) in combination with

chemotherapy. The study was conducted with approval from the scientific

committee of Reliance Life Science Pvt Ltd. The study design was shared with the

hospitals for obtaining clinical information of the patients who were referred to


227

Reliance Life Sciences Pvt. Ltd. for mutational analysis of the samples. Formalin

fixed specimens was stained with haematoxylin – eosin (HE) and histological

assessment was done. Direct sequencing was performed for KRAS, NRAS, BRAF

and PIK3CA mutational analysis.

6.3 Statistics

Statistical analysis was done using GraphPad Prism 7 (GraphPad Software Inc,

CA, USA) and MedCalc for Windows, version 16.0 (MedCalc Software, Ostend,

Belgium). The Kaplan- Meier method was used for plotting survival curves.

Progression free survival (PFS) is defined as the time from initial administration

of treatment until the first objective evidence of disease progression or death from

any cause.

Overall survival (OS) is defined as the time from the initiation of the treatment

until the death of the patient. Patients were censored at the time of last follow up or

if they were alive after the end of the study, which was August 2016.

To assess the differences in survival, log rank test was used. Univariate hazard

ratios were identified along with multivariate using COX proportional hazard

analysis. p value less than or equal () to 0.05 was considered as significant.


228

6.4 Results:

Basic information data from the CRC patients is given in Table 6.1.

Table 6.1a: Basic Data of CRC patients (n=30)

Pt

No. Age

Date of

diagnosis

Date of

death or lost

follow up

Alive (A) or

dead (D) or

left (L)

No of

years

Round

Down

No. of

years

No. of

days

1 66 5/03/13 5/03/14 D 1 1 365

2 74 18/06/12 1/07/13 D 1 1.04 378

3 44 1/04/11 1/03/15 L 3 3.92 1430

4 70 16/07/13 1/08/14 D 1 1.04 381

5 69 24/08/13 15/07/15 L 1 1.89 690

6 58 27/11/13 3/03/15 L 1 1.26 461

7 45 6/09/13 6/07/15 D 1 1.83 668

8 70 20/11/14 6/04/16 A 1 1.38 503

9 56 2/01/14 1/08/15 D 1 1.58 576

10 62 7/06/11 12/03/16 A 4 4.77 1740

11 57 30/07/13 12/01/16 A 2 2.45 896

12 59 6/07/15 12/05/16 A 0 0.85 311

13 66 12/04/13 4/04/16 A 2 2.98 1088

14 56 30/08/14 8/05/16 A 1 1.69 617

15 52 6/08/13 10/06/15 D 1 1.84 673

16 48 31/07/13 20/05/14 D 0 0.80 293

17 63 1/03/14 11/05/15 L 1 1.19 436

18 78 27/09/12 4/12/12 D 0 0.19 68

19 60 28/02/13 1/02/15 L 1 1.93 703

20 55 7/03/13 8/11/15 D 2 2.67 976

21 45 17/12/14 29/04/16 A 1 1.37 499


229

22 51 26/01/13 25/02/16 A 3 3.08 1125

23 62 8/05/13 18/06/15 D 2 2.11 771

24 48 12/02/15 10/03/16 A 1 1.07 392

25 50 6/06/13 21/07/15 D 2 2.12 775

26 52 16/02/14 24/05/16 L 2 2.27 828

27 65 26/03/13 19/07/16 A 3 3.32 1211

28 67 27/05/13 13/08/16 A 3 3.22 1174

29 71 14/07/14 6/08/16 D 2 2.07 754

30 80 21/01/14 15/07/16 D 2 2.48 906


230

Table 6.1b: Clinicopathological Data of CRC patients (n=30)

Pt No Age Date of diagnosis

Date of death or lost follow up

Alive or dead or left

No.of yrs Gende

r Site

Tumor differentiation

Lymphnode mets Therapy Mutation P/M

1 66 5/03/13 5/03/14 D 1 M Colon PDA pT3N2

CAPIRI+ERBUTIX BRAF M

2 74 18/06/12 1/07/13 D 1 F Rectum PDA ypT3N2b CAPOX ND P

3 44 1/04/11 1/03/15 L 3 M Rectum PDA ypT4N2b

FOLFOX+CAPIRI ND M

4 70 16/07/13 1/08/14 D 1 M Colon MDA pT3pN0pM1 CAPIRI ND M

5 69 24/08/13 15/07/15 L 1 F Rectum MDA pT4bN1aMx FOLFOX KRASCD12 M

6 58 27/11/13 3/03/15 L 1 F Colon MDA T3N0 FOLFOX KRASCD12 P

7 45 6/09/13 6/07/15 D 1 F Colon PDA T3N2MX FOLFOX ND P

8 70 20/11/14 6/04/16 A 1 M Rectum MDA T3N1MX FOLFOX ND P

9 56 2/01/14 1/08/15 D 1 F Colon PDA T3N2

FOLFOX+CAPIRI ND M

10 62 7/06/11 12/03/16 A 4 M Rectum MDA T3NOMX CAPOX ND P

11 57 30/07/13 12/01/16 A 2 M Rectum MDA T3N1MX FOLFOX KRASCD12 P

12 59 6/07/15 12/05/16 A 0 M Rectum MDA T3N0 CAPOX ND P

13 66 12/04/13 4/04/16 A 2 M Rectum MDA T4N2M1 CAPOX+CAPIRI ND M

14 56 30/08/14 8/05/16 A 1 M Rectum MDA T3N2MX

FOLFIRI+AVASTIN KRASCD12 M

15 52 6/08/13 10/06/15 D 1 M Rectum MDA T3N0 FOLFOX ND M

16 48 31/07/13 20/05/14 D 0 F Colon PDA T4N2

FOLFOX+AVASTIN ND P

17 63 1/03/14 11/05/15 L 1 F Colon MDA T4N2

FOLFOX+ERBUTIX ND P

18 78 27/09/12 4/12/12 D 0 M Rectum MDA T3N2MX CAPOX ND P

19 60 28/02/13 1/02/15 L 1 F Colon MDA T3N1MX CAPOX ND P

20 55 7/03/13 8/11/15 D 2 F Colon PDA T3N1 FOLFOX KRASCD12 P


231

21 45 17/12/14 29/04/16 A 1 F Colon MDA T3N1 FOLFOX ND M

22 51 26/01/13 25/02/16 A 3 M Colon MDA T3N0

FOLFIRI+AVASTIN ND P

23 62 8/05/13 18/06/15 D 2 F Rectum MDA T3N2MX FOLFOX KRASCD12 P

24 48 12/02/15 10/03/16 A 1 M Rectum PDA T3N0 CAPOX ND P

25 50 6/06/13 21/07/15 D 2 F Colon PDA T3N1 FOLFOX ND M

26 52 16/02/14 24/05/16 L 2 F Colon MDA T3N0 CAPOX ND P

27 65 26/03/13 19/07/16 A 3 M Rectum MDA T3N1 FOLFOX ND P

28 67 27/05/13 13/08/16 A 3 F Rectum MDA T4N2

FOLFOX+ERBUTIX ND P

29 71 14/07/14 6/08/16 D 2 M Colon PDA T3N2MX FOLFOX ND M

30 80 21/01/14 15/07/16 D 2 M Colon PDA T3N1MX FOLFOX KRAS CD12 M

D-Dead, A-Alive, L-Lost follow up, P-Primary tumor, M-Metastatic tumor


232

6.4.1 Estimation of survival

The first requirement for estimation of survival time is a well-defined starting point.

In this study, the date of diagnosis was taken as the starting time. The outcome

was defined as the death of the patient, so the death of patient was recorded.

Survival time is the time between the starting point (occurrence of disease) and the

outcome (death of the patient). The data of patients’ survival time in increasing

duration is shown in Figure 6.1. The X axis shows the time in years and Y axis

shows the patient. The bars in red denote the death of that particular patient.


233

Figure 6.1: The data of 30 patients aligned in order of their survival time. Red bars

indicate the dead patients and yellow ones indicate the alive patients.

0 1 2 3 4 5 6

10

3

27

28

22

13

20

30

11

26

25

23

29

19

5

15

7

14

9

8

21

6

17

24

4

2

1

12

16

18

Years

Pat

ien

t n

um

be

r


234

The data, in summary, is represented in the form of a tree diagram (Figure 6.2).

Figure 6.2: Tree diagram for CRC patients (n=30).

Figure Legend: The upper arm represents the dead patients and the lower arm

represents the alive patients. Based on this data, the probability of dying can be calculated

as 13/25=0.52=52%.

30

13-Dead

17-Alive


235

6.4.2 Censored observations

A closed group consists of patients having all complete observations. However, in

the clinical scenario of a retrospective study this is not possible as there would be

some patients who would join the study at different time points and other patients

that would have been lost to follow up due to migration. Such patients who have

early termination of follow up are called as censored patients.

In the present study, there were 6 patients who left during the study period. These

patients were termed as censored. Hence, the results can be expressed as a tree

diagram having three arms, one for patients who are alive, one for dead patients

and another one for censored patients as shown in Figure 6.3. The probability of

dying can then be calculated as D/N-(0.5XL) i.e.13/25-(0.5X6) =0.59=59%.

Figure 6.3: Tree diagram showing 30 CRC cases of which 13 are dead, 6 have

lost follow up and 11 are alive.

30

13-Dead

11-Alive

06-Lost Follow up


236

6.4.3 Actuarial Life Table

The data of these 30 patients is also presented in the form of Actuarial life table in

which cumulative survival is calculated along with probability of dying and

probability of survival. Table 6.2 summarises actuarial life table for 30 patients and

the survival curve is shown in Figure 6.4.

Table 6.2: Actuarial Life Table for patients (n=30)

No. of

years

No. of patients

(N)

No. of Dead (D)

No. of Alive or Left A/L

(N-0.5L) Prob. of Dying

Prob. of Surviving

Cumulative Survival

0 30 2 1 29.5 0.07 0.932 0.93

1 28 6 8 24 0.25 0.75 0.47

2 14 5 3 12.5 0.40 0.600 0.2

3 6 0 2 5 0.00 1.000 0.13

4 4 0 1 3.5 0.00 1.000 0


237

Figure 6.4: Actuarial survival curve (n=30)

Figure Legend:

Probability of dying = D/N-(0.5L)

Probability of survival = 1- Probability of death

Cumulative survival = Multiplication of probability survivals of each year


238

6.4.4 Kaplan – Meier Survival Curve

The method of estimation of survival probabilities when the exact time of death or

censored data is known is called Kaplan-Meier survival curve analysis. It is a

stepped line plot in which each step denotes the death of a patient.

Figure 6.5: Kaplan-Meier survival curve for CRC patients (n=30).

Figure Legend: The 3-year overall survival is 40% with median survival of 2.48 years i.e.

29 months as shown in Figure 6.5 .The dots on the plot represent the censored patients

and the drops are the patients, which died in the study.

0 2 4 6

0

5 0

1 0 0

T im e

Pe

rc

en

t s

urv

iva

l


239

6.4.4.1 Estimation of Progression free survival (PFS) and Overall Survival

(OS)

Patients were divided according to the different clinicopathological characteristics.

PFS and OS were calculated using Kaplan-Meier curves with respect to different

clinicopathological features. Figure 6.6 summarises the PFS and OS curves of

Indian CRC patients.

Figure 6.6: Kaplan Meier plots of PFS and OS for CRC patients (n=30).

A

B

10

20

30

40

50

60

70

80

90

100

Age wise PFS

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Time in years

Sur

viva

l pro

babi

lity

(%)

Number at risk

Group: Below 60 years

15 15 10 7 2 0 0

Group: Above 60 years

15 14 10 5 2 1 0

Age

Below 60 years

Above 60 years

30

40

50

60

70

80

90

100

Age wise Overall Survival

0 1 2 3 4 5

Time in years

Sur

viva

l pro

babi

lity

(%)

Number at risk

Group: Below 60 years

15 13 6 2 0 0

Group: Above 60 years

15 11 7 3 1 0

Age

Below 60 years

Above 60 years


240

C

D

0

20

40

60

80

100

Differentiation wise PFS

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Time in years

Sur

viva

l pro

babi

lity

(%)

Number at risk

Group: MDA

11 11 6 4 1 0 0

Group: PDA

19 18 14 8 3 1 0

Differentiation

MDA

PDA

10

20

30

40

50

60

70

80

90

100

Differentiation-Overall Survival

0 1 2 3 4 5

Time in years

Sur

viva

l pro

babi

lity

(%)

Number at risk

Group: PDA

11 8 5 1 0 0

Group: MDA

19 16 8 4 1 0

Differentiation

PDA

MDA


241

E

F

0

20

40

60

80

100

Lymphnode metastasis wise PFS

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: No

8 8 5 4 2 1 0

Group: Yes

22 21 15 8 2 0 0

Lymphnode_mets

No

Yes

30

40

50

60

70

80

90

100

Lymphnode metastasis-Overall Survival

0 1 2 3 4 5

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: No

8 6 3 2 1 0

Group: Yes

22 18 10 3 0 0

Lymphnode_mets

No

Yes


242

G

H

10

20

30

40

50

60

70

80

90

100

Primary or Metastatic tumor wise PFS

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: Primary

18 17 12 8 3 1 0

Group: Metastatic

12 12 8 4 1 0 0

Primary_or_metastatic

Primary

Metastatic

20

30

40

50

60

70

80

90

100

Primary or metastatic tumor-Overall Survival

0 1 2 3 4 5

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: Primary

18 14 8 4 1 0

Group: Metastatic

12 10 5 1 0 0

Primary_or_metastatic

Primary

Metastatic


243

I

J

Figure Legend:

A and B: Age wise PFS and OS ; C and D : Tumor differentiation wise PFS and OS; E and

F :Lymph node metastasis wise PFS and OS ; G and H : Primary or metastatic tumor wise

PFS and OS ; I and J: Tumor site wise PFS and OS. MDA-Moderately differentiated

adenocarcinoma ; PDA-Poorly differentiated adenocarcinoma

10

20

30

40

50

60

70

80

90

100

Tumor site wise PFS

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: Rectum

15 14 11 7 3 1 0

Group: Colon

15 15 9 5 1 0 0

Tumor_site

Rectum

Colon

10

20

30

40

50

60

70

80

90

100

Tumor site wise Overall survival

0 1 2 3 4 5

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: Rectum

15 12 7 4 1 0

Group: Colon

15 12 6 1 0 0

Tumor_site

Rectum

Colon


244

According to age factor, two groups were made, less than 60 years old and other

more than 60 years. PFS and OS were compared in these two groups. The median

PFS of patients below 60 years was 0.7 years (n=15, HR=0.834,95%CI=0.3543 to

1.9636) and above 60 years was 0.6 years (n=15, HR=0.94,95%CI=0.3178 to

2.8032) . The median OS of patients below 60 years was 2.7 years (n=15,

HR=0.94,95%CI=0.3178 to 2.8032) and above 60 years was 2.5 years (n=15,

HR=1.06,95%CI=0.3567 to 3.1462). There was no significant association between

the two plots both by univariate and multivariate analysis (p>0.05) (Figure 6.6 A

and B, Table 6.3).

As per histological differentiation - moderately differentiated adenocarcinoma

(MDA) and poorly differentiated adenocarcinoma (PDA), median PFS of PDA

(n=11, 0.5 years, 95%CI= 0.40-0.70) was shorter than that of MDA (n=19, 0.8

years, 95%CI= 0.50-0.90), as verified in both univariate (HR=0.503, 95%CI=

0.2001 to 1.2643, p=0.08 ) and multi variate (HR= 0.331, 95%CI=0.1047 to 1.0468,

p=0.058) analysis.

The median OS of PDA was significantly shorter than that of MDA as verified both

by univariate (HR=0.25, 95%CI= 0.07682 to 0.7499, p=0.009 ) and multivariate

analysis (HR=0.1605, 95%CI= 0.0237 to 1.0895, p=0.05 ) (Figure 6.6 C and D,

Table 6.3)

In the case of location of tumor i.e colon or rectum, primary or metastatic tumor

and lymph node metastasis no significant association was observed for PFS and

OS both by univariate and multivariate analysis (p>0.05). The PFS and OS was


245

shorter in case of tumors located in rectum, metastatic tumors and tumors with

lymph node metastasis (Figure 6.6 E-J, Table 6.3) .

Table 6.3: Univariate and Multivariate analysis of PFS and OS

Univariate

PFS OS

Clinicopathological features

Reference category

OR 95% CI p OR 95% CI p

Age >60/<60 yrs 0.834 0.3543 to 1.9636

0.65 0.94 0.3178 to 2.8032

0.72

Site Colon/Rectum 0.96 0.4061 to 2.2771

0.9 2.63 0.8756 to 7.911

0.08

Differentiation

PDA/MDA 0.503 0.2001 to 1.2643

0.08 0.25 0.07682 to 0.7499

0.009*

Lymph node involvement

Yes/No 0.55 0.2164 to 1.4007

0.23 0.53 0.1517 to 1.828

0.39

Metastatic or Primary

Primary/Metastatic 0.79 0.3264 to 1.9152

0.56 0.56 0.1875 to 1.727

0.3

Mutation status

Wild Type/ Any mutant

0.68 0.2566 to 1.8262

0.36 0.86 0.2548 to 2.918

0.81

Therapy Chemo/Chemo+biological agent

0.91 0.2984 to 2.8045

0.86 0.83 0.2021 to 3.4482

0.8

Multivariate

PFS OS


Reference category

OR 95% CI p OR 95% CI p

Age >60/<60 yrs 1.4722

0.5944 to 3.6461

0.4033

0.456 0.1233 to 1.6866

0.2393

Site Colon/Rectum 0.6488

0.2294 to 1.8349

0.4147

1.7074

0.4122 to 7.0717

0.4606

Differentiation

PDA/MDA 0.3311

0.1047 to 1.0468

0.0598

0.1605

0.0237 to 1.0895

0.0542*

Lymph node involvement

Yes/No 0.76 0.1136 to 5.1044

0.779 0.7525

0.1133 to 4.9961

0.7684

Metastatic or Primary

Primary/Metastatic 0.9074

0.3490 to 2.3591

0.842 0.8336

0.2093 to 3.3207

0.7963

Mutation status

Wild Type/ Any mutant

1.5031

0.5732 to 3.9417

0.4073

0.6749

0.1709 to 2.6663

0.5749

Therapy Chemo/Chemo+biological agent

0.5973

0.1827 to 1.9529

0.3939

0.77 0.1450 to 4.0739

0.76

*Statistically significant


246

PFS-Progression free survival, OS-Overall survival, OR-Odds ratio, 95%CI-95%Confidence interval, PDA-Poorly differentiated adenocarcinoma, MDA-Moderately differentiated adenocarcinoma Further in this study, twenty two patients had tumors with no mutations (all wild-

type tumors) and 8 had tumors with mutation in either KRAS codons 12 or 13 or

BRAF (any of the mutations). Among the 8 patients with any of the mutations, 7

had KRAS codon 12 or 13 mutations one had BRAF mutation (Table 6.4).

Patients with tumor mutations were more likely to have worse PS in comparison

with all wild-type tumors. No other significant difference was seen between the two

groups (Table 6.4).


247

Table 6.4: Patient, disease and treatment characteristics (n=30).


Wild type (n=22) Any mutant (n=8) p value

Age

>60 11 (50.0) 4 (50.0) 0.99

<60 11 (50.0) 4 (50.0)

Gender

Male 12 (54.5) 4 (50.0) 0.99

Female 10 (45.5) 4 (50.0)

Dead/Alive

Dead 9 (40.9) 4 (50.0) 0.69

Alive 13 (59.1) 4 (50.0)

Site

Colon 11 (50.0) 4 (50.0) 0.99

Rectum 11 (50.0) 4 (50.0)

Tumor Diff

MDA 14 (63.6) 5 (62.5) 0.99

PDA 8 (36.4) 3 (37.5)

Lymph node metastasis

YES 15 (68.1) 7 (87.5) 0.39

NO 7 (31.9) 1 (12.5)

Therapy

Chemo 18 (81.8) 6 (75.0) 0.64

Chemo+ Biological agent

4 (18.2) 2 (25.0)

Primary/metastasis

Primary 14 (63.6) 4 (50.0) 0.67

Metastasis 8 (36.4) 4 (50.0)

Mutation

KRAS 0 7

NRAS 0 0

BRAF 0 1

PIK3CA 0 0


248

Figure 6.7: Kaplan Meier plots for comparison between patients with tumor

mutation or wild type tumors.

A

B

Figure Legend:

A- PFS plot and B-OS plot

Any mutation- KRAS +BRAF mutation

0

20

40

60

80

100

Mutation wise PFS

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: Wild Type

22 21 13 9 4 1 0

Group: Any Mutant

8 8 7 3 0 0 0

Mutation

Wild Type

Any Mutant

0

20

40

60

80

100

Mutationwise Overall Survival

0 1 2 3 4 5

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: Wild type

22 17 9 5 1 0

Group: Any mutation

8 7 4 0 0 0

Mutation

Wild type

Any mutation


249

Figure 6.8: Kaplan Meier plots for comparison between different therapy options.

A

B

Figure Legend: A-PFS plot and B-OS plot.

10

20

30

40

50

60

70

80

90

100

Therapy wise PFS

0.0 0.2 0.4 0.6 0.8 1.0 1.2

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: Chemo + Biological agent

6 6 3 2 1 0 0

Group: Chemotherapy

24 23 17 10 3 1 0

Therapy

Chemo + Biological agent

Chemotherapy

30

40

50

60

70

80

90

100

Therapy wise Overall survival

0 1 2 3 4 5

Time in years

Surv

ival pro

babili

ty (

%)

Number at risk

Group: Chemo+ Biological agent

6 4 2 2 0 0

Group: Chemotherapy

24 20 11 3 1 0

therapy

Chemo+ Biological agent

Chemotherapy


250

Among the 8 patients with mutations, one was treated with second-line anti-EGFR-

containing regimen, one was treated with second line anti VEGF treatment i. e

bevacizumab and six were treated with chemotherapy.

The median PFS of patients with KRAS or BRAF mutations (n = 8; 6 months; 95%

CI, 5-7 months) was shorter than that of patients with all wild-type tumors (n = 22; 7

months; 95% CI, 4-9 months), as verified in both univariate (HR 1.46; 95% CI,

0.5476 to 3.8978; P = 0.36) and multivariate analyses (HR 1.50; 95% CI, 0.5732 to

3.9417; P = 0.407) (Figure 6.7 A, Table 6.3).

The median OS of patients with KRAS or BRAF mutations (n = 8; 2.1 years; 95%

CI, 1.800 to 2.1 years) was shorter than that of patients with all wild-type tumors (n

= 22; 2.5 years; 95% CI, 2.100 to 2.700 years), as verified in both univariate (HR

1.1; 95% CI, 0.3316 to 3.6657; P = 0.86) and multivariate analyses (HR 0.75; 95%

CI, 0.2033 to 2.7878; P = 0.6) (Figure 6.7 B, Table 6.3).


251

6.5 Discussion

In the past few years, there have been several studies done to determine the

association of a range of variables with the survival of CRC patients (Laohavinij et

al., 2010, Moghimi-Dehkordi et al., 2008, Ratto et al., 1998, Desolneux et al., 2010,

Ghazali et al., 2010). The age, gender, site of CRC, tumor differentiation, invasion

of tumor, lymph node metastases and other variables have been studied. However,

determination of prognostic factor is still a challenge. The overall survival rate

currently in Asian countries is approximately 60% as the majority of

adenocarcinomas are still diagnosed at the later stages. If the disease is

diagnosed at an early stage then the survival rate is observed as 90% (Moghimi-

Dehkordi and Safaee, 2012). In this study, the effect of different clinico-pathological

features on the Indian patients survival rate was evaluated.

It was observed that overall survival was 37% with median survival of 29 months

which is similar to the survival rate observed in a study published by Yeole et al. in

2001 on the Indian population (Yeole et al., 2001). The incidence rates of colon

and rectal tumors are low in comparison to the population in Western developed

countries. In India, rectal tumors are more common than colon (Mohandas and

Desai, 1998). However, a significant increase has been noted in colon cancer

cases over the past two decades. The present study data shows that the incidence

of colon cancer in India was more in comparison to rectal cancer (colon -35% and

rectum – 28%).

Few other researchers from China have also reported the decline in rectal cancer

cases (Xu et al., 2006, Wan et al., 2001).The reason for this change is unclear, it


252

could be due to improvisation in early diagnosis, changing dietary habits or

etiological changes (McMichael and Potter, 1985, Mohandas and Desai, 1998). For

example, alcohol consumption has been associated with rectal cancers while

family history is strongly associated with colon cancers (Bongaerts et al., 2008,

Andrieu et al., 2004). Literature indicates that patients having colon as the site of

tumor have a better prognosis than those having rectum (Moghimi-Dehkordi et al.,

2008, Ratto et al., 1998, Wang et al., 2008). Further, it has been observed that

poorer survival has been associated with proximal colon, rather than distal colon

and as such there is not much difference in the survival rate of distal colon and

rectal tumors (Hemminki et al., 2010, Meguid et al., 2008, Wray et al., 2009).

These differences in survival according to tumor site could instead be due to

differences in tumor aggressiveness or due to screening methodologies used. Few

studies have shown that colonoscopy and sigmoidoscopy are associated with

higher incidence and mortality with proximal colon cancers in comparison to distal

colon cancers (Newcomb et al., 2003, Atkin et al., 2010, Brenner et al., 2009,

Baxter et al., 2009). Proximal colon cancers exhibit rapid tumor progression, which

could be due to their diagnosis as interval cancers. Also these tumors frequently

show the presence of CIMP, MSS/MSI-L along with BRAF mutated status. These

all factors are associated with poorer survival in proximal tumors (Baxter et al.,

2011, Shaukat et al., 2010, Phipps et al., 2013). Similarly it has been reported MSI-

H tumors are significantly present in proximal colons and MSI-H tumors have

favourable survival outcome (Guastadisegni et al., 2010).


253

In the current study the median overall survival of patients with colon tumors was

observed to be 21 months. There was however no significant association observed

for PFS and OS with respect to site of tumor origin.

The likelihood of being diagnosed with CRC increases after the age of 40 years

and the occurrence of CRC cases is higher in patients after the age of 50 years

(Fund and Research, 2007). Incidence rate is seen to higher in patients with age

above 60 years, however, the incidence of CRC is seen to be increasing in the

younger population of 40 years and below (O'Connell et al., 2004, You et al., 2012,

Siegel et al., 2009). Some studies have shown that early onset of the disease is

associated with poorer survival outcome (Fang et al., 2010, Gharbi et al., 2010,

Moghimi-Dehkordi et al., 2008, Zhang et al., 2010).

The increasing incidence of CRC in younger populations could be due to lack of

screening at a younger age, behavioural factors such as alcohol consumption,

smoking and lifestyle factor like obesity. The pesticide consumption in India has

increased several hundred folds from 154 metric tons in 1954 to 41,822 metric tons

in 2009-2010. In low income countries like India only 10% of the contaminated

water is treated rest all is discharged into water bodies. This highly contaminated

water can cause adverse health effects including cancer. In an epidemiological

study from Egypt, researchers have shown prevalence of young onset CRC in

people with exposure to pesticides (Lo et al., 2010).

Some studies that have shown the better survival in younger patients which could

be due to aggressive therapy regimes used for younger patients, low risk of


254

postoperative complications and higher treatment completion rates of surgery and

adjuvant therapy. Also the younger patient group may include hereditary CRC like

Lynch syndrome which is seen to have better survival rates (O'Connell et al.,

2004).

To study the effect of age on survival the patients were divided in two categories

above 60 years and below 60 years. No significant association was observed

between the survivals of these two groups. However, the median overall survival

for patients above the age of 60 was 28 months and below 60 years was 30

months which concurs with few studies reporting poor survival rate in older patients

when compared with that of younger ones (Rosenberg et al., 2008, Laohavinij et

al., 2010). It has been observed that in the young patients CRC is more aggressive

and has poor pathological features (Chou et al., 2011).

In this study, significant association was observed between patients survival rates

and poorly differentiated adenocarcinoma (PDA). PDA is associated with poor

survival in comparison to moderately differentiated adenocarcinoma (p<0.05)

(Figure 6.8) as seen in other studies (Laohavinij et al., 2010, Moghimi-Dehkordi et

al., 2008). Histologically, PDA account for around 4.8% to 23% of all colorectal

cancer cases (Benedix et al., 2010, YOSHIDA et al., 2011, Xiao et al., 2013). PDA

is directly associated with a poor prognosis as reported in previous studies(Ishihara

et al., 2012, Bjerkeset et al., 1987). PDA cases mostly occur in advanced stages or

metastatic stages which could be the reason for poor survival.


255

Lymph node metastasis is a critical predictor of survival and recurrence in CRC.

Several studies have found a significant association between numbers of lymph

node resected and improved survival (Chang et al., 2007, Gunderson et al., 2010,

Chen and Bilchik, 2006). As increased survival is noted in patients with lymph node

involvement, better therapeutic advantage is suggested in higher lymph node

retrieval. In the current study, the overall survival of patients with lymph node

involvement was 25 months, which was seen to be better than patients with no

lymph node involvement. However, the survival curves are non-significant. ASCO,

NCCN and the American College of Surgeons Commission on Cancer (CoC)

indicate that a minimum of 12 lymph node count is associated with improved

outcome in the patients (Nelson et al., 2001).The actual mechanism between

lymph node count and survival is unclear. However, there are various factors that

affect the number of lymph node examined like patient age, extent of surgical

resection and tumor location. It has been observed that right-sided tumors show

the presence of higher number of lymph nodes (Chang et al., 2007). Numbers of

lymph nodes involved reflect the patients improved immune response. More lymph

node involvement indicates a greater immune response and hence improved

survival (Pagès et al., 2005).

CRC can be prevented if detected at an early phase and if adenomatous polyps

are removed early. If the tumor is diagnosed when it is in localised stage then the

survival outcome of the patient is better than in those cases where in the diagnosis

occurs at the metastatic stage (Fatemi et al., 2010). In the current study, it was


256

observed that if the tumor is localised i.e. present at the primary stage then the

median survival is 31 months in comparison to the metastatic tumors in which the

median survival is 26 months. However, the survival curve was not statistically

significant. In one study published on a Dutch population, it was observed that

metastatic tumors have significantly improved survival. This change observed

could be due to increased used and improvisation in chemotherapy, use of

adjuvant and neoadjuvant therapies and better selection of patients eligibility for

surgery (Meulenbeld et al., 2008).

Together with the clinico-pathological features, mutation in the RAS –RAF pathway

is observed in around 30-50% of colorectal cancer tumors implying that only the

remaining 50% of patients would benefit from anti-EGFR therapy. Cetuximab plus

FOLFOX helps in improving the survival rate and disease free progression

(Bokemeyer et al., 2008). Cetuximab and FOLFIRI both improve the survival and

response rate both in KRAS wild type tumors (Assenat et al., 2011). In current

study, the overall survival rate in mutated tumors with mutation in KRAS or BRAF

genes studied was 25 months and in wild type was 29 months although statistical

significance was not observed.

These experimental evidences of survival rates of CRC patients in relation to

different clinico-pathological features in this retrospective analysis of the Indian

population suggests that there are many factors which could influence the

prognosis of colorectal cancer patients. However, the present study has limitations.

Due to a smaller sample size, current study may not exactly reflect the prevalence


257

of colorectal cancer in the entire Indian population, however it reflects the nature of

disease and the effect of different clinico-pathological features on survival for

Indian CRC cases. This study indicates the differences in presentation of CRC in

Indian population and also the effect of various factors on survival that may differ

from the population in Western developed nations. One of the major limitation of

the present study was that owing to the small number of patients, no data was

available specifying BRAF, NRAS and PIK3CA mutation due to which impact of

these mutations on the survival could not be studied. As seen in Chapter 5, the

frequency of BRAF, NRAS and PIK3CA mutations in Indian population is 5.9%, 2%

and 4% respectively. Further the survival analysis involved only seven patients for

KRAS and single patient for BRAF out of a small sample size selected (n=30), from

the total patients studied for mutation analysis (n=203). According to the published

literature patients with BRAF mutations are often refractory to systemic

chemotherapy and have poor prognosis hence screening for BRAF mutations has

become important. However, the study findings are extrapolative and hypothesis

generating which can be further analysed in larger cohort.

This study supports the hypothesis that clinical and pathological characteristics are

better determinants of prognosis in CRC patients. Amongst all the

clinicopathological features studied through univariate and multivariate analyses,

the feature that has the significant impact on the survival outcome, is the tumor

differentiation status. Thus, early detection and timely evaluation of tumor becomes

extremely important in CRC, which can further lead to improved survival.

Chapter 7. Summary and Future Work

258

Chapter 7 Summary and Future Work

Colorectal cancer is one of the leading causes of cancer worldwide. It is the most

common of all gastrointestinal malignancies. The chapter 2 highlights that in

existing literature wide geographical, racial and ethnic difference in incidence are

observed for this cancer. The majority of studies showing genetic and epigenetic

changes correlation with CRC have been carried out in the population of Western

developed nations. Very little data is available on the Indian population. This study

was hence undertaken with an aim to evaluate the genetic alterations in KRAS,

BRAF, NRAS and PIK3CA genes and the correlation of these molecular alterations

with clinicopathological characteristics in 203 CRC patients. Further, the correlation

between clinicopathological features and survival was studied in in a subset of

Indian population sample size (n=30). The percentage of molecular alterations

observed in this study corresponds with those reported in literature for CRC cases

described in COSMIC database.

All the molecular analysis performed in this study was according to current

recommendations of CAP and NABL guidelines. Hence, the molecular data

obtained from this study can be associated to the clinical data and errors possibly

related to technical issues are unlikely. Also, the samples analyzed in the current

study constituted a random fraction of Indian CRC patients and is hence a

balanced representation of entire Indian population.


259

7.1 Mutation Studies in 203 CRC patients

Analysis of mutation distribution in KRAS, BRAF, NRAS and PIK3CA genes was

carried out using Sanger sequencing, which is the cost effective methodology and

‘Gold Standard’ for mutation analysis. Sanger sequencing allowed evaluation of all

the hotspot regions in all four genes. Various steps were undertaken in our

laboratory to ensure optimal procedures for mutational testing through direct

sequencing. As mentioned above, these included strict adherence to current

recommendations by CAP and NABL guidelines and involvement of experienced

pathologists in representative tissue sample section and for performing tumour

macrodissection. Furthermore, mutational analyses were performed using widely

accepted protocols. The laboratory is also registered in external quality control

audits. The minimum allelic sensitivity of Sanger sequencing was established as

20% using commercially available reference standards. Samples having tumor

percentage of 20% were processed by macro dissection to enrich the tumor

content. Samples below the tumor percentage of 20 were not included in the study.

In a total of 36% of CRC cases at least one mutation in the analyzed hot spot

region was observed. The prevalence of KRAS, BRAF, NRAS and PIK3CA

mutations in the present study were 24%, 6%, 2% and 4%, respectively which

concurs well with the COSMIC database reported frequencies. Hence, it can be

seen that approximately 12% of CRC patients have mutations in NRAS, BRAF or

PIK3CA in KRAS wild type population. However, it was observed that the mutation

frequency of BRAF V600E was relatively lower in the Indian population as


260

compared to what is reported in the COSMIC database. The reason for this

difference may be the structure of COSMIC database that screens for the

information available in literature for the somatic mutations and displays its

relationship to the particular human cancer. This amino acid substitution of V to E

at codon position 600 in BRAF was observed in 6% of cases versus 10.1% in the

COSMIC database. However, this observed frequency of 6% was in concordance

with the study performed by Bagadi et. al. and Bisht et. al. on the Indian population

(Bagadi et al., 2012, Bisht et al., 2014).BRAF mutations were found to mutually

exclusive with KRAS mutations. Three cases showed coexistence of PIK3CA and

KRAS mutations together, which confirms the reported observations that PIK3CA

mutations can coexist with other molecular alterations (Thesis Chapters 4 and 5).


261

7.2 Correlation of mutations in KRAS, BRAF, NRAS and PIK3CA genes

with clinico-pathological data for a 203 Indian CRC patient cohort

The statistical analysis of clinicopathological characteristics and mutation analysis

was performed using Chi-square tests. Significant positive association was

observed for KRAS mutations with age and tumor differentiation (p<0.05). The

mutation rate in patients above 50 years was higher than the rate in patients below

50 years. Also, KRAS mutations were significantly associated positively with

moderately and poorly differentiated adenocarcinoma, as compared to well-

differentiated adenocarcinoma. Other clinicopathological findings like gender,

tumor location, stage and lymph-node metastasis, showed no significant

association with KRAS mutations (p>0.05), which is in accordance with recent

reports for the Indian population.

In the case of BRAF, a statistically significant correlation was observed in

moderately differentiating and poorly differentiating adenocarcinomas, but not in

well-differentiated adenocarcinomas. This study supports previous reports that

found that BRAF mutation status is correlated with specific clinicopathological

features and hence identifies a distinctive subgroup of patients having specific

clinico-pathological features (Li et al., 2011).

No significant association was observed between any of the clinico-pathological

features with NRAS or PIK3CA mutations. However, this study agrees well with

other population based studies not only in terms of distribution of mutations and

clinical and pathological features but also in terms of association between these

mutations and the clinical data (Thesis Chapter 5).


262

7.3 Correlation of clinico-pathological data with survival in Indian patient

cohort

In the past few years, there have been many studies carried out to determine the

association of numerous variables with the survival in case of CRC (Laohavinij et

al., 2010, Moghimi-Dehkordi et al., 2008, Ratto et al., 1998, Desolneux et al., 2010,

Ghazali et al., 2010). The age, gender, site of CRC, tumor differentiation, invasion

of tumor, lymph node metastases and other variables have been studied. However,

determination of prognostic factor is still a challenge. The overall survival rate

currently in Asian countries is approximately 60% as the majority of

adenocarcinomas are still diagnosed at the later stages while the highest survival

rate is observed in the USA as 64% (Moghimi-Dehkordi and Safaee, 2012).

Amongst the Asian countries, the highest survival rate is seen in China and the

lowest in India (Shiono et al., 2005, Yeole et al., 2001). In this study, it was

observed that overall survival was 37% with median survival of 25 months. In terms

of anatomic location, the median survival for colon was seen to be 21 months. The

differences in survival according to tumor site could be due to differences in tumor

aggressiveness or due to screening methodologies used. Further, the median

survival for patients above the age of 60 was 25 months and below 60 years was

30 months which concurs with few studies reporting poor survival rate in older

patients when compared with that of younger ones. However, recently it has been

observed that the incidence of CRC is rising in the younger population in India. It

has been observed that in the young patients CRC is more aggressive and has

poor pathological features (Chou et al., 2011). With regard to differentiation of


263

tumor, the current study data reveals a significant association in the survival rate of

patients with poorly differentiated adenocarcinoma (PDA). PDA is positively

associated with poor survival (median survival of 21 months) in comparison to

MDA (p0.05) as observed in previous studies. PDA cases mostly occur in

advanced stages or metastatic stages which could be the reason for poor survival.

The overall survival in the case of patients with lymph node involvement was found

to be 25 months, which was higher than patients with no lymph node involvement.

Numbers of lymph nodes involved reflect the patients’ improved immune response.

The more the lymph node involvement, the greater the immune response and

hence improved survival.

If the tumor is diagnosed when it is in a localised stage then the survival outcome

of the patient is better than those cases where in the diagnosis occurs at the

metastatic stage. In the current study, it was observed that if the tumor is

localised, i.e. present at primary stage, then the median survival was 31 months in

comparison to the metastatic tumors in which the median survival was 26 months.

The survival rate in mutated tumors was 25 months and in wild type was 29

months though statistical significance was not observed (Thesis Chapter 6).

Patients’ selection has recently entered a new era of personalised therapy. The

establishment of biomarkers and clinicopathological features prior to treatment can

lead to improved survival. The impact of different genetic and epigenetic alterations

such as mutations, SNP’s, methylation status and copy number, required for

efficacy of treatment, requires further study to determine the mechanisms of action

for the specific drug molecule used. This thesis investigated a variety of CRC


264

cases in the Indian population for studying the effect of different factors on survival.

Taken together, the findings of current study shows for the first time that at the

genetic level, mutations in one of the four genes (KRAS, BRAF, NRAF and

PIK3CA) occur at a lower frequency than in the population in Western developed

countries. Data supports the hypothesis that (i) rate of mutations in critical CRC

genes involved in the tumor growth and survival i.e. KRAS, BRAF, NRAS and

PIK3CA differ according to racial differences, and (ii) that different

clinicopathological factors would have impact on clinical outcome of the patient in

the context of Indian patient cohort. Results from therapeutic data analysis

(Chapter 6) shows that the knowledge of tumor differentiation status can influence

decision making for patient and hence improve response rate and outcome of CRC

patient. This data needs to be validated in larger cohort to potentially influence

treatment decisions in Indian patients, and hence is a step forward towards

personalised treatment.

In brief, the following conclusions can be drawn from this study:

1. The prevalence of KRAS, BRAF, NRAS and PIK3CA mutations in CRC

patients in the present study was 24%, 6%, 2% and 4%, respectively,

which is at a much lower frequency when compared to the data available

for populations in Western developed nations.

2. BRAF mutations were found to be mutually exclusive with KRAS

mutations. However, coexistence of PIK3CA mutations with KRAS

mutant patients was observed. These results concur with the published

literature on populations in Western developed nations.


265

3. Significant statistical association (p<0.05) was observed between the

following parameters:

a. KRAS mutations with age and tumor differentiation. Mutation rate in

patients above 50 years was higher than for patients below 50 years

of age. KRAS mutations were significantly associated positively with

moderately and poorly differentiated adenocarcinoma, as compared

to well-differentiated adenocarcinoma.

b. BRAF statistically significant positive correlation was observed in

moderately differentiating and poorly differentiating adenocarcinomas

than in well-differentiated adenocarcinomas.

4. No significant association was observed between any of the

clinicopathological features with NRAS or PIK3CA mutations in Indian cases.

5. In terms of correlation between survival and clinicopathological features, the

following observations were made:

a. The 3- year overall survival in Indian patients was observed to be 37%, with

median survival of 25 months, which is much lower in comparison to that in

developed nations.

b. Significant association was observed in the survival rate of patients with

poorly differentiated adenocarcinoma (PDA). PDA is inversely associated with poor

survival (median survival of 21 months) in comparison to moderately differentiated

adenocarcinoma (MDA) (p0.05).

c. It was observed that if the tumor is localised, that is, present at the primary

stage, then the median survival is 31 months, in comparison to metastatic tumors


266

in which the median survival is 26 months. The survival rate in mutated tumors was

25 months and in wild type was 29 months, although statistical significance was

not observed.

The current study data reflects the nature of disease and the effect of different

clinicopathological features on survival in case of Indian CRC cases. These

experimental evidences of survival rates of CRC patients in relation to different

clinico-pathological features in the Indian population indicates that there are

numerous factors that influence the prognosis of colorectal cancer patients.

However, life expectancy has not increased much in these years. Though the data

has inherent limitations due to small sample size analysis, this retrospective study

supports the hypothesis (based on existed literature) that clinical and pathological

characteristics, especially tumor differentiation are good determinants of prognosis

in CRC patients.

Recently mutations in KRAS exon4 and NRAS exon 4 have been shown to have

an effect on therapeutic response. However, the reported percentage of these

mutations is low, ranging from 0.5 to 2.2%. Further studies are required to

establish the prevalence and effect of these mutations of exon 4 in the Indian

population.

In terms of a forward path, additional studies are required in the Indian CRC

population to determine the effect of additional genetic and epigenetic markers,

such as AKT, PTEN, MAPK, and other receptors molecules such as MET, MSI,

CIMP, which could provide an alternative pathway to survival. Also, larger cohort


267

needs to be studied to evaluate the effect of different mutations in correlation with

clinicopathological features on survival. Thus the future strategy to improve survival

outcomes and clinical management of CRC, lies in personalized therapy which is

still an evolving approach with a focus to identify highly specific and sensitive

predictive biomarkers. Hence, there is a strong need to identify, develop and

validate more biomarkers that will assist with clinical decision-making. As reviewed

recently (Patil et al 2016), Next Gen Sequencing and multi-gene sequencing

(parallel sequencing technology), data reveals that along with the mutations in

genes of EGFR pathway, mutations SMAD-4 and FBXW7 are also responsible for

resistance to therapy. Also, advances in imaging techniques such as FDG-PET,

DWI, DCE-MRI could potentially serve as predictive imaging biomarkers to anti-

angiogenesis inhibitors(Atreya and Goetz, 2013). Considering the current progress

and focus in personalized medicine, and with the recent genomic profiling of CRC

patient tumors and the development of new proteomic and modeling studies,

selecting and stratifying CRC patients based on their molecular profile will be

improved in future.

Appendix

268

Appendix

I. Haematoxylin and eosin (H&E) Analysis

Preparation of reagents:

1. Haematoxylin Solution (Harris):

Potassium or ammonium (alum): 100 g

Distilled water: 1000 ml

Heat to dissolve. Add 50 ml of 10% alcoholic haematoxylin solution and heat to boil

for 1 min. Remove from heat and slowly add 2.5 g of mercuric oxide (red). Heat to

the solution and until it becomes dark purple color. Cool the solution in cold water

bath and add 20 ml of glacial acetic acid (concentrated). Filter before use and store

at room temperature.

2. Eosin-Phloxine B Solution:

Eosin Stock Solution:

Eosin Y: 1 g


Mix to dissolve.

Phloxine Stock Solution:

Phloxine B: 1 g


Mix to dissolve.

3. Eosin-Phloxine B Working Solution:

Eosin stock solution: 100 ml

Phloxine stock solution: 10 ml

Appendix

269

Ethanol (95%): 780 ml

Glacial acetic acid: 4 ml

Mix well and store at room temperature.

4. 1% Acid Alcohol Solution (for differentiation):

Hydrochloric acid: 3 ml

70% ethanol: 300 ml

Mix well and store at room temperature.

II. Reagents required for DNA extraction using Invitrogen Purelink Genomic DNA

kit:

Preparation of Reagents:

1. Purelink Wash Buffer WB1:

Buffer WB1 was diluted with 80 ml of 100% ethanol to make the volume to 200 ml.

2. Purelink Wash Buffer WB2:

Buffer WB2 was diluted with 80 ml of 100% ethanol to make the volume to 185 ml.

III. Gel electrophoresis Reagent Preparation:

1. 1XTBE Buffer

Tris-180 grams

EDTA-9.3 grams

Boric Acid-55 grams

pH-7.5

Volume adjusted to 1 liter with MilliQ water

Appendix

270

List of Chemicals

Sr

No. Chemical Company Catalogue

1 SeaKem LE Agarose Lonza 50004

2 FG, BDT V3.1 RR1000 ABI 4337456

3 Boric acid Merck 6505001730

4 BSA Sigma A7030- 100 g

5 DMSO Sigma D2650

6

DNA Extraction kit:

Purelink Invitrogen K1820-02

7 EDTA Thomas Baker 74298

8 Ethidium bromide Sigma 160539

9 Ethanol Changshu Yangyuan XK-13-201-00185

10 Generuler 100bp Ladder MBI Fermantas SM0241

11

GOTAQ(R)Flexi DNA

Polymerase Promega M829B

12 Hi Di Formamide ABI 4311320

13

N-RAS G12V Reference

Standard Horizon Diagnostics HD203

14

N-RAS Q61K Reference

Standard Horizon Diagnostics HD247

15 PCR Nucleotide Mix Promega C114H

Appendix

271

16 10 X EDTA Buffer Thermo Scientific 402824

17 5X Seq Buffer Thermo Scientific 4339843

18 HPLC grade water Merck O61765010001730

19

Montage PCR u96

Cleanup Plates Millipore LSKMPCR50

20

Montage PCR u96 Seq.

Rxn Cleanup Kit Millipore LSKS09624

21 POP6 Thermo Scientific 4316357

22 Primers Sigma -

23 1XPBS Gibco 20012-050

24 5X Seq Buffer Thermo Scientific 4339843

25

Flat Deck Thermo-Fast 96

detection plate Thermo Scientific AB-1400

26 DPX Mountant Merck AF2 AF 52226

27 Formaldehyde solution Qualigens 12755

28 Formamide SRL 62758

29 Hydrogen Peroxide Qulaigens 15465

30 Tri Sodium citrate Qulaigens 14005

31 Hydrogen Peroxidase Thomas Baker 90383

32 Xylene Xlar Qualigens 32295

33 Reference Standards Horizon Diagnostics HD203

34 Trizma base Sigma T1503

Appendix

272

List of Instruments

Sr

No. Instrument Model Company

1 Biosafety Cabinate 1590V Klenzaids

2 Fume Hood AFA1000 Kewaunee

3

Gel Electrophoresis

Appratus SubcellGT Biorad

4 pH Meter PICO+ LabIndia

5 Incubator 450X450 mm Trishul Equipment

6 Hot Air Oven PEW180ASS Pathak Electric Work

7 Floatation bath 3120058

Thermo Electron

Corporation

8 Microtome Finesse ME

Thermo Electron

Corporation

9

Bright field Olympus

Microscope BX51 BX51 Olympus

10

CCD camera for

microphotography ProgRes C3 Olympus

11 Genetic Analyser 3100 3100 Life Technologies

12 Dry Bath DB-3D Techne

13 PCR Thermal Cycler MyCycler Biorad

14 Micro Centrifuge 5415D Eppendorf

Appendix

273

15 Gel Documentation System

Pharmacia Biotech

011E991 GE Biosciences

16

Nano drop

Spectrophotometer ND-1000 Nanodrop

17 Laminar Hood 1560R Klenzaids

18 Microwave CE118KF Samsung

19 Analytical Balance BP121S Sartorius

20 Semianalytical Balance BP1200 Sartorius

Appendix

274

List of Software’s

Sr.

No. Software Company

1 Sequencing Analysis Software Life Technologies

2 Image Total Master GE Biosciences

3 BioEdit www.mbio.ncsu.edu/bioedit/bioedit.html

4 GraphPad QuickCals GraphPad Software, Inc

5 GraphPad Prism GraphPad Software, Inc

6 MedCalc MedCalc Software, Ostend, Belgium

Accreditations and Approvals

i. Accreditation with College of American Pathologist (CAP) - LAP No: 7194405,

AU ID-1449073, CLIA No: 99D20118815, Issue date: 12 Sep 2013

ii. Accreditation with National Accreditation Board for testing and calibration

Laboratory (NABL) in accordance with ISO15189:2007 - NABL No. M-0090,

Issue date:19 Dec 2015

iii. Registration with Maharashtra Industrial Development Corporation,

Registration No.11/24/MIDC/001.IEM No. 686,687,688/SIA/IMO/2008/

References

275

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