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