Have We Made Progress in Pharmacogenomics and in the Implementation of Molecular
Markers in Colorectal Cancer ?
Have We Made Progress in Pharmacogenomics and in the Implementation of Molecular
Markers in Colorectal Cancer ?
Axel Grothey
Mayo Clinic College of Medicine
Rochester, MN
Axel Grothey
Mayo Clinic College of Medicine
Rochester, MN
DefinitionsDefinitions
• Pharmacogenomics: • Assessment of influence of genetic variation on
drug response by correlating gene expression or single-nucleotide polymorphisms (SNPs) with a drug's efficacy or toxicity
• Whole genome application of pharmacogenetics, which examines single gene interactions with drugs
• Biomarker:• Property of the tumor or the host associated with
clinical outcome• Either single trait or grouping of traits
(signature)
• Pharmacogenomics: • Assessment of influence of genetic variation on
drug response by correlating gene expression or single-nucleotide polymorphisms (SNPs) with a drug's efficacy or toxicity
• Whole genome application of pharmacogenetics, which examines single gene interactions with drugs
• Biomarker:• Property of the tumor or the host associated with
clinical outcome• Either single trait or grouping of traits
(signature)
To Distinguish…To Distinguish…
• Predictive vs prognostic markers
• Some biomarkers are predictive AND prognostic
• Biomarkers can be used to predict efficacy
and/or toxicity
• Somatic vs germline markers (mutations)
• Single marker analysis vs genome-wide
approach
• Predictive vs prognostic markers
• Some biomarkers are predictive AND prognostic
• Biomarkers can be used to predict efficacy
and/or toxicity
• Somatic vs germline markers (mutations)
• Single marker analysis vs genome-wide
approach
Single marker analysis - ChemotherapySingle marker analysis - Chemotherapy
Agent Marker PrognosticPredictive
Efficacy Toxicity
5-FU TS + +
DPD (+) +
TP (+)
Irinocetan UGT1A1 +
Oxaliplatin GSTP1 + +
ERCC1 + +
XPD (ERCC2) +
5-FU: Predictive Markers5-FU: Predictive Markers
FUH2
FUPA
FBAL
DPDDPDFUrd FUMP FUDP FUTP
FUdR
FdUMP FdUDP FdUTP
dUMP dTMP
5,10-CH3THF DHF
DNADNA
RNARNA
FU
TSTS
LV
TP
DPD, TS and TP Gene Expression vsResponse to 5-FU/LV in Colorectal Cancer
0
0.2
0.4
0.6
0.8
1
1.2
13
5
13
7
15
0
15
4
16
5
20
4
28
9
36
1
37
4
57
4
43
8 7
91
12
1
15
2
16
4
18
9
19
6
21
7
22
0
27
0
27
8
28
8
35
9
39
6
40
1
45
8
52
6
55
9
58
2
58
3
58
5
10
5m
DPD
TS
TP
Response Non response
Patient ID Number
Danenberg
Tum
or
Pro
file
Sca
le
Salonga et al. Clin Cancer Res 2000
Irinotecan-MetabolismIrinotecan-Metabolism
UGT
SN-38 (=active agent)
Inhibition of topoisomerase I
Carboxylesterase
Irinotecan
N NC
O
CH3
CH2
N
ON
O
O
OCH2CH3
HO
CH3
CH2
N
HON
O
O
OCH2CH3
HO
Glucuronidation(Detoxification)
(TA)6
(TA)7
UGT1A1*1
UGT1A1*28
UGT1A1 Polymorphism Predicts Severe Neutropenia on Irinotecan: 7/7 vs 6/7 + 6/6 Genotypes
UGT1A1 Polymorphism Predicts Severe Neutropenia on Irinotecan: 7/7 vs 6/7 + 6/6 Genotypes
Author
n/N (%)
Est. Odds Ratio 95% CI7/7 6/6 + 6/7
Innocenti 3/6 (50%) 3/53 (6%) 16.7 2.3 - 120.6
Rouits 4/7 (57%) 10/66 (15%) 7.5 1.4 - 38.5
Marcuelloa 4/10 (40%) 18/85 (21%) 2.5 0.6 - 9.7
Andob 4/7 (57%) 22/111 (20%) 5.4 1.1 - 25.9aGr 3+ neutropenia. bGr 4 leukopenia and/or Gr 3+ diarrhea.
From Parodi et al, FDA Subcommittee presentation, November, 2004
UGT1A1 genotype IFL FOLFOX IROX All
6/6 6.8% (3/44)
19.4% (26/134)
9.6% (5/52)
14.8% (34/230)
6/7 11.1% (6/54)
22.2% (28/126)
15.0% (6/40)
18.2% (40/220)
7/7 18.2% (2/11)
36.0% (9/25)
54.5% (6/11)
36.2% (17/47)
p-Value* 0.46 0.11 0.004 0.007
N9741 - Rates of Grade 4 Neutropenia for Genotype by Treatment. *Based on test of trend
McLeod et al. ASCO GI 2006
Glutathione-S-Transferase P1 I105V Polymorphism
Glutathione-S-Transferase P1 I105V Polymorphism
• GSTP1 = detoxifying enzyme that catalyzes the conjugation of glutathione to an electrophilic center in the toxic compound
• Single-nucleotide polymorphism (SNP) at residue 105 (C or T) determines enzymatic activity
• T (Isoleucine) C (Valine) substitution leads to
• Lower enzymatic activity
• Lower thermal stability
Reduced detoxicating properties of GSTP1
• GSTP1 = detoxifying enzyme that catalyzes the conjugation of glutathione to an electrophilic center in the toxic compound
• Single-nucleotide polymorphism (SNP) at residue 105 (C or T) determines enzymatic activity
• T (Isoleucine) C (Valine) substitution leads to
• Lower enzymatic activity
• Lower thermal stability
Reduced detoxicating properties of GSTP1
Johansson et al., J Mol Biol 1998
GST-P1 I105V (TC) Polymorphism Predicts Early Onset of Oxaliplatin-mediated Neurotoxicity
GST-P1 I105V (TC) Polymorphism Predicts Early Onset of Oxaliplatin-mediated Neurotoxicity
0
5
10
15
20
25
30
<600 <800
C/C (N=38) orC/T (N=130)
T/T (N=120)
% Grade 2/3 Neurotoxicity
P=0.030
Grothey et al., ASCO 2005
mg/m2 cum. oxaliplatin-dose
Multifactor Analysis 5-FU/Oxaliplatin-Treated Patients
Multifactor Analysis 5-FU/Oxaliplatin-Treated Patients
XPD, ERCC1, TS, GSTP1
5.4 mo
17.4 mo
Stoehlmacher et al. BJC 2004
Eve
nt-
free
Pro
bab
ility
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Weeks from Randomization
0 8 16 24 32 40 48 56
Hazard ratio=0.54 (95% CI: 0.44, 0.66)
Stratified log-rank testP < .000000001
Panitumumab vs. BSC: PFS
Panitumumab
BSC
Van Cutsem et al., JCO 2007
Only a subgroup of patients benefits Only a subgroup of patients benefits from EGF-R targeted therapyfrom EGF-R targeted therapy
Selected Potential Predictors of Anti-EGFR Therapy in CRC
Selected Potential Predictors of Anti-EGFR Therapy in CRC
• Tumor-related factors• EGFR mutations• EGFR expression levels• Alterations in EGFR signaling pathway
• Patient-related factors• Intensity of skin rash• Genetic polymorphism in, e.g.
components of EGFR pathway, ADCC activation
• Tumor-related factors• EGFR mutations• EGFR expression levels• Alterations in EGFR signaling pathway
• Patient-related factors• Intensity of skin rash• Genetic polymorphism in, e.g.
components of EGFR pathway, ADCC activation
12.77/5524.722/89Weak/moderate
0.00/722.26/27>20 - ≤35%
31.35/1620.04/20>10 - ≤20%
9.43/3224.215/62>35%
EGFR-staining intensity
4.81/2120.811/53Faint
11.84/3422.717/75Strong
7.14/5622.925/109≤10%
Percentage of EGFR-expressing cells
Cetuximab
n/N (%)
Cetuximab + Irinotecan
n/N (%)
No Correlation of Response Rate and EGFR Expression
No Correlation of Response Rate and EGFR Expression
Cunningham et al. NEJM 2004
Gene Copy Number of EGFR and Response to EGFR Antibodies
Gene Copy Number of EGFR and Response to EGFR Antibodies
• 31 pts with CRC treated with cetuximab- or panitumumab-based therapy
• Increased EGFR copy number in
• 8/9 pts with response• 1/21 pts without
response(p<0.0001)
Moroni et al., Lancet Oncol 2005
FISH
Dual color FISH assays for probes of EGFR (red) and Chr 7 (CEP7, green)
KRAS Mutation Status Predictive of Response to Cetuximab?
KRAS Mutation Status Predictive of Response to Cetuximab?
Lievre et al. Cancer Res 2006
• 30 patients with CRC on cetuximab
• PR: 11/30 patients (37%)• KRAS mutation in
• 0/11 responders• 13/19 non-responders
(68%)• p=0.0003
• Increased EGFR gene copy number in 10%
• significantly associated with response (p=0.04)
16.3 mo
6.9 mo
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
0 3 6 9 12 15 18 21 24
Months since start of cetuximab treatment
Est
imat
ed p
roba
bilit
y o
f su
rviv
al Adjusted log-rank p value = 0.028
All low expressions (n = 12)
Any high expression (n = 16)
Vallböhmer et al., JCO 2005
COX-2, IL-8 and EGFR Gene Expression Levels Associated with Survival on Cetuximab
COX-2, IL-8 and EGFR Gene Expression Levels Associated with Survival on Cetuximab
Genome-Wide Approaches Genome-Wide Approaches
• Potential to obtain comparative gene expression profiles and genetic fingerprints
• Can lead to identification of novel biomarkers and potential therapeutic target
• Different technologies applied:• Expression profiling microarrays• SNP arrays• Array-based comparative genomic
hybridization (CGH)
• Potential to obtain comparative gene expression profiles and genetic fingerprints
• Can lead to identification of novel biomarkers and potential therapeutic target
• Different technologies applied:• Expression profiling microarrays• SNP arrays• Array-based comparative genomic
hybridization (CGH)
Genome-Wide Approaches Genome-Wide Approaches
Eschrich et al. JCO 2005
32,000 gene microarray78 tumors (Dukes B/C)53 prognostic genes identified
Gene Signatures: Limitations and Challenges
Gene Signatures: Limitations and Challenges
• Fresh Frozen Tissue versus Formalin-Fixed Paraffin-Embedded Tissue
• Tissue Specific Array versus Non Tissue Specific Arrays
• Quantitative Gene Expression Profiles versus Arrays
• Fresh Frozen Tissue versus Formalin-Fixed Paraffin-Embedded Tissue
• Tissue Specific Array versus Non Tissue Specific Arrays
• Quantitative Gene Expression Profiles versus Arrays
Candidate Gene Approach Genomic Health
Candidate Gene Approach Genomic Health
• Expert selection of genes of interest
• 142 genes exhibited a significant linear relationship with RFI (p<0.05) in NSABP C-01/02
• 78 genes exhibited a significant linear relationship with RFI (p<0.05) after controlling for important covariates
• The prognostic genes in colon cancer are different from those in breast cancer
• Preliminary analysis of NSABP C-04 indicate that many genes are confirmed to be prognostic in colon cancer
• Expert selection of genes of interest
• 142 genes exhibited a significant linear relationship with RFI (p<0.05) in NSABP C-01/02
• 78 genes exhibited a significant linear relationship with RFI (p<0.05) after controlling for important covariates
• The prognostic genes in colon cancer are different from those in breast cancer
• Preliminary analysis of NSABP C-04 indicate that many genes are confirmed to be prognostic in colon cancer
O’Connell et al. ASCO 2006
Candidate Gene ApproachCandidate Gene Approach
O’Connell et al. ASCO 2006
ChallengesChallenges
• Combination therapy complicates choice of appropriate biomarkers
• Identification of biomarkers lags behind standard of care and agents used in clinical trials
• Most biomarkers identified in retrospective analysis without (or pending) prospective validation
• Complex, step-wise trial designs to validate usefulness of biomarkers• Large sample size
• Combination therapy complicates choice of appropriate biomarkers
• Identification of biomarkers lags behind standard of care and agents used in clinical trials
• Most biomarkers identified in retrospective analysis without (or pending) prospective validation
• Complex, step-wise trial designs to validate usefulness of biomarkers• Large sample size
Trial Designs:1. Marker by Treatment Interaction
Trial Designs:1. Marker by Treatment Interaction
Register Test Marker
Marker +
Marker -
Treatment A
Treatment B
Treatment A
Treatment B
R
R
Validation of marker as predictor for response to specific treatmentNo proof yet that marker-based treatment strategy is superior
Sargent et al. JCO 2005
Trial Designs (Example):1. Marker by Treatment Interaction
Trial Designs (Example):1. Marker by Treatment Interaction
Register Test TS
TS low
TS high
5-FU/Irino
Oxali/Irino
5-FU/Irino
Oxali/Irino
R
R
Validation of marker as predictor for response to specific treatmentNo proof yet that marker-based treatment strategy is superior
Sargent et al. JCO 2005
Trial Designs:2. Marker-Based Strategy
Trial Designs:2. Marker-Based Strategy
Register
Marker +
Marker -
Treatment A
Treatment B
Treatment A
Treatment B
R
Validation that marker-based treatment strategy is superior to random choice of therapy
Sargent et al. JCO 2005
R
Marker-basedstrategy
Non-marker-based strategy
Test Marker
Trial Designs (Example):2. Marker-Based StrategyTrial Designs (Example):2. Marker-Based Strategy
Register
TS low
TS high
5-FU/Irino
Oxali/Irino
5-FU/Irino
Oxali/Irino
R
Validation that marker-based treatment strategy is superior to random choice of therapy
Sargent et al. JCO 2005
R
Marker-basedstrategy
Non-marker-based strategy
Test TS
Phase II
Phase II NCCTG/ECOG ProposalMarker-driven First-Line CRC
Phase II NCCTG/ECOG ProposalMarker-driven First-Line CRC
KRAS wt orEGFR ampl.
KRAS mut andno EGFR ampl
FOLFOX + EGFR-mAb
FOLFOX + Bevacizumab
KRAS analysisEGFR gene amplification
Statistical calculations:• Primary Endpoint: RR• FOLFOX+Cetuximab 70%• FOLFOX+BEV 50%• N=200=0.10 (two-sided)• 90% power
ConclusionsConclusions
• Biomarker-driven treatment strategies hold promise of individualized, tailored therapeutic approaches with
• Higher efficacy• Lower toxicity• Improved cost-effectiveness
• Biomarkers are can be derived from retrospective analysis of single/multiple factors or from comparative genomic screening
• Prospective validation of biomarkers in clinical trials are challenging, but necessary
• Biomarker-driven treatment strategies hold promise of individualized, tailored therapeutic approaches with
• Higher efficacy• Lower toxicity• Improved cost-effectiveness
• Biomarkers are can be derived from retrospective analysis of single/multiple factors or from comparative genomic screening
• Prospective validation of biomarkers in clinical trials are challenging, but necessary
