Personalized Health Care and Cancer Therapeutics (Biomarkers and Treatment)

2. Personalized Health Care and Cancer Therapeutics (Biomarkers and Treatment) April 17,, 2011 Dr Howard L. McLeod Eshelman Distinguished Professor and Director Institute for Pharmacogenomics and Individualized Therapy (IPIT) University of North Carolina – Chapel Hill, NC

3. “A surgeon who uses the wrong side of the scalpel cuts her own fingers and not the patient; if the same applied to drugs they would have been investigated very carefully a long time ago” Rudolph Bucheim Beitrage zur Arzneimittellehre, 1849

4. Personalized medicine, schmersonalized medicine! • Medicine has always been personalized • Medicine is moving toward greater 'customer accountability' • Medicine will never be personalized • it is a change in expectation as well as some practical, process changes

5. Drivers of Personalized Medicine • Technology • Significant new opportunities over the past 5 years • Patient financial burden • When you are paying more, you want more say • Less personal care • Who will be my 'doctor' today? • Cost of care • Even the USA can't afford treating 100% to benefit 20%

6. Preemptive action is a clinical major weapon Drug interactions Renal dysfunction Age Vaccination Antimalarial TB Mammography colonoscopy

9. $ $ $ $ $ $ $ $ $ $ $ $ • The clinical problem • Multiple active regimens for the treatment of most diseases • Variation in response to therapy • Unpredictable toxicity $ With choice comes decision

11. Pharmacogenomic examples-2011 • bcr/abl or 9:22 translocation—imatinib mesylate* • HER2-neu—trastuzumab** • C-kit mutations—imatinib mesylate** • Epidermal growth factor receptor mutations—gefitinib • Thiopurine S-methyltransferase—mercaptopurine and azathioprine* • UGT1A1-irinotecan** • CYP2D9/VKORC1-warfarin* • HLA-B*5701-abacavir * • HLA-B*1502-carbamazepine * • CYP2C19-clopidogrel • Cytochrome P-450 (CYP) 2D6—5-HT3 receptor antagonists, antidepressants, ADHD drugs, and codeine derivatives, tamoxifen*

12. What needs to be done to determine hope vs hype? • Find the 'right' biomarkers • Validate in robust datasets • Apply them!

13. We do not know very much about drugs ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? ???? Irinotecan cell membrane ABCB1 Irinotecan APC CYP3A4 CES1 Irinotecan NPC CYP3A5 CES2 CES1 CES2 SN-38 UGT1A1 SN-38 SN-38G ABCB1 ABCG2 ABCC1 ABCC2 SN-38 TOP1 ADPRT XRCC1 TDP1 NFKB1 CDC45L Cell Death

14. HapMap Linkage Model systems Expression array Association controls cases Discovery Strategies

15. What needs to be done to determine hope vs hype? • Find the 'right' biomarkers • Validate in robust datasets • Apply them!

16. Correlative science: business as usual Phase I In vivo Mechanism Phase II Biomarker assessment Phase III Biomarker validation

17. C90401; n=1020 C40101; n=4646 C80203/80405; n=2200 C30502; n=270 C80203/80405; n=2200 C30502; n=270 C50303; n=430 C50303; n=430 C10105; MDS C80303; n=528 C80303; n=528 C80101 gastric; n=800 2011 Estimated US Cancer Cases* Men710,040 Women662,870 Prostate 33% Lung and bronchus 13% Colon and rectum 10% Urinary bladder 7% Melanoma of skin 5% Non-Hodgkin 4% lymphoma Kidney 3% Leukemia 3% Oral Cavity 3% Pancreas 2% All Other Sites 17% • 32% Breast • 12% Lung and bronchus • 11% Colon and rectum • 6% Uterine corpus • 4% Non-Hodgkin lymphoma • 4% Melanoma of skin • 3% Ovary • 3% Thyroid • 2% Urinary bladder • 2% Pancreas • 21% All Other Sites *Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder. Source: American Cancer Society, 2005.

18. C90401; n=1020 C40101; n=4646 C30502; n=270 C80203/80405; n=2200 C30502; n=270 C80203/80405; n=2200 C50303; n=430 C50303; n=430 C10105; MDS C80303; n=528 C80303; n=528 C80101 gastric; n=800 GWAS x 2 GWAS NextGEN 2010 Estimated US Cancer Cases* Men710,040 Women662,870 Prostate 33% Lung and bronchus 13% Colon and rectum 10% Urinary bladder 7% Melanoma of skin 5% Non-Hodgkin 4% lymphoma Kidney 3% Leukemia 3% Oral Cavity 3% Pancreas 2% All Other Sites 17% • 32% Breast • 12% Lung and bronchus • 11% Colon and rectum • 6% Uterine corpus • 4% Non-Hodgkin lymphoma • 4% Melanoma of skin • 3% Ovary • 3% Thyroid • 2% Urinary bladder • 2% Pancreas • 21% All Other Sites GWAS GWAS *Excludes basal and squamous cell skin cancers and in situ carcinomas except urinary bladder. Source: American Cancer Society, 2005.

19. What needs to be done to determine hope vs hype? • Find the 'right' biomarkers • Validate in robust datasets • Apply them!

20. Fundamental questions When is surgery enough? Should we use chemotherapy? difficult to reverse practice Which treatment should we use? toxicity-many 'equal' therapies efficacy dosage

21. When should we use chemotherapy? Tumor tissue DNA Chip Analysis “Gene signature” Single value on “Gene signature” Relapse Hazard Score • Assay result: • low- or high- risk group • probability of distant relapse

22. 1.0 Good prognosis N = 20 0.8 0.6 N = 16 Distant Relapse-free Survival Poor prognosis 0.4 0.2 P-value = 0.0001 0.0 0 20 40 60 80 Time (months) Prediction of disease recurrence after surgery in Stage II colon cancer

23. Treat with therapy patients with stage II disease Watch and wait Not Predisposed to relapse

24. Development and Validation of a Multi-Gene RT-PCR Colon Cancer Assay NSABP and CCF Collaborations - 761 genes studied in 1,851 patients to select genes which predict recurrence and/or differential 5FU/LV benefit Clinical Validation of final assay in a large, prospectively-designed independent study Colon Cancer Technical Feasibility Development Studies Surgery + 5FU/LV NSABP C-04 (n=308) NSABP C-06 (n=508) Development Studies Surgery Alone NSABP C-01/C-02 (n=270) CCF (n = 765) Selection of Final Gene List & Algorithm Validation of Analytical Methods Clinical Validation Study – Stage II Colon Cancer QUASAR (n=1,436) Test Prognosis and Treatment Benefit

25. QUASAR RESULTS: Colon Cancer Recurrence Score Predicts Recurrence Following Surgery Group Risk (by Kaplan-Meier) 22% 12% 18% Prospectively-Defined Primary Analysis in Stage II Colon Cancer (n=711) RECURRENCE SCORE Calculated from Tumor Gene Expression STROMAL FAP INHBA BGN CELL CYCLE Ki-67 c-MYC MYBL2 GADD45B REFERENCE ATP5E GPX1 PGK1 UBB VDAC2 p=0.004

26. Fundamental questions When is surgery enough? Should we use chemotherapy? difficult to reverse practice Which treatment should we use? toxicity-many 'equal' therapies efficacy dosage

27. Shc Grb2 PI3-K Sos-1 Ras AKT Raf MEKK-1 MEK mTOR MKK-7 ERK JNK Angiogenesis Metastasis Apoptosis Resistance Proliferation The Epidermal Growth Factor Receptor Pathway

28. Retrospective studies supporting K-ras and lack of anti-EGFR response

29. Single agent panitumumab: N=208 K-Ras Mutation Wild-Type K-Ras Panitumumab registration trial Amado RG, et al. J Clin Oncol. 2008;26:1626-1634.

30. Mutations aplenty! Your patient with stage III sigmoid mucin neg adenocarcinoma has mutations in KRAS, BRAF, FGFR3, and CDK4 WHAT DO YOU DO?

31. Patient biology Lymph node status Tumor biology Cancer Outcome Distant metastasis Surgical technique Access to care

32. Disease Genotypes Infection Defense Genotypes Toxicity-risk Genotypes Supportive Care Genotypes Comprehensive optimization of patient care