predicting safety and efficacy of treatment for colon cancer clinical science symposium towards...
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Predicting Safety and Efficacy of Treatment for Colon Cancer
Clinical Science SymposiumTowards Personalized Medicine: Trials and
Technologies That Will Lead to Individualized Therapy for Cancer
Neal J. Meropol, M.D.Fox Chase Cancer Center
Philadelphia, PAMay 31, 2008
Disclosures
• Consulting or Advisory role– Amgen– Astra Zeneca– Genentech– Genomic Health– Imclone– Saladax– Sanofi Aventis– Zealand Pharma
• Stock Ownership– Saladax
The Context
• Multiple treatment options for patients with colorectal cancer
• No single “correct” treatment algorithm• All available treatments are toxic• All available treatments have modest activity• No obvious new agents on the horizon
The Treatment Discovery Cycle in Oncology: Where are we?
Demonstrate Clinical Activity
Optimize Use
Drug Discovery
Examples• 5FU modulation• Newer cytotoxics• Antibodies
Personalized Medicine Prerequisites: Target, Drug, Classifier
Diagnosis Select Treatment
Diagnosis
Select Treatment
Apply DiagnosticClassifier
Select Treatment
Apply DiagnosticClassifier
Revise Treatment
Old paradigm: Empirical Medicine
New paradigm: Personalized Medicine
It’s all about variability:“Predictive” vs. “Prognostic”
• Predictive: explains variability in response to treatment
• Prognostic: explains variability irrespective of treatment
Variability exists in the host (germline) and tumor (somatic)
Why weren’t validation studies undertaken until recently?
• It wouldn’t affect clinical care– Limited options for alternative therapy– Results not sufficiently discriminating
• Love for new drugs• Technical aspects of assay performance
But
The times have changed
What should we expect from a classifier?
• It must assist in decision making– Must it be perfect as a discriminator?
• Yes - If no competing therapies • No - If competing therapies
• Possible results– Patient will definitely benefit – doesn’t tell
who not to treat– Patient will definitely not benefit – doesn’t
tell who to treat– Patient will be more or less likely to benefit
Potential Predictive Markers for Colon Cancer Treatment
Drug Marker
Fluoropyrimidines TS, DPD*, TP, MSI, MTHFR expression/polymorphisms
Irinotecan UGT polymorphisms*, MSI, transporter polymorphisms
Oxaliplatin ERCC1, GST P1, XPD expression, transporter polymorphisms
EGFR Antibodies gene amplification/polymorphism, RAS mutation, BRAF mutation, ligand expression, PTEN expression, VEGF levels
VEGF inhibitors VEGF polymorphisms, ICAM polymorphisms/levels, E-selectin levels, HIF1, Glut-1, VEGFr gene expression
General Circulating tumor cells
*FDA-recognized Yellow = presented at ASCO 2008
The personalized approach to treatment of colorectal cancer has
arrived
PFS benefit of panitumumab only seen in patient with
wild-type KRAS
R. Amado et el. JCO 2008
Mutant RAS
WT RAS
When added to FOLFIRI, the benefit of cetuximab is restricted to patients with
WT RAS tumors
Van Cutsem et al. ASCO Plenary, 2008
Wild type RAS (N=348)
Mutant RAS (N=192)
Response
FOLFIRI vs. FOLFIRI/Cetuximab
Favor cetuximab
P = 0.0025
No difference
Progression-Free Survival
FOLFIRI vs. FOLFIRI/Cetuximab
Favor cetuximab
HR = 0.68
P = 0.017
No difference
Tumor gene expression and K-Ras mutations in fixed paraffin-embedded tissue predict response to cetuximab in metastatic colon cancer
AuthorsJ.B. Baker1, D. Dutta1, D. Watson1, T. Maddala1, S. Shak1, E.K. Rowinsky2, L. Xu3 , E. Clark3 , D.J. Mauro3 , S. Khambata-Ford3
1Genomic Health, Inc. Redwood City, CA2Imclone Systems, Inc., New York, NY
3Bristol Myers Squibb, Princeton, NJ
Baker et al. Summary
• 226 patients with metastatic colorectal cancer treated with single-agent cetuximab
• Retrospective analysis of banked tissue from 3 studies (~425 patients in parent studies)
• Association of RAS mutation and quantitative gene expression with clinical outcomes
• Key findings:– Gene expression can be reliably measured in
FFPE tissue– RAS mutation associated with lack of response– 4-gene model discriminates outcomes (“disease
control” and PFS) in patients with WT RAS
If validated, is this test “good enough” to assist in treatment decision making?
WT RAS
Response + SD 87 (60%)
Disease Progression
57 (40%)
Total 144 (100%)
If validated, is this test “good enough” to assist in treatment decision making?
WT RASLow Response
Gene ScoreHigh Response
Gene Score
Response + SD 87 (60%) 16 (27%) 71 (85%)
Disease Progression
57 (40%) 44 (73%) 13 (15%)
Total 144 (100%) 60 (100%) 84 (100%)
Clinical utility is dependent on other available options
Things I’d like to know more about
• Platform characteristics– e.g. how frequent are indeterminate results?
• Prediction vs. Prognosis– Is gene expression profile associated with
response or only “disease control”?– “Disease control” may be heavily influenced by
natural history rather than treatment• Will equivalent results be obtained with other
EGFR inhibitors?• Will the use of this test result in improved patient
outcomes for patients?• These data are worthy of validation in an
independent patient sample
H. L. McLeod, K. Owzar, D. Kroetz, F. Innocenti, S. Das, P. Friedman, K. Giacomini, R. Goldberg, A. Venook, M. J. Ratain
Univ of North Carolina-Chapel Hill, Chapel Hill, NC; Duke, Durham, NC; UCSF, San Francisco, CA; University of Chicago, Chicago, IL; CALGB, Chicago, IL
Cellular transporter pharmacogenetics in metastatic colorectal cancer: initial analysis of
C80203
McLeod et al. Summary
• Genomic DNA from 180 of 238 patients on C80203 (FOLFOX vs. FOLFIRI +/- cetuximab)
• Genotyping of transporter genes involved in irinotecan and oxaliplatin clearance:– ABCC2, ABCC4, ABCG2, SLCO1B1, SLC22A1,
and SLC22A2• Association of genotype with response and toxicity
• Key findings:– ABCG2 34 G>A associated with response to
FOLFOX, resistance to FOLFIRI– No associations with toxicity
Pharmacogenetics (Genetic Variation) Impacts Pharmacokinetics and Pharmacodynamics
Pharmacokinetics-Absorption-Distribution-Metabolism-Excretion
PharmacodynamicsTumor Normal Tissue
Response Toxicity
Dose andCompliance
Drug focused
Target focusedTarget focused
The promise and challenge of pharmacogenetics
• The promise– Mechanism-based– Non-invasive– Response and toxicity prediction
• The challenge– Low-frequency alleles– Multiple interrelated systems– Large sample sizes required to develop
and validate models
What have we learned?
• Germline DNA collection is possible in an Intergroup clinical trial
• ABCG2 34 G>A polymorphisms are uncommon• Association with treatment effect requires clinical
validation and mechanistic support (previous published work suggests no impact on irinotecan PK and increased in vitro sensitivity)
• Large sample sizes will be required to identify predictive associations with low frequency SNPs
• Individual SNPs as predictive markers will likely be rare given the complexity of drug metabolism and clearance, target expression and function, and mutidrug regimens
• Candidate gene selection based on pathway understanding complements genome wide screening efforts
We must be prepared to integrate new findings
• Patient care– Recognize that predictive markers will generally
not provide absolute guidance– Assess and communicate value and comparative
effectiveness of personalized approaches– Develop streamlined systems for tumor and
germline marker assessment • Research
– Emphasize prospective tissue acquisition– Anticipate and react promptly to new data that
impact ongoing research studies and patient care
The impact of personalized medicine for pharma is uncertain
Advantage?• Costs
– Development time ?– Production =
• Risks– Success rate ?
• Returns– Market size -– Duration of treatment +– New treatment market +– New diagnostic market ?– Competition +– Pricing (value, novelty, need) ?
Conclusions
• We can successfully personalize the therapy of patients with colorectal cancer
• We must continue to build well-annotated tissue repositories in prospective randomized clinical trials
• Now more than ever, industry and academia must identify shared goals
• With more effective personalized treatment approaches we have an opportunity to shift emphasis from the traditional focus on p-values (and marginal benefit) to focus on (and demand for higher) value of new innovation