axel grothey mayo clinic college of medicine rochester, mn
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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. Definitions. Pharmacogenomics: - PowerPoint PPT PresentationTRANSCRIPT
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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
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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)
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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
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Single marker analysis - ChemotherapySingle marker analysis - Chemotherapy
Agent Marker PrognosticPredictive
Efficacy Toxicity
5-FU TS + +
DPD (+) +
TP (+)
Irinocetan UGT1A1 +
Oxaliplatin GSTP1 + +
ERCC1 + +
XPD (ERCC2) +
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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)
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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
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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
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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)
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Genome-Wide Approaches Genome-Wide Approaches
Eschrich et al. JCO 2005
32,000 gene microarray78 tumors (Dukes B/C)53 prognostic genes identified
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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
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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
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Candidate Gene ApproachCandidate Gene Approach
O’Connell et al. ASCO 2006
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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
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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
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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
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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
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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
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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
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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