molecular characterisation of c11orf67 in luminal breast ...bacterial empyema pathogens, induces...

Molecular Characterisation of C11orf67 in Luminal Breast Cancer

Rabab Rashwan

MBBS, MSc

School of Anatomy, Physiology and Human Biology

2016

This thesis is presented for the degree of Doctor of Philosophy

University of Western Australia

i

Abstract

The recent integration of both genomic and transcriptomic datasets have added a further

dimension to the landscape of breast cancer subtyping, defining novel functional

subgroups with distinctive oncogenic drivers that carry important implications for

therapy. This integrative clustering has unveiled a novel subtype of hormone receptor

positive (HR+) breast cancer associated with high proliferation and very poor survival

characterised by copy number amplification and overexpression of a cluster of candidate

oncogenic drivers at 11q13.5-q14 amplicon.

At the heart of this amplicon we have demonstrated the selective overexpression of

C11orf67/AAMDC (Adipogenesis associated Mth938 domain containing) which encodes

a hypothetical protein of 122 amino acids with unknown function. In a pilot tissue

microarray of 75 breast cancer cases C11orf67 amplification and expression were

significantly correlated with hormone receptor positivity, these positive cases also

demonstrated high risk features with, in particular high grade of the tumour.

In functional elucidation studies, knockdown of C11orf67 in the highly expressing T47D

cell line resulted in decreased cell proliferation, cell migration, anchorage independent

cell growth and induction of cellular senescence. T47D xenografts with stable shRNA-

induced C11orf67 knockdown injected into BALB/c mice showed significantly lower

tumour volumes relative to T47D with empty vector. A genome wide analysis of these

T47D C11orf67 shRNA cells compared to T47D empty vector cells using the Illumina

HumanHT-12 platform demonstrated 40 differentially expressed genes. Network analysis

revealed a proliferation node, enriched in cell cycle proteins, and a metabolic node

comprising several biosynthetic enzymes such as MTHFD1L involved in one-carbon

folate metabolism. Supporting this link and pointing to potential utility in chemotherapy

selection, induction of ectopic C11orf67 expression in MCF7 cells increased sensitivity

to 5-fluorouracil and methotrexate but not to taxol.

ii

Investigating potential novel binding partners and effectors, in yeast two hybrid screening

C11orf67 was found to associate strongly with RABGAP1L, a protein involved in

controlling GTPase signalling, protein trafficking, and autophagy.

Exploring the molecular cues that control C11orf67 expression, our data suggest the locus

is regulated by transcription factors associated with high proliferation and metabolic

control, notably Myc and NF-κB, as well as HRs. Estrogen leads to a significant

downregulation of C11orf67 in T47D cells, which was reversed by the antiestrogen drug

tamoxifen, whereas Progesterone significantly increased C11orf67 levels. In keeping

with this, MCF7 cells ectopically expressing C11orf67 were resistant to the anti-

proliferative effects of tamoxifen compared to the parent cell line.

These observations endorse C11orf67 as a novel oncogenic driver with exciting

therapeutic potential, which could serve to distinguish the HR+ tumours at high risk of

relapse and guide both the selection of current chemotherapeutical and endocrine

treatments as well as the design of future precision therapeutics, notably anti-folate one-

carbon drugs and novel endocrine agents.

iii

Acknowledgements

All praises to Allah for giving me the strength to completing this thesis.

Special appreciation goes to my supervisor, Associate Professor Pilar Blancafort.

Conducting this PhD project and writing up this thesis would be totally impossible if it is

not for her. I would like to express my heartfelt thanks and appreciation to her for her

invaluable guidance, inspiration, help, support and advice provided throughout the entire

years.

Big thanks to Dr Jeremy Parry and Mr Nathan Acott for the great help with the TMA

staining. Thanks to Dr Iwona Kardas and Dr Magda Ratajska for setting up the FISH

protocol, Dr Piotr Kozlowski for the cBioPortal analyses, and Mr Peter Fleming for

helping me with the immunohistochemistry staining. I also wish to thank Dr Anabel

Sorolla, for helping me with the western blot analyses and for her friendship, enduring

positivity and encouragement especially at times when I have needed it most. Sabine,

Ben, Colette, Mahira, Edi, and Agustin, thank you all for all the love, laughs, support, and

friendship through my PhD years.

I would like to extend my sincerest thanks to my siblings and my parents, Aida and

Kamal, thank you for your emotional support, and prayers. Thanks for believing in me

and being proud of me no matter what path I choose.

Words cannot express my appreciation and love for my lovely kids, Sama and Mostafa,

who have given me much happiness and keep me hopping. I hope I have been a good

mother and that I have not lost too much during the time of my study.

Saving the most important for last, I wish to give my heartfelt thanks to my husband,

Ebrahim, you have continued to love and support me when I have been at my worst during

this process. Thank you for being my rock throughout my PhD, for taking care of me, for

your encouragement and motivation.

iv

Publications

Journal Articles

Stolzenburg S, Beltran AS, Swift-Scanlan T, Rivenbark AG, Rashwan R, Blancafort P.

Stable oncogenic silencing in vivo by programmable and targeted de novo DNA

methylation in breast cancer. Oncogene 2015, 34:5427-35.

Sorolla A, Ho D, Wang E, Evans CW, Ormonde CF, Rashwan R, Singh R, Iyer KS,

Blancafort P. Sensitizing basal-like breast cancer to chemotherapy using nanoparticles

conjugated with interference peptide. Nanoscale 2016, 8:9343-53.

Rashwan R, Sorolla A, Parry J, Redfern A, Blancafort P. Characterization of the novel

oncogenic role of C11orf67 in luminal breast cancer. In Preparation (2016).

Rashwan R, Varano J, Lansley S, Lee G. Streptococcus pneumoniae, but not other

bacterial empyema pathogens, induces mesothelial cell death. In Preparation (2016).

Conferences

Rashwan R, Sorolla A, and Blancafort P (2014) “Characterization of the novel oncogenic

role of C11orf67 in luminal breast cancer”. Poster Presentation, 7th Cairo International

Biomedical Engineering Conference CIBEC, Cairo, Egypt. December 2014.


role of C11orf67 in luminal breast cancer”. Oral Presentation, Cancer Council 5th

Biennial Research Symposium, Perth, Australia. October 2014.


role of C11orf67 in luminal breast cancer”. Poster Presentation, 26th Lorne Cancer

Conference, Melbourne, Australia. February 2014.

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Rashwan R, and Blancafort P (2013) “Characterization of the novel oncogenic role of

C11orf67 in luminal breast cancer”. Oral Presentation, 8th State Cancer Conference,

Perth, Australia. October 2013.

Rashwan R, and Blancafort P (2013) “Characterization of the novel oncogenic role of

C11orf67 in luminal breast cancer”. Oral Presentation, COMBIO, Perth, Australia.

October 2013.

Rashwan R, Varano J, Lansley S, and Lee G (2013) “Streptococcus pneumoniae, but not

other bacterial empyema pathogens, induces mesothelial cell death”. Poster Presentation,

Thoracic Society of Australia and New Zealand TSANZ, Darwin, Australia. March 2013.

vi

Awards

2015

UWA Completion scholarship, University of Western Australia

To support my thesis completion and submission

2014

UWA Graduate Research School Travel Award, University of Western Australia

To support my travel to the 7th CIBEC meeting, Egypt 2014

2012

LIWA PhD Top-Up scholarship award (2012-2013), Lung Institute of Western

Australia

To support my postgraduate studies at the University of Western Australia

2011

International Egyptian scholarship award (2011-2015), Mission department and

scholarship office, Egypt

To support my postgraduate studies at the University of Western Australia

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Table of Contents

Chapter 1: General Introduction .......................................................... 1

1.1 Breast cancer ................................................................................................... 2

1.1.1 Incidence and mortality ................................................................................. 2

1.1.2 Normal mammary gland development .......................................................... 2

1.1.3 Breast cancer heterogeneity .......................................................................... 4

1.1.4 Prognosis of breast cancer ............................................................................. 6

1.1.4.1 Histological classification ..................................................................... 6

1.1.4.2 The Nottingham histological grade ....................................................... 6

1.1.4.3 Tumour Node Metastasis Staging ......................................................... 6

1.1.4.4 Immunohistochemical markers ............................................................. 7

1.1.5 Management of breast cancer........................................................................ 7

1.1.5.1 Surgery .................................................................................................. 7

1.1.5.2 Radiotherapy ......................................................................................... 8

1.1.5.3 Chemotherapy ....................................................................................... 8

1.1.5.4 Targeted therapies ................................................................................ 9

1.2 Breast cancer subtypes ................................................................................. 10

1.2.1 Clinical and molecular classification of breast cancer ................................ 10

1.2.2 Intrinsic breast cancer subtypes .................................................................. 11

1.2.2.1 Luminal A breast cancer ..................................................................... 12

1.2.2.2 Luminal B breast cancer ..................................................................... 12

1.2.2.3 Basal-like breast cancer ...................................................................... 13

1.2.2.4 HER2-enriched breast tumours........................................................... 14

1.2.2.5 Claudin-low subtype ........................................................................... 14

1.2.3 Inter-cluster breast cancer subtypes ............................................................ 15

1.3 The ER pathway and tamoxifen resistance ................................................ 17

1.3.1 Estrogen Receptor (ER) .............................................................................. 17

1.3.2 ER domain structure .................................................................................... 18

1.3.3 ER signalling pathway ................................................................................ 18

1.3.4 Transcriptional output of ER signalling ...................................................... 21

1.3.5 Endocrine therapy and ER+ breast cancer ................................................... 21

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1.3.6 Mechanisms of tamoxifen resistance .......................................................... 21

1.3.6.1 Coregulators of the ER ....................................................................... 22

1.3.6.2 Loss of ER and activation of growth factor receptor pathways .......... 23

1.3.6.3 Cell cycle signalling regulators .......................................................... 23

1.4 Molecular pathogenesis of breast cancer .................................................... 25

1.4.1 Oncogenes and tumour suppressor genes ................................................... 25

1.4.2 The “case” of the 11q13-q14 amplicon in breast Cancer ............................ 26

1.4.2.1 CCND1 ................................................................................................ 29

1.4.2.2 EMSY ................................................................................................... 29

1.4.2.3 PAK1 ................................................................................................... 29

1.4.2.4 AQP11 ................................................................................................. 30

1.4.2.5 RSF1 .................................................................................................... 30

1.4.2.6 GAB2 ................................................................................................... 30

1.4.2.7 C11orf67 ............................................................................................. 31

1.5 Statement of aims .......................................................................................... 32

Chapter 2: C11orf67 as a Novel Biomarker in Breast Cancer ......... 34

2.1 Introduction ................................................................................................... 35

2.2 Materials and Methods ................................................................................. 38

2.2.1 Cell lines and cell culture ............................................................................ 38

2.2.2 RNA extraction ........................................................................................... 38

2.2.3 Reverse transcription and cDNA synthesis ................................................. 39

2.2.4 Real-time Polymerase Chain Reaction ....................................................... 39

2.2.5 Total protein extraction and western blotting ............................................. 39

2.2.6 Tissue Microarrays ...................................................................................... 40

2.2.7 Immunocytochemistry ................................................................................ 41

2.2.8 Fluorescence microscopy ............................................................................ 41

2.2.9 Fluorescence In Situ Hybridization ............................................................. 42

2.2.10 Statistical analysis ................................................................................... 43

2.3 Results ............................................................................................................ 44

2.3.1 Bioinformatic and structural analyses of C11orf67 .................................... 44

2.3.2 Identification of the full-length cDNA sequence and spliced transcripts of

C11orf67. ................................................................................................................ 44

2.3.3 Subcellular localization of Flag-tagged C11orf67 Isoform_2 .................... 49

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2.3.4 The pattern of C11orf67 gene alteration in cancer...................................... 50

2.3.5 C11orf67 expression profile in breast cell lines .......................................... 52

2.3.6 C11orf67 is overexpressed in HR+ and high-grade breast cancer ............... 56

2.3.7 Copy number amplification of C11orf67 in HR+ breast cancer .................. 60

2.1 Discussion ............................................................................................................. 63

Chapter 3: Functional Consequences of C11orf67 Gene Alteration 67

3.1 Introduction ................................................................................................... 68

3.2 Materials and Methods ................................................................................. 71

3.2.1 Reagents and Antibiotics............................................................................. 71

3.2.2 Lentiviral C11orf67 shRNA infection ........................................................ 71

3.2.3 Lentiviral C11orf67 cDNA infection .......................................................... 72

3.2.4 MTT cell viability assays ............................................................................ 73

3.2.5 Immunofluorescence ................................................................................... 73

3.2.6 Anchorage-independent colony formation assays ...................................... 74

3.2.7 In Vitro cell migration assay ....................................................................... 74

3.2.8 TUNEL assay .............................................................................................. 75

3.2.9 In vivo tumourigenicity assays.................................................................... 76

3.2.10 Immunohistochemistry of tumour sections ............................................. 76

3.2.11 Senescence-associated β-galactosidase staining ..................................... 77

3.2.12 Illumina microarray analysis ................................................................... 77

3.2.12.1 RNA preparation ............................................................................. 77

3.2.12.2 Labelling and Purification .............................................................. 78

3.2.12.3 Hybridization and data export ........................................................ 78

3.2.12.4 Raw data preparation and statistical analyses ............................... 78

3.2.13 Statistical analysis ................................................................................... 79

3.3 Results ............................................................................................................ 80

3.3.1 Functional consequences of C11orf67 ablation in T47D cells ................... 80

3.3.1.1 Knockdown of C11orf67 by shRNAs ................................................... 80

3.3.1.2 C11orf67 ablation reduces the proliferative potential of T47D ......... 83

3.3.1.3 C11orf67 knockdown decreases tumourigenic phenotype of T47D cells

in vitro ............................................................................................................. 85

3.3.1.4 C11orf67 promotes migratory behaviour in ER+ breast cancer cells 87

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3.3.1.5 Downregulation of C11orf67 expression inhibits tumour growth in a

xenograft model................................................................................................... 88

3.3.1.6 C11orf67 knockdown is not involved in cellular apoptosis ................ 90

3.3.1.7 C11orf67 knockdown induces cellular senescence ............................. 92

3.3.1.8 C11orf67 knockdown deactivates AKT/mTOR and MAPK pathways 92

3.3.1.9 Identification of genes and biological pathways downstream of

C11orf67 ............................................................................................................. 95

3.3.2 Functional analyses of C11orf67 overexpression in breast cell lines ......... 97

3.3.2.1 C11orf67 activates genes involved in metabolism .............................. 99

3.3.2.2 C11orf67 overexpression increases sensitivity to anti-metabolites .. 101

3.4 Discussion ..................................................................................................... 103

Chapter 4: Regulation and Binding Partners of C11orf67 ............. 107

4.1 Introduction ................................................................................................. 108

4.2 Materials and Methods ............................................................................... 111

4.2.1 Reagents and Antibodies ........................................................................... 111

4.2.2 Cell Culture ............................................................................................... 111

4.2.3 siRNA Transfection .................................................................................. 112

4.2.4 Rat AAMDC qRT-PCR ............................................................................ 112

4.2.5 Chromatin immunoprecipitation assay ..................................................... 113

4.2.6 Luciferase assay ........................................................................................ 113

4.2.7 Yeast Two-Hybrid Analysis...................................................................... 114

4.2.8 Statistical analysis ..................................................................................... 115

4.3 Results .......................................................................................................... 116

4.3.1 11q13.5-q14 locus is enriched in transcription factor binding sites ......... 116

4.3.2 Estrogen modulates C11orf67 gene expression. ....................................... 117

4.3.3 Estrogen downregulates C11orf67 expression in an ER-dependent manner ..

................................................................................................................... 121

4.3.4 Tamoxifen upregulates expression of multiple oncogenes in the 11q13.5-

q14 cluster ............................................................................................................. 121

4.3.5 Aberrant expression of C11orf67 alters the sensitivity to tamoxifen ....... 124

4.3.6 C11orf67 is upregulated in pregnancy ...................................................... 126

4.3.7 NF-κB regulates the expression of C11orf67 ........................................... 128

4.3.8 C11orf67 acts as a regulator of NF-κB transcriptional activity ................ 130

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4.3.9 RABGAP1L as a binding partner of C11orf67 ......................................... 132

4.3.10 RABGAP1L expression co-localises with C11orf67 in ER+ breast

cancers ............................................................................................................... 133

4.4 Discussion ..................................................................................................... 136

Chapter 5: General Discussion .......................................................... 141

Chapter 6: References ........................................................................ 153

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List of Figures

Figure 1-1 Schematic representation of the mammary gland ........................................... 3

Figure 1-2 Hormonal control of female mammary gland development ........................... 4

Figure 1-3 Possible models of origin of breast cancer subtypes ....................................... 5

Figure 1-4 The clinical outcomes of the inter-cluster subgroups .................................... 16

Figure 1-5 Schematic representation of ER protein and ER signalling pathways .......... 20

Figure 1-6 Detailed schematic diagram of the 11q13-q14 amplicon .............................. 27

Figure 2-1 Selective overexpression of C11orf67 in breast cancer subtypes ................. 36

Figure 2-2 Phylogenetic analysis and structural similarity of C11orf67 ........................ 46

Figure 2-3 Alternative splicing of the C11orf67 gene .................................................... 47

Figure 2-4 Cellular localization of C11orf67 Isoform_2 ................................................ 49

Figure 2-5 Alteration frequency and survival probability of C11orf67 in different

cancers ............................................................................................................................. 51

Figure 2-6 Expression pattern of C11orf67 in breast cell lines ...................................... 55

Figure 2-7 IHC staining of C11orf67 in 75 cases of breast cancer TMA ....................... 58

Figure 2-8 C11orf67 amplification detected by FISH in ER+ breast cancer .................. 62

Figure 3-1 Illustration of the PI3K/Akt/mTOR pathway ................................................ 69

Figure 3-2 Knockdown of C11orf67 by shRNAs in T47D cells .................................... 82

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Figure 3-3 C11orf67 knockdown decreases cellular proliferation .................................. 84

Figure 3-4 C11orf67 knockdown decreases cellular colony formation .......................... 86

Figure 3-5 C11orf67 knockdown decreases cellular migration ...................................... 87

Figure 3-6 C11orf67 knockdown inhibits in vivo tumour growth .................................. 89

Figure 3-7 C11orf67 knockdown is not associated with cellular apoptosis.................... 91

Figure 3-8 C11orf67 knockdown induces cellular senescence ....................................... 93

Figure 3-9 C11orf67 knockdown deactivates Akt/mTOR and MAPK pathways .......... 94

Figure 3-10 Expression profiling of C11orf67 knockdown cells ................................... 96

Figure 3-11 C11orf67 overexpression does not increase cellular proliferation .............. 98

Figure 3-12 Changes in gene/protein expression in response to C11orf67 overexpression

....................................................................................................................................... 100

Figure 3-13 C11orf67 overexpression increases the sensitivity to one- carbon folate

antagonists ..................................................................................................................... 102

Figure 4-1 Canonical and alternative pathways of NF-κB activation ........................... 109

Figure 4-2 Transcription factor binding sites of the 11q13.5-q14 amplicon ................ 118

Figure 4-3 C11orf67 is regulated by Estrogen .............................................................. 120

Figure 4-4 Estrogen regulates C11orf67 expression in an ER dependent manner ....... 122

Figure 4-5 Effect of estradiol and tamoxifen on the expression of multiple oncogenes at

11q13.5-q14 .................................................................................................................. 123

xiv

Figure 4-6 Changes in C11orf67 mRNA levels alter the sensitivity of T47D cells to

tamoxifen ...................................................................................................................... 125

Figure 4-7 Effect of pregnancy hormones on C11orf67 expression ............................. 127

Figure 4-8 C11orf67 is regulated by NF-κB ................................................................. 129

Figure 4-9 Effect of C11orf67 knockdown on NF-κB activity ..................................... 131

Figure 4-10 Y2H screen reveals RABGAP1L and SF3B1 as binding partners of

C11orf67 ....................................................................................................................... 134

Figure 4-11 The intracellular pattern of RABGAP1L expression corresponds to that of

C11orf67 expression in breast cancer sections and cell lines ....................................... 135

Figure 5-1 Proposed mechanisms underlying C11orf67-induce tamoxifen resistance in

breast cancer .................................................................................................................. 148

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List of Tables

Table 1-1 Potential oncogenes residing in the 11q13.5-q14 cis-acting amplicon........... 28

Table 2-1 Characteristics of the C11orf67 isoforms ....................................................... 48

Table 2-2 Source, clinical and pathological features of breast cancer cell lines used in

this study ......................................................................................................................... 53

Table 2-3 C11orf67 expression in 75 cases of breast cancer patients............................. 59

Table 3-1 Nucleotide sequences and exon number of C11orf67-specific shRNAs ........ 72

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List of abbreviations

4EBP1 Eukaryotic initiation factor 4E binding protein

5-FU 5-Fluorouracil

aa Amino acid

AAMDC Adipogenesis associated Mth938 domain containing

AD Activating domain

AF-1 Activation function-1

AF-2 Activation function-2

AIHW Australian Institute of Health and Welfare

AIs Aromatase inhibitors

APQ 6Amino-4(4-phenoxyphenylethylamino) quinazoline

AR Androgen receptor

bp Base pair

BSA Bovine Serum Albumin

CCND1 Cyclin D1

CCNE1 Cyclin E1

CDK Cyclin-dependent kinase

ChIP Chromatin immunoprecipitation

CMF Cyclophosphamide, Methotrexate and 5-Fluorouracil

CNA Copy number amplification

CSS Charcol-stripped seum

DBD DNA binding domain

DCIS Ductal Carcinoma In Situ

DHFR Dihydrofolate reductase

DMEM Dulbecco’s Modified Eagle Medium

DMSO Dimethylsulphoxide

dUTP Deoxyuridine triphosphate

E2 17β-Estradiol

EDTA Ethylenediaminetetraacetic acid

EGF Epidermal growth factor

EGFR1 Epidermal growth factor receptor 1

ER Estrogen receptor

xvii

ER+ Estrogen receptor positive

ERBB2 Erythroblasts leukemia viral oncogene homolog 2

ERE Estrogen Response Elements

FBS Fetal Bovine Serum

FFPE Formalin-Fixed, Paraffin-Embedded

FISH Fluorescence In Situ Hybridization

GHR Growth hormone receptor

HER2 Human epidermal growth factor receptor-2

HR Hormone receptor

HR+ Hormone receptor positive

HuMEC Human mammary epithelial cells

ICC Immunocytochemistry

IDC Invasive ductal carcinoma

IHC Immunohistochemistry

IKKα/β Inhibitor of nuclear factor kappa-B kinase subunit alpha and

beta

ILC Invasive lobular carcinoma

IntClust 2 Inter-cluster 2 subtype

IκBα Inhibitor kappa B alpha

LBD Ligand-binding domain

LN Lymph node

MAPK Mitogen-activated kinases

Mif Mifepristone

Mth938 Methanobacterium thermoautotrophicum

MTHFD2 Methylenetetrahydrofolate dehydrogenase

mTOR Mammalian target of rapamycin

MTX Methotrexate

MYC v-myc avian myelocytomatosis viral oncogene homolog

NAT Normal adjacent breast tissue

NF-κB Nuclear factor Kappa B

NHG Nottingham Histological Grade

NLS Nuclear localisation signal

NSCLC Non-small cell lung cancer

ORF Open reading frame

P13K Phosphoinositide 3-kinase

xviii

PAK1 p21-activated kinase 1

PBS Phosphate Buffered Saline

PCR Polymerase chain reaction

Pen/Strep Penicillin-Streptomycin

PFA Paraformaldehyde

PG Progesterone

PIP2 Phosphatidylinositol 4,5 bisphosphates

PIP3 Phosphatidylinositol 3,4,4-triphosphate

PPARGC1 Peroxisome proliferator-activated receptor gamma,

coactivator 1

Ppp4 Phosphoprotein phosphatase 4

PR Progesterone receptor

PR+ Progesterone receptor positive

qRT quantitative Real-time

RABGAP1L Ras-related in brain GTPase-activating protein 1-like

RB Retinoblastoma

RSF1 Remodeling and spacing Factor 1

S6K1 40S ribosomal protein S6 kinase 1

SC Subcutanous

SD Standard deviation

SDS Sodium dodecyl sulfate

SE Standard error

SERDs Selective estrogen receptor downregulators

SERMs Selective estrogen receptor modulators

SFM Serum-free media

sh1 C11orf67 shRNA 1



SHMT Serine hydroxymethyl transferase

shRNA Short hairpin RNA

SM Second messengers

SOP Standard operating procedures

Tam Tamoxifen

TBS-T Tris-buffered saline/Tween

TCGA The Cancer Genome Atlas

xix

TDLU Terminal ductal lobular units

TdT Terminal deoxy-transferase

TF Transcription factor

TFBS Transcription factor binding site

TMA Tissue microarray

TNBC Triple-negative breast cancer

TNFα Tumour necrosis factor alpha

TNM Tumour Node Metastasis

TS Thymidylate synthase

TSS Transcription start site

TUNEL Terminal deoxynucleotidyl transferase-dUTP nick end

labeling

UAS Upstream activation sequences

VEGFA Vascular endothelial growth factor A

Y2H Yeast two-hybrid

β-gal β-galactosidase

Chapter 1:

General Introduction


2

1.1 Breast cancer

1.1.1 Incidence and mortality

Breast cancer is the most common invasive tumour in women and one of the leading

causes of cancer-related deaths worldwide (Jemal et al., 2011, Torre et al., 2015).

According to the Australian Institute of Health and Welfare (AIHW) Report 2014, breast

cancer is the third most commonly diagnosed cancer in Australia. It is expected that

15,740 new cases of breast cancer will be detected in 2015, with an age-standardised

incidence rate of 59 cases per 100,000 of the population (AIHW, 2014).

In Australia, 2015 statistical records show that breast cancer remains the fourth most

common cause of death from cancer (AIHW, 2014). The mortality rate from breast cancer

in Australia is predicted to increase from 2,819 deaths in 2012 to 3,065 deaths in 2015

(25 males and 3,040 females). Although the number of breast cancer cases are increasing

each year, the five-year relative survival from breast cancer improved from 72% between

1982-1986 to 90% between 2007-2011 (AIHW, 2014).

1.1.2 Normal mammary gland development

It is important first to introduce the structure of the normal mammary gland and its

hierarchical tissue organization to understand the pathogenesis of breast cancer. The

human breast is a tubuloalveolar gland characterized by a branching network of ducts.

Clusters of small ducts constitute the terminal ductal lobular units (TDLUs) (Visvader,

2009) (Figure 1-1A). The TDLUs eventually mature into a more complex structure

containing several lobules per lobe, with each lobe functioning as a separate gland (Mills

et al., 2011, Lanfranchi, 2014). The cellular epithelial architecture is composed of a

bilayer of luminal cells surrounding an inner lumen and an external layer of myoepithelial

cells that contact the basement membrane (Figure 1-1B). These epithelial cells are

surrounded by fibroblasts and adipocytes, which compose the stroma of the mammary

gland (Anderson, 2002, Visvader, 2009).


3

The development of the mammary gland starts during embryogenesis; further

development occurs with puberty, pregnancy and lactation (Figure 1-2) (Russo and

Russo, 1998, Ali and Coombes, 2002). Both the mammary gland growth and

differentiation is regulated and maintained mainly by hormonal cues. Estrogen and

growth hormone induce branching of the mammary ducts (Hovey et al., 2002).

Progesterone and prolactin act synergistically to stimulate the lobuloalveolar

development of the mammary gland during pregnancy in preparation for lactation.

Oxytocin, adrenal steroids, thyroid hormones and insulin regulate alveolar

morphogenesis of the mammary gland (Anderson, 2002, Hovey et al., 2002, Neville et

al., 2002).

Prolonged prolactin secretion increases the proportion of mammary cells that are

differentiated and refractory to cancer. For this reason, pregnancy and lactation have been

considered as protective factors against the development of breast cancer (Adami et al.,

Figure 1-1 Schematic representation of the mammary gland

(A) Macroscopic structure of the human mammary gland. (B) Cellular

composition and architectural organization of a human mammary duct.

Adapted from Visvader (2009).


4

1998). However, elevated circulating prolactin is associated with higher risk of breast

cancer development with poorer patient outcomes (Swaminathan et al., 2008).

1.1.3 Breast cancer heterogeneity

Heterogeneity is one of the hallmarks of breast cancer (Kim et al., 2012). Intratumoural

heterogeneity accounts for the intrinsic differences within individual tumours, leading to

extensive variation in phenotypic properties and a different pattern of expression of

molecular markers (Visvader, 2011). Intertumoural heterogeneity leads to the

classification of different tumour subtypes with different morphology, molecular profile

and expression of specific biomarkers (Skibinski and Kuperwasser, 2015).

Two hypothetical mechanisms have been proposed to explain the intertumoural

heterogeneity of breast cancer. Different genetic events may be concurrent within the

Figure 1-2 Hormonal control of female mammary gland development

All hormone receptors (pink boxes) are required in the mammary epithelium.

The growth hormone receptor (GHR) is required in the stroma. Adapted from

Brisken and O'Malley (2010).


5

same target cell, resulting in varied tumour morphology (Figure 1-3A). Alternatively

different cells of origin could give rise to distinct tumour subtypes (Figure 1-3B).

Nevertheless, both cellular and molecular mechanisms could also act together to

influencing diverse aspects of tumour progression and behavior (Visvader, 2011,

Skibinski and Kuperwasser, 2015).

Figure 1-3 Possible models of origin of breast cancer subtypes

(A) In the genetic mutation model, different mutations results in different

tumour subtypes. (B) In the cell-of-origin model, different cell populations are

responsible for different cancer subtypes. Modified from Visvader (2011).


6

1.1.4 Prognosis of breast cancer

1.1.4.1 Histological classification

Breast cancer can be classified histologically as non-invasive (referred to in situ) and

invasive. Ductal Carcinoma In Situ (DCIS) is the most common type of non-invasive

cancer where cancer cells are restricted to the basement membrane (Malhotra et al., 2010,

Weigelt et al., 2010). Invasive breast cancer occurs when cancer cells spread beyond the

basement membrane, and is associated with a high risk of distant metastasis involving

the spread of cancer from the breast to secondary sites).

The most common histological type is invasive ductal carcinoma (IDC), which accounts

for about 50-80% of all breast cancers (Zheng et al., 2013). Invasive lobular carcinoma

(ILC) comes next and is found in 5-15% of breast malignancies (Rakha et al., 2008b).

Histological classifications, however, are purely descriptive and in most cases do not

confer prognostic information.

1.1.4.2 The Nottingham histological grade

Grading tumours according to the Nottingham histological grade (NHG) has proven very

useful for predicting disease aggressiveness. In this type of classification tumours are

assigned a grade from I (well differentiated) to III (poorly differentiated), by evaluating

morphological features of the tumour such as tubule formation, nuclear polymorphism,

and mitotic index (Rakha et al., 2008a). The prognostic implication of NHG has been

validated in several independent patient cohorts (Fong et al., 2015, Santos et al., 2015b).

1.1.4.3 Tumour Node Metastasis Staging

The Tumour Node Metastasis (TNM) staging system is a standard method used to classify

breast cancer patients in the routine clinic (Sobin, 2003). In this system, tumour size (T),

lymph node involvement (N) and distant metastasis (M) are taken into account. By


7

combining three factors which each contributes to prognostic information, an estimate of

the clinical stage of the disease is obtained (Kwan et al., 2012).

1.1.4.4 Immunohistochemical markers

Several immunohistochemical markers are assessed clinically to provide prognostic

information, and most importantly, predictive treatment information. Molecular

biomarkers, such as the estrogen receptor (ER), progesterone receptor (PR), and human

epidermal growth factor receptor-2 (HER2), will be discussed in detail in the following

sections.

The proliferative marker Ki-67 is evaluated immunohistochemically to determine the

proliferative activity of a tumour. However, the clinical use of Ki67 is limited, partly due

to the lack of a clearly defined cut-off value, and difficulties in standard operating

procedures (SOP) of the immunohistochemical method (Yerushalmi et al., 2010, Luporsi

et al., 2012).

1.1.5 Management of breast cancer

1.1.5.1 Surgery

Surgery is the principal treatment modality used for breast cancer treatment. It is

conducted either as modified radical mastectomy, partial mastectomy or breast-

conserving surgery. Breast-conserving surgery is the primary choice for patients with

early stage breast cancer (Fisher et al., 2002, Veronesi et al., 2002). During the surgical

procedure, a sentinel lymph node (LN) biopsy is performed to determine the potential

presence of malignant cells disseminating from the primary site of the breast lesion. In

the case of a negative biopsy, further resection of the LNs is not necessary, sparing the

patient from side effects associated with LN removal (Senkus et al., 2013, Gherghe et al.,

2015).


8

1.1.5.2 Radiotherapy

Postoperative radiotherapy is administered to eradicate possible residual microscopic

disease at and around the localization of the tumour (Veronesi et al., 1993). Postoperative

radiotherapy of the breast and thoracic wall is administered when the risk of local

recurrence within the next ten years is greater than 20% (Darby et al., 2011).

Radiotherapy is recommended for breast cancer patients who undergo breast-conserving

therapy, mastectomy, or if the tumour is larger than 20 mm (Del Barco et al., 2013,

Senkus et al., 2013).

Loco-regional radiotherapy is recommended for all patients with metastases in four or

more LNs (Del Barco et al., 2013, Senkus et al., 2013). Radiotherapy induces acute side

effects, such as erythema of the skin and pneumonitis, and late side effects, including

neuropathy of the affected brachial plexus, lymphedema of the upper extremities and

increased mortality from cardiac disease. However, it seems that the improved precision

of modern techniques has reduced the risk of developing these side effects (Clarke et al.,

2005, Darby et al., 2011).

1.1.5.3 Chemotherapy

Chemotherapy is unselective and targets all dividing or proliferating cells. The synergic

effect of several cytotoxic agents is achieved by targeting multiple pathways (Cazzaniga

et al., 2006, Sukel et al., 2008, Greenberg et al., 2011, Del Barco et al., 2013). A

combination of cyclophosphamide, methotrexate and fluorouracil (CMF) was the

standard regimen during the late 1970s and the first half of the 1980s. Further

improvement of breast cancer treatment outcomes was observed after the addition of the

taxanes (Gines et al., 2011). Both methotrexate (MTX) and 5-fluorouracil (5-FU) target

the one-carbon metabolic pathway and consequently inhibit the cells from synthesizing

nucleic acids inducing cell cycle arrest and apoptosis (Xu and Chen, 2009). MTX and 5-

FU interfere with the biosynthesis of thymidylate and purine by inhibiting the

mitochondrial enzyme dihydrofolate reductase (DHFR) and the thymidylate synthase


9

(TS) respectively. Therefore, these two agents show selectivity to rapidly proliferating

cancer cells overexpressing genes coding for folate metabolism enzymes (Vazquez et al.,

2013).

The standard chemotherapy regimens used today include different combinations of

anthracyclines, taxanes, cyclophosphamide, MTX and 5-FU (Gianni et al., 2011).

However, because of the lack of cellular selectivity, chemotherapy regimens are

associated with significant toxicities. For some patients, chemotherapy is effective at the

beginning of the treatment, but later resistance is observed, particularly in the metastatic

setting (when the tumour has already spread from the site of origin). This is a particular

problem for aggressive breast cancers that present with recurrence. For these patients, the

development of targeted therapeutic approaches would represent a very significant

advance over non-specific approaches.

1.1.5.4 Targeted therapies

Targeted therapies offer the possibility of efficient and tailored treatment based on the

molecular profile of the tumour (Saji and Kimura-Tsuchiya, 2015). The combination of

targeted therapy with either chemotherapy or endocrine therapy based on patient

predictive biomarkers has been shown to significantly enhance the overall survival in

patients with breast cancer (Kawalec et al., 2015).

An example of a novel targeted agent is the mammalian target of rapamycin (mTOR)

inhibitor everolimus (Fedele et al., 2012). Adding everolimus to endocrine therapy has

improved the survival of hormone receptor (HR) positive patients compared to endocrine

therapy alone (Bachelot et al., 2014, Lin et al., 2015, Xie et al., 2015).

However, targeted therapies are not yet standard strategies for the management of breast

cancer patients. Better trial designs, and tumour and patient selection criteria will be

critical to understanding the complexity of the targeted therapy (Fedele et al., 2012). In

any event, stratification of breast cancers in molecular subtypes represents the first critical

step in the development of novel tailored treatments.


10

1.2 Breast cancer subtypes

1.2.1 Clinical and molecular classification of breast cancer

Three well-understood molecular markers with established prognostic and predictive

outcomes are routinely used in the clinic: ER, PR, and HER2 (Davies et al., 2011). Based

on the expression levels of these receptors in breast tumour biopsies breast cancers are

clinically classified as hormone receptor-positive (HR+), HER2+, or triple-negative if they

lack the expression of all three receptors (ER-, PR-, and HER2-) (Cianfrocca and

Goldstein, 2004). Receptor levels are scored by a pathologist using standardized

immunohistochemical procedures.

The nuclear receptors ER and PR facilitate cellular growth and proliferation by binding

with their cognate ligands estrogen and progesterone. Ligand-bound receptors

consequently act as transcriptional regulators that stimulate the expression of pro-

proliferative genes in mammary epithelial cells. Thus, disruption of the molecular

mechanisms of regulation of these receptors can contribute to carcinogenesis. In general,

HR expression is associated with a good prognosis and predicts a positive response to

hormonal treatment (Utsumi et al., 2007). In contrast, HER2 overexpression is associated

with copy number amplification and, to a lesser extent, chromosomal polysomy, is a

marker for a poor prognosis and indicates treatment with HER2 inhibitors such as

trastuzumab (Hudis, 2007).

Triple-negative breast cancer (TNBC) which lacks expression of ER, PR and HER2,

represents 30% of primary breast cancers and is usually associated with larger tumour

size, lymph nodal positivity, and poor outcome (Bae et al., 2015). Due to its molecular

nature and lack of well-studied targets, neither antibody nor hormone therapies benefit

patients with TNBC (Han et al., 2015). Because of its poor prognosis, this type of breast

cancer is in great need of novel therapeutic strategies.


11

1.2.2 Intrinsic breast cancer subtypes

The development of DNA Microarray technology has greatly expanded understanding of

cancer biology at both the molecular and transcriptional level through the interrogation

of tens of thousands of expressed genes simultaneously (Creighton, 2012).

Hierarchical clustering of breast cancer patients based on differentially expressed intrinsic

genes has led to the identification of novel molecular subclasses of breast cancer. Intrinsic

molecular classification of human breast cancer includes luminal A, luminal B, basal-

like, normal-type and HER2+ subtypes (Perou et al., 2000). Results from these seminal

studies have shown strong reproducibility across many different datasets. Therefore, these

five defined subtypes are usually referred to as the “intrinsic subtypes of breast cancer”

(Santos et al., 2015a).

Importantly, the molecular subtyping of breast tumours mostly reflects the established

clinical and histopathological-based classifications, with the basal-like subtype

representing ER-/HER2- diseases, HER2-enriched representing ER-/HER2+, and the

normal-like and luminal A/B subtypes representing ER+ (Creighton, 2012). More

recently, larger dataset studies have identified additional molecular subtypes including

the interferon-rich, claudin-low and molecular apocrine subtypes (Hu et al., 2006,

Herschkowitz et al., 2007, Reis-Filho and Pusztai, 2011). Larger and independent cohorts

of patients have been used to validate the clinical value of this classification (Sorlie et al.,

2003).

Although gene expression analyses do reflect outcomes, patterns of clinical behavior and

response to therapy, genome-wide gene expression analyses are not sufficiently cost

effective to be implemented in a routine test for breast cancer patients. Instead, a much

smaller gene set predictor comprising 50 genes (PAM50) has been used to stratify breast

cancer samples into the intrinsic subtypes using a quick and cost-effective quantitative

real-time Polymerase Chain Reaction (qRT-PCR) assay (Parker et al., 2009). The PAM50

assay also provides a risk of relapse score for each patient and is highly predictive of

treatment response (Nielsen et al., 2010, Dowsett et al., 2013).


12

1.2.2.1 Luminal A breast cancer

Luminal-like breast cancer derives its name from the finding that these tumours show

similar expression profiles of keratins 8/18 typically associated with normal luminal

epithelial cells (Perou et al., 2000).

Luminal A breast cancers are ER+, HER2- and represent 40% of all breast cancers (Guiu

et al., 2012). The molecular profiling of this subtype has shown that these tumours have

relatively lower rates of proliferation and high expression of ER-regulated genes (Cancer

Genome Atlas, 2012). Luminal A tumours are generally sensitive to anti-hormonal

therapy and patients tend to have better clinical outcomes than patients with other intrinsic

subtypes (Parker et al., 2009, Prat and Perou, 2011).

1.2.2.2 Luminal B breast cancer

Luminal B breast cancers represent 20% of all breast malignancies and 30% of ER+ breast

cancers. These luminal tumours are highly proliferative (mostly Ki-67 positive) and

generally do not benefit from anti-hormonal therapy or chemotherapy and consequently

have a high rate of relapse after treatment (Allred et al., 2004, Cui et al., 2005).

Luminal B breast tumours are generally more heterogeneous than the luminal A subtype.

They characteristically express lower levels of ER-related genes than luminal A, show

variable HER2 expression and in many cases these tumours lose PR expression, and

become dependent on other signalling pathways, such as high epidermal growth factor

receptor 1 (EGFR1) pathway (Prat and Perou, 2011, Guiu et al., 2012, 2012).

Luminal B tumours have also been shown to have frequent focal regions of chromosomal

amplification, p53 mutations, and aneuploidy (presence of an abnormal number of

chromosomes in a cell) (Cancer Genome Atlas, 2012, Habashy et al., 2012).

Consequently, patients with luminal B malignancies have significantly worse outcomes

and a relatively higher risk of relapse than luminal A patients, and treatment protocols

vary significantly on a tumour to tumour basis (Prat and Perou, 2011).


13

The Cancer Genome Atlas (2012) (TCGA) consortium has yielded a catalogue of genes

that are amplified or deleted in luminal B cancers and many of these aberrations may have

significant roles in breast cancer development and progression. Having mapped potential

genomic aberrations in these tumours, the real challenge is to distinguish the genomic

alterations that drive the development and progression (Creighton, 2012). As drivers can

evolve during disease progression; another challenge is to distinguish mutations that are

important during tumourigenesis but are dispensable during the clonal evolution of the

tumour (Koch, 2014).

1.2.2.3 Basal-like breast cancer

Basal-like tumours include an extremely heterogeneous group representing

approximately 15% of all breast cancer cases. Approximately 80% of basal tumours are

TNBCs. These tumours have high proliferative rates and show gene expression patterns

more similar to that of basal mammary epithelial cell populations, as opposed to the

luminal epithelium (Cancer Genome Atlas, 2012). Basal-like tumours display high rates

of p53 mutations (84%), and have high expression of DNA repair proteins.

Unsurprisingly, they have very high genomic instability, and most samples show

aneuploidy (Jiao et al., 2014).

Patients with basal-like tumours have significantly poorer outcomes than patients with

other intrinsic types (Prat and Perou, 2011). Many of the established molecular therapies

that work in other intrinsic subtypes are minimally effective in basal-like tumours due to

the high percentage that are TNBCs. However, novel treatments for basal-like and TNBC

tumours are continually being tested both at the bench and clinically, employing both

traditional DNA-damaging chemotherapeutic agents as well as targeted molecular

therapies for other pathways associated with TNBC (Griffiths and Olin, 2012, Shastry

and Yardley, 2013).


14

1.2.2.4 HER2-enriched breast tumours

HER2 is a member of the EGFR family, consisting of four different receptor tyrosine

kinases: HER1 (EGFR), HER2, HER3, and HER4. Upon ligand binding, EGFRs dimerise

and cross-phosphorylate each monomer to initiate downstream signalling pathways,

facilitating cellular proliferation, differentiation, adhesion, and migration. Unlike other

EGFR family members, HER2 does not require ligand binding to dimerise and initiate

downstream signalling (Ciardiello and Tortora, 2008). Instead, it forms heterodimers with

the other HER receptors, thereby extending ligand interaction and prolonging pathway

activation (Harari and Yarden, 2000, Schmitt, 2009, Barros et al., 2010).

Overexpression of HER2 occurs in 15% of breast cancers and is associated with poor

clinical outcomes (Slamon et al., 1987, Borg et al., 1990, Tolaney et al., 2015).

Trastuzumab selectively antagonizes HER2 proteins and improves overall survival

(Barros et al., 2010, Giampaglia et al., 2010). The level of HER2 expression is determined

by immunohistochemistry (IHC), and scored either 0, 1+, 2+ or 3. Gene amplification is

evaluated using in situ hybridization techniques in the cases with high IHC scores

(Piccart-Gebhart et al., 2005, Romond et al., 2005, Gianni et al., 2011).

1.2.2.5 Claudin-low subtype

The claudin-low group was identified by Herschkowitz et al. (2007). This subtype has

low expression of claudin genes which are associated with tight junctions and cell-cell

adhesion, and therfore referred to as mesenchymal cancers. Other hallmarks of these

tumours include the enrichment of stem cell-like markers (high CD44/CD24 ratios) and

high lymphocyte infiltration (Prat and Perou, 2011). Patients with, these tumours show

poor prognosis, reduced survival curves, and variable response to therapy that is

intermediate between basal and luminal subtypes (Prat et al., 2010).


15

1.2.3 Inter-cluster breast cancer subtypes

Integrated genomic and transcriptomic dataset analyses of breast tumour samples has

helped define novel functional intrinsic subtypes of breast cancer and determine possible

somatic drivers in the process of breast cancer development. Curtis et al. (2012) defined

levels of gene expression and both copy number variants and somatic variants in a group

of primary breast cancer patients.

The clustering analyses suggested ten integrative clusters (labeled IntClust 1-10). Some

of these IntClust groups correspond to distinct intrinsic subtypes, but many split the

sample into unique groups. For example, IntClust 3 is composed predominantly of

luminal A tumours that have a good prognosis. IntClust 4 includes both ER+ and ER-

tumours, a variety of intrinsic subtypes, and is characterized by a favorable outcome.

IntClust 5 contains HER2 enriched and luminal tumours (ER+) that may benefit from

targeted therapy. This subtype exhibits the worst disease-specific survival at both five and

15 years, possibly because trastuzumab was not availabile at the time patients enrolled in

the study (Curtis et al., 2012) (Figure 1-4). IntClust 10 includes the majority of all basal-

like tumours and patients with these tumours have relatively good long-term outcomes

after five years. IntClust 1, 6, and 9 are several intermediate prognosis groups composed

mainly of ER+ cancers. IntClust 7 and 8 include luminal A patients with similar profiles

and good outcomes.

Of central interest for this thesis is the IntClust 2 subtype that represents a new ER+

subgroup that is characterized by poor prognosis, a steep mortality curve and elevated

hazard ratios. This represents an especially high-risk subgroup (Figure 1-4). One of the

hallmarks of this subgroup is the copy number amplification (CNA) and overexpression

of a cluster of candidate oncogenic drivers at the 11q13-q14 amplicon. Several driver

genes located in this region have been linked to endocrine resistance and poor breast

cancer prognosis (Hughes-Davies et al., 2003, Santarius et al., 2010) and ovarian cancer

(Brown et al., 2008).


16

The following section describes the ER pathway and its role in the development of

endocrine resistance as well as the mechanisms of resistance to endocrine therapy. The

significance of 11q13-q14 amplification in IntClust 2 subtype of breast cancer is then

presented, followed by the clinical importance of ER+ IntClust 2 subtype.

Figure 1-4 The clinical outcomes of the inter-cluster subgroups

Kaplan-Meier plot of disease-specific survival for the ten inter-cluster

subtypes. The IntClust 2 subtype is represented by a green line (marked red),

showing steep mortality curve and poor specific survival probability. For each

cluster, the number of samples at risk is indicated as well as the total number

of deaths (in parentheses). Adapted from Curtis et al. (2012).


17

1.3 The ER pathway and tamoxifen resistance

Estrogen is essential in women for a diversity of physiological processes. It influences

the growth, differentiation, and the function of tissues in the reproductive system,

including the breast, uterus, vagina, and ovaries (Schwabe et al., 1993). It also induces

expression of immediate and delayed hormone-responsive genes in normal and

transformed mammary epithelial cells (Altucci et al., 1996). However, prolonged

exposure to high levels of estrogen increases the risk of breast cancer by constitutively

activating the transcription of genes mainly involved in metabolism and cell cycle

regulation (Hervouet et al., 2013).

The ER was first described in the 1960s by Jensen and Jacobson when tissue uptake and

retention of radiolabeled estradiol was detected in the uterus of rats (Jensen, 1962). By

the end of the 1970s it was established that patients with ER+ tumours were more likely

to respond to endocrine treatment compared to patients with ER- tumours (Jensen et al.,

1968, McGuire, 1975).

While ER is still considered a critical predictive marker for the response to endocrine

treatment, some patients with ER+ cancer will eventually present with recurrent disease.

For this reason, many researchers are focusing on finding additional markers for

predicting endocrine response. Below is an introduction to the structure and function of

ER followed by a brief overview of what is known thus far of the mechanisms of action

of the anti-hormonal therapy, and why certain tumours develop resistance.

1.3.1 Estrogen Receptor (ER)

The ER is a nuclear hormone receptor belonging to the steroid nuclear receptor

superfamily of transcription factors (Parker, 1993). ER exists in two different isoforms,

ERα, and ERβ, transcribed from two distinct genes located on separate chromosomes

(Menasce et al., 1993, Enmark et al., 1997).


18

ERα is the predominant isoform expressed in the uterus, mammary gland, testis, pituitary,

liver, kidney, heart and skeletal muscle. In contrast, the expression of the ERβ transcript

is restricted to the ovary and prostate (Kuiper et al., 1997, Couse and Korach, 1999).

These two transcripts are rarely expressed within the same cell type, indicating distinct

functions of the two isoforms (Kuiper et al., 1996, Couse and Korach, 1999).

Studies have shown that ERα knockout mice display impaired mammary gland

development with a morphology a lifelong resembling that of newborn mice.

(Bocchinfuso and Korach, 1997). In contrast, ERβ knockout mice show normal ductal

structure of the mammary glands, which appear to undergo normal differentiation during

pregnancy and lactation. These studies suggest that ERα is the predominant receptor

during normal mammary gland development and regulation (Couse and Korach, 1999,

Gustafsson and Warner, 2000). In this thesis, ER will refer to ERα if not otherwise

specified.

1.3.2 ER domain structure

The ER protein consists of six functional domains (Figure 1-5A) The activation function-

1 (AF-1) is located within domains A and B, which in conjunction with the activation

function-2 (AF-2) of domain E is involved in mediating transcription. The ligand-binding

domain (LBD) is located in the same region as AF-2. The DNA binding domain (DBD)

of region C is required for the activated receptor to bind to specific DNA elements for

transcription initiation. Domain D functions as a flexible hinge between regions C and E

and contains several nuclear localisation signals (NLS) (Kumar et al., 1987, MacGregor

and Jordan, 1998).

1.3.3 ER signalling pathway

Estrogen and the ER are crucial regulators of complex biological networks controlling

cellular proliferation, apoptosis, invasion and angiogenesis (Rochefort et al., 1998, Ali et

al., 2000). The ER is activated by three major forms of estrogen in the human body,

namely estrone, estradiol, and estriol. In premenopausal women, ovaries produce 17β-


19

estradiol (E2), which is the more potent activating ligand of ER (Kuiper et al., 1997,

Cheskis et al., 2007). Upon binding of E2 to the LBD, the ER undergoes an allosteric

change into a dimerised active receptor complex that translocates into the nucleus. Several

co-factors are also recruited to the complex (McKenna et al., 1999). The various pathways

through which ER may activate transcription are outlined below and illustrated in Figure

1-5B.

In the classical ligand-dependent pathway, the activated ER complex binds directly to

DNA motifs known as estrogen response elements (ERE) in the proximity of target gene

promoters. However, in the non-classical ligand dependent pathway, the activated ER

complex tethers to already bound transcription factors (TFs) acting as a co-regulator

(Klein-Hitpass et al., 1988, Kushner et al., 2000). The ER has also been suggested to be

activated near the plasma membrane where it may modulate and interact with several

different pathways in a non-genomic mode. This is followed by initiating signalling

cascades via second messengers (SM), eventually leading to a rapid physiological

response that does not involve gene regulation (Levin, 1999). This interaction is most

likely a factor contributing to the resistance to tamoxifen frequently observed in ER+ and

HER2-overexpressing tumours (Shou et al., 2004, Li et al., 2012). Additionally, the ER

might be activated in a ligand-independent manner. In the absence of the ligand, growth

factor signalling leads to activation of kinases that may phosphorylate and activate the

ER. This pathway is thought to explain the hormone-independent growth observed in

some tumour subtypes (Lee et al., 2000, Campbell et al., 2001, Razandi et al., 2003,

Arpino et al., 2009).


20

Figure 1-5 Schematic representation of ER protein and ER signalling

pathways

(A) Modular organisation of the ER protein consisting of six functional

domains termed A, B, C, D, E and F. Modified from Hervouet et al. (2013). (B)

Distinct molecular pathways involved in regulatory actions of ER. Adapted

from Heldring et al. (2007).

AF-1: activation function-1, DBD: DNA binding domain, LBD: ligand-binding

domain, AF-2: activation function-2, ER: estrogen receptor, TF: transcription

factor, SM: second messengers, GF: growth factor.


21

1.3.4 Transcriptional output of ER signalling

More than 1000 genes are thought to be regulated by the ER (Kok and Linn, 2010). Gene

expression profiling of E2-stimulated breast cancer cells has shown downregulation of

the majority of the transcripts. Nevertheless, the net result is an increase in proliferation-

associated processes and suppression of apoptosis (Frasor et al., 2003). Well-known

upregulated transcripts upon E2 stimulation include Myc, CCND1 and PR (Dubik et al.,

1987, Altucci et al., 1996, Flototto et al., 2004).

1.3.5 Endocrine therapy and ER+ breast cancer

Endocrine therapy is the first line of medical treatment for ER+ breast cancer. This therapy

involves the manipulation of the endocrine system through the administration of agents

that inhibit the downstream activity or production of estrogen. Three categories of anti-

estrogen drugs are used in the clinic: selective estrogen receptor modulators (SERMs),

selective estrogen receptor downregulators (SERDs) and aromatase inhibitors (AIs)

(Bean et al., 2014).

SERMs affect ER directly by direct competition with the ligand for binding to ER.

Binding of a SERM to ER leads to insufficient conformation changes of the receptor and

inhibition of transcription. This group includes tamoxifen, raloxifen, and toremifine

(Jordan, 2004). SERDs (ICI182780, and CI164384) act by inducing a conformational

change of ER that promotes ER for degradation by the proteasome (Dauvois et al., 1992).

AIs (Letrozole) inhibit the enzyme aromatase, which prevents the conversion of

androgens into estrogens leading to inhibition of estrogen synthesis (Goss et al., 2003,

Kalidas and Brown, 2005).

1.3.6 Mechanisms of tamoxifen resistance

Tamoxifen is widely used in both the treatment of breast cancer and in the preventive

setting for patients with a high risk of developing breast cancer. The use of tamoxifen has


22

reduced breast cancer recurrence and thus, significantly increased survival rates (Brown

and Lippman, 2000, Early Breast Cancer Trialists' Collaborative, 2005). However, one-

third of women treated with the recommended five-year course of tamoxifen will relapse

within 15 years. Identifying novel biomarkers that can predict response to tamoxifen is

challenging (Early Breast Cancer Trialists' Collaborative, 2005).

Resistance to tamoxifen can be described as either intrinsic (de novo) or acquired. De

novo resistance exists before any treatment is given, while the acquired resistance

develops during tamoxifen administration after an initial period of response (Osborne and

Schiff, 2011). Some of the mechanisms that have been suggested to contribute to

endocrine resistance will be addressed in the following sections.

1.3.6.1 Coregulators of the ER

Coactivators and corepressors of the ER constitute a group of proteins that have been

repeatedly associated with tamoxifen resistance. The ER coactivator protein SRC-3 is

frequently amplified and overexpressed in breast cancer (Anzick et al., 1997, Bautista et

al., 1998). Studies of this co-activator both in vitro and in xenograft models have linked

its overexpression to tamoxifen resistance. High SRC-3 levels have been associated with

an impaired tamoxifen response in patients (Osborne et al., 2003). Another ER

coactivator is SRC-1 which has also been clinically associated with mediating tamoxifen

resistance and with reduced disease-free survival (Fleming et al., 2004, Redmond et al.,

2009).

Corepressors recruited to the tamoxifen-bound receptor, such as N-CoR, are thought to

have the opposite effect and play an important role in mediating the inhibitory effect of

tamoxifen. Low N-CoR mRNA expression has been significantly associated with

decreased relapse-free survival in a patient cohort and a xenograft mouse model

(Lavinsky et al., 1998, Girault et al., 2003).


23

Furthermore, activation of transcription factors (NF-κB and AP-1) promote ER binding

to specific gene promoters, have also been associated with endocrine resistance (Zhou et

al., 2007).

1.3.6.2 Loss of ER and activation of growth factor receptor

pathways

Another possible mechanism for resistance to endocrine therapy is the loss of ER

expression which occurs in 20% of patients treated with anti-hormonal therapy

(Encarnacion et al., 1993, Gutierrez et al., 2005). Upregulation of growth factor receptor

signalling pathways provides alternative escape pathways for proliferation and survival

of tumour cells which are no longer driven by estrogen (Osborne and Schiff, 2011).

Overexpression of HER2, as well as excessive EGFR, lead to improper activation of ER,

and consequently tamoxifen insensitivity (Campbell et al., 2001, Hutcheson et al., 2003,

Knowlden et al., 2003).

Loss of PR is another mechanism for resistance to endocrine therapy that occurs even

more frequently than ER and leads to more aggressive tumours (Brankovic-Magic et al.,

2002, Martinez et al., 2006). PR loss is associated with upregulation of the

phosphoinositide 3-kinase (PI3K)/AKT and p42/44 mitogen-activated kinases (MAPK)

pathways which also downregulate PR and ER expression (Arpino et al., 2005, Cui et al.,

2005).

1.3.6.3 Cell cycle signalling regulators

The third category of pathways implicated in tamoxifen resistance involves cell cycle

regulatory proteins. Overexpression of the positive regulators Myc, cyclins E1 (CCNE1),

and cyclin D1 (CCND1) have been involved in mediating tamoxifen resistance in patients

(Kenny et al., 1999, Stendahl et al., 2004). However, lack of subgroup stratification for

patients with CCND1 amplified tumours makes it difficult to determine the role of

CCND1 in endocrine resistance (Lundgren et al., 2012).


24

Inactivation of the retinoblastoma (RB) pathway is also thought to lead to tamoxifen

resistance in cell lines and xenograft models (Bosco et al., 2007, Lehn et al., 2011). High

expression of the cell cycle regulator p27 has been found to predict response to tamoxifen,

whereas exclusive cytoplasmic expression of p21 has been associated with tamoxifen

resistance (Perez-Tenorio et al., 2006, Chu et al., 2008, Stendahl et al., 2010).

In addition to overexpression of positive regulators and RB inactivation, upregulation of

downstream signalling pathways (PI3K/AKT, and MAPK), and activation of some

transcription factors (NF-κB), have also been shown to mediate cell survival and

contribute to endocrine resistance (Ali and Coombes, 2002).


25

1.4 Molecular pathogenesis of breast cancer

Genome instability is one of the hallmarks that characterize the pathogenesis of breast

cancer (Hanahan and Weinberg, 2011, Hainaut and Plymoth, 2013). Genetic changes that

drive and sustain cancer growth and metastasis are mainly categorized into two key

classes: 1) loss of function of tumour suppressor genes and 2) gain of function of

oncogenes.

1.4.1 Oncogenes and tumour suppressor genes

An oncogene is a mutated and overexpressed gene that alone, or in collaboration with

other changes, promotes cellular transformation, growth, and invasion. In contrast, a

tumour suppressor gene under normal conditions counteracts cell growth or other

processes that may increase invasive and metastatic potential and whose loss of function

promotes malignancy (Zhu et al., 2015).

The most frequently activated and best characterized oncogenes in breast cancer are

ERBB2 (erythroblasts leukemia viral oncogene homolog 2) (Shih et al., 2015), P1K3CA

(phosphoatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha) (Ibrahim et al.,

2015), MYC (Nadal et al., 2015), and CCND1 (Long et al., 2015).

The most frequently altered tumour suppressor genes in breast cancer are the tumour

suppressor protein p53 gene (TP53) (Evangelisti et al., 2015), the breast cancer

susceptibility genes 1 and 2 (BRCA1 and BRCA2) (Zghair et al., 2015), and the

retinoblastoma gene (RB1) (Witkiewicz and Knudsen, 2014).

Undoubtedly, many more oncogenes and tumour suppressor genes contribute to breast

carcinogenesis. Given the heterogeneity of breast cancer, a better understanding of

genetic lesions that drive tumourigenesis in the mammary gland will lead to

improvements in the clinical management of breast cancer patients.


26

1.4.2 The “case” of the 11q13-q14 amplicon in breast Cancer

DNA amplification is a common non-random cellular event in cancer that often involves

many genes and large segments of DNA defined as “amplicons”. Chromosome locus

11q13-q14 is amplified in some human malignancies including 15% of breast cancers

(Karlseder et al., 1994, Ormandy et al., 2003, Lundgren et al., 2008). The high frequency

of this particular amplification suggests that the region contains oncogenes that contribute

to positive selection for cell proliferation and survival. The four distinct core regions

within 11q13-q14 can be amplified independently or concurrently in different

combinations (Karlseder et al., 1994).

The four cores of 11q13-q14 amplicon are arranged unevenly from centromere to

telomere, and their boundaries and compositions are summarized in Figure 1-6

(Wilkerson and Reis-Filho, 2013). Although the region harbours several genes with

known or suspected oncogenic potential, the multipart structure of the amplicon has

hindered the determination of specific gene function and this remains a complex and

continuing process (Ormandy et al., 2003).

Breast cancers harbour amplification of all cores in this region (Courjal et al., 1996,

Schraml et al., 2003). Interestingly, there is a frequent concurrent amplification of 8p11.2-

p12 related to the 11q13-q14 amplicon that could confer additional advantages to cancer

cells (Courjal and Theillet, 1997, Bautista et al., 1998).

Expression profile landscape by Curtis et al. (2012) suggests a separate amplicon from

11q13.5-11q14 centred around C11orf67 and spanning PAK1-GAB2. The presence of 13

genes (Table 1-1) in this oncogenic cluster makes it more challenging to distinguish the

driver in the IntClust 2 breast cancer subtype. Several studies provide strong evidence

that these genes are important oncogenic elements. Potential drivers of the 11q13

amplicon in epithelial tumours are discussed in the following sections.


27

Figure 1-6 Detailed schematic diagram of the 11q13-q14 amplicon

Chromosome 11 ideogram showning in detail the 11q13-q14 amplicon with the

four cores highlighted green. Genes contained within each core are shown in

boxes (with the start and end points of each core), with the proposed driver

C11orf67 highlighted in red. Adapted from Wilkerson and Reis-Filho (2013).


28

Table 1-1 Potential oncogenes residing in the 11q13.5-q14 cis-acting

amplicon

Gene

Function

PAK1 p21 protein (Cdc42/Rac)-

activated kinase 1

Cytoskeleton organization, migration,

survival and nuclear signalling

(Bostner et al., 2007)

AQP11 Aquaporin 11 Membrane channel (water, ions)

(Okada et al., 2008)

CLNS1A Chloride channel, nucleotide-

sensitive, 1A

cytoskeleton organization,

ribonucleoprotein synthesis (Scandella

et al., 2000)

RSF1 Remodeling and spacing factor 1 Chromatin Remodeler (Yang et al.,

2014)

C11ORF67

(AAMDC)

Adipogenesis associated,

Mth938 domain containing

Unknown, putative anti-apoptotic

protein, regulation of (fat) cell

differentiation

INTS4 Integrator complex subunit 4

a multiprotein mediator of small

nuclear RNA processing associates

with RNA polymerase II

THRSP Thyroid hormone responsive

Transcriptional activator, putative role

in controlling tumour lipid metabolism

(Wu et al., 2012)

NDUFC2 NADH dehydrogenase

(ubiquinone) 1, subcomplex

Accessory subunit of the

mitochondrial membrane respiratory

chain NADH dehydrogenase

(Complex I) (Igci et al., 2015)

KCTD14

Potassium channel

tetramerization domain

containing 14

Unknown

ALG8 Alpha-1,3-glucosyltransferase

Catalyzes the addition of a glucose

residue to the lipid-linked

oligosaccharide precursor

(Remminghorst et al., 2009)

KCTD21

Potassium channel

tetramerization domain

containing 21

Unknown

USP35 Ubiquitin specific peptidase 35 Human ubiquitin-specific protease

(Liu et al., 2015)

GAB2 GRB2-associated binding

protein 2

Activator of phosphatidylinositol-3

kinase (Herrera Abreu et al., 2011)


29

1.4.2.1 CCND1

CCND1 encodes the cyclin D1 cell cycle protein, which forms an active complex with

CDK4 and is responsible for the cell cycle progression through G1-S phases. Its

overexpression increases proliferation of cancer cells. Cyclin D1 has been found to be

amplified in 10-20% of breast cancer cases (Bostner et al., 2007, Lundgren et al., 2012,

Tobin and Bergh, 2012) (see section 3.6.3 for more details).

1.4.2.2 EMSY

EMSY encodes a putative oncogenic product interacting with the transactivation domain

of BRCA2. Overexpression of EMSY was found to induce chromosomal instability in

normal human breast epithelial cells similar to that reported in BRCA2-deficient cells

(Benusiglio et al., 2005, Raouf et al., 2005). EMSY is amplified in 13% of sporadic breast

cancers, 18% of high-grade ovarian cancers, and is associated with a poor prognosis

(Hughes-Davies et al., 2003). There is also a strong association between EMSY gene

amplification and overexpression (Rodriguez et al., 2004, Brown et al., 2006).

1.4.2.3 PAK1

The gene encoding p-21-activated kinase 1 (PAK1) has been reported to be amplified in

breast (Bekri et al., 1997) and ovarian cancer (Schraml et al., 2003, Brown et al., 2008).

PAK1 regulates cytoskeletal structure, motility and mitosis of normal mammary

epithelial cells (Vadlamudi et al., 2000, Li et al., 2002, Vadlamudi et al., 2005). In breast

cancer, nuclear expression of PAK1 predicts resistance to tamoxifen therapy, while

cytoplasmic levels correlate with recurrence rate and mortality (Bostner et al., 2007,

Bostner et al., 2010). It was also found that PAK1 gene regulates anchorage-independent

growth and invasiveness of human breast cancer cells and is closely associated with the

invasive phenotypes of breast cancer cells and tumour grades (Vadlamudi et al., 2000,

Wang et al., 2002, Menard et al., 2005).


30

1.4.2.4 AQP11

Aquaporins (AQPs) are a family of channel-forming membrane proteins that facilitate the

transport of water and some low-molecular-weight solutes (Agre, 2006). AQPs are

expressed in various tumours (Mobasheri et al., 2005), and AQP expression often

correlates with tumour grade (Verkman et al., 2008, Ribatti et al., 2014), metastasis and

local invasion (Hu and Verkman, 2006).

1.4.2.5 RSF1

Remodeling and spacing factor 1 (RSF1) is a family member of a chromatin remodeling

complex that has been shown to play an essential role in transcriptional regulation, cell

cycle progression and carcinogenesis (Vignali et al., 2000, Wolffe, 2001, Sheu et al.,

2010). Increasing evidence suggests that the RSF1 gene is amplified and overexpressed

in ovarian cancer (Shih Ie et al., 2005) and in breast cancer (Mao et al., 2006).

Elevated levels of RSF1 are correlated with poor prognosis (Liang et al., 2012). RSF1

knockdown reduces proliferation of ovarian cancer cells in vitro (Sheu et al., 2008). RSF1

was also found to be upregulated in paclitaxel-resistant ovarian cancer cell lines (Choi et

al., 2009).

1.4.2.6 GAB2

GRB2-associated binding protein 2 (GAB2) is a scaffolding adaptor protein responsible

for the transduction of extracellular signals through some receptor tyrosine kinases, such

as HER2. GAB2 is overexpressed in breast cancer cell lines and primary tumours (Bekri

et al., 1997, Ormandy et al., 2003, Bentires-Alj et al., 2006). Moreover, it is implicated

in the metastatic spread of both breast cancer and melanoma (Ke et al., 2007, Horst et al.,

2009). Overexpression of GAB2 in mammary epithelial cells lead to marked changes in

cytoskeletal organization, maturation of cell matrix and cell motility (Herrera Abreu et

al., 2011).


31

1.4.2.7 C11orf67

Human C11orf67 is also known as Adipogenesis associated Mth938 domain containing

(AAMDC), encoding a hypothetical protein of unknown function. C11orf67 is located in

the centre of the 11q13-14 locus and is one of the novel genes overexpressed and

amplified by copy number in the ER+ subgroup of breast cancer. This subgroup is

characterized by poor prognosis (Curtis et al., 2012).

Studies of the overexpression of the murine homolog of AAMDC show that it can induce

adipogenesis in murine preadipocytes, suggesting a potential role in lipid metabolism (Ma

et al., 2012). Moreover, C11orf67 was found to be one of the novel key regulators of the

NF-B pathway in the 293T embryonic kidney cells (Gewurz et al., 2012). In addition,

C11orf67 was found to be overexpressed in some breast cancer cell lines (Kwek et al.,

2009), but the biological function of the protein has not yet been investigated in human

tumours.


32

1.5 Statement of aims

C11orf67 is a novel gene that is amplified and overexpressed in a subgroup of HR+ breast

cancer that show resistance to endocrine therapy resulting in poor patient outcomes.

However, the biological function of this protein has not been previously investigated in

human tumours. I hypothesize that C11orf67 is a new hormone-dependent oncogene

amplified and overexpressed in a subset of HR+ breast cancers associated with poor

outcome, and its expression is necessary to maintain high proliferation in the IntClust 2

tumours. The following four aims were developed to test this hypothesis.

Aim 1. Demonstrate the amplification and overexpression of C11orf67

In chapter 2, we analyse the protein structure of C11orf67 and its pattern of expression

in breast cancer cell lines and breast cancer tissues. We test the amplification of

C11orf67 in breast cancer and its possible correlation with poor outcomes.

(A) Test the transcriptional and protein expression levels of C11orf67 in

breast cancer cell lines and cellular localization of the C11orf67 protein.

(B) Immunohistochemical analysis of commercially available tissue

microarrays, and correlate the overexpression with tumour grade.

(C) Fluorescence in situ hybridization of HR+ breast cancer tissues, and

correlate the amplification with LN metastasis.

Aim 2. Investigate the molecular function of C11orf67 in HR+ breast cancers

In chapter 3, we test the functional consequences of C11orf67 knockdown and gain

of function in breast cancer cell lines. Moreover, we identify candidate signalling

pathways that are affected by changes in C11orf67 gene expression.

(A) Determine the capacity of C11orf67 knockdown to control

tumourigenesis and tumour progression.


33

(B) Investigate the effect of C11orf67 overexpression on drug sensitivity.

(C) Map the signalling pathways affected by perturbation of C11orf67

expression.

Aim 3. Investigate the possible mechanisms of regulation of C11orf67 and the

intracellular binding partners of C11orf67

In chapter 4, we explore the effect of hormonal treatment on the expression levels of

C11orf67, along with the different effect of tamoxifen in cases of high or low

C11orf67 expression. We also investigate possible cross-talk between C11orf67 and

NF-κB in breast cancer. Finally, we screen for the binding partners of C11orf67.

(A) Define the molecular cues that control the expression of C11orf67 at

11q13.5-q14.

(B) Explore the effect of the molecular control of C11orf67 on the endocrine

resistance of the HR+ breast cancer subtypes.

(C) Investigate potential binding partners of C11orf67 by yeast two-hybrid

screens.

Chapter 2:

C11orf67 as a Novel

Biomarker in Breast Cancer

C11orf67 as a Novel Biomarker in Breast Cancer

35

2.1 Introduction

Breast cancer is one of the leading causes of cancer-related deaths in Australia and

worldwide (AIHW, 2014). In recent years, genomic profiling of breast cancers has

allowed researchers to develop novel predictive biomarkers and guide targeted therapy.

In this chapter, we take advantage of the cancer genome project and other databases to

describe C11orf67 as a novel target in hormone receptor positive (HR+) breast cancer.

We propose that C11orf67 is a predictive biomarker that could help define a group of

poor prognosis ER+ tumours at risk of relapse.

Oncogenic activation in breast cancer is often the result of the loss, rearrangement or

amplification of particular regions of the genome (Luo et al., 2006). There is a

considerable interest to identify the genes in which chromosomal abnormality occurs

since they are likely to determine the behavior of the tumour (Gillett et al., 1994). The

chromosome region (11q13.5-q14) in chromosome 11 is amplified in almost 10% of

breast cancer and it is associated with very poor clinical outcome (Borg et al., 1991, Hui

et al., 1998, Ormandy et al., 2003). Gene(s) within this amplicon may contribute to the

clinical aggressiveness of the tumour.

For decades, breast cancer has been classified at the molecular level into five intrinsic

subtypes; luminal A, luminal B, HER2+, basal-like and normal-like (Perou et al., 2000).

However, gene expression data does not directly inform about potential drivers that lead

to essentially different breast cancer disease (Turner et al., 2010, Wilkerson and Reis-

Filho, 2013).

To search candidate driver oncogenes, the recent sequencing of the breast cancer genome

has provided new insights about the genes that are actually causative of the disease. By

integrating both genomic aberrations, such as copy number amplifications, and gene

expression data, the cancer genome project has defined novel subgroups of breast cancer

with very important consequences for therapy (Curtis et al., 2012). One of these

functional subgroups is characterized by copy number amplification and overexpression

in a cluster of potential drivers in the 11q13.5 chromosomal region, namely Intercluster


36

2 (IntClust 2) subtype. This IntClust 2 subtype is a HR+ breast cancer associated with

poor prognosis and possibly resistance to endocrine therapy (Curtis et al., 2012).

Therefore, it is crucial to analyse the key drivers of this subgroup to develop novel

biomarkers that predict the poor outcome and ultimately improve survival for these

patients.

Within the 11q13.5-q14 oncogenic cluster we observed the selective overexpression of

C11orf67 in the IntClust 2 subtype (Figure 2-1). C11orf67 (also known as adipogenesis

Figure 2-1 Selective overexpression of C11orf67 in breast cancer subtypes

Boxplots of log2 expression of C11orf67 in (A) the Intercluster subtypes, and

(B) the Intrinsic subtypes of breast cancer. IntClust 2 and luminal subtypes

are indicated with red boxes. Figure modified from Curtis et al. (2012).


37

associated Mth938 domain containing, AAMDC) encodes a hypothetical protein of

unknown function. Conserved domain analyses suggest that C11orf67 has a very similar

folding to a predicted protein (Mth938) from Methanobacterium thermoautotrophicum

which has suggested lateral gene transfer from bacteria to mammalian cells (Das et al.,

2001). However, the biological role of C11orf67 protein in human tumours has not been

studied previously.

In this chapter, we first characterized the C11orf67 gene organisation and the domain

structure of the protein encoded by C11orf67. Second, we developed antibody based

assays to determine the subcellular localization of C11orf67 in mammalian cells. Third,

to understand the extent and the significance of C11orf67 in the prognosis of breast

cancer, we examined a series of breast cancer tissues for the presence of the C11orf67

gene amplification. Lastly, we investigated the expression of a C11orf67 protein by IHC

in breast cancer tissues.

This study demonstrated the amplification and overexpression of C11orf67 in HR+ breast

cancer tissues and breast cancer cell lines over normal tissues. Our results are the first to

highlight the prognostic value of C11orf67 as a novel biomarker in HR+ breast cancer.

Our work suggests that C11orf67 could be used clinically to identify ER+ breast cancer

patients belonging to this IntClust 2 subtype associated with very poor prognosis.


38

2.2 Materials and Methods

2.2.1 Cell lines and cell culture

All breast cell lines were purchased from American Type Culture Collection (ATCC,

Manassas, VA) and cultured in complete growth medium as described by the company.

All media and supplements were purchased from Invitrogen, Waverley, Australia, unless

stated otherwise.

Briefly, human breast cancer cell lines T47D, ZR751, SKBR3 and BT474 cells were

cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and

1% Penicillin-streptomycin (Pen/Strep). HEK293T and MDAMB-231 cells were cultured

in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% FBS and 1%

Pen/Strep. MCF7 cells were cultured in Minimum Essential Medium (MEM)

supplemented with 1.5 mg/ml sodium bicarbonate, 0.1 mM non-essential amino acids

(NEAA), 1 mM sodium pyruvate, 10% FBS and 1% Pen/Strep. MCF12A cells were

cultured in DMEM/F12 (1:1) containing 20 ng/ml epidermal growth factor (EGF)

(Sigma-Aldrich, St Louis, MO, USA), 100 ng/ml cholera toxin (Sigma-Aldrich, St Louis,

MO, USA), 0.01 mg/ml insulin, 500 ng/ml hydrocortisone (Sigma-Aldrich, St Louis, MO,

USA), 5% horse serum, and 1% Pen/Strep. Human mammary epithelial cells (HuMEC)

were cultured in HuMEC media, supplemented with 1% bovine pituitary extract, and

1%Pen/Strep. All the cells were incubated in 5% CO2-containing humidified atmosphere

at 37°C. Once the cells reached 60-70% confluency, they were trypsinised with 0.25%

trypsin-ethylenediaminetetraacetic acid (EDTA) and passaged accordingly.

2.2.2 RNA extraction

Total RNA was extracted from breast cancer cells using TRIzol reagent according to the

manufacturer’s protocol (Qiagen, Hilden, Germany). Briefly, cells were homogenized

using 1 ml TRIzol reagent per one well of the six-well plate. The homogenized samples

were incubated for 5 minutes at room temperature to permit complete dissociation of


39

nucleoprotein complexes. 0.2 ml chloroform per 1 ml TRIzoL reagent was added to the

homogenized samples followed by vigorous shaking for 15 seconds. After 2-3 minutes

incubation at room temperature, samples were centrifuged at 12,000 g for 15 minutes at

4°C. After centrifugation, the samples separated into three phases, and the aqueous phase

was transferred to a new tube. For RNA precipitation, 0.5 ml isopropyl alcohol was added

to the aqueous phase. The samples were incubated for 10 minutes at room temperature

and centrifuged at 12,000 g for 10 minutes at 4°C. The supernatant was discarded, and

the RNA gel-like pellet was washed once with 200 μl 70% ethanol, vortexed and

centrifuged at 7,500 g for 5 minutes at 4°C. Ethanol was aspirated, and the RNA pellet

was air-dried, dissolved in RNase-free water and stored at -80°C until required.

2.2.3 Reverse transcription and cDNA synthesis

Up to 3 μg of total RNA was reverse transcribed to complimentary DNA (cDNA) using

the High Capacity cDNA Kit (Invitrogen, Waverley, Australia) according to the

manufacturer’s protocol, the samples were kept on ice for the next step or stored at -20°C

2.2.4 Real-time Polymerase Chain Reaction

Quantitative real-time Plymerase chain reaction (qRT-PCR) was carried out to detect

levels of C11orf67 mRNA expression using Taqman probes (Applied Biosystems,

Scoresby, Australia) for C11orf67 (FAM/MGB #4331182) and the housekeeping gene

GAPDH (FAM/MGB #4333764F). Data were analyzed using the comparative ΔΔ Ct

method (ABPrism software, Applied Biosystems) using GAPDH as an internal

normalization control (Livak and Schmittgen, 2001).

2.2.5 Total protein extraction and western blotting

For protein extraction, cells were washed twice with PBS and lysed in ice-cold lysis buffer

(2% sodium dodecyl sulfate (SDS), 125 mmol/L Tris/HCL pH 6.8). Lysates were


40

centrifuged at 12,000 g for 30 minutes at 4°C; the supernatant was stored in -80°C until

assayed.

Protein concentration was measured with the Bio-Rad DC Protein Assay kit (BioRad

Laboratory, Hercules, CA) according to the manufacturer protocol, with various

concentration of Bovine Serum Albumin (BSA) (Sigma-Aldrich, St Louis, MO, USA)

prepared in the lysis buffer as standards for this assay.

For western blotting, cell lysates (20 μg) was mixed with Laemmli sample buffer (BioRad

Laboratory, Hercules, CA) and dithiothreitol (DTT) (Sigma-Aldrich, St Louis, MO,

USA). The samples were then denatured by boiling at 95°C for 5 minutes. Samples were

loaded onto 4-15% pre-cast Mini gels (BioRad Laboratory, Hercules, CA). Proteins were

then electroblotted to nitrocellulose membranes by using Trans-Blot turbo transfer system

(BioRad Laboratory, Hercules, CA). and blocked for 1 hour at room temperature in Tris-

buffered saline containing Tween (TBS-T) buffer (50 m M Tris – HCL, pH 7.5, 150 m M

NaCl, 0.1% Tween-20), containing 5% non-fat milk. After blocking, blots were incubated

overnight at 4°C with the required primary antibodies: rabbit anti-C11orf67 polyclonal

antibody 1:250 (Santa Cruz Biotechnology, Texas, USA), and mouse anti-α tubulin

antibody 1:2000 (Sigma-Aldrich, St Louis, MO, USA). The next day, membranes were

washed three times with TBS-T, and then incubated with horseradish peroxidase-

conjugated secondary Anti-rabbit and anti-mouse respectively (1:10,000) for one hour at

room temperature. After three more washes with TBS-T, proteins were visualized by

enzymatic Chemiluminescence Detection Kit (Millipore, Victoria, Australia).

2.2.6 Tissue Microarrays

Commercially available tissue microarrays (TMAs) (Biomax, Rockville, MD, USA)

containing Formalin-fixed, Paraffin-embedded (FFPE) tissues from 75 breast cancer

patients, with two representative cores 1.5 mm diameter from each tumour were used in

this study.


41

Immunohistochemistry (IHC) was performed at Royal Perth Hospital by Mr Nathan Acott

on Ventana Benchmark Ultra, using Ventana optiView detection system (760-700).

Antigen retrieval was performed on deparaffinized sections by boiling them at 99°C in

citrate buffer (10 mM sodium citrate buffer, pH 6.0) for 16 minutes using board Ventana

Benchmark Ultra. Endogenous peroxidase was blocked with 3% H2O2 followed by

incubation with rabbit polyclonal anti-C11orf67 antibody (Santa Cruz Biotechnology,

Texas, USA) diluted at 1:100 in DAKO diluent (S0809) for 32 minutes at 36°C, followed

by the EnVision+ System (DAKO, Carpinteria, CA) using the peroxidase method.

Scoring was performed by breast pathologist Dr Jeremy Parry, intensity score ranging

from 0 to 2+ to evaluate C11orf67 immunoreactivity in tumour samples.

2.2.7 Immunocytochemistry

For immunocytochemistry (ICC), VitroView LSAB IHC Kit, Rabbit IgG (Genecopoeia,

Rockville, MD, USA) was used according the manufacturer’s protocol. Briefly, cells

were fixed with 10% paraformaldehyde (PFA) in Phosphate Buffered Saline (PBS) for

20 minutes, permeabilized in 0.025% Triton X-100, and incubated with normal goat

serum for 30 minutes to block non-specific binding. Rabbit polyclonal anti-C11orf67

antibody (Santa Cruz Biotechnology, Texas, USA) was added to the cells at 1:100 dilution

in 10% goat serum in PBS and incubated overnight at 4°C. Peroxidase blocking was

performed by incubation of cells with 0.3% hydrogen peroxide in PBS for 10 minutes at

room temperature; then cells were incubated with the biotinylated anti-rabbit secondary

antibody for 30 minutes at room temperature. Detection was finally performed by

incubating cells with Streptavidin-Horseradish Peroxidase (HRP) for 30 minutes at room

temperature, followed by DAB solution for signal development. Positive C11orf67 cells

were detected under light microscope Olympus IX71 (Singapore).

2.2.8 Fluorescence microscopy

T47D cells were cultured on coverslips until 70% confluent, then transiently transfected

using Lipofectamine-2000 reagent (Invitrogen, Waverley, Australia) with C11orf67


42

isoform_2 cDNA fused to an N-terminal 3xFLAG epitope (GenScript, NJ, USA)

according to the manufacturer's protocol. After 36 hour incubation, cells were fixed with

4% PFA in PBS at room temperature for 20 minutes, permeabilized with 0.5% Triton-X

in PBS at 4°C for 10 minutes and blocked with 2% BSA in PBS at 4°C for 24 hours. Cells

were incubated with mouse monoclonal Anti-Flag antibody 1:1600 (Cell Signaling, Gold

Coast, QLD, Australia) overnight, then goat anti-mouse secondary Alexa Fluor 488-

conjutated antibody (1:500) for 1 hour in the dark . To visualize nuclei, cells were

incubated with 1:5000 dilution of Hoechst nuclear stain (Sigma-Aldrich, St Louis, MO,

USA) at room temperature for 5 minutes. Coverslips were mounted on slides, preserved

using Fluorosave and examined with the Nikon Fluorescence microscope with

microscope camera (DS-Fi2).

2.2.9 Fluorescence In Situ Hybridization

Fluorescence in situ hybridization (FISH) was conducted in collaboration with the

department of Biology and Genetics, Medical University, Poland by Dr Iwona Kardas.

FISH protocol was performed using commercially available probes (Empire Genomics,

Buffalo, NY) labelled red for C11orf67 gene, and green pericentromeric region of

chromosome 11 (5-Fluorescein dUTP). After deparaffinization and rehydration, slides

were soaked in pre-treatment solution (Histology FISH Accessory Kit, Dako) and heated

in a microwave oven for 10 minutes, followed by 17 minutes Pepsin digestion (room

temperature, RTU Pepsin solution, Histology FISH Accessory Kit, Dako). Subsequently,

slides were dehydrated and 10 μl of probe working solution was applied to each slide.

After co-denaturation at 78℃ for 5 minutes, slides were placed in a humid chamber at 37

°C overnight. The next day residual probe was washed out and Fluorescence Mounting

Medium (Histology FISH Accessory Kit, Dako) was applied. After 30 minutes, sections

were observed under a fluorescence microscope (ZEISS, Germany). Digital images were

recorded using the integrated camera (AxioCamMRc, ZEISS) and analysed using Isis

Fluorescence Imagining (MetaSystems, Germany). For each sample at least 40 nuclei

were counted. Cells with a ratio of gene specific probe (C11orf67) to control probe

(pericentromeric region of chromosome 11) ≥ 2.0 were considered to have amplification.


43

2.2.10 Statistical analysis

Each experiment was repeated at least three times. Data are presented as mean ± the

standard deviation (SD) of each experiment. Data analysis was performed using

GraphPad Prism. Student’s t-test was used to calculate the numerical data. Chi-square test

was utilised for comparisons of categorical data. Statistical tests were two-sided, and p

values less than 0.05 were considered statistically significant.


44

2.3 Results

2.3.1 Bioinformatic and structural analyses of C11orf67

Bioinformatic analyses were performed using BLAST, Ensembl and NCBI databases

(www.ncbi.nlm.nih.gov/protein/NP_078960.1). These analyses showed that human

C11orf67 is located on chromosome 11, and the corresponding open reading frame (ORF)

encodes a hypothetical protein of 122 amino acids (aa) with a predicted MW of 13.3 kilo

Dalton (kD).

Nucleotide sequence conservation analysis of C11orf67 indicated that the gene is

conserved across different species including human, chimpanzee, rhesus monkey, cow,

dog, rat, mouse, chicken, zebrafish, and even bacteria (Figure 2-2A). Furthermore, the

resulting protein domain structure was found highly similar to that of MTH938 domain

from Methanobacterium, suggesting lateral gene transfer from bacteria to eukaryotic cells

(Das et al., 2001) (Figure 2-2B). The crystal structure of the MTH938 domain reveals a

unique tertiary fold consisting of three β-sheets and three α-helices. The β-sheets from

each monomer comprise 111 aa and associate to form a dimer with a cleft in the

interphase, which potentially bind double-stranded nucleic acid (Das et al., 2001).

2.3.2 Identification of the full-length cDNA sequence and

spliced transcripts of C11orf67.

To obtain the cDNA sequences of the C11orf67 gene, we used GeneBank

(www.ncbi.nlm.nih.gov/gene/28971) and UCSC genome browser databases. C11orf67

was mapped to human chromosome 11, and its 6 exons spanned 75.6 Kbps. UCSC

Genome Browser analysis of C11orf67 cDNA sequences revealed the existence of three

alternative spliced variants (Figure 2-3A). The homology between these three C11orf67

transcripts resides mainly in their NH2-terminal domain, whereas the amino COOH-

terminal region is much more divergent. The C11orf67 isoforms differ in their nucleotide

sequence, position in chromosome 11 and number of coding exons; thereby, these

http://www.ncbi.nlm.nih.gov/protein/NP_078960.1


45

isoforms may encode protein products of distinct MW and aa sequence (Table 2-1).The

length of the splicing transcript 1 was 264 bp (includes exon 1, 2, and 6), transcript 2 was

366 bp (includes exon 1, 2 and 3), transcript 3 was 441 bp (includes exon 1, 2, 4 and 5).


46

Figure 2-2 Phylogenetic analysis and structural similarity of C11orf67

(A) Phylogenetic tree analysis of C11orf67. (B) Structural similarity between

C11orf67 and MTH938 domain from Methanobacterium, modelled using

PyMol software. 1IHN and 2AB1 indicate the X-ray structure of the gene

product.


47

Figure 2-3 Alternative splicing of the C11orf67 gene

(A) Sketch map of the C11orf67 alternative splicing model. The boxes show

the exons and their sizes. (B) cDNA sequence and predicted aa sequence of

human C11orf67 Isoform_2. Shared aa sequence between the three isoforms

are underlined. * indicates a stop codon.


48

Table 2-1 Characteristics of the C11orf67 isoforms

C11orf67

isoform

Position

Chr11

Number

of aa

Coding

exon

count

Mw

(kD)

Isoform_1 77,553,543-

77,629,145 88 3 9.57

Isoform_2 77,553,543-

77,583,361 122 3 13.33

Isoform_3 77,553,543-

77,611,732 147 4 16.37


49

2.3.3 Subcellular localization of Flag-tagged C11orf67

Isoform_2

To investigate the intracellular localization of C11orf67 Isoform_2, we cloned the cDNA

into the PcDNA3.1 expression vector to allow high expression of the protein into T47D

and MCF7 breast cancer cell lines. The cDNA was N-terminally fused with an epitope

tag (3xFLAG) to allow the detection of the protein and intracellular localisation by

immunofluorescence (IF). Our results show that Flag-tagged C11orf67 isoform_2 was

predominantly localised in the cytoplasm of T47D and MCF7 with some highly

concentrated staining around the nucleus. However, the Flag-tagged protein translocated

to the nucleus in some cells, which could be explained by the small size of the protein

(122 aa) (Figure 2-4). In conclusion, these data suggest that C11orf67 protein exerts its

physiological role in the cytoplasm, the nucleus and might be membrane-associated.

Figure 2-4 Cellular localization of C11orf67 Isoform_2

Fluorescent microscopy analysis of Flag-tagged C11orf67 Isoform_2 protein

(green) and nuclear-specific fluorescent dye Hoechst (blue) was performed

after 36 hours transfection of T47D cells and MCF7 cells. Scale bars are 50 μm.


50

2.3.4 The pattern of C11orf67 gene alteration in cancer

After analyzing the genomic organisation and protein structure of C11orf67 we next

investigated whether C11orf67 was amplified and overexpressed in different tumour

types. In addition, we were interested to explore if its expression is of any prognostic

value in different tumours.

In order to achieve these objectives, we first investigated the pattern of C11orf67

alteration in a large-scale cancer genomic datasets with collaboration with Piotr

Kozlowski, utilizing cBioPortal for Cancer Genomics (http://www.cbioportal.org/public-

portal/). Our results show that C11orf67 tends to be amplified in a variety of cancers,

most predominantly in breast invasive carcinoma (~25% of cases), followed by ovarian

carcinoma (15% of cases) (Figure 2-5 A). In addition, a strong correlation between the

copy number amplification (CNAs) of C11orf67 and mRNA overexpression was

validated by examination of the Cancer Genome Atlas database. This correlation was also

described by Curtis et al in the context of the IntClust 2 subtype.

To assess the prognostic value of C11orf67 overexpression, we used the PPISURV

PORTAL tool to query the prognostic value of C11orf67 expression levels in a published

dataset of patients with different types of cancers. As expected from being an oncogenic

driver, patients with low C11orf67 expression had a significantly higher survival

probability than patients with high C11orf67 expression in breast cancer (p=0.0117),

ovarian cancer (p=0.00659), and lung non-small cell lung cancer (NSCLC) (p=0.0109)

(Figure 2-5B). This data support the notion that overexpression of C11orf67 has a

prognostic value in breast and other cancers.


51

Figure 2-5 Alteration frequency and survival probability of C11orf67 in

different cancers

(A) Alteration frequency of C11orf67 (y-axis) in various cancer sequencing

studies (x-axis). Results are obtained via cBioPortal for cancer genomics.

Mutations are illustrated in green, deletions in blue, amplifications in red, and

multiple alterations in grey. Individual cancer types are shown along x-axis.

(B) Kaplan-Meier survival curves for patients with breast cancer, ovarian

cancer, and non-small cell lung cancer expressing different levels of C11orf67

(high C11orf67 expression: red line; low C11orf67 expression: green line).

Low C11orf67 expression correlates with higher survival probability in breast

cancer patients (p=0.0117), ovarian cancer patients (p=0.00659), and lung

cancer patients (p=0.0109).


52

2.3.5 C11orf67 expression profile in breast cell lines

C11orf67 was found to be amplified and overexpressed in IntClust 2 breast cancer

subtype. This subgroup includes HR+ patients characterised with poor clinical outcome

and tendency to relapse (Curtis et al., 2012). Next, we asked the question whether

C11orf67 would be overexpressed in representative ER+ breast cancer cell lines. We

screened a panel of 8 tumourigenic and non-tumourigenic breast cell lines using a

C11orf67 antibody by western blot. Breast cell lines used were representative of luminal,

Her2+, and TNBC. Normal-like cell lines were used in our analyses as negative controls.

(Table 2-2).

First, qRT-PCR was performed to analyse the mRNA level of C11orf67 in the different

cell lines tested. Our results demonstrated that C11orf67 was overexpressed in several

breast cancer cell lines relative to the non-transformed breast epithelial cell line MCF12A.

The highest expression of C11orf67 was detected in the ER+ luminal T47D cell line

(p<0.0001), followed by the luminal cell lines BT474, MCF7 and ZR751 (p<0.0001).

C11orf67 was also found to be upregulated in the ER-, HER2+ cell line SKBR3 (p<0.001),

and in the basal cell line MDA-MB231 (p<0.0001) relative to MCF12A cells, but in less

extent that in the luminal lines. Normal-like HUMEC cells did not show any significant

difference in the expression of C11orf67 compared to MCF12A cells (Figure 2-6A).

Collectively, these data indicated that luminal T47D cells showed the highest levels of

C11orf67 mRNA.


53

Table 2-2 Source, clinical and pathological features of breast cancer cell

lines used in this study

Cell line Gene

cluster ER PR HER2 TP53 Source

Tumour

type

Age

(yrs) Ethnicity

T47D Lu + [+] ++M PE IDC 54

BT474 Lu + [+] + + P.Br IDC 60 W

MCF7 Lu + [+] +/-WT PE IDC 69 W

ZR751 Lu + [+] - AF IDC 63 W

SKBR3 Lu - [-] + PE AC 43 W

MDA-

MB231 Ba - [-] + PE AC 51 W

MCF12A Ba - [-] + P.Br F 60 W

HUMEC Ba - [-] NA NA NA

Lu, luminal; Ba, Basal; AC, adenocarcinoma; AF, ascites fluid; IDC, invasive

ductal carcinoma; P.Br, primary breast; PE, pleural effusion; W, White;

ER/PR/HER2/TP53 status: ER/PR positivity, ErbB2 overexpression, TP53

protein levels and mutational status (M, Mutant protein; WT, wild type protein)

are indicated. Square brackets indicate that levels are inferred from mRNA

levels alone where protein data is not available. Table adapted from Neve et al.

(2006).


54

At the protein level, we first confirmed the specificity of the polyclonal anti-C11orf67

antibody by western blot analysis. We generated stable MCF7 cells transfected with a

C11orf67 Isoform_2 lentiviral vector as our positive control. We found that the anti-

C11orf67 antibody recognized two protein bands of a MW of 13 kD and 16 kD,

corresponding to C11orf67 Isoform_2 and Isoform_3, respectively. The western blot

analysis did not reveal any bands corresponding to the C11orf67 isoform_1 of a MW of

9 kD. Positive control cells showed an increase in the intensity of the band at the MW of

13 kD, corresponding to the expected C11orf67 Isoform_2, which indicates specificity of

the antibody.

Western blot analyses of the ER+ and ER- breast cancer cell lines showed a similar trend

of expression to that of the qRT-PCR analyses. Consistent with the transcriptional data,

MCF12A and HUMEC cells did not show detectable C11orf67 protein expression under

the same experimental conditions (Figure 2-6B).

To confirm the specific binding of the anti-C11orf67 antibody in cells, we tested the

expression pattern of C11orf67 protein in the high- and the low-expressing cells by ICC.

We stained the T47D luminal cell line that showed the highest levels of endogenous

C11orf67 mRNA, and the MCF12A normal-like cell line carrying the lowest levels of

expression. T47D cells showed cytoplasmic as well as peri-nuclear staining of the

C11orf67 protein, while MCF12A cells showed hardly detectable levels of C11orf67

staining both in the cytoplasm and nucleus (Figure 2-6C).

These data collectively validate the specificity of the anti-C11orf67 antibody and allowed

us to use this reagent in patient’s samples.


55

Figure 2-6 Expression pattern of C11orf67 in breast cell lines

(A) Transcript levels of C11orf67 were determined in the indicated cell lines

by qRT-PCR and normalized to GAPDH. The data are normalized to T47D

cells. (B) Protein expression levels were determined by western blot, equal

loading of lanes was confirmed by expression of -tubulin. (C) C11orf67

protein localization was determined by ICC in T47D and MCF12A cells (***

p<0.0001, ** p<0.001).


56

2.3.6 C11orf67 is overexpressed in HR+ and high-grade breast

cancer

We next examined the pattern and intensity of C11orf67 protein expression in breast

cancer specimens using a commercial breast cancer TMA by IHC staining. The breast

cancer TMA contained 3 cases of normal adjacent breast tissue (NAT), 3 cases of

fibroadenoma (benign breast tumours) and 69 cases of a malignant breast tumour (in

duplicate blocks). Information about the age, ER, PR, Androgen receptor (AR), p53,

Ki67, HER2, pathological type, TNM and grade of each patient sample was available.

Scoring was performed by breast pathologist Dr Jeremy Parry. All sections were divided

into negative (-), intermediate (+) and high (++) C11orf67 expression groups. A positive

reaction was defined as discrete localization of the chromogen in the cytoplasm, nucleus,

and membrane of stained sections (Figure 2-7A).

We found that C11orf67 was mainly localized in the cytoplasm, although it also showed

perinuclear localization in some breast cancer samples as well as membranous and

nuclear expression in other samples (Figure 2-7B)

Staining results for all cases are shown in Table 2-3. In 75 breast cores, the positive

expression rate of C11orf67 protein was 64%. Of these, 89.6% were malignant breast

cancer samples. It was observed that all 3 sections of NAT and 2 out of 3 cases of benign

breast conditions showed moderate (+) C11orf67 expression in luminal ductal epithelial

cells, either cytoplasmic or both cytoplasmic and nuclear. This observation suggests that

C11orf67 is weakly expressed in normal breast ductal epithelium (Figure 2-6A middle

panel).

Next, we investigated whether there is any correlation between the expression of

C11orf67 and available clinical/pathological information such as hormonal status and

grade. Our results indeed showed that in intermediate (+) and high (++) expression

groups, 48.6% and 53.8% of patients respectively were ER+, whereas only 14.8% of the

low expression group were ER+. This difference was statistically significant, p=0.0054


57

(Figure 2-7C). This finding validated the bioinformatic analyses of genomic databases

and confirms that C11orf67 is predominantly overexpressed in the HR+ subgroups of

breast cancer.

Moreover, we investigated the correlation between C11orf67 expression and the PR status

of the samples. Although not all the ER+ tumours were PR+, we were still able to find a

significant correlation between the expression of C11orf67 and PR status of sections.

Analyses of TMA staining revealed that 53.8% of patients were PR+ in the high

expression group and 37.1% in the intermediate expression group, as compared to only

22.2% in the low expression group. This difference was also statistically significant,

p=0.0443 (Figure 2-7D), yet not as high as the correlation with ER+ tumours.

Tumour grade is a score that determines how abnormal the cancer cells and the tumour

tissue appear under a microscope. It is also an indicator of how quickly a tumour is likely

to grow and spread. Grades 1 and 2, well and moderately differentiated respectively, tend

to grow and spread at a slower rate than poorly and undifferentiated tumours (grades 3

and 4, respectively). Importantly, we were able to correlate high C11orf67 expression

with poorly differentiated high tumour grade (grade 3), p=0.0386 (Figure 2-7E). While

46.2% of high C11orf67 expression group was grade 3, only 13.6% and 14.8% of low

and intermediate expression groups respectively were grade 3.

No significant correlation was found between C11orf67 expression and any of the other

parameters available from the stained TMA sections, including Ki67, p53, Her2, AR, or

tumour stage (Table 2-3). It is worth mentioning that commercial TMA sections do not

allow access to response to treatment, follow-up or 5-year survival information of the

patient samples used in the assay.


58

Figure 2-7 IHC staining of C11orf67 in 75 cases of breast cancer TMA

(A) Representative images for the intensity of C11orf67 expression from low

(-), intermediate (+) and high (++). (B) Localization of C11orf67 expression:

cytoplasmic, membranous or nuclear (bars represent 50μm). High C11orf67

(++) expression was significantly correlated with ER+ (C), PR+ (D) and high-

grade breast cancers (E).


59

Table 2-3 C11orf67 expression in 75 cases of breast cancer patients

a indicates unavailable information, ** p<0.01, * p<0.05.

Characteristics Total

No.

Low

(-)

No (%)

Intermediate

(+)

No (%)

High

(++)

No (%)

P

value

Type

Benign

Malignant

75 1 (3.7)

26 (96.3)

5 (14.3)

30 (85.7)

0 (0)

13 (100) 0.9423

Age

<=50

>50

75 15 (55.6)

12 (44.4)

21 (60)

14 (40)

7 (53.8)

6 (46.2) 0.9930

ER status

Positive

Negative

75 4 (14.8)

23 (85.2)

17 (48.6)

18 (51.4)

7 (53.8)

6 (46.2)

0.0054

**

PR status

Positive

Negative

75 6 (22.2)

21 (77.8)

13 (37.1)

22 (62.9)

7 (53.8)

6 (46.2)

0.0443

*

AR status

Positive

Negative

75 8 (29.6)

19 (70.4)

13 (37.1)

22 (62.9)

4 (30.8)

9 (69.2) 0.8171

Her2

Positive

Negative

75 2 (7.4)

25 (92.6)

7 (20)

28 (80)

3 (23.1)

10 (76.9) 0.1483

P53

Positive

Negative

75 19 (70.4)

8 (29.6)

22 (62.9)

13 (37.1)

8 (61.5)

5 (38.5) 0.5242

Ki67

Positive

Negative

75 11 (40.7)

16 (59.3)

16 (45.7)

19 (54.3)

3 (23.1)

10 (76.9) 0.4230

Lymph node

metastases

Positive

Negative

67a 6 (23.1)

20 (76.9)

7 (25)

21 (75)

4 (30.8)

9 (69.2) 0.6213

Grade

Low grade 1,2

High grade ≥ 3

62a 19 (86.4)

3 (13.6)

23 (85.2)

4 (14.8)

7 (53.8)

6 (46.2)

0.0386

*


60

2.3.7 Copy number amplification of C11orf67 in HR+ breast

cancer

Our bioinformatic analyses revealed that C11orf67 is overexpressed and amplified in the

IntClust 2 subtype of breast cancer. In the previous section, we confirmed the

overexpression of C11or67 in breast cancer TMA. In this section, we experimentaly

confirmed the amplification of C11orf67 in breast cancer and we correlated the extent of

the amplification with the protein overexpression. Unfortunately, it was incompatible to

test C11orf67 amplification by FISH in the same TMA settings due to damaged nuclei or

multi-layer aggregates of tumour cells that all led to unsuccessful hybridization of tumour

blocks. Alternatively, to validate the amplification of C11orf67 in clinical specimens, we

conducted FISH analysis on 32 available ER+ breast cancer cases through our

collaboration with the pathology department, Gdansk University Hospital, Poland.

FISH was performed using detection probes for C11orf67 and Chromosome 11

centromere (CEN-11, empire genomics). Breast cancer samples were evaluated

microscopically by their C11orf67/CEN-11 ratios with a ratio ≥2 considered as positive

amplification (Figure 2-8A).

Our data has validated the amplification of C11orf67 in 9 ductal carcinomas (all ER+) out

of 32 cases tested (28%) with amplification indexes varying between 2.2 and 4.7. We also

had available information about the grade of the tumour and the TNM status.

While there was no significant difference in tumour grade between positive amplification

cases (33%) versus amplification negative cases (22%) p=0.08, we were able to

significantly correlate the cases with amplification to the LN metastases. 78% of the

tumours with C11orf67 amplification presented LN metastases over 26% of the negative

amplification group p<0.0001, keeping with expected aggressive pathobiology (Figure 2-

8B).

However, it is important to note that the statistical power of this analysis is limited

because of the small number of cases, lack of normal-like cases, and unavailable data


61

about response to treatment or resistance. In the near future we are planing to conduct a

much extensive study in a larger available TMA of 1000 cases.

In summary, in this chapter we have validated for the first time the overexpression and

the amplification of C11orf67 in HR+ breast cancer. As expected, a general positive

correlation between C11orf67 overexpression and ER and PR expression, tumour grade

and LN involvement. Bioinformatic analyses revealed that C11orf67 indeed had

prognosis value not only in breast cancer but also in other tumours, particularly ovarian

and lung cancer. Lastly we also experimentally validated the existence of C11orf67

different isoforms in luminal cells and revealed the intracellular distribution of isoform 2

in luminal cells. Collectively our data suggest that C11orf67 could be a novel biomarker

to identify HR+ breast cancers with very poor prognosis.


62

Figure 2-8 C11orf67 amplification detected by FISH in ER+ breast cancer

(A) Representative images of breast tumours positive and negative for the

C11orf67 amplification as assessed by FISH. Red dots represent C11orf67

FISH detection probe, and green dots represent Chromosome 11 centromere.

(B) Summary of the data analysis of FISH performed on 32 ER+ breast cancer

patients.


63

2.1 Discussion

Breast cancer is a genetically heterogeneous disease with multiple genetic alterations

influencing its initiation and progression. Copy number amplification of several

oncogenic drivers at 11q13.5-q14 is a hallmark chromosomal aberration in the IntClust 2

subtype of breast cancer. These group of cancers are characterized by poor prognosis and

disease recurrence after treatment (Curtis et al., 2012). The identification of new

prognostic markers that stratify patients with poor prognosis, is very important to improve

the overall survival of ER+ breast cancer.

There are several established putative oncogenes harboured in the 11q13 region,

including MEN1, CCND1, FGF3, EMS1, PAK1, RSF1 and GAB2 (Shih Ie et al., 2005).

However, no previous reports have attempted to characterize the amplification of

C11orf67 gene as a significant candidate oncogene featuring amplification and

overexpression in this chromosome region.

The tertiary structure of the human AAMDC (C11orf67) protein that is higly homologus

to the bacterial protein MTH938 from Methanobacterium. This unique tertiary structure

of the protein has suggested that the gene has invaded the eukaryotic genomes by lateral

gene transfer (Das et al., 2001). Similar to the murine homologue of AAMDC (LI2),

human C11orf67 is highly conserved in human, mouse, rat, cow, chicken, and zebrafish

(Ma et al., 2012).

Bioinformatics analysis of subcellular localization suggests that human C11orf67 protein

is distributed in the cytoplasm (http://www.uniprot.org/uniprot/Q9H7C9). Our study

confirmed that flag-fused C11orf67 Isoform_2 protein is located in the cytoplasm with

some condensations around the nucleus in T47D and MCF7 breast cancer cell lines,

suggesting that C11orf67 protein has a physiological role in the cytoplasm and the

nucleus. This finding is not inconsistant with the study performed by Ma et al. (2012),

which showed that the murine homologue of AAMDC protein follows cytoplasmic

distribution in fibroblast cells (Ma et al., 2012).

http://www.uniprot.org/uniprot/Q9H7C9


64

In this chapter we described the existence of three different isoforms of C11orf67 and we

hypothesised that these transcript variants are originated by alternative splicing occurring

at the C-terminus. Spliced variants of C11orf67 produces multiple mature mRNAs and

protein isoforms with distinct structural and functional properties, which could contribute

to the oncogenic potential of the C11orf67 protein and its ability to influence several

aspects of tumour formation, progression and resistance to therapy.

To the best of our knowledge, only one other study had investigated the mRNA

expression levels of C11orf67 in a number of breast cancer cell lines, together with

expression levels of other genes in the same locus (Kwek et al., 2009). However, this

study neither investigated the protein expression levels of C11orf67 in the tested cell lines

nor correlated the mRNA expression levels to the HR expression status of those cell lines.

Here we reported the expression of human C11orf67 at both the mRNA and protein levels

in different breast cancer lines with variable HR status, and we correlated the high

overexpression of C11orf67 in HR+ breast cell lines compared to the normal-like breast

cells.

A significant number of reports have detected the amplification of chromosome 11q13.5

in several types of human cancer, and most of them have linked the presence of the

amplicon with a worse clinical outcome and shorter overall survival (Ormandy et al.,

2003, Hui et al., 1998, Borg et al., 1991). These findings suggest that the 11q13.5

amplicon may contain important potential drivers that could participate to clinical

aggressiveness of certain tumour subtypes.

In the current study, we performed a detailed genomic analysis on a novel gene located

in the 11q13.5 amplicon that has not been studied before in the context of breast cancer.

The selective overexpression and copy number amplification of C11orf67 in IntClust 2

ER+ subgroup of breast cancer that is associated with low survival, makes C11orf67 an

attractive potential target which could serve to distinguish those ER+ tumours at risk of

relapse from those of low risk (Curtis et al., 2012, Stephens et al., 2012).


65

We foud that C11orf67 protein expression in the breast cancer TMA significantly

correlated with positive ER and PR status and high tumour grade. On the other hand,

amplification analyses in a different cohort of ER+ patients by FISH revealed another

correlation with LN metastases. However, the statistical power of this analysis is limited

because of the small number of cases in the gene-amplified subgroups. However, these

observations suggest the predictive prognostic power of C11orf67 overexpression in ER+

patients, and strongly argue that C11orf67 amplification could represent a driver lesion

in breast carcinogenesis.

Choi et al. found that C11orf67 was amplified and overexpressed in high-grade ovarian

serous carcinoma, but this study focused more on neighbouring genes with higher

correlation between DNA and transcript copy number (e.g. RSF-1, INTS4, CLNS1A,

ALG8, GAB2, and PAK1) (Choi et al., 2009).

These findings are consistent with other studies that proved the involvement of 11q13

oncogenic targets in the prognosis of breast cancer and other epithelial cancers. CCND1

is one of the most important drivers in 11q13 chromosome locus and had been examined

by a significant number of studies. Lundgren et al. and others have found that gene

amplification of CCND1 is linked to poor clinical prognosis, detrimental response to

tamoxifen treatment, and is associated with high tumour grade (Roy et al., 2010,

Lundgren et al., 2012). Also, high CCND1 expression by IHC defines a subgroup of ER+

patients with a high risk of relapse and death (Beca et al., 2015).

Another important potential driver located in the same amplicon is PAK1 (p21-activated

kinase 1), which has been studied extensively in the context of breast cancer.

Experimental studies have recently shown increasing evidence that the overexpression of

PAK1 is associated with decreased tamoxifen sensitivity and poor clinical outcome in

breast cancer, particularly in postmenopausal women (Holm et al., 2006, Bostner et al.,

2007, Bostner et al., 2010).

RSF-1 (remodelling and spacing factor) is the neighbouring gene to C11orf67, and may

be coregulated with C11orf67 (this is discussed in more detail in chapter 4). RSF-1 is also


66

an important biomarker that predicts prognosis in breast and other epithelial cancers. In

gastric adenocarcinoma and colorectal carcinoma, RSF-1 overexpression is known to

predict an unfavorable prognosis and poor overall survival (Hu et al., 2012, Liu et al.,

2012). High expression levels of RSF-1 have also been associated with aggressive

phenotypes and poor clinical outcome in breast cancer patients and paclitaxel resistance

in ovarian cancer patients (Choi et al., 2009, Ren et al., 2014).

In conclusion, there are many reports suggesting that other candidate genes in the 11q13

amplicon may contribute to tumour development and poor clinical outcome in breast

cancer. However, this is the first report that has clearly established C11orf67 as one of

the novel biomarkers in 11q13.5 in breast cancer that can stratify patients and identify

those patients with a higher risk of poor clinical outcome.

In the future, it will be of interest to conduct C11orf67 staining of FFPE tissues from

biopsies of patients with HR+ breast cancer, along with clinical follow-up of those

patients. This will be a necessary translational step to introduce C11orf67 staining to the

clinic. Also, generation of cell lines derived from patient samples that show amplification

of C11orf67 is a significant step to having a model that can be used in the development

of a routine clinical test in the future.

Chapter 3:

Functional Consequences of

C11orf67 Gene Alteration

Functional Consequences of C11orf67 Gene Alteration

68

3.1 Introduction

Despite increasing efforts towards developing new advances in surgery and novel

therapeutic agents, breast cancer remains a leading cause of cancer-related death in

women worldwide (Calaf et al., 2015). Identification of novel functional biomarkers is

essential for the developent of novel targeted therapies for specific breast cancer subtypes,

specially those characterised with poor prognosis (IntClust 2 subtype) (Nakshatri and

Badve, 2007).

In the previous chapters, we have demonstrated that C11orf67 is both amplified and

overexpressed in ER+ breast cancers and is associated with poor prognosis. These patients

usually develop highly proliferative tumours, which have a high incidence of metastases

and become resistant to endocrine therapy and chemotherapy (Curtis et al., 2012). As

C11orf67 is a fundamentally uncharacterised gene, its role as an oncogenic driver in

breast cancer has not been previously investigated.

To assess the role of C11orf67 in breast cancer tumourigenesis and tumour progression,

in this chapter we artificially altered the endogenous levels of C11orf67 in ER+ breast

cancer cells, by either loss of function (RNAi-mediated knockdown) in cell lines

expressing high levels of the gene, or by gain of function (exogenous cDNA

overexpression) in cell lines carrying very low levels of C11orf67. We hypothesized that

this alteration of C11orf67 expression would affect proliferation, tumourigenicity and

other aspects of tumour progression such as resistance to drugs. Indeed, our data revealed

that knockdown of C11orf67 was able to arrest proliferation and induced cellular

senescence of ER+ breast cancer cells. Furthermore, a C11orf67 gain of function

increased the sensitivity of ER+ breast cancer cells to antifolate chemotherapeutic agents.

Tumourigenesis is caused by genetic alterations that activate oncogenes or inactivate

tumour suppressor genes. Genes involved in tumourigenesis are usually associated with

aberrant cellular proliferation, apoptosis, angiogenesis, and cellular senescence (Hainaut

and Plymoth, 2013). Senescence is a state of irreversible and permanent growth arrest

that occurs in response to either aging (replicative senescence), stress (stress-induced


69

senescence), or mediation of an oncogenic factor (oncogene-induced senescence)

(Collado et al., 2007). Triggering the senescence of tumour cells may contribute to

successful cancer therapy (Lleonart et al., 2009).

Activation of the tumour suppressor p53 or RB pathways is usually essential for the

induction of the senescence program (Lowe et al., 2004). However, cell cycle arrest and

the inhibition of the AKT signalling pathway are also possible mechanisms that induce

senescence, and participant proteins of these pathways are used as essential markers for

the identification of senescent cells (Kuilman et al., 2010).

The phosphoinositide 3 kinase/Akt/mammalian target of rapamycin (PI3K/Akt/mTOR)

pathway plays a substantial role in tumour cell growth, proliferation, and has been

associated with resistance to endocrine therapy (Paplomata and O'Regan, 2014). A

simplified schematic representation of the PI3K/Akt/mTOR pathway is illustrated in

Figure 3-1.

Figure 3-1 Illustration of the PI3K/Akt/mTOR pathway

Adapted from Holmes (2011).


70

Growth fator stimulation leads to activation of tyrosine kinase receptors, whichs induce

the recruitment and activation of different proteins such as PI3K (Cantley, 2002). PI3K

phosphorylates phosphatidylinositol 4,5 bisphosphate, or PIP2, to phosphatidylinositol

3,4,4-triphosphate, or PIP3. This leads to the phosphorylation of Akt in two residues,

threonine 308 and serine 473, after which it is fully activated. Phosphorylated Akt then

phosphorylates several targets in the nucleus and cytoplasm, including mTOR. mTOR

induces cellular growth and proliferation through its effect on the downstream molecules

S6K1 (40S ribosomal protein S6 kinase 1), and 4EBP1 (eukaryotic initiation factor 4E

binding protein) (Kenerson et al., 2002, Dowling et al., 2010, Holz, 2012). Indeed,

rapamycin, the inhibitor of mTOR, is currently used for the treatment of HR+ breast

cancer (Ciruelos Gil, 2014). Furthermore, experimental studies have found an interaction

between Akt activity and the response to chemotherapy in breast and ovarian cancer (Page

et al., 2000, Stal et al., 2003).

In this chapter, we analyzed the effect of C11orf67 knockdown and overexpression on

the progression of breast cancer cells, to elucidate the mechanisms and molecular

pathways by which C11orf67 may induce cellular senescence and resistance to therapy.


71


3.2.1 Reagents and Antibiotics

Estradiol valerate, peanut oil, Methotrexate, 5-Fluorouracil , and Taxol were purchased

from Sigma-Aldrich, St Louis, MO, USA.

For IF and western blotting, primary antibodies specific to Ki-67, caspase-3, p-AKT

(Serine 473) , p-AKT (Threonine 308), p-mTOR (Serine 2448), p-MAPK p42/44,

CDK4, CDK6, CCND1, and CCNE1 were purchased from Cell Signaling, Gold Coast,

QLD, Australia.

3.2.2 Lentiviral C11orf67 shRNA infection

Knockdown of C11orf67 expression was performed using Mission TRC human

shRNA/pLKO.1 clone sets (Sigma-Aldrich, St Louis, MO, USA). This system allows

efficient intracellular delivery and stable expression of the small hairpin RNA (shRNA)

sequences specific to the C11orf67 mRNA (Table 3-1). Lentiviruses were produced by

transfecting HEK293T packaging cells at 80% confluency with the following packaging

plasmids: 1.54 μg VSV-G Envelop (vesicular stomatitis virus G), 1.1 μg RSV-Rev, 2.88

μg Gag-pol, and 4.5 μg of each pLKO.1/shRNA plasmid, using Lipofectamine-2000

reagent (Invitrogen, Waverley, Australia) according to the manufacturer’s protocol. An

empty pLKO.1 lentiviral vector was used as a negative control.

Recipient (host) T47D cells were seeded at 1.5×105 cells per 10 cm diameter tissue culture

dish and incubated for 24 hours at 37°C, and 5% CO2 atmosphere. Host cells were then

transfected 3 times every 8 hours with culture media containing viral particles collected

48, 56 and 72 hours post-infection. Viral particles were filtered through a 0.22 μm sterile

filter unit (Thermo Scientific, USA), and treated with 8 μg/ml polybrene (Sigma-Aldrich,

St Louis, MO, USA) before being added to the host cells. Stable T47D cells were selected


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with 5 µg/ml puromycin (Invitrogen, Waverley, Australia) for 5 days before functional

assays were performed.

3.2.3 Lentiviral C11orf67 cDNA infection

The gain of function of C11orf67 was performed using C11orf67 Isoform_2 cDNA

pLV105 lentiviral plasmid (Genecopoeia, Rockville, MD, USA). MCF7 cells were

transfected with virus harvested from HEK293T cells, as described above. An empty

pLV105 lentiviral vector was used as a negative control.

Table 3-1 Nucleotide sequences and exon number of C11orf67-specific

shRNAs

Nucleotides involved in base pairing to C11orf67 transcript is shown in red.

C11orf67

shRNA Sequence & Location (Exon no.)

sh1 5'CCGGAGGCTCTAATACAACCTATAACTCGAGTTATA

GGTTGTATTAGAGCCTTTTTTG (Exon 1)

sh2 5'CGGCAGATGTGAAGGAAGTTGTTGCTCGAGCAACA

ACTTCCTTCACATCTGTTTTTG (Exon 2)

sh5 5'CCGGGTGTACAGACTCTTGTGATTGCTCGAGCAATC

ACAAGAGTCTGTACACTTTTTG (Exon 2)


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3.2.4 MTT cell viability assays

Evaluation of cell proliferation and cell cytotoxicity was performed using MTT

colorimetric assay (Sigma-Aldrich, St Louis, MO, USA). Either T47D or MCF7 breast

cancer cell lines were transduced with the corresponding lentiviral vectors (C11orf67

shRNAs for knockdown, or C11orf67 cDNA for overexpression) or empty vector

lentiviral control. Transduced cells were seeded at a density of 1000 cells/well in 96-well

flat-bottom culture plates in triplicate for each condition.

For proliferation assays, cells were incubated for 0, 24, 48, 72, or 96 hours and the

percentage of proliferating cells was measured according to the MTT manufacturer’s

instructions. Briefly, 100 μl of filter sterilized 3-(4, 5-Dimethylthiazol-2-yl)-2, 5-

Diphenyltetrazolium Bromide (MTT) solution (final concentration 0.5 mg/ml in complete

media) was added to the media in each well. Following a 1 hour incubation period with

MTT, media were removed and the blue formazan crystals trapped in cells were dissolved

in sterile dimethyl sulfoxide (DMSO) (50 μl/well) (Sigma-Aldrich, St Louis, MO, USA)

by incubating at 37ºC for 5 minutes. The absorbance was measured at 570 nm using a

microplate spectrophotometer (Labsystems Multiskan RC). Background absorbance was

measured at 690 nm. The results were represented as mean ± SE of three independent

experiments. Results were normalized to values obtained immediately after seeding of

the cells (day=0).

For cytotoxicity assays, transduced cells were treated with various concentrations of

indicated drugs (methotrexate: 0.5, 1.0, and 5.0 μM; 5-fluorouracil: 0.5, 5.0, and 10.0 μM;

taxol: 1, 10, 20, 30, and 40 nM) for 48 or 72 hours. The percentage of viable cells in each

treatment group was normalized to that of vehicle control treated cells.

3.2.5 Immunofluorescence

2x104 T47D cells transduced with C11orf67 shRNAs or empty lentiviral vector were

seeded on coverslips and incubated overnight at 37°C to allow the cells to attach. Cells

were fixed with 4% PFA in PBS for 20 minutes, permeabilized with 0.5% Triton X-100


74

in PBS for 10 minutes at 4°C, blocked with 4% BSA in PBS for 60 minutes at room

temperature, incubated with the primary antibody (1:400) diluted in blocking solution

overnight at 4°C, and finally incubated with a secondary Alexa Fluor 488-conjutated

antibody (1:1000) for 1 hour in the dark. Nuclei were stained blue with Hoechst nuclear

stain (1:5000) (Sigma-Aldrich, St Louis, MO, USA), and coverslips were mounted in

Fluoromount aqueous mounting medium (Sigma-Aldrich, St Louis, MO, USA). The

percentage of positive cells was assessed by counting the percentage of green fluorescent

cells in 9 fields of views using a fluorescent Olympus IX71 microscope (Singapore). Data

were normalised to empty vector control cells.

3.2.6 Anchorage-independent colony formation assays

Anchorage-independent growth was assessed with soft agar colony formation assays.

T47D cells (10000 cells/well) were seeded in triplicate in six-well plates, in 0.48%

agarose (Sea Plaque Low-Melt Agarose, Sigma-Aldrich, St Louis, MO, USA) over a solid

layer of 0.8% agarose in RPMI medium with 20% FBS. 0.5 ml of growth media was

added to each well and gently replaced every 3 days. Plates were then incubated in 5%

CO2 humidified conditions at 37°C for 4 weeks.

The resulting colonies were fixed and stained with 4% PFA-0.01% crystal violet in PBS

for 5 minutes, washed in PBS, and then counted by Leika light microscope in 12 random

fields. Results were expressed as a percentage of colony count ± SE from three

independent experiments relative to the empty vector control.

3.2.7 In Vitro cell migration assay

To assess migratory capacity of T47D knockdown cells, a CytoSelect 24-well cell

migration assay (Cell Biolabs, San Diego, USA) was used according to the

manufacturer’s instructions. Briefly, the transwell polycarbonate membrane filter inserts

with 8-µm pore size were rehydrated and placed in 24-well tissue culture plates. T47D

cells transduced with either C11orf67 shRNAs or empty vector control were serum

starved overnight and then resuspended into serum free media (SFM). 1x 106 cells/well


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from serum-free cell suspension were added to the top chambers of the transwells, and

10% serum-containing RPMI media was added to bottom chambers, which served as a

chemoattractant. The plates were incubated at 37°C to allow the cells to migrate. After

24 hours, the culture media was carefully aspirated, and the non-migrating cells were

removed from the top chamber of the membrane using a cotton swab without causing any

punctures to the polycarbonate layer. To visualise the migrating cells, the inserts were

incubated with 400 μl cell stain solution (Cell Biolabs, San Diego, USA) for 10 minutes

at room temperature. Pictures were captured using a digital camera attached to a cell

culture inverted Leika light microscope to quantitate the number of cells that had passed

through the porous membrane in 12 random fields. Results were expressed as average

values ± SE relatively to the empty vector control cells.

3.2.8 TUNEL assay

The terminal deoxy-transferase (TdT)-mediated deoxyuridine triphosphate (dUTP) nick-

end labeling (TUNEL) assay was used to detect the extent of DNA fragmentation caused

by apoptosis. The in Situ cell death detection kit (Roche, Castle Hill, Australia) was used

to detect the percentage of apoptotic cells in T47D cells transduced with either the

C11orf67 shRNAs or with empty vector control by flow cytometry according to the

manufacturer’s instructions.

Briefly, T47D cells were fixed in 2% PFA in PBS for 60 minutes at room temperature

and then permeabilized in 0.1% Triton X-100 in 0.1% sodium citrate for 2 minutes on

ice. Cells were incubated with 50 μl of freshly prepared TUNEL reaction mixture

according to the kit’s instruction for 60 minutes at 37°C in the dark. Fluorescein-

conjugated dUTP incorporated in nucleotide polymers was detected and quantified using

flow cytometry. As a positive control for apoptosis, T47D cells were treated with 20 nM

taxol for 72 hours before the experiment and treated the same way as the treatment groups.

Approximately 10,000 cells per sample were acquired and quantified using Cell-Quest

software (Becton-Dickinson) for the analysis of cell apoptosis (Bao et al., 2014).


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3.2.9 In vivo tumourigenicity assays

All animal experiments were approved under and performed in accordance with the

animal ethics protocol RA 3/100/1159, University of Western Australia. 5x106 T47D

cells stably transduced with either pLKO.1 empty lentiviral vector or C11orf67 shRNAs

2 (sh2) were injected subcutaneously (sc) in the right flank of 5 weeks old female BALB/c

Foxn1/Arcmouse (nude) mice (Animal Resources Centre, WA, Australia). Prior to

inoculation, cells were resuspended (1 in 1) in a solution of SFM and BD Matrigel High

Concentration (BD Bioscience, North Ryde, NSW, Australia). A total of 100 μl of cell

suspension was slowly injected using a 26-gauge needle sc in the flank of nude mice. 1μg

of estradiol valerate in 100 μl peanut oil (Sigma-Aldrich, St Louis, MO, USA) was

injected sc into the back of the mice every 4 days to allow the estradiol-dependent T47D

cells to proliferate (Sartorius et al., 2003). Width and length of tumours were measured

every 3-4 days by a digital caliper, and tumour volumes were calculated using the

modified ellipsoid formula; V= [(width2) x ½ x length]. Animals bearing tumours

>800mm3 were humanely sacrificed, and tumours were dissected and extracted. Tumour

tissues were fixed in 4% PFA in PBS, washed 3 times with PBS and incubated in 70%

ethanol for up to one week. The tumours were embedded in paraffin and 5 µm sections

were made by Cell Central (University of Western Australia).

3.2.10 Immunohistochemistry of tumour sections

C11orf67 IHC staining was performed on FFPE tissue biopsies from mice. Tissue

sections were deparaffinized with xylene and rehydrated with 100% and then 75%

ethanol. Sections were then subjected to heat-induced antigen retrieval in 10 mM Sodium

citrate buffer, pH 6.0, using a microwave oven at high power for 10 minutes before the

power was lowered for another 10 minutes. Following that, the tissue sections were

cooled to room temperature in the same buffer and rinsed with distilled water.

Endogenous peroxidase activity was blocked by adding 3% hydrogen peroxide solution

(Sigma-Aldrich, St Louis, MO, USA) in methanol for 30 minutes at 4°C. The tissue

sections were then washed with distilled water before they were incubated with 1% BSA


77

in PBS for 1 hour at room temperature to block non-specific protein binding. The anti-

C11orf67 primary antibody (Santa Cruz Biotechnology, Texas, USA) was added

overnight at 4°C (1:100). Immunodetection was performed by incubation with

biotinylated goat anti-rabbit secondary antibody (GeneCopeia, Maryland, United States)

for one hour followed by incubation with streptavidin-horseradish peroxidase

(Genecopoeia, Rockville, MD, USA) at room temperature for 30 minutes. Sections were

then incubated with DAB solution (Genecopoeia, Rockville, MD, USA) for 5 minutes

until signal development and counterstained with Haematoxylin for 1 minute. After

rinsing with distilled water and then tap water, the sections were dehydrated through

increasing concentrations of ethanol (70%, 95% and 100%) and then xylene. Finally,

mounting of the coverslips was done using Acrymount IHC mounting media (McKinney,

Texas, USA).

3.2.11 Senescence-associated β-galactosidase staining

A senescence cellular staining kit (Sigma-Aldrich, St Louis, MO, USA ) was utilized for

the detection of senescent T47D cells according to the manufacturer's instructions.

Briefly, stable T47D cell lines transduced with either C11orf67 shRNAs or with empty

vector cells were seeded in a sex-well plate at a density of 4x105 cells/well. The cells were

washed with PBS and fixed in the kit’s fixative solution before staining with the X-gal

solution. After 24 hours of incubation at 37°C, cells were observed under the microscope

and the percentage of positive cells was calculated by counting the blue-stained cells

(senescent cells) versus total cells in at least 12 random microscopic fields.

3.2.12 Illumina microarray analysis

3.2.12.1 RNA preparation

For microarray gene expression analysis, total RNA was extracted using Trizol

(Invitrogen Life Technologies, Carlsbad, USA) and purified using RNeasy columns

(Qiagen, Valencia, USA) according to the manufacturers’ protocol. After processing with

DNase digestion, RNA samples were quantified and aliquoted and stored at -80°C until


78

use. For quality control, RNA purity and integrity were evaluated by denaturing gel

electrophoresis, OD 260/280 ratio, and analyzed on Agilent 2100 Bioanalyzer (Agilent

Technologies, Palo Alto, USA).

3.2.12.2 Labelling and Purification

Total RNA was amplified and purified using the Ambion Illumina RNA Amplification

kit (Ambion, Austin, USA) to yield biotinylated cRNA according to the manufacturer’s

instructions. Gene expression analysis were carried by Macrogen (Seoul Rep, Korea).

Briefly, 550 ng of total RNA was reverse-transcribed to cDNA using a T7 oligo (dT)

primer. Second-strand cDNA was synthesized, in vitro transcribed, and labeled with

biotin-NTP. After purification, the cRNA was quantified using the ND-1000

Spectrophotometer (NanoDrop, Wilmington, USA).

3.2.12.3 Hybridization and data export

750 ng of labeled cRNA samples were hybridized to each human HT-12 expression v.4

bead array for 16-18 h at 58°C, according to the manufacturer's instructions (Illumina,

Inc., San Diego, USA). Detection of array signal was carried out using Amersham

fluorolink streptavidin-Cy3 (GE Healthcare Bio-Sciences, Little Chalfont, UK) following

the bead array manual. Arrays were scanned with an Illumina bead array Reader confocal

scanner according to the manufacturer's instructions.

3.2.12.4 Raw data preparation and statistical analyses

The quality of hybridization and overall chip performance were monitored by visual

inspection of both internal quality control checks and the raw scanned data. Raw data

were extracted using the software provided by the manufacturer (Illumina GenomeStudio

v2011.1 (Gene Expression Module v1.9.0)). Array probes were transformed by logarithm

and normalized by quantile method. Statistical significance of the expression data was

determined using Independent t-test and fold change in which the null hypothesis was


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that no difference exists among groups. False discovery rate (FDR) was controlled by

adjusting p-value using Benjamini-Hochberg algorithm. For a differentially expressed

genes (DEG) set, Hierarchical cluster analysis was performed using complete linkage and

Euclidean distance as a measure of similarity. Gene-Enrichment and Functional

Annotation analysis for significant probe list was performed using DAVID

(http://david.abcc.ncifcrf.gov/home.jsp). All data analysis and visualization of

differentially expressed genes were (www.r-project.org).


Statistical significance was detected using one-way ANOVA to compare differences

between mean values in cell viability, colony formation, cell migration and invasion

assays in shRNA-transduced samples relative to empty vector control group. Multiple

group comparisons were analyzed with Dunnet multiple comparison tests. Statistical

analyzes were performed using Prism software, p < 0.05 was considered to be statistically

significant. All data are represented as mean value ± SE of at least triplicate experiments.

http://david.abcc.ncifcrf.gov/home.jsp

http://www.r-project.org/


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3.3 Results

In previous chapters, we demonstrated that C11orf67 is a novel prognostic biomarker that

is overexpressed and amplified in ER+ breast cancer. However, the precise role C11orf67

in the tumourigenesis and tumour progression in breast cancer cell lines has not been

investigated before.

In this chapter, we hypothesized that C11orf67 has a role in breast carcinogenesis and

influences the response of breast cancer cells to different therapeutic agents. To

experimentally address this, we first identified suitable cellular model systems expressing

C11orf67 (Section 2.3.5). We selected T47D breast cancer cell line to be used in the

functional characterization of C11orf67 knockdown showing highest levels of C11orf67

expression, and MCF7 cells in the C11orf67 cDNA overexpression expressing lower

levels of the gene.

In the following sections, we will take advantage of in vitro and in vivo tumour biology

assays to analyse the consequences of the genetic manipulation of C11orf67 expression

to address the role of C11orf67 in breast cancer tumourigenesis.

3.3.1 Functional consequences of C11orf67 ablation in T47D

cells

3.3.1.1 Knockdown of C11orf67 by shRNAs

For stable knockdown of C11orf67 expression in T47D cells, we used the pLKO.1

lentiviral vector. This system allows efficient intracellular delivery and stable expression

of shRNA sequences to target gene of interest in mammalian cells. shRNA sequences

were designed to specifically targeting C11orf67 mRNA at the first and the second exons.

To account for potential off-target effects, we used three different shRNA constructs

specific to C11orf67 transcript in all the functional studies, shRNA1 (sh1), shRNA2


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(sh2), and shRNA5 (sh5). As a negative control, we used pLKO.1 empty vector and

untransfected T47D (mock) cells.

Lentiviral particles were generated by co-transfection of lentiviral constructs with

packaging constructs in the recipient HEK293T cells, and supernatants were used to infect

the host lines. After transfection, the transduced cells were analyzed for quantitative

changes in C11orf67 expression by qRT-PCR, western blot, and ICC analyses (Figure 3-

2).

We found that C11orf67 sh1 and sh2 constructs yielded a significant knockdown of

C11orf67 expression at the mRNA level (90% and 99% respectively), p<0.0001. sh5

produced an incomplete yet significant downregulation of C11orf67 expression (30-50%)

as compared to the empty vector control, p<0.001 (Figure 3-2A). Western blot analysis

confirmed the existence of at least two isoforms of human C11orf67 in T47D cells.

Isoform_2 revealed a MW of 13 kD, and was completely downregulated with the sh2

construct, while Isoform_3 with a MW of 16 kD showed partial downregulation (Figure

3-2B).

We also confirmed the cytoplasmic and perinuclear downregulation of C11orf67

expression by sh2 at the protein level by ICC. Our results confirmed the intracellular

compartmentalization studies of the protein conducted in section 2.3.3 (Figure 3-2C).


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Figure 3-2 Knockdown of C11orf67 by shRNAs in T47D cells

(A) C11orf67 mRNA expression analysis by qRT-PCR in T47D cells

transduced with mock control, empty vector, or specific C11orf67 shRNAs

(sh1, sh2 and sh5) (*** p<0.0001, ** p<0.001). (B) Western blot analysis of

the indicated cell lines blotted with an anti-C11orf67 antibody (Santa Cruz); -

tubulin was used as a loading control. MW markers are shown on the right. (C)

ICC staining of T47D cells transduced with empty vector or C11orf67 sh2

constructs.


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3.3.1.2 C11orf67 ablation reduces the proliferative potential

of T47D

After C11orf67 downregulation by shRNAs was validated at the mRNA and the protein

level, we secondly investigated whether C11orf67 depletion had an effect on the

proliferation rates of the ER+ breast T47D cells by MTT cell viability assays.

T47D cells transduced with either empty vector, or C11orf67 shRNAs (sh1, sh2, and sh5)

were seeded at the same density in triplicate for each experimental point, then cell

proliferation was monitored over time for a total period of 96 hours. We observed that

T47D cells expressing C11orf67 shRNAs (sh1, sh2, sh5) exhibited a significant reduction

in cell growth relative to the empty vector cells at 48 hours, 72 hours, and 96 hours.

(p<0.0001) (Figure 3-3A).

To further confirm this finding, we quantitated the expression of the proliferation marker

Ki-67 in the shRNA cells by IF (Figure 3-3B). shRNA-transduced cells showed a

significant reduction in the percentage of cells exhibiting nuclear positivity of Ki-67 as

compared with the empty vector control cells (Figure 3-3C). C11orf67 sh1 and sh2 cells

showed about 70% and 85% drop in the expression of Ki-67 protein, respectively

(p<0.0001), while sh5 cells had 40% less Ki-67 expression relative to that of the empty

vector control (p=0.0001).

Overall, the above results indicate that C11orf67 is necessary for the proliferation of

T47D cells.


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Figure 3-3 C11orf67 knockdown decreases cellular proliferation

(A) MTT cell proliferation analysis of T47D cells transduced with either empty

vector or C11orf67 shRNAs. (B) Representative IF images of Ki-67 positive

cells in the indicated cell lines (green), nuclei were labelled with Hoechst

nuclear stain (blue). (C) Percentage of Ki-67 positive cells in C11orf67 shRNA

cells normalized to the empty vector control (*** p<0.0001, ** p<0.001).


85

3.3.1.3 C11orf67 knockdown decreases tumourigenic

phenotype of T47D cells in vitro

As many oncogenic drivers induce tumourigenic behaviour, we next investigated whether

the loss of C11orf67 expression could affect the ability of T47D cells to grow in an

anchorage-independent fashion and to form colonies in soft agar, which monitors

tumourigenic capacity in vitro. To this end, T47D cells transduced with either the

C11orf67 shRNAs or control empty vector were overlaid over a solid layer of agar at a

clonogenic density of 1x104 cells per well in a six-well plate, and the cells were allowed

to grow for three weeks until visible colonies were formed.

Consistent with the results obtained from the proliferation assays, we observed a

significant decrease in the colony forming efficiency upon C11orf67 knockdown in T47D

cells (Figure 3-4A). Our results show that both mock treated and empty vector transduced

cells formed abundant foci in soft-agar while C11orf67 depletion by sh1 and sh2 caused

a dramatic and significant reduction in the number of colonies formed relative to controls

(75% and 83% reduction respectively, p<0.0001). sh5 cells demonstrated 60% decrease

in the percentage of the number of colonies, p=0.001 (Figure 3-4B). In addition to colony

number the size of the colonies was also affected by the C11orf67 shRNAs with an overall

trend similar to that of the colony scoring experiment (Figure 3-4C). Thus, our data shows

that colony forming ability is inversely correlated with the silencing efficiency achieved

with the various lentiviral vectors (lower efficiency of C11orf67 sh5).

In summary, these experiments demonstrated that C11orf67 was necessary for the

formation and proliferation of colonies in anchorage-independent assays, suggesting that

C11orf67 is playing a role in tumour formation.


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Figure 3-4 C11orf67 knockdown decreases cellular colony formation

(A) Images of stained soft agar colonies from cells transduced with the

C11orf67 shRNAs or with empty vector control and incubated for 4 weeks. (B)

Percentage of colony formation normalised to the empty vector control cells.

(C) Percentage of colony size relative to the empty vector control cells (***

p<0.0001, ** p<0.001).


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3.3.1.4 C11orf67 promotes migratory behaviour in ER+ breast

cancer cells

We also hypothesized that the knockdown of C11orf67 might impair the migratory

behaviour of ER+ breast cancer cells. To investigate this, we conducted a CytoSelect

migration assay to quantitate the migration capacity of T47D cells transduced with the

empty vector and the C11orf67 shRNAs. Importantly, we found that C11orf67

knockdown had a potent inhibitory effect on T47D cell migration (Figure 3-5A).

C11orf67 sh1 and sh2 significantly suppressed cell migration by more than 90% as

compared with the empty vector control (p<0.0001). Incomplete knockdown of C11orf67

by sh5 suppressed cell migration to a lesser extent (75%, p<0.0001) (Figure 3-5B).

Figure 3-5 C11orf67 knockdown decreases cellular migration

(A) Representative images of migratory T47D cells transduced with C11orf67

shRNAs or the empty vector control. (B) Quantification of migratory cells in

each condition normalised to the empty vector control cells (*** p<0.0001).


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3.3.1.5 Downregulation of C11orf67 expression inhibits

tumour growth in a xenograft model

The finding that the C11orf67 knockdown strongly inhibited proliferation, tumour

formation and migration in vitro, prompted us to investigate the effect of C11orf67

knockdown in a tumourigenic model in vivo. In our in vivo experimental design, we used

cells transduced with the empty vector as a control and the T47D transfected with the

C11orf67 sh2 since these cells showed the most potent repression of C11orf67 gene

transcripts and protein. Cells were implanted into the posterior flank of nude mice, and

tumour growth was monitored every other day. Our results show that the knockdown of

C11orf67 by sh2 significantly reduced the tumour growth in mice bearing T47D sh2 cells

compared to the empty vector control (p< 0.0001) (Figure 3-6A). C11orf67 sh2 mediated

inhibition of tumour growth was evident in most of the animals at several time points

post-injection (Figure 3-6B).

Examination of the tumour sections by IHC staining using an anti-C11orf67 antibody

demonstrated a higher density of tightly packed tumour cells in the empty vector control

cells. In contrast, C11orf67 sh2 tumours showed small islands of tumour cells (Figure 3-

6C).

In summary, our in vivo analysis indicated that the tumour suppressive functions of the

C11orf67 shRNA weakened the aggressiveness of T47D cells and decreased tumour cell

proliferation in an animal model of breast cancer, suggesting an important role of this

oncogene in promoting breast cancer in vivo.


89

Figure 3-6 C11orf67 knockdown inhibits in vivo tumour growth

(A) Representative images of tumour sections collected at day 10 post-injection

of cells. (B) Tumour volume measurements at day 3 and day 10 post-injection

(N= 8 animals per group) (*** p<0.0001, ** p<0.001). (C) C11orf67 IHC

staining of empty vector and C11orf67 sh2 in mice tumour sections at day 10

post-injection of cells.


90

3.3.1.6 C11orf67 knockdown is not involved in cellular

apoptosis

Since C11orf67 knockdown caused growth suppression of ER+ T47D breast cancer cells,

we further explored the underlying molecular mechanisms related to the proliferation

inhibition. Suppression of expression of many oncogenic drivers has been attributed to

programmed cell death or apoptosis (Yu et al., 2007, Al Dhaheri et al., 2013). Thus, we

analyzed whether the C11orf67 knockdown induced apoptosis of T47D cells.

To quantitate cellular apoptosis in response to C11orf67 knockdown, we first scored the

levels of the active caspase-3 by IF using an anti-cleaved caspase-3 antibody. Caspase-3

is a protease from the caspase family (cysteinyl aspartate-specific proteases) that is

implicated with the initiation of cellular death and therefore is a well suited read-out in

an apoptosis assay (Nicholson et al., 1995). C11orf67 shRNA cells showed a very small,

yet significant percentage of caspase-3 positive cells relative to that of the empty vector

control. The highest percentage of positive apoptotic cells were found in the C11orf67

knockdown sh1 (8%) compared to the empty vector control cells (p=0.0006), while sh2

and sh5 showed only 3% and 4 % of caspase-3 positive cells respectively (p<0.05) (Figure

3-7A). The low percentage of apoptotic cells in case of C11orf67 knockdown, may

indicate that apoptosis is not the primary mechanism of the decreased cellular

proliferation induced by C11orf67 knockdown.

We also investigated later stages of apoptosis in the C11orf67 knockdown by analyzing

DNA fragmentation by TUNEL assay. The TUNEL reaction preferentially labels DNA

strand breaks generated during apoptosis, by TdT (Labat-Moleur et al., 1998, Negoescu

et al., 1998). Fluorescein labels incorporated in nucleotide polymers are detected and

quantified by flow cytometry.

Flow cytometric analysis of apoptosis by TUNEL assay confirmed a minor yet significant

increase in the percentage of TUNEL positive cells in C11orf67 sh1 knockdown

compared to the empty vector control (p<0.05). Insignificant changes in the percentage


91

of TUNEL positive cells were observed with C11orf67 sh1 or sh5 when compared to the

empty vector control, p>0.05 (Figure 3-7B). Collectively, these results suggest that the

C11orf67 knockdown does not induce cancer cell death predominantly through induction

of cellular apoptosis.

Figure 3-7 C11orf67 knockdown is not associated with cellular apoptosis

(A) Representative IF images of anti-cleaved caspase-3 antibody positive cells

(green) in the indicated cell lines, nuclei were labeled with Hoechst (blue),

(quantification of positive cells is indicated on the right panel). (B) Flow

cytometry histograms (Cell-Quest software) of TUNEL positive cells

compared to the empty vector control (quantification is indicated on the right

panel) (** p<0.001, * p<0.05).


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3.3.1.7 C11orf67 knockdown induces cellular senescence

To explore other possible mechanisms underlying C11orf67 knockdown-induced

decrease in cell proliferation, we also investigated cellular senescence. For this purpose,

we stained the cells with β-galactosidase (-gal), a specific marker used to identify

senescent cells which are characterised by increased lysosomal β-gal (Dimri et al.,

1995).

We found that transduction of T47D cells with C11orf67 shRNAs significantly increased

the number of cells showing β-gal activity as compared with the T47D empty vector cells

(Figure 3-8A). In addition we found that in C11orf67 knockdown cells the percentage of

β-gal positive staining cells peaked at 24 hours with a value of approximately 34% for

sh1 (p<0.0001), 50% for sh2 (p<0.0001), and 18% for sh5 (p=0.003) compared to less

than 5% in the empty vector (Figure 3-8A). In conclusion, these results support the notion

that C11orf67 depletion induces arrest in cell growth via induction of cellular senescence.

3.3.1.8 C11orf67 knockdown deactivates AKT/mTOR and

MAPK pathways

In the search for the molecular pathways altered by C11orf67, we first analysed the

Akt/mTOR and MAPK pathways since they have been shown to control proliferation and

cellular senescence (Young et al., 2009, Kennedy et al., 2011, Xu et al., 2014).

Since all phosphorylated residues are known to regulate cell proliferation and senescence

(Park et al., 2013), we analyzed the levels of phosphorylated forms of different regulatory

proteins participating in the Akt/mTOR and MAPK pathways in C11orf67 shRNA cells

and control cells by western blot using specific phospho-antibodies. We found that

C11orf67 knockdown by sh2 reduced the activity of p-Akt at both serine 473 and

threonine 308, and also reduced the p-MAPK (Thr202/Tyr204). On the other hand,

C11orf67 sh1 decreased the p-Akt levels at threonine 308, but not at serine 473 or the p-

MAPK, a result that could be explained by the incomplete knockdown of the C11orf67


93

protein accomplished by sh1. We also tested the effect of C11orf67 depletion on the

phosphorylated mTOR, which is a downstream target of Akt. Our results show that

C11orf67 knockdown by sh1, sh2 and sh5 resulted in decreased phosphorylation of

mTOR protein at serine 2448 (Figure 3-9). Altogether, these data suggest that C11orf67

knockdown might decrease the activity of the Akt/mTOR and MAPK pathways.

Figure 3-8 C11orf67 knockdown induces cellular senescence

(A) Representative images of β-gal staining of T47D cells treated with empty

vector control or C11orf67 shRNAs. (B) Quantification of the percentage of

positive cells (blue) in C11orf67 shRNA cells compared to the empty vector

control (*** p<0.0001, ** p<0.001).


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Figure 3-9 C11orf67 knockdown deactivates Akt/mTOR and MAPK

pathways

Western blot analysis of p-Akt, p-mTOR and p-MAPK proteins in the

C11orf67 shRNA T47D cells and empty vector control, α-tubulin was used as

a loading control. MW markers are indicated on the right.


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3.3.1.9 Identification of genes and biological pathways

downstream of C11orf67

To identify genes and biological pathways downstream from C11orf67, we used global

transcriptome analyses. We determined the genes differentially regulated by the C11orf67

knockdown (sh1 and sh2) relative to the empty vector control cells by Illumina gene

expression arrays. Statistical analyses of three independent biological replicates were

conducted by t-test analyses (p<0.05). C11orf67 knockdown achieved by sh1 and sh2

constructs co-regulated 40 genes that are differentially regulated by the two shRNA

constructs relative to control cells (Figure 3-10A).

In order to investigate the biological relevance of these genes, we performed a STRING

network analyses (Jensen et al., 2009). Interestingly, one of the network nodes was highly

enriched in genes with known function in cell cycle regulation such as Cyclin-dependent

kinases, CDK4 and CDK6 which play a key role in regulating cell cycle progression and

regulation of cellular senescence (Anders et al., 2011), which is in agreement with our

functional studies (Figure 3-10B).

These network analyses also revealed a metabolic node, comprising several biosynthetic

enzymes, notably the methylenetetrahydrofolate dehydrogenase 1-like (MTHFD1L) that

protein, may link the mitochondria and the cytoplasm in the mammalian model of one-

carbon folate metabolism (Vazquez et al., 2013) (Figure 3-10C).

For subsequent validation, we performed qRT-PCR and western blot analyses for 16

important genes that were differentially regulated by C11orf67 sh1 and sh2 knockdown

in T47D cells (p<0.001) (Figure 3-10D).

In conclusion, these gene expression assays indicated that C11orf67 regulates genes

involved in cell cycle regulation and metabolism. The link between C11orf67 and

metabolic control (one-carbon folate) is particularly exciting and could explain the

induction of cellular senescence by depletion of fundamental metabolic pathways

required for cancer proliferation.

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3.3.2 Functional analyses of C11orf67 overexpression in breast

cell lines

In parallel to the functional ablation studies, we analyzed the consequences of C11orf67

gain of function in breast cancer cells. We used MCF7 cells as a cellular model system of

tumour mammary cells that are ER+, PR+ and features low basal levels of C11orf67

mRNA transcript compared to that of T47D cells. We used a lentiviral construct to stably

overexpress C11orf67 in MCF7 cells. The pLV105 lentiviral vector containing the human

cDNA of C11orf67 Isoform_2 (Genecopoeia, Rockville, MD, USA) was utilized in this

assay. This system allows constitutive and stable expression of C11orf67 protein. Cells

were lentivirally transduced with either pLV105 empty vector or with a pLV105-

C11orf67 construct to yield stable overexpression of the protein. Stably transduced MCF7

cells were analyzed for C11orf67 expression at transcript and protein levels. qRT-PCR

and western blot analyses showed that lentiviral-mediated overexpression of C11orf67

yielded approximately 15-fold increase in the mRNA and protein levels, respectively, as

compared to the empty vector control or the un-transduced MCF7 (Mock) cells, p<0.0001

(Figure 3-11A and B).

To determine whether C11orf67 overexpression is sufficient per se to induce

tumourigenicity in MCF7 cells, we analyzed cell proliferation rate of MCF7/C11orf67

cells compared to the empty vector control and mock cells by MTT cell proliferation

assay. Our results revealed that ectopic C11orf67 overexpression was not associated with

significant increase in MCF7 cell proliferation (Figure 3-11C). Indeed, there were no

evident differences in terms of growth morphology, size, and number of outgrowths

detected between C11orf67 cDNA-transduced cells relative to controls. This could be

explained by the fact that other factors in addition to C11orf67 might be required to induce

tumourigenicity. It could also be possible that other isoforms, in addition to isoform_2,

might be responsible for the oncogenic potential of C11orf67. Moreover, results in

chapter 4 demonstrate that C11orf67 isoform_2 overexpression induces tamoxifen

resistance, and thus the overexpression of the cDNA appears not to change proliferation

but rather a resistance to certain forms of therapy.


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Figure 3-11 C11orf67 overexpression does not increase cellular proliferation

(A) qRT-PCR analyses of C11orf67 mRNA levels in the C11orf67/MCF7,

empty vector control, or mock cells. (B) Western blot analyses of the indicated

cells blotted with anti-C11orf67 antibody, α-tubulin was used as a loading

control. (MW markers are shown on the right). (C) MTT cell proliferation

analysis of indicated cells, cell viability was monitored over 96 hours. Results

are plotted as fold increase with respect to day 0.


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3.3.2.1 C11orf67 activates genes involved in metabolism

Our cDNA microarray data analyses revealed that C11orf67 knockdown lead to the

downregulation of many cell cycle regulation related genes and genes involved in

metabolism and biosynthesis, in addition to genes associated with senescence. In this

section, we explored the effect of C11orf67 overexpression on these targets identified in

the knockdown experiments.

We first used western blot analyses to score the expression and/or activation state of 8

proteins that were affected by the C11orf67 knockdown: p-Akt ser473, p-Akt threo308,

p-mTOR ser2448, p-MAPK p42/44, CDK4, CDK6, CCNE1, and CCND1 (Figure 3-

12A). Our western blot data showed that only CCND1 protein was upregulated in the

C11orf67 overexpressing cells relative to empty vector control cells. However it is

important to note that the cells tested here are MCF7 cells, which are different from T47D

cells used in the knockdown analyses.

We next performed qRT-PCR analyses to detect the expression level of some of the

targets of the C11orf67 shRNAs identified by gene expression microarrays. Here again

we found that C11orf67 overexpression in MCF7 leads to upregulation of genes involved

in metabolic control, particularly one-carbon folate metabolism and phospholipase

activity (MTHFD1L, ALDH1L2, PPARGC1A, PLCE1), p<0.001 (Figure 3-12B).

Overall these results suggest that C11orf67 isoform_2 altered the expression of the cell

cycle regulator CCND1 in MCF7 cells and also induced enzymes involved in metabolic

control, reinforcing a role of the gene in controlling metabolic pathways.


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Figure 3-12 Changes in gene/protein expression in response to C11orf67

overexpression

(A) Western blot analyses of several signalling pathways affecting proliferation

and cell cycle control in MCF7 cells overexpressing C11orf67 cDNA relative

to control cells, this has been validated twice in two independent experiments.

(B) qRT-PCR analyses of genes involved in metabolism in case of C11orf67

overexpression and empty vector MCF7 (* p<0.01).


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3.3.2.2 C11orf67 overexpression increases sensitivity to anti-

metabolites

Significant upregulation of genes involved in one-carbon folate metabolism raised the

hypothesis that C11orf67 overexpression could increase ER+ breast cancer cell sensitivity

to folate antagonists, such as methotrexate (MTX) and 5-fluorouracil (5-Fu).

To evaluate the role of C11orf67 conferring sensitivity to these drugs, we took advantage

of MCF7 cells stably expressing C11orf67 cDNA isoform_2 and control empty vector

cells. These lines were seeded in 96-well plates and challenged to increasing

concentrations of drugs. Cell viability assays were carried out to determine the percentage

of surviving cells by MTT assays.

We found that cells overexpressing the C11orf67 cDNA had significantly enhanced the

sensitivity of MCF7 cells to the anti-tumour effect of 5-Fu and MTX at a time and dosage-

dependent manner compared to the empty vector control cells (p<0.001), an effect that

did not occur with non-antimetabolite drugs such as taxol (p>0.05) (Figure 3-13).


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Figure 3-13 C11orf67 overexpression increases the sensitivity to one-

carbon folate antagonists

MCF7 cells transfected with C11orf67 cDNA vector, or the empty vector

control were treated for 72 hours with different doses of (A) methotrexate, (B)

5-fluorouracil, and (C) taxol. Percentage of cell cytotoxicity was assessed by

MTT assay (** p<0.001).


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3.4 Discussion

An important question addressed in this chapter is how C11orf67 deregulation is

mechanistically implicated in breast cancer, and which cellular processes are deregulated

on its depletion. So far, no studies have addressed the role of C11orf67 in breast cancer

tumourigenesis and progression. In this chapter, we mounted evidence that C11orf67 is

necessary for breast cancer tumourigenesis and progression. We are the first to report that

efficient C11orf67 knockdown results in decreased proliferation, tumour formation and

migration of ER+ breast cancer cells in vitro, and also tumour formation in a xenograft

model in vivo. In addition, we were able to demonstrate that apoptosis was not the main

mechanism by which downregulation of C11orf67 expression inhibited cellular growth

and proliferation. In contrast, deregulation of C11orf67 expression caused high levels of

induction of cellular senescence.

Several reports demonstrated that cellular senescence functions as a potent tumour

suppressor mechanism that is essential for preventing unregulated growth and malignant

transformation (Campisi and d’Adda di Fagagna, 2007). We thus tested the potential role

of C11orf67 in regulating cellular lifespan. A senescent phenotype was readily observed

in ER+ breast cancer cells, T47D following the inhibition of C11orf67 expression. This

result suggests that cellular senescence induced by C11orf67 knockdown might be the

mechanism underlying its potential promoting malignant transformation and sustained

cell growth effects. Moreover, cellular senescence induced by loss of function of

oncogenes has been considered as a therapeutic approach and has been verified by

previous studies (Zou et al., 2002, Lleonart et al., 2009, Li et al., 2012).

We were also able to demonstrate a potential link between C11orf67 depletion and

senescence induction through decreased activity of phosphorylated AKT/mTOR proteins.

However, AKT/mTOR pathway is complicated and contradicting reports in the literature

argue the ability of AKT/mTOR pathway to induce senescence. Some reports have

pointed out that activation of the AKT pathway induces senescence in mouse embryonic

fibroblasts and prostate, esophageal, and colon cancer cells (Oyama et al., 2007,


104

Majumder et al., 2008, Nogueira et al., 2008, Park et al., 2013). Other reports have

concluded that AKT/mTOR activity is downregulated in senescence , and can antagonize

senescence (Courtois-Cox et al., 2006, Young et al., 2009, Kennedy et al., 2011, Xu et

al., 2014). In this study, we show that C11orf67 knockdown impairs the Akt activity and

induce a senescencent phenotype in ER+ breast cancer cells. Thus, our results point out

towards the second hypothesis.

The notion that AKT/mTOR is associated with resistance to endocrine therapy in breast

cancer (Paplomata and O'Regan, 2014) is of special interest as it supports the idea of

stratifying breast cancer patients and predicting those with poor prognosis and endocrine

treatment resistance. Mounting evidence indicates that the combination of mTOR

inhibitors with the hormonal therapy can overcome the resistance and improve the

outcome of HR+ breast cancer patients (Baselga et al., 2012, Bachelot et al., 2014).

Furthermore, strategies to downregulate C11orf67 in endocrine resistant breast cancer in

combination with mTOR inhibitor might improve the response to endocrine therapy and

consequently the outcome of patients, a hypothesis that needs to be further validated.

To confirm the involvement of PI3K/Akt/mTOR in C11orf67 induced senescence, more

checkpoints in the pathway require further analyses. For instance, the PI3K the upstream

effector of AKT, which phosphorylate PIP2, to PIP3. On the other hand, we showed that

phosphorylated mTOR protein is downregulated in response to C11orf67 knockdown;

however, the intermediate proteins (TSC1/2 and Rheb-GTP), the other subunits of the

mTORC1 complex (Raptor, PRAS40, and mLST8), and the mTORC2 complex have not

been checked to be affected or not in response to C11orf67 depletion and required to be

dissected for the full understanding of the AKT/mTOR pathway involvement in the

cellular senescence induced by C11orf67 knockdown.

We also investigated whether alteration of C11orf67 expression in breast cancer cell lines

induces changes in the transcriptomic landscape of these cells. To our knowledge, this is

the first report conducted a genome-wide expression microarray analyses to identify

differentially regulated genes linked to C11orf67 knockdown in T47D cells, we identified

40 genes to be commonly co-regulated by both shRN1 and shRNA2 constructs specific


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to C11orf67 transcript. The fact that these genes are regulated in these shRNAs in 6

independent transfections give high confidence that these targets are bona fide

downstream genes of C11orf67, and not the result of off-targets of the shRNAs. To date,

we have validated the regulation of 16 out of 40 genes by qRT-PCR and western blot

analyses.

Moreover, to gain insights into the biological relevance of the global transcriptional

changes induced by C11orf67 downregulation, we performed network pathway analysis.

This analyses resulted in the novel identification of a cell cycle control network and

metabolism network regulated by C11orf67 knockdown, suggesting an important role of

C11orf67 in the regulation of these processes.

It was not surprising that we found a cell cycle gene network regulated by C11orf67

knockdown, taking in consideration the results obtained in our functional studies, which

revealed that C11orf67 depletion in breast cancer cell lines decreased cellular

proliferation, and also in light of the global transcriptome changes conducted by the

cancer genome project in the context of the InClus2 subtype (Curtis et al., 2012).

Moreover, cell cycle regulation might provide another explanation for the gain of

senescence induced by C11orf67 knockdown. Cellular senescence caused by C11orf67

knockdown could be explained by the downregulation of many cell cycle regulation-

related genes (CCNE1, CCND1, CDK4, CDK6, PPP4R4, CEBPG, and PPARGC1). It is

important to note that suppression of most of these genes was usually found to be

associated with cell cycle arrest, cellular senescence or both as discussed below.

CDK4 and CDK6 are positive regulators of cell cycle entry, and inhibit senescence in

melanoma cells (Anders et al., 2011). CEBPG is a growth-promoting transcription factor

and its depletion induced senescence in lung tumour cells and primary mouse fibroblasts

(Huggins et al., 2013). Ppp4R4 (Phosphoprotein phosphatase 4) has been implicated in

DNA damage checkpoint signalling, NF-κB activation, and mTOR pathway regulation

(Zhou et al., 2002, Cohen et al., 2005, Nakada et al., 2008). PPARGC1 (peroxisome

proliferator-activated receptor gamma, coactivator 1) plays a critical role in the


106

transcriptional control of mitochondrial biogenesis and respiratory function (Scarpulla,

2011).

In addition to the cell cycle gene network found in the pathway analyses, we also found

a biosynthesis and a metabolism gene network that is deregulated in response to C11orf67

knockdown. Indeed, C11orf67 depletion led to remarkable lower expression of the

mitochondrial glycine biosynthetic pathway and also downregulation of mitochondrial

folate enzymes that include serine hydroxymethyltransferase (SHMT2),

methylenetetrahydrofolate dehydrogenase (MTHFD (Scarpulla, 2011, Vazquez et al.,

2013). The downregulation of these mitochondrial enzymes correlated with decreased

proliferation caused by C11orf67 ablation. The expression levels of the above mentioned

mitochondrial enzymes contribute to the sensitivity or the resistance of a tumour cell to

antifolate cancer therapy (MTX and 5-FU) (Vazquez et al., 2013), where there is a strong

correlation between the sensitivity to MTX treatment and increased expression of

mitochondrial enzyme MTHFD1L (Sorich et al., 2008).

Consequently, we raised the hypothesis that overexpression of C11orf67 in cancer cells

will upregulate the expression of mitochondrial one-carbon pathway, and glycine

consumption that render those cells to be more potently inhibited by MTX, whereas cells

with lower levels of C11orf67 would not be as sensitive. In this chapter, we are the first

to provide evidence that the anti-metabolites MTX, and 5-FU might show selectivity to

tumours showing higher levels of C11orf67 with the subsequent upregulation of the

mitochondrial one-carbon pathway components. These data support the hypothesis that

MTX, 5-FU and potentially other inhibitors of thymidylate and purine biosynthesis could

be used to target ER+ breast cancers with high expression levels of C11orf67.

In summary, our data demonstrates that C11orf67 has a novel oncogenic role in ER+

breast cancer and is required for breast cancer cell proliferation and survival. Further, we

identified the induction of cellular senescence as a potential mechanism underlying

C11orf67 malignant transformation effect. In addition, our results suggest that one-carbon

metabolism antagonists might increase the response of cancer cells overexpressing

C11orf67 which increase the dependence of cancer cells on this metabolic pathway.

Chapter 4:

Regulation and Binding

Partners of C11orf67

Regulation and Binding Partners of C11orf67

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4.1 Introduction

To define the significance of C11orf67 in the initiation and progression of ER+ breast

cancer, it is essential to identify both its transcriptional regulation mechanisms and its

molecular interactions with putative proteins and cofactors. This information will also be

important at the cellular level to potentially understand the mechanisms of response to

therapy mediated by C11orf67, as well as the identification of more effective treatments

for tumours expressing this novel biomarker.

Approximately 75% of breast cancers are ER+, and different anti-hormonal based

treatments exert their action through various mechanisms to antagonize tumour

proliferation stimulated by estrogen (Zhang et al., 2013). One of the accepted models of

ER function is via binding of estrogen to ER to form the estrogen/ER complex, which

binds specifically to ERE of the DNA. Binding of the ERE initiates gene transcription

either directly, or indirectly through the interaction with other transcription factors such

as NF-κB (Thomas and Gustafsson, 2011, Baumgarten and Frasor, 2012).

Current anti-estrogen approaches include the use of selective estrogen receptor

modulators (SERM), such as tamoxifen, which antagonize ER activity by competing with

estrogen for ER binding. Although tamoxifen is very efficient in the treatment of ER+

breast cancer, resistance and tumour relapse occurs in 30% to 40% of patients who

showed an initial response to tamoxifen (Chia et al., 2005). Indeed, it is challenging to

find definitive methodology identifying ER+ patients that are more likely to benefit from

tamoxifen from those who are not (Jung et al., 2013).

The activity of the transcription factor NF-κB is thought to deliver means for tumour cells

to escape the antagonistic effect of tamoxifen, by either downregulating ER protein

expression, or by enhancing ER activity in a ligand-independent manner (Biswas et al.,

2000, Van Laere et al., 2007). Different inflammatory cytokines, such as tumour necrosis

factor α (TNFα), activate NF-κ B proteins to form dimers that bind to NF-κB response

elements of the DNA (Hoffmann et al., 2003, Chen and Greene, 2004, Sun, 2011).


109

Activation of NF-κ B occurs through either the canonical pathway or the alternative

pathway (explained in Figure 4-1) (Sen and Smale, 2010).

Figure 4-1 Canonical and alternative pathways of NF-κB activation

In the canonical pathway, cellular activation leads to phosphorylation of the

inhibitor of NF-κB α-protein (IκBα) by a macromolecular complex containing

an inhibitor of NF-κB kinase 1 and 2 (IKKα and IKKβ) and others.

Phosphorylation of IκBα stimulates the rapid ubiquitylation and degradation of

this cytoplasmic inhibitor by the 26S proteasome complex, which leads to

liberation and rapid translocation of NF-κB dimers (p65 and p50) into the

nucleus. In the non-canonical pathway, RelB/NF-κB2 dimer undergoes

inducible proteolytic processing of the NF-κB2 gene product, p100. (Ko et al.,

2010, Sun, 2011). Adapted from Dolcet et al. (2005).


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In this chapter, we used bioinformatic analyses and different experimental approaches to

understand novel regulatory mechanisms that govern the expression of C11orf67 and

neighboring genes in the oncogenic cluster at 11q13.5-q14. We are the first to report that

C11orf67 and other genes of the cluster are co-regulated by a group of transcription

factors inducing proliferation including ER, Myc and NF-κB. The results of our analyses

suggest that transcriptional upregulation of C11orf67 by tamoxifen underlies the

development of resistance to endocrine therapy in these patients. Therefore, the C11orf67

expression could represent a novel biomarker for predicting tamoxifen resistance, which

occurs in 30% of patients.

Moreover, we also utilized yeast two-hybrid (Y2H) to identify the direct interaction

partners of C11orf67 protein, as an important step toward systematically defining the

C11orf67 protein function. Two independent Y2H screens revealed RABGAP1L (Ras-

related in brain GTPase-activating protein 1-like) (Mitra et al., 2011) as a very high

confidence prey physically interacting with C11orf67. RABGAP1L is a critical cell

signalling molecule known to be involved in a variety of pivotal cellular processes

including intracellular vesicular transport and membrane trafficking (Spiegel et al.,

2014). The outcomes of this research have revealed C11orf67 and other interacting

partners as a new signalling axis defining tamoxifen resistance in ER+ tumours.


111


4.2.1 Reagents and Antibodies

17β-Estradiol, Tamoxifen, Progesterone, Mifepristone, Prolactin and Oxytocin were

purchased from Sigma-Aldrich, St Louis, MO, USA. NF-κB inhibitor was purchased

from Calbiochem, Kilsyth, Victoria, Australia. TNFα was a kind gift from Professor

Juliana Hamzah (University of Western Australia).

For western blotting, primary antibodies specific to p-IKKα/β, IκBα , p-p65, NFκB2

p100/p52, and c-Myc were purchased from Cell Signaling, Gold Coast, QLD, Australia,

and were used at 1:1000. The anti-p65 antibody was purchased from Santa Cruz

Biotechnology, Texas, USA (1:500). Anti-RABGAP1L was purchased from Proteintech,

Chicago, USA, and was used for immunohistochemistry (1:400), and western bloltting

(1:100).

4.2.2 Cell Culture

Human breast cancer T47D, MCF7, and SKBR3 cell lines were routinely grown in the

appropriate media (Section 2.2.1). To evaluate the effect of hormonal signals, cells were

cultured in phenol red-free media supplemented with 5% dextran coated, charcoal-

stripped serum (CSS) (Invitrogen, Waverley, Australia) for 3 to 5 days, before being

incubated with either the indicated hormones or the antihormonal agent (Perillo et al.,

2000). 4x105 cells were seeded in a six-well plate and grown until they reached 60-70%

confluency. Cells were then treated with the indicated concentrations of hormones, drug

or vehicle control (mock) for 48 to 72 hours. Cells were trypsinized, and total RNA was

collected with the RNeasy Kit and converted to cDNA by reverse transcription (Sections

2.2.2, and 2.2.3). qRT-PCR analysis of human C11orf67 was performed with a PCR

Taqman Gene Expression Assay, using human GAPDH as a housekeeping gene (Section

2.2.4).


112

4.2.3 siRNA Transfection

The sequences of siRNA oligos specific for C11orf67 (Sigma-Aldrich, St Louis, MO,

USA) were as follows: 5’-ACUUGGGAUUGGAGAGAAA-3’ and 5’-

UUUCUCUCCAAUCCCAAGU-3’. The sequences of negative universal control siRNA

(Sigma-Aldrich, St Louis, MO, USA) were as follows: 5’-

UUCUCCGAACGUGUCACGUTT-3’ and 5’-ACGUGACACGUUCGGAGAATT-3’.

T47D cells were cultured in six-well plates until they reached 50-60% confluency. Cells

were transfected with C11orf67 siRNA or control siRNA according to the manufacturer's

protocol. Briefly, 1 μl (2 μg) of the siRNAs oligos was added to 99 μl of Opti-MEM

Reduced-Serum Medium (Invitrogen, Waverley, Australia) and mixed gently. At the

same time, 4 μl of Lipofectamine-2000 (Invitrogen, Waverley, Australia) was added to

96 μl of Opti-MEM and left at room temperature for 5 minutes. The media including the

siRNA was added gently to the media including the Lipofectamine and mixed by

pipetting. After 20 minutes, 200 μl of the mixture was added to each well and made up to

a final volume of 500 μl with Opti-MEM, and the plates were incubated at 37 °C and 5%

CO2 for 24 hours. C11orf67 mRNA levels were detected by qRT-PCR, and MTT

cytotoxicity assays were performed on transiently transfected cells up to 72 hours after

transfection (Section 3.2.4).

4.2.4 Rat AAMDC qRT-PCR

Pregnant rats (24 days of gestation) and control female rats were humanely sacrificed

under Ethics Approval number RA/3/100/1318 of the University of Western Australia.

Embryos were collected for a separate study while the mammary gland and

retroperitoneal fat pads were collected under aseptic conditions and snap frozen. RNA

was extracted from the collected fat pads using Qiagen RNA extraction kit (Hilden,

Germany) and processed as stated previously (Section 2.2.2). Rat AAMDC mRNA

expression levels were detected using TaqMan primers and probes (Applied Biosystems,

Scoresby, Australia), using rat GAPDH as a housekeeping gene.


113

4.2.5 Chromatin immunoprecipitation assay

Chromatin immunoprecipitation assay (ChIP) was performed as described by Yang et al.

(2014). Briefly, T47D cells were either maintained in their basal media or stimulated with

10 ng/ml TNF-α for 4 hours. Cells were fixed, sonicated, before being incubated with the

anti-p65 (Santa Cruz Biotechnology, Texas, USA) antibody for 16 hours at 4°C (1

mg/reaction). Chromatin was precipitated using Dynabeads Protein A (Invitrogen,

Waverley, Australia) according to the manifucturer’s protocol. DNA was extracted with

phenol/chloroform, precipitated with ethanol and analyzed by PCR using primers

flanking the predicted p65 binding sites. The immunoprecipitated sample DNA was

compared with the input DNA to determine the percent of the immunoprecipitated

product. The primers used to amplify a distinct region of the C11orf67 promoter with

putative NF-κB binding sites (determined using Transcription Factor software) were: 5’-

CCTCTGGTCCACTTGGGATA-3’ and 5’-GTGATCTCAGTGGCAAGCAA-3’, with

the following conditions: Cycle 1, 3 minutes at 95°C; Cycle 2, 30 sec at 95°C; Cycle 3,

30 sec at 60°C; Cycle 4, 1 minutes at 72°C; then repeat cycles 2 to 4 35 times followed

by a final step of 10 minutes at 72°C. The amplified products were analysed by

electrophoresis on a 2% agarose gel and visualized by ethidium bromide staining under

UV light.

4.2.6 Luciferase assay

Stable T47D cells that had been transfected with C11orf67 shRNAs or empty vector (see

section 3.2.3) were then transfected with the NF-κB–Luc reporter plasmid (a kind gift

from Dr. Xavier Dolcet, University of Lleida, Spain). Transfection was performed using

Lipofectamine-2000 transfection reagent according to manufacturer’s instructions. Cells

were lysed using Luciferase Reporter Assay System (Promega, Madison, WI, USA)

according to the manufacturer’s instructions. Luciferase activity was measured using the

EnVision 2012 Multilabel Reader, PerkinElmer (Waltham, MA, USA)


114

4.2.7 Yeast Two-Hybrid Analysis

Yeast two-hybrid screening was performed by Hybrigenics Services, S.A.S., Paris,

France (http://www.hybrigenics-services.com).

The coding sequence for Homo sapiens C11orf67 (GenBank accession number gi:

34328078) was PCR-amplified and cloned into the plasmid pB27 as a C-terminal fusion

to LexA (N-LexA-C11orf67-C), and into pB66 as a C-terminal fusion to Gal4 DNA-

binding domain (N-Gal4-C11orf67-C). The constructs were verified by sequencing and

used as bait to screen a random-primed cDNA library from human breast tumour cells

(T47D, MDA468, MCF7, and BT20) cloned into pP6. pB27, pB66 and pP6 derive from

the original pBTM116 (Vojtek and Hollenberg, 1995), pAS2ΔΔ (Fromont-Racine et al.,

1997), and pGADGH (Bartel et al., 1993) plasmids, respectively.

For the LexA bait construct, we screened 85 million clones (8-fold the complexity of the

library) using a mating method with YHGX13 (Y187 ade2-101:loxP-kanMX-loxP, matα)

and L40ΔGal4 (mata) yeast strains as previously described (Fromont-Racine et al., 1997).

A total of 16 His+ colonies were selected on a medium lacking tryptophan, leucine, and

histidine.

For the Gal4 construct, 68 million clones (6-fold the complexity of the library) were

screened using the same mating approach with HGX13 (Y187 ade2-101::loxP-kanMX-

loxP, matα) and CG1945 (mata) yeast strains. A total of 53 His+ colonies were selected

on a medium deficient in tryptophan, leucine, and histidine. PCR was used to amplify the

prey fragments of the positive colonies and then were sequenced at their 5’ and 3’ ends.

The NCBI GenBank database was used to identify the resulting sequence for the

corresponding interacting proteins. A Predicted Biological Score for each interaction was

attributed (Formstecher et al., 2005).


115


Statistical data are presented as the mean ± SD of three individual experiments performed

in triplicate. Statistical analysis was carried out using the Student’s t-test or one-way

ANOVA, and the level of significance was established at a P value of <0.05.


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4.3 Results

4.3.1 11q13.5-q14 locus is enriched in transcription factor

binding sites

13 genes mapping in 11q13.5-q14 are found to be amplified in the IntClust 2 subtype of

breast cancer (Curtis et al., 2012). We have described the validation of the C11orf67

amplification by FISH and described the oncogenic potential of this protein in ER+ breast

cancer in previous chapters. However, the transcriptional regulation of C11orf67 and

other oncogenic drivers at 11q13.15-q14 has not been investigated before.

A key factor in determining the transcriptional expression of the 11q13.5-q14 genes is the

binding of sequence-specific DNA binding proteins (i.e. transcription factors, TFs) to

regulatory regions of these genes (i.e. promoters and enhancers) within a few hundred

nucleotides from the transcription start site (TSS). In addition, TFs can also bind

enhancers which act via chromatin loops many Kbps away from their binding site. In fact,

examination of ENCODE TF data in MCF7 cells demonstrates that chromatin

interactions can link many oncogenes in the 11q13.5-q14 amplicon even located more

than 500 Kbps away. Whilst these TFs show sequence-specific DNA binding properties,

they also bind more than one sequence, which makes it difficult to identify ultimately all

functional binding sites in a given cell line.

To determine the relevant TF binding sites, we first employed the transcription factor

binding site (TFBS) search program to scan sequences for putative binding sites in the

whole 11q13 amplicon, and particularly in the RSF1-C11orf67-INTS4 region. Outcomes

from TF analysis demonstrated a high-density of binding sites for ER, PR, p65 and Myc

in the promoter and other regulatory regions along 11q13.5-q14. A sketch map of the TF

binding sites and their relative position in the 11q13.5-q14 locus is illustrated in Figure

4-2A.


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Interestingly, we found 4 out of 13 p65 binding sites in the C11orf67 promoter region are

a consensus with high-score motifs GGGAGTTCCC, GGGGGCCTCC, GGGACTTCT

and GGGAAATTCC. Furthermore, we observed that the RSF1 gene promoter sequence

(the gene located adjacently to C11orf67 start site) also contains several NF-κB binding

sites, which suggests possible co-regulation with the C11orf67 gene.

We next investigated the possibility of concomitant co-regulation of the C11orf67 gene

with the other genes in the amplicon. Interestingly, we found that C11orf67 is

significantly co-amplified with other distal genes in the locus, particularly the proximal

genes, AQP11, CLNS1A, RSF1 and INTS4 (Cancer Genome Atlas, 2012), with the

highest probability of co-amplification with the neighboring genes in the amplicon.

Collectively, these results suggest the possibility of chromatin interactions within the

11q13.5-q14 locus, and that the expression of the C11orf67 gene and other genes residing

in this amplicon might be regulated by ER, PR, p65, and Myc TFs.

4.3.2 Estrogen modulates C11orf67 gene expression.

In previous chapters, we have found that C11orf67 is overexpressed in ER+ breast

cancers. Consistently, here we have determined its gene promoter contains ER binding

sites, yet the exact transcriptional effect of estrogen on C11orf67 gene expression has not

previously been investigated. With this aim in mind, we chose ER+ T47D cells which are

ER+, PR+ and show high levels of C11orf67 mRNA expression as a model to understand

the transcriptional regulation of C11orf67 in hormone sensitive luminal breast cancers.

To study hormonal cues in gene expression in general two types of media are commonly

used: Phenol red-containing (red) medium supplemented with FCS, which is the standard

medium for optimum cellular growth, and Phenol red-free (white) medium supplemented

with CSS, which is used to analyse the expression of hormone-dependent genes, since

phenol red is known to activate ER-dependent gene regulation. For all the hormonal

assays, T47D cells were cultured in white media for 72 hours, to abolish the uncontrolled

effect of exogenous estrogens on gene expression.


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Figure 4-2 Transcription factor binding sites of the 11q13.5-q14 amplicon

(A) Sketch map representing the ENCODE chromatin interaction data of the

11q13.5-q14.5 amplicon, indicating the TF-binding sites and their relative

position, with an enlarged image of the RSF1-C11orf67-INTS4 region with

putative NF-κB binding sites. (B) Co-amplification of C11orf67 with the other

immediate neigboring oncogenes at 11q13.5-q14.5 in breast cancer (analyses

performed by Dr. A. Woo at the Harry Perkins institute using Cancer Genome

Atlas input data).


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We first investigated whether changing the culture media from red to white could affect

the expression levels of C11orf67 mRNA. To this end, T47D cells were grown in red

media, and switched to white media for 48 hours, after which C11orf67 mRNA levels

were analyzed. Importantly, we found that C11orf67 mRNA levels increased by at least

two-fold (p<0.0001) when the cells were cultured in white media, which suggests that

removing the estrogens from the growing media increases the C11orf67 mRNA levels

(Figure 4-3A).

To validate these findings, T47D cells were treated with increasing physiological

concentrations of 17β-Estradiol (E2), ranging from 1nM to 100 nM, for 48 hours before

conducting a qRT-PCR analysis to quantitate C11orf67 mRNA levels. Consistent with

our previous finding, we noticed a two-fold downregulation (p<0.0001) of C11orf67

mRNA levels in response to treatment of increasing doses of E2, compared to the mock

control cells (DMSO treated) (Figure 4-3B).

Tamoxifen (Tam) is an anti-estrogen drug that is most commonly used for the treatment

of ER+ breast cancer. To explore the effect of Tam on C11orf67 mRNA levels, T47D

cells were treated with increasing concentrations of Tam (0.5 μM, 1, and 5 μM) for 48

hours, before C11orf67 mRNA levels were measured and compared to that of the mock

control cells. Our findings did not demonstrate any significant changes in transcriptional

activity of C11orf67 upon treatment with any dose of Tam (Figure 4-3C).


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Figure 4-3 C11orf67 is regulated by Estrogen

qRT-PCR analysis of C11orf67 mRNA levels in T47D cells in response to

different hormonal growing conditions: Phenol red-free media (A), Estradiol

treatment (B), and Tamoxifen treatment (C) (*** p<0.0001).


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4.3.3 Estrogen downregulates C11orf67 expression in an ER-

dependent manner

We next investigated whether E2 exerts its action on C11orf67 expression in a way

dependent on ER expression. To this end, we treated the ER+ line T47D with gradually

increasing doses of the specific ER antagonist, Tam (0.5, 1, and 5 μM), to 1 nM of E2.

Interestingly, C11orf67 mRNA downregulation observed with E2 alone was gradually

abolished by adding Tam to the growing media, until it reached significantly higher levels

of expression of C11orf67 in response to 5μM Tam (as compared to the mock-treated

cells) (p<0.0001) (Figure 4-4A).

Moreover, we tested the same approach (E2 and Tam treatment) in an ER- cell line,

SKBR3. As expected from being hormone-independent cells, neither E2 nor Tam led to

a significant difference in the mRNA levels of C11orf67 expression in SKBR3 cells. It is

worth noting that 5μM Tam in the presence of 1nM E2 resulted in a slight yet significant

increase in C11orf67 mRNA levels compared to the mock-treated cells (p<0.05) (Figure

4-4B). Collectively, these data suggest that the effect of E2 on C11orf67 is mediated

through ER and is rescued by the anti-estrogen drug tamoxifen.

4.3.4 Tamoxifen upregulates expression of multiple oncogenes

in the 11q13.5-q14 cluster

Based on the previous finding of possible co-regulation and chromatin interactions among

multiple oncogenes residing at 11q13.5-q14, we next explored the effect of 5μM Tam in

combination with 1nM E2 on the expression levels of all 13 genes in the amplicon.

Strikingly, we detected significant upregulation of all 13 genes in the amplicon upon

treatment with Tam (p<0.001) as assessed by qRT-PCR. In addition, our results indicated

that 8 out of 13 genes in the amplicon were significantly transcriptionally downregulated

by E2 (p<0.01).


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These results suggest that exposure to anti-estrogens might have an undesirable effect

by activating multiple oncogenes in Chromosome 11 and subsequently the IntClust 2

subtype of breast cancer.

Figure 4-4 Estrogen regulates C11orf67 expression in an ER dependent

manner

Effect of Estradiol (E2), Tamoxifen (TAM), or both on the mRNA levels of

C11orf67 in ER+ cell line; T47D (A) and ER- cell line; SKBR3 (B) (***

p<0.0001, * p<0.05)

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4.3.5 Aberrant expression of C11orf67 alters the sensitivity to

tamoxifen

Previously, we have demonstrated that tamoxifen treatment results in upregulation of

genes in the oncogenic cluster 11q13.5-q14 in ER+ cell lines. To further validate this

observation and establish the role of C11orf67 in antiestrogen resistance, we examined

the effect of aberrant expression of C11orf67 on the response to tamoxifen treatment.

To this aim, we generated T47D cells with lower levels of C11orf67 by transient

transfection with siRNA oligos specific for C11orf67. We validated the successful

knockdown by qRT-PCR analysis of C11orf67 mRNA levels. siRNA transfection

resulted in 60% knockdown of C11orf67 mRNA levels compared to the negative control

siRNA-transfected cells. It is worth noting that we did not use C11orf67 shRNAs as an

alternative for the knockdown in this experiment, since they resulted in extensive

decreased proliferation of cells (Section 3.3.1.2), which masked the effect of Tam

treatment. To complement this study, we also generated MCF7 cells with higher levels of

C11orf67 by stable cDNA lentiviral plasmid (Section 3.2.3).

Our results indicate that T47D cells transfected with C11orf67 siRNA were more

sensitive to Tam treatment than T47D transfected with control siRNA (p<0.001) (Figure

4-6A). Reciprocally, MCF7 cells lentivirally expressing C11orf67 cDNA were more

resistant to Tam treatment compared to the MCF7/empty vector cells (p<0.0001) (Figure

4-6B).

Collectively, these results suggest that Tam treatment upregulates the expression of

C11orf67 and other genes, resulting in increased resistance of these cells to Tam. This

finding explains why anti-estrogen treatment might not be the proper approach for

treating this aggressive ER+ breast cancer subtype.


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Figure 4-6 Changes in C11orf67 mRNA levels alter the sensitivity of T47D

cells to tamoxifen

(A) qRT-PCR analysis of C11orf67 mRNA expression in T47D/control siRNA

and T47D/C11orf67 siRNA cells (left panel), indicated cells were treated with

different concentrations of Tam for 48 hours and cell viability was measured

by MTT assay (right panel). (B) qRT-PCR analysis of C11orf67 mRNA

expression in MCF7/empty vector and MCF7/C11orf67 cells (left panel),

indicated cells were treated with different concentrations of Tam for 48 hours

and cell viability was measured by MTT assay (right panel) (*** p<0.0001, **

p<0.001).


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4.3.6 C11orf67 is upregulated in pregnancy

Progesterone Receptor (PR) is one of the transcription factors that has high frequency of

predicted binding sites in promoter region of C11orf67, but whether it has any

transcriptional regulation of the expression of C11orf67 has not yet been investigated.

We next studied the effect of Progesterone (PG) and other pregnancy hormones (Prolactin

and Oxytocin) on the expression levels of C11orf67.

Our results show that physiological doses of PG treatment (100 nM) in T47D cells for 48

hours led to significant activation of C11orf67 mRNA levels by approximately 1.7 fold

(p<0.0001). This effect was rescued by treating the cells with the anti-PG compound

Mifepristone (Mif) at 1 nM, 10 nM and 100 nM (p<0.001, p>0.5 and p<0.001

respectively). 100 nM Mifepristone alone did not significantly alter the expression level

of C11orf67 (Figure 4-7A).

Similarly, physiological doses of other pregnancy hormones such as Prolactin and

Oxytocin, which are known to increase during pregnancy, resulted in significant

upregulation of C11orf67 mRNA levels (p<0.01) (Figure 4-7B).

The physiological responsiveness to hormones in vivo was confirmed by analyzing the

C11orf67 homolog in rats (rat AAMDC) mRNA expression in the mammary fat pads from

pregnant and control rats. Fat pads from pregnant rats (in which PG levels peak) exhibited

3-6 folds higher levels of AAMDC mRNA relative to non-pregnant rats (p<0.0001). While

no changes in expression were observed in non-mammary (retroperitoneal) fat pads

between the two groups (Figure 4-7C).

Collectively, these results suggest that pregnancy, and pregnancy hormones upregulate

the expression of C11orf67 (or rat AAMDC) at the transcriptional level. The opposing

effects of E2 and PG on C11orf67 gene expression could be due to differential

associations with co-repressors and co-activators .


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Figure 4-7 Effect of pregnancy hormones on C11orf67 expression

(A) Effect of Progesterone and anti-Progesterone drug Mifepristone on

C11orf67 expression. (B) Effect of Prolactin and Oxytocin on C11orf67

expression. (C) Effect of pregnancy hormones on rat AAMDC mRNA

expression in mammary and retroperitoneal fat pads (*** p<0.0001, **

p<0.001, * p<0.01).


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4.3.7 NF-κB regulates the expression of C11orf67

Our previous TF search analysis revealed the transcription factor p65 (from the NF-κB

family) has strong consensus sites mapping in the regulatory regions of C11orf67, as well

as the neighboring gene RSF1. In previous sections, we showed that the C11orf67

promoter sequence contains 13 p65 binding sites, four of which are high consensus sites,

suggesting that p65 might regulate C11orf67 mRNA expression at the transcriptional

level. p65 is a multifunctional transcription factor that elicits its physiological function

by regulating target gene expression upon NF-κB activation. To determine whether NF-

κB transcriptionally controls the expression of C11orf67 in ER+ cells, we analyzed the

effect of either activation or inhibition of NF-κB pathway on the transcriptional levels of

C11orf67.

First, we tested the effect of TNFα on the transcriptional levels of C11orf67 mRNA in

T47D cells. Stimulation of T47D cells with 10 ng/ml and 100 ng/ml TNFα for 4 hours

significantly increased C11orf67 mRNA levels by 3 and 5 fold, respectively (p<0.001),

as compared with untreated (mock) cells (Figure 4-8A).

Second, we used the NF-κB inhibitor, APQ [6Amino-4(4-phenoxyphenylethylamino)

quinazoline], a cell-permeable quinazoline compound that acts as a potent inhibitor of

NF-κB transcriptional activation by inhibiting NF-κB translocation into the nucleus. APQ

treatment of T47D cells at 10 μM and 100 μM for 48 hours resulted in a significant dose-

dependent downregulation of C11orf67 mRNA levels by approximately 0.4 and 0.2 fold,

respectively, as compared with the untreated control (p<0.0001) (Figure 4-8B).

To physically validate the binding of p65 to the κB binding site in the C11orf67 promoter

in vivo, we performed a ChIP assay in T47D cells. The binding of p65 to the C11orf67

promoter was detected in the basal conditions (no TNFα), and this binding was

significantly higher upon stimulation with 10 ng/ml TNFα for 4 hours. Protein-DNA

complexes were immunoprecipitated with specific anti-p65 antibody. The PCR

quantification of the 177 bp ChIP product was performed with specific primers

encompassing the p65 consensus binding sites in the C11orf67 proximal promoter region.


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Upon stimulation with TNFα, nuclear p65 binding to the human C11orf67 gene promoter

increased relative to the basal levels (Figure 4-8C).

ChIP results suggest the direct binding of the p65 transcription factor to the C11orf67

proximal promoter. This enhanced binding is consistent with the activation of C11orf67

mRNA expression induced by TNFα. In summary, these results suggest that p65 regulates

C11orf67 transcription by directly binding to the C11orf67 promoter in a TNFα

dependent fashion.

Figure 4-8 C11orf67 is regulated by NF-κB

C11orf67 mRNA levels were analysed by qRTPCR in T47D cells after being

treated with 10 ng/ml and 100 ng/ml of TNFα (A) and with 1µM and 10µM

NF-κB inhibitor APQ (B) for 48 hours. (*** p<0.0001, ** p<0.001). (C) ChIP

analysis of p65 in presence or absence of 10 ng/ml TNFα treatment for 4 hours.


130

4.3.8 C11orf67 acts as a regulator of NF-κB transcriptional

activity

To determine the molecular function of C11orf67 we first searched the genome-wide

siRNA phenotypic screening databases and found that C11orf67 was identified as one of

the 155 targets regulating NF-κB activity in 293T cells (Gewurz et al., 2012). To further

investigate this role in breast cancer cell lines, we performed a luciferase assay using an

NF-κB response element-derived reporter (pNF-κB–Luc) to monitor NF-κB activity.

Reducing C11orf67 using three independent shRNAs having satisfactory knockdown

efficiency to reduce levels of C11orf67 (see section 3.3.1.1), resulted in a significant

decrease in the activity of NF-κB reporter at both the constitutive (basal) levels or after

TNFα stimulation, p<0.001 (Figure 4-9A).

We also tested the effect of C11orf67 knockdown on the mRNA levels of some important

genes downstream the NF-κB pathway; c-Myc, RSF1, and Vascular endothelial growth

factor A (VEGFA). qRT-PCR analysis confirmed that C11orf67 knockdown was

accompanied with a significant downregulation of c-Myc, RSF1 and VEGFA transcripts

(p<0.001), which also follows the same trend of C11orf67 downregulation by the

different shRNAs (Figure 4-9B).

To identify the exact mechanisms of regulation of the endogenous NF-κB by C11orf67,

whole cell lysates were prepared from T47D cells transfected with C11orf67 shRNAs or

empty vector control. Western blotting was performed using specific antibodies against

checkpoints in the NF-κB signalling pathway (Figure 4-9C). Western blot results

indicated that C11orf67 knockdown led to blocked phosphorylation of IKKα/β at Ser

176/180, and also downregulated c-Myc expression at the protein level. However, no

significant changes were observed in IκBα, p65, p-p65, NF-κB2 (p100/p50) protein

expression (Figure 4-9C). Collectively, these data suggest that C11orf67 is an essential

upstream signalling factor controlling NF-κB activity and consequently downstream

targets.


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Figure 4-9 Effect of C11orf67 knockdown on NF-κB activity

(A) Luciferase NF-κB reporter activity was assessed in T47D transfected with

C11orf67 shRNAs or empty vector control. (B) qRT-PCR analysis of genes

downstream NF-κB pathway in the indicated cell lines (** p<0.001). (C)

Western blot analyses of proteins of the NF-κB pathway in knockdown cells

and empty vector T47D, α-tubulin was used as a loading control. (MW markers

are indicated on the right).


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4.3.9 RABGAP1L as a binding partner of C11orf67

The previous sections in this chapter have described transcriptional regulation of

C11orf67. Moreover, the identification of novel post-transcriptional protein-protein

interactions involving C11orf67 represents a critical step towards the elucidation of its

biochemical function.

To identify the direct binding partners of C11orf67 we conducted a Yeast two-hybrid

(Y2H) screen. Briefly, Y2H it is a technique conducted in yeast that takes advantage of

the properties of the galactose gene activating the transcription factor (GAL4) or other

similar modular TFs such as LexA, of the yeast Saccharomyces cerevisiae (S. cerevisiae)

(Fields and Song, 1989). The GAL4 protein consists of two separable domains: the N-

terminal domain and the C-terminal domain. The N-terminal domain, also called DNA

binding domain (DBD), recognizes and binds to specific sequences in the DNA upstream

of a promoter. These sequences are termed upstream activation sequences or UAS. The

C-terminal domain (activating domain, AD) stimulates transcription by binding RNA

polymerase. By fusing a protein to each of these domains, one can detect if two proteins

interact by transformation into S. cerevisiae. Provided that the two proteins fused to the

two separable domains interact, the reconstituted GAL4 protein activates transcription of

one or more reporter genes that enable a colour reaction or growth on specific media. The

protein “X” that is fused to the BD of GAL4 is termed the ‘bait’, whilst the second protein,

“Y”, fused to the AD of GAL4 is termed the ‘prey’ (Figure 4-10A) (Fields and Song,

1989, Phizicky and Fields, 1995, Topcu and Borden, 2000, Lentze and Auerbach, 2008).

In our case the screen was performed with the bait plasmids pB27 (N-LexA-bait-C fusion)

and pB66 (N-GAL4-bait-C fusion), and obtained 14 positive clones out of 75 million

interactions tested with a 7-fold coverage of the luminal breast cancer cDNA library. Our

results revealed RABGAP1L as a very high-confidence prey (score A), given the number

(7/14) of independent and overlapping clones obtained from the screen.

In addition to RABGAP1L, only two preys have been identified with a significant score:

SF3B1 (score C, good confidence interaction), and COPG (score D, moderate confidence


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with a high possibility of false-positive interaction). SF3B1, is the splicing factor 3B

subunit 1 protein and is involved in RNA splicing particularly prespliceosome formation

(Will et al., 2002, Maguire et al., 2015), COPG, is the coatomer protein subunit gamma,

and is one of seven protein coatomers that coat vesicles as they bud from the Golgi

complex (Futatsumori et al., 2000). In the further analysis we will be focusing on

RABGAP1L as it has a very high confidence interaction (score A) obtained from the Y2H

screen.

4.3.10 RABGAP1L expression co-localises with C11orf67 in

ER+ breast cancers

The Y2H screen revealed RABGAP1L as a very high-confidence binding partner of

C11orf67. We next asked whether RABGAP1L follows the same pattern of expression

in breast cancer to that of C11orf67. To this aim, we took advantage of our breast cancer

TMA and performed IHC using anti-RABGAP1L and anti-C11orf67 specific antibodies

to assess co-localisation in the breast tumours. Our IHC staining revealed that

RABGAP1L follows a cytoplasmic, perinuclear and membrane-associated pattern of

expression very similar to that of C11orf67 (Figure 4-11A). With regards to the intensity

of staining, RABGAP1L showed weak expression (-) in the ER-/PR- breast cancer

patients, intermediate expression (+) in normal and benign breast tissues, and high

expression (++) in ER+/PR+ breast sections.

Moreover, we also investigated the effect of C11orf67 gene knockdown by shRNAs (see

Section 3.3.1) on both the mRNA and protein expression levels of RABGAP1L.

Interestingly, the C11orf67 knockdown by sh1 and sh2, but not control vector, led to

significant downregulation of RABGAP1L at the transcript and the protein level,

following the same trend of C11orf67 downregulation (Figure 4-11B). These results

suggest that C11orf67 and its binding partner RABGAP1L might participate the same

intracellular pathway. Further analysis might be required to understand the detailed

interaction and existence of the 2 proteins.


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Figure 4-10 Y2H screen reveals RABGAP1L and SF3B1 as binding partners

of C11orf67

(A) Schematic illustration of the two-hybrid-based protein-protein interactions

in the yeast nucleus. Modified from James et al. (1996). (B) Y2H screen

parameters for proteins interacting with C11orf67 (Score A: very high

confidence in the interaction, Score C: good confidence in the interaction).


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Figure 4-11 The intracellular pattern of RABGAP1L expression

corresponds to that of C11orf67 expression in breast cancer sections and cell

lines

(A) Representative IHC images of breast cancer TMA with C11orf67 and

RABGAP1L expression in ER-/PR-, Normal breast tissue, and ER+/PR+ breast

cancer sections. (B) The effect of C11orf67 gene knockdown by shRNAs on

the mRNA and protein expression levels of RABGAP1L by qRT-PCR and

western blot analyses respectively (*** p<0.0001).


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4.4 Discussion

C11orf67 has previously been identified as a novel candidate gene located at the heart of

an oncogenic cluster in 11q13.5-q14 that is amplified and overexpressed in an ER+

subgroup of breast cancer (Curtis et al., 2012). The IntClust 2 subtype is characterized by

poor prognosis and resistance to endocrine therapy.

In previous chapters, we proposed C11orf67 as a novel biomarker in ER+ breast cancer

that may have a role in predicting poor prognosis. We also identified the oncogenic

potential of C11orf67 and its significance as a therapeutic target in ER+ breast cancer. In

this chapter, we delineated the regulation mechanisms leading to C11orf67 upregulation

and its involvement in the development of the endocrine resistance to tamoxifen treatment

in ER+ breast cancer. We also evaluated potential protein-protein interactions involving

C11orf67 and other signalling molecules which may be reponsible for its biochemical

and biological role.

One of the major clinical obstacles for the management of the IntClust 2 subtype is the

failure of tamoxifen to prevent early relapse in a percentage of patients receiving

treatment, which results in a poor clinical outcome. Moreover, lack of predictors of

tamoxifen response makes it difficult to distinguish those patients who will not respond

to tamoxifen treatment.

The 11q13.5-q14 amplicon comprises C11orf67 and 12 other potential oncogenes.

Among them, amplification of PAK1 has been well associated with poor response to

tamoxifen in ER+ breast cancer. It has also been suggested that therapies targeting PAK1

expression and activity represent a novel strategy to increase endocrine response in breast

cancer (Kilker and Planas-Silva, 2006, Jirstrom et al., 2005, Bostner et al., 2007, Holm et

al., 2006).

In agreement with these findings, we are the first to highlight the role of the C11orf67

gene in tamoxifen resistance in breast cancer. The exact response to tamoxifen treatment

varied among breast cancer cell lines with high or low C11orf67 expression. C11orf67


137

overexpression reduced the antitumour effect of tamoxifen in MCF7 cells, whereas

siRNA depletion of C11orf67 combined with tamoxifen increased its antitumour effects.

These results provide a novel explanation for cellular mechanisms of tamoxifen

resistance. In summary, our findings suggest C11orf67 may provide a potential novel

targeted therapy to increase the response to endocrine treatment in poor prognosis breast

cancer subtype.

It has been reported that amplification of 11q13.5-q14 might occur individually or

simultaneously, resulting in co-amplification of multiple oncogenes (Karlseder et al.,

1994, Bekri et al., 1997, Courjal et al., 1997). In line with these studies, we are the first

to report that the C11orf67 promoter region shares transcriptional factor binding sites that

are distributed over the 11q13.5-q14 cluster as a whole. We also demonstrated that the

transcriptional regulation of this oncogenic cluster includes hormonal binding sites, p65

and Myc binding sites and the possibility of variable chromatin interactions occurring in

different regions of the cluster. This finding suggests that this oncogenic cluster might be

co-regulated leading to co-amplification of potential oncogenes including C11orf67.

Despite the fact that the 11q13.5-q14 locus might be coregulated in ER+ poor prognosis

breast cancer, no previous studies have investigated the effect of tamoxifen on the gene

expression of the whole cluster. We were the first to test this novel hypothesis, and found

that treating ER+ breast cancer cells with tamoxifen leads to remarkable upregulation of

all the genes (including C11orf67) residing in the 11q cluster. This provides an

explanation for the potential adverse effect of treating this category of patients with the

anti-estrogen drug tamoxifen.

The molecular mechanisms involved in the development of resistance to tamoxifen are

poorly understood. However, growing evidence indicates that the NF-κB signalling

pathway plays an important role by regulating the expression of genes critical for cell

survival (deGraffenried et al., 2004, Oida et al., 2014, Kastrati et al., 2015). Direct

binding of the p65 component to the RSF1 gene promoter is one of the possible

mechanisms by which NF-κB elicits its role in resistance to therapy of cancer cells (Yang

et al., 2014). Thus, we invesigated the binding of p65 in the C11orf67 locus using ChIP.


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Our data support a direct binding of p65 to the proximal promoter of C11orf67 in ER+

T47D breast cancer cells. Moreover, activating the NF-κB pathway by inflammatory

mediators (TNFα) or inhibiting its translocation into the nucleus resulted in significant

proportional changes in the expression of the C11orf67 transcript.

Both the direct binding of NF-κB to the C11orf67 promoter and the changes in

transcriptional levels of C11orf67 validate the possible role of NF-κB in regulating the

expression of C11orf67. This could be the underlying mechanism of developing

endocrine resistance. Additionally, these data also suggest that C11orf67 and RSF1 genes

are more likely to be co-regulated under the control of NF-κB.

A genome-wide siRNA screen for the regulators of NF-κB activity highlighted C11orf67

as one of 133 genes that if transiently knocked down by siRNA led to a significant

suppression of NF-κB activity in HEK 293T cells (Gewurz et al., 2012). However, this

study did not identify any mechanistic pathways underlying the effect of C11orf67 in

regulating NF-κB pathway, and findings in this embryonic cell line may not be applicable

to breast cancer. However, this finding added another dimension to our understanding of

the potential relation between C11orf67 and NF-κB, and motivated us to investigate the

effect of C11orf67 knockdown on NF-κB activity.

The outcomes of our experimental approach were consistent with the findings of Gewurz

(2012) and also answered additional questions. We showed that C11orf67 knockdown

dramatically decreased NF-κB reporter activity at both the basal level and after

stimulation with TNFα. We also showed downregulation of genes downstream of the NF-

κB pathway. Additionally, we pointed to one possible mechanism behind the decreased

activity of NF-κB, an impaired phosphorylation of IKKα and β levels associated with

C11orf67 knockdown. IKKα and β proteins are responsible for phosphorylation of the

inhibitor proteins stimulating its degradation by the proteasome, thus blocking

phosphorylation of IKKα and β proteins keeps p65 in the inactive form in combination

with IκB. Similar findings were observed after the RSF1 knockdown in ovarian cancer

cells by short hairpin RNAs, which suppressed the transcriptional activity of NF-κB

(Yang et al., 2014).


139

In summary, our preliminary data endorse C11orf67 as an essential, upstream signalling

factor controlling NF-κB activity and consequently downstream targets associated with

proliferation and resistance to anti-oestrogens and some forms of chemotherapy. On the

other hand, since C11orf67 and possibly other loci in 11p13.5 are regulated by p65, our

data provide evidence that C11orf67 acts in a feed-forward loop to amplify NF-κB

activation.

One of our striking findings is the novel discovery of RABGAP1L as a very high-

confidence prey by Y2H to be the binding partner of C11orf67, given the number (7/14)

of independent and overlapping clones obtained from the screen.

Ras-like proteins in brain (Rab) is a family of small GTPases that control the vesicular

trafficking that ensures appropriate binding of vesicles from their site of synthesis, with

the proper acceptor compartment and without any uncontrolled interactions or mixing.

Dysregulation of vesicular trafficking leads to diverse pathological consequences (Mitra

et al., 2011). In the cytosol, Rab proteins are kept in the inactive form by constant binding

with Guanine diphosphate (GDP). The Rab GTPase-activating proteins (RABGAP)

accelerate the intrinsic GTPase activity to promote the formation of the inactive GDP-

bound form. RABGAP proteins are “master organizers” that alternate the switches

between an inactive GDP-bound and an active Guanine triphosphate (GTP)-bound form

(Novick and Zerial, 1997, Mitra et al., 2011).

RABGAP1L, also known as HHL or KIAA0471, and is located in the 1q25.1 (Kim et al.,

2013). It is a signal transduction protein that regulates Rab GTPases and controls critical

pathways including vesicular trafficking, cell signalling, protein stability and cell death

(Spiegel et al., 2014). RABGAP1L encodes for a 298 aa protein, which is a GTPase-

activating protein, containing a putative phosphotyrosine-binding domain that binds

phospholipids and cell receptors, suggesting it is the tyrosine-kinase target in signalling

transduction (Roberti et al., 2009).


140

Importantly, RABGAP1L was found to be upregulated in human esophageal and oral

squamous cell carcinomas, and its downregulation was detected in Alzheimer’s disease

(Sharma et al., 2003, Arora et al., 2005).

In mammalian cells, Rab family include about 70 putative members, some with

particularly exciting roles. For instance, Rab 31 protein was found to be overexpressed in

IntClust 2 tumours (Curtis et al., 2012) and was also known to be one of the proteins

controlling NF-κB activity (Gewurz et al., 2012). Additionally, Rab 28 was found to

regulate p65 nuclear transport (Jiang et al., 2013), which could explain molecular

mechanisms by which C11orf67 control the NF-κB activity, resulting in transcriptional

activation of gene programs supporting proliferation and metabolic reprogramming.

In summary, our data demonstrates the important role of C11orf67 as a novel molecule

in tamoxifen resistance induced by the activation of the NF-κB pathway. Our findings

provide a potential mechanism of tamoxifen resistance in ER+ breast cancer subtype. It

can be predicted that targeting C11orf67 and other genes in the 11q13.5-q14 amplicon

can increase the efficiency of endocrine therapy in patients with C11orf67 overexpressing

breast cancer. In addition, tamoxifen should be used with caution in patients with

C11orf67 overexpression.

Chapter 5:

General Discussion

General Discussion

142

Breast cancer is one of the most prevalent malignancies in women around the world.

Although the relative survival from breast cancer is improving, the number of deaths from

this disease is likely to rise by the end of 2015 (AIHW, 2014). Breast cancer heterogeneity

is a major problem that gives rise to different clinical outcome and response to treatment

despite the same clinical diagnosis and prognostic profiles (Zhang et al., 2014).

Over the past few years, the introduction of advanced genomic platforms has added a new

dimension to the landscape of breast cancer and resulted in the identification of new

molecular subtypes. This integrative clustering approach has revealed the IntClust 2

subtype as a novel HR+ tumour subtype associated with high proliferation and very poor

survival. IntClust 2 is characterized by the overexpression of a cluster of candidate

oncogenic drivers in the 11q13.5-q14 locus.

Amplification of the 11q13.5-q14 amplicon has been detected in some types of human

cancer, and is associated with resistance to therapy and poor clinical prognosis (Hui et

al., 1997, Hui et al., 1998, Ormandy et al., 2003, Bostner et al., 2007). However, the

molecular mechanisms accounting for the poor response to treatment and the unfavorable

clinical outcomes in these patients are not understood. In the present study, we performed

functional genomic assays on the C11orf67 locus located in the centre of 11q13.5-q14

amplicon for its roles in tumourigenesis and the development of drug resistance in ER+

breast cancer.

C11orf67 encodes a hypothetical 122 aa protein of unknown function. It has been

previously shown that the protein structure is highly homologous to the bacterial protein

MTH938 (Das et al., 2001), and we have validated the existence of at least three different

oncogenic isoforms derived from C-terminal differential splicing. Importantly, the

function and regulation of C11orf67 is still largely unexplored, particularly in the breast

cancer field.

In the present study, we first assessed C11orf67 expression in clinical breast cancer

specimens and the potential prognostic role of C11orf67 in ER+ breast cancer patients.

Furthermore, we utilized model breast cancer cell lines to investigate the molecular

General Discussion

143

mechanisms of C11orf67 in promoting resistance to endocrine therapy, and proposed an

alternative approach for treating patients with C11orf67 overexpression.

C11orf67 is a novel prognostic biomarker in breast cancer

Incorporation of novel biomarkers that can be assessed in large patient cohorts into

routine clinical practice is very important towards prognostic improvement over the

traditional clinicopathological markers (ER, PR, HER2). In the current study, C11orf67

overexpression at the protein level (as assessed by IHC) appeared to be a strong

prognostic factor associated with HR+ status and the high grade breast cancer sections.

On the other hand, our preliminary data has validated the copy number amplification of

C11orf67 in ER+ breast tumours with a significant correlation with LN metastases. This

finding provides support to the notion that C11orf67 could be used as a novel biomarker

able to stratify breast cancer patients and identify those patients with a higher risk of

tumour recurrence and endocrine resistance.

However, in the near future larger cohorts of breast cancer patients are required for the

validation of the prognostic value of C11orf67. Further collaboration is planned to

validate the amplification and the overexpression of C11orf67 in clinical specimens by

performing IHC and FISH on invasive breast carcinomas from large Western Australian

patient cohorts. Both overexpression and amplification of C11orf67 need to be integrated

in the same patient cohort and then correlated with the risk of recurrence, resistance to

therapy and 5 year survival of patients. Significant correlations are imperative prior to the

development of clinical tests that can be implemented as prognostic biomarker of ER+

breast cancer patients with risk of recurrence and poor outcome.

C11orf67 is a novel oncogene in breast cancer

It is often unclear whether a given molecular alteration is a driver or a passenger in the

process of tumourigenesis and tumour progression. In the case of a driver, the expectation

is that tumour cells harbouring a given alteration are dependent on such alteration for the

origin and/or maintenance of the malignant phenotype (Koch, 2014). Several lines of

General Discussion

144

evidence presented in this thesis indicate that C11orf67 expression is a driver of some

aspects of ER+ breast cancer malignancy. However, it is presently unknown whether

C11orf67 functionally collaborates with some other flanking oncogenes. Clarification

will require combinatorial knockdowns of those genes. Current work ongoing in the

laboratory is implementing a knockdown multiplexing technology based on the

CRISPR/Cas9 system, to carry out multiple loss-of-function mutations in C11orf67 and

the flanking oncogenes and address their effect in the cell death phenotype.

C11orf67 is overexpressed in ER+ breast cancer cell lines, and as expected from being an

oncogene, has low expression levels in normal mammary epithelial cells. IHC was

conducted to evaluate C11orf67 expression in breast cancer specimens. High C11orf67

expression was mainly found in ER+/PR+ breast cancers compared to the normal breast

tissues or benign breast lesions.

More importantly, we demonstrated that silencing of C11orf67 expression in ER+ breast

cancer cell line by shRNA resulted in a significant decrease in the proliferation, colony

formation, in vitro migration, and in vivo tumourigenic potential of breast cancer cells.

Further supporting a causative role of C11orf67 in breast carcinogenesis, we showed that

C11orf67 depletion resulted in cellular senescence, which was evident by positive

staining of the β-gal assay.

C11orf67 is a potential therapeutic target in tamoxifen resistant breast tumours

ER+ breast cancers comprise 70% of all breast cancer cases, and these tumours exhibit a

relatively better prognosis than the ER- breast cancers. However, 30% of ER+ cases show

resistance to hormonal therapy and overall poor outcome (Goldhirsch et al., 2011).

Accordingly, the ER status is not the only factor that controls hormonal responsiveness.

Comprehensive understanding of the molecular signature profiles and identification of

novel predictors of tamoxifen resistance are essential for identifying patients who are

more likely to respond to tamoxifen treatment, and therefore designing novel therapeutic

strategies to improve the clinical outcome.

General Discussion

145

The effects of tamoxifen treatment were distinct between breast cancer cell lines with

high or low C11orf67 expression. We found that C11orf67 isoform_2 overexpression

increased the resistance to tamoxifen in ER+ breast cancer cell lines, whereas the siRNA-

mediated knockdown of the gene increased sensitivity. Furthermore, our molecular

studies provide a basis for potential mechanisms underlying tamoxifen resistance in ER+

breast cancer. Some proposed mechanisms for C11orf67-induced tamoxifen resistance

are discussed below, taking into consideration some gaps of knowledge and missing

information that were not fulfilled in our time frame, which will need to be explored in

further studies.

Loss or phosphorylation of ER

The ER status of breast cancer provides the primary target for endocrine therapy.

However, varying degrees of loss of ER signalling have been detected in about 20% of

tamoxifen-resistant breast cancer (Gutierrez et al., 2005). This phenomenon is not

contradictory with our results showing that C11orf67 is highly expressed in ER+ breast

cancer patients. However, further studies are required to determine if C11orf67

overexpression indeed correlates with ER loss in breast cancer patients treated with

tamoxifen.

Several pathways have been involved in tamoxifen resistance, including activation of

Akt/mTOR, MAPK, and NF-κB pathways. The activation of these proliferative pathways

induces the phosphorylation of ER, resulting in altered response to tamoxifen (Schiff et

al., 2004, Levin and Pietras, 2008, Santen et al., 2009). For instance, phosphorylation of

ERα at Serine 118 by p-MAPK, p-IKKα, or p-mTOR, increases ER activity and decreases

ER affinity to tamoxifen (Vendrell et al., 2005, de Leeuw et al., 2011). All the kinases

mentioned above were shown to be downregulated in the C11orf67 knockdown, and

could explain a possible mechanism of tamoxifen resistance. However, the

phosphorylated ER at S118 is an important checkpoint to be further tested in case of

C11orf67 depletion.

General Discussion

146

Growth factor receptor signalling pathways

Activation of proliferative pathways (EGFR and HER2) leads to the formation of ER-

independent transcriptional activity by either downregulating ER protein or by enhancing

ER activity in a ligand-independent manner, allowing tumour cells to escape the

inhibitory action of tamoxifen. Tamoxifen resistance, however, can also occur indirectly

by activation of other signalling pathways. In this study we found that the expression

levels of phosphorylated p42/44 MAPK, a downstream target of EGFR, decreased in the

case C11orf67 knockdown, an effect that could be associated with the reported

EGFR/HER2-induced tamoxifen resistance in ER+ tumours (Arpino et al., 2008,

Chakraborty et al., 2010). We plan to investigate the overexpression and/or the activation

state of EGFR/HER2 signalling pathways in the case of C11orf67 overexpression or

knockdown in the future.

Akt/mTOR pathway

The Akt/mTOR pathway can directly alter the sensitivity to tamoxifen leading to

tamoxifen resistance in ER+ breast cancer (Baselga et al., 2012, Cavazzoni et al., 2012).

ER+ breast cancer patients with high expression levels of p-Akt (Serine473) and p-mTOR

(Serine2448) exhibit an increased recurrence rate and poor outcome when they receive

tamoxifen treatment (Kirkegaard et al., 2005). However, the factors that induce the

activation of Akt/mTor signalling remain unknown. We are the first to show that

C11orf67 knockdown inhibits the phosphorylation of Akt/mTor proteins, suggesting that

a C11orf67-based target therapy is more likely to improve the response to tamoxifen in

ER+ breast cancer patients. It will also be interesting to define further the effect of mTOR

inhibitors combined with C11orf67 knockdown to increase the antitumour effect of

tamoxifen.

Cell cycle regulators and Myc

The expression and activity of the cyclins and cyclin-dependent kinases proteins have

been shown to have an impact on tamoxifen response. Increased expression of CCND1

General Discussion

147

has been reported in breast cancer cells in association with the development of tamoxifen

resistance, and with restored sensitivity in case of CCND1 depletion (Kilker et al., 2004).

Also, several studies have identified a significant correlation between CCND1

overexpression and poor response to tamoxifen treatment (Stendahl et al., 2004, Rudas et

al., 2008). This finding could be of particular interest, as it explains why C11orf67

knockdown cells showed lower levels of CCND1, while C11orf67 overexpression led to

upregulation of CCND1.

Furthermore, CCNE1 overexpression has been shown to induce resistance to anti-

estrogen therapy (Dhillon and Mudryj, 2002), and is considered one of the most critical

markers that predict endocrine resistance in ER+ breast cancer (Viedma-Rodriguez et al.,

2014). CCNE1 was found in our gene expression array to be downregulated in C11orf67

knockdown cells; these results offer another possible mechanism of tamoxifen resistance

mediated by C11orf67.

Another important marker for tamoxifen resistance is Myc, which is a nuclear

transcription factor that modulates the regulators of cell cycle progression (Nicholson et

al., 2005). We found that Myc was transcriptionally downregulated in the C11orf67

knockdown. Mounting evidence suggests that the overexpression of Myc is associated

with tamoxifen resistance in ER+ breast cancer patients. Myc activation occurs in

response to activation of the upstream mitogenic signalling pathways or the NF-κB

pathway that are both downregulated in C11orf67 ablation.

NF-κB activation

There is strong evidence in the literature that NF-κB activation is associated with

resistance to endocrine therapy (deGraffenried et al., 2004, Zhou et al., 2007). The

mechanisms of NF-κB activation are complex and crosstalk between multiple signalling

pathways is involved (Zhou et al., 2005, Van Laere et al., 2007, Godwin et al., 2013,

Zhang et al., 2013). Activation of Akt/mTor and MAPK pathways can in turn activate

NF-kB pathway (Sanchez-Perez et al., 2002, Crowley et al., 2005). A previous study

conducting a genome-wide screen in HEK 293T cells identified C11orf67 as one of the

General Discussion

148

targets that regulate NF-κB activity. In the current study, we confirmed this finding in the

context of the breast cancer cells, and also demonstrated that NF-κB regulated the

expression of C11orf67 by direct binding of p65 to the promoter of C11orf67. Our data

provide evidence that C11orf67, and possibly the 11q13.5-q14 amplicon, act as a

feedback loop to amplify NF-κB activation (Figure 5-1).

Figure 5-1 Proposed mechanisms underlying C11orf67-induce tamoxifen

resistance in breast cancer

Red dotted arrows indicate downstream direct effects that have been validated

in the study. Black dotted arrows and question marks indicate proposed effects

that have not been validated in the study.

General Discussion

149

Tamoxifen might be detrimental to ER+ patients with 11q13.5-q14 amplification

All the mechanisms mentioned above explain the potential role of C11orf67 in promoting

resistance to tamoxifen. On the other hand, we were able to demonstrate that treating ER+

breast cancer cells with a combination of estrogen and tamoxifen enhanced the expression

levels of almost all the genes residing in the 11q13.5q14 amplicon. This novel finding

suggests that the exposure to tamoxifen might have a detrimental effect on IntClust 2

patients through the activation of multiple oncogenes in 11q13.5-q14 amplicon. Indeed,

exposure to tamoxifen might represent a feedback loop that leads to more upregulation of

the expression levels of oncogenes, more resistance to tamoxifen, and so on.

Antimetabolites are a better choice for C11orf67 overexpression

Metabolic reprogramming of cancer cells is one of the hallmarks of cancer that is thought

to be essential to trigger rapid cancer cell proliferation and thus it is associated with poor

survival in breast cancer (Vazquez et al., 2013). Metabolic reprogramming of cancer cells

is expressed as increased nutrient uptake, upregulated consumption of glycine, and

increased expression levels of folate mitochondrial enzymes (SHMT2, MTHFD2, and

MTHFD1L) (Nilsson et al., 2014). The dependence of cancer cells on nucleotide

metabolism forms the basis that these cancer cells are potentially more inhibited by

chemotherapeutic agents targeting these metabolic enzymes (Jain et al., 2012) .

In the current study, we utilized genome-wide and targeted gene expression analyses to

study the expression patterns of metabolic enzymes in the case of C11orf67 knockdown

or overexpressing cancer cells. In particular, we demonstrated that enzymes of the

mitochondrial folate metabolic pathway are highly upregulated in cancer cells

overexpressing C11orf67, and markedly downregulated in C11orf67-depleted cancer

cells. Further, we provide evidence to show that clinically approved inhibitors of

thymidylate and purine biosynthesis (MTX and 5-FU) show selectivity to C11orf67-

overexpressing MCF7 cells. These findings were also consistent with other studies which

found a strong correlation between MTX sensitivity and high expression of folate

mitochondrial enzymes (Vazquez et al., 2013).

General Discussion

150

Our work suggests that C11orf67 overexpression in breast cancer is one of the underlying

reasons for the concomitant expression of folate metabolism-associated genes, which is

also are targets of Myc (Zeller et al., 2003). This highlights a novel pathway connecting

C11orf67, Myc, and metabolic enzymes in the responsiveness to MTX. Consequently,

overexpression of C11orf67 in breast cancer cells might provide a basis of stratifying the

patients that are more likely to benefit from MTX or 5-FU therapy; these tumours could

be more dependent on folate metabolism.

Concluding remarks

Outcomes from the present study may have several biological, clinical and prognostic

implications in the field of ER+ breast cancer.

First, the current results provide a potential explanation of how the 11q13.5-q14

amplification in the IntClust 2 subtype of breast cancer contributes to shorter overall

survival as compared to the other inter cluster subtypes. It is likely that tumours with

increased DNA copy number of C11orf67 overexpress C11orf67 protein that renders the

de novo tamoxifen resistance. Thus, they are likely related to poor clinical outcomes in

those patients.

Second, the C11orf67 knockdown sensitized breast cancer cells with high C11orf67

expression to the anti-cancer effects of tamoxifen. This result might have translational

consequences for the development of new, targeted cancer therapies to enhance the

sensitivity to tamoxifen. This could be achieved by inactivating the C11orf67 gene or by

interrupting the interaction with its potential binding partners, such as the RABGAP1L

protein in cancer cells.

Third, we found that C11orf67 was required for cell proliferation and survival in T47D

cells, indicating that cells with high expression of C11orf67 may be molecularly

“addicted” to C11orf67 expression.

General Discussion

151

Fourth, C11orf67 expression in ER+ breast cancer specimens could be used as a surrogate

marker alone or in combination with other candidate genes in the 11q13.5-q14 amplicon

(e.g. RSF1, INTS4) to predict treatment response to tamoxifen. To this end, future cohort

studies are required to validate the usefulness of C11orf67 immunoreactivity as a

potential prognostic test for ER+ breast cancer, or for other cancer types with C11orf67

amplification and overexpression.

There are several possible mechanisms that could explain how C11orf67 overexpression

contributes to tumour cell survival and growth in the presence of tamoxifen. First, an

upregulated C11orf67 might modulate the transcriptional activity of a set of genes that

participate in tamoxifen resistance. In fact, based on comparison of gene profiles between

C11orf67 knockdown and control T47D cells, we found that C11orf67 expression was

associated with changes in the expression of CCND1, CCNE1, and Myc. These gene

products have been previously reported to be involved in developing tamoxifen resistance

(Span et al., 2003, Butt et al., 2005).

Furthermore, using interaction network analysis, it appears that several major molecular

hubs were identified in this network, including NF-κB, MAPK, and Akt/mTOR. These

pathways have been suggested to participate in the development of tamoxifen resistance

in cancer cells. Further studies are required to demonstrate the detailed mechanisms of

how the C11orf67/NF-κB complex contributes to tumour development through its

downstream mediators.

While we have proposed the model outlined above, there are alternative interpretations to

consider. First, the shRNA approach used in this study suggests that C11orf67 is the main

gene within the 11q13.5-q14 amplicon responsible for tamoxifen resistance. However,

other gene(s) within the 11q13.5 amplicon might also play a role in the aggressive

behaviour of 11q13.5-amplified carcinomas. For example, PAK1 and Rsf-1 genes have

been shown to be associated with cancer cell proliferation and resistance to endocrine and

chemotherapy in breast and ovarian cancers (Holm et al., 2006, Mao et al., 2006, Bostner

et al., 2007, Keilty et al., 2013, Yang et al., 2014).

General Discussion

152

In summary, our data highlights an important role of C11orf67 as a novel marker in

tamoxifen resistant breast cancers. Our findings provide a possible mechanism of

tamoxifen-resistance in ER+ breast cancers by regulation of several pathways such as

Akt/mTOR and NF-κB. We propose that targeting C11orf67 and its downstream

signalling pathways could increase the efficacy of endocrine therapy in patients with

C11orf67-overexpressing breast cancer. In addition, tamoxifen should be used with

caution in patients presented with C11orf67 overexpression, as it leads to the upregulation

of the genes residing in the oncogenic cluster 11q13.5-q14. Instead, folate antagonists

might present a better choice for this subtype, as C11orf67 overexpression might increase

the dependence on folate metabolism.

Chapter 6:

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