Predicting Safety and Efficacy of Treatment for Colon Cancer Clinical Science Symposium Towards Personalized Medicine: Trials and Technologies That Will Lead to Individualized Therapy for Cancer Neal J. Meropol, M.D. Fox Chase Cancer Center Philadelphia, PA May 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 DiagnosisSelect Treatment Diagnosis Select Treatment Apply Diagnostic Classifier Select Treatment Apply Diagnostic Classifier 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 DrugMarker FluoropyrimidinesTS, DPD*, TP, MSI, MTHFR expression/polymorphisms IrinotecanUGT polymorphisms*, MSI, transporter polymorphisms OxaliplatinERCC1, GST P1, XPD expression, transporter polymorphisms EGFR Antibodiesgene amplification/polymorphism, RAS mutation, BRAF mutation, ligand expression, PTEN expression, VEGF levels VEGF inhibitorsVEGF polymorphisms, ICAM polymorphisms/levels, E-selectin levels, HIF1, Glut-1, VEGFr gene expression GeneralCirculating tumor cells *FDA-recognizedYellow = 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 = No difference Progression-Free Survival FOLFIRI vs. FOLFIRI/Cetuximab Favor cetuximab HR = 0.68 P = No difference

Tumor gene expression and K-Ras mutations in fixed paraffin-embedded tissue predict response to cetuximab in metastatic colon cancer Authors J.B. Baker 1, D. Dutta 1, D. Watson 1, T. Maddala 1, S. Shak 1, E.K. Rowinsky 2, L. Xu 3, E. Clark 3, D.J. Mauro 3, S. Khambata-Ford 3 1 Genomic Health, Inc. Redwood City, CA 2 Imclone Systems, Inc., New York, NY 3 Bristol 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 + SD87 (60%) Disease Progression 57 (40%) Total144 (100%)

If validated, is this test “good enough” to assist in treatment decision making? WT RAS Low Response Gene Score High Response Gene Score Response + SD87 (60%)16 (27%)71 (85%) Disease Progression 57 (40%)44 (73%)13 (15%) Total144 (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 Pharmacodynamics Tumor Normal Tissue ResponseToxicity Dose and Compliance Drug focused Target 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