1. Al B. Benson III, MD, FACP Professor of Medicine Associate Director for Clinical Investigations Robert H. Lurie Comprehensive Cancer Center of Northwestern University Biomarker-Driven Design: Complexities Using A Colon Cancer Model

2. Prognostic Markers versus Predictive Markers www.cancerdiagnosis.nci.nih.gov Prognostic marker Indicates the likelihood of outcome (tumor recurrence or patient survival) regardless of the specific treatment the patient receives Predictive marker Indicates the likelihood of response to a specific therapy Markers may have both prognostic and predictive value –This can complicate assessment

3. Prognostic versus Predictive Markers

4. Comparative Effectiveness Individual factors contribute to differences in clinical outcomes –Race or ethnic diversity –Co-morbidities –Drug-drug interactions –Tumor heterogeneity –Tumor genetics –Host genetics

5. Standard therapy Responders and Patients Not Predisposed to Toxicity All patients with same diagnosis Alternate therapy non-responders and toxic responders

6. Why Correlational Studies in Colorectal Cancer? Trials represent a “generic” population –Predictably a high % will have no benefit Tumors are heterogenous Numerous new “targeted” therapies, e.g., EGFR, VEGF Models: Breast cancer, GIST Toxicities

7. Ann Thor, ECOG, 2002 From “Marker” to “Test” Significant and independent value Validated by clinical testing Feasibility, reproducibility and widely available with quality control (robust) Performance should benefit the patient

8. Comparison and Applicability of Different Methodologies for Assessment of Tumor Markers YYNNNNY Application to routine diagnosis YYNNNNN Cellular localization evaluable NNYYYYY Microdissection needed YYNNYYY Use in formalin- fixed, wax- embedded tissue Protei n DNA / mRNA mRNA DNA Cellular constituent examined ICHISH Northern blotting RT- PCR LOH PCR SSCP PCR McLeod HL and Murray GI. British J of Cancer 79(2)191-203, 1999

9. Prevalence of Alterations 18qL0H 17pL0H p53 overexpr. p21 waf1 expr. 8pL0H Prevalence (%  95%CI)

10. Current patients A B All patients receive standard treatment (A) Clinical trials survival benefit from A Future patients Molecular analysis of tumor and patients A B C D Choice of treatment dependent upon molecular profile of tumor and on patient genotype

11. Cancer Outcome Lymph node status Distant metastasisSurgical technique Patient biology Tumor biology Access to care

12. All patients with same diagnosis

13. Standard therapy Responders and Patients Not Predisposed to Toxicity All patients with same diagnosis Alternate therapy non-responders and toxic responders

14. Marker Analyses from Clinical Trials Retrospective Analyses –Majority of marker reports –Incomplete tissue collection –Small numbers of patients –Various methodologies –Can be hypothesis generating –Exception = Kras

15. Marker Analyses from Clinical Trials Prospective Correlative Studies in Clinical Trials –Tissue collection not mandated –Statistically significant number of patients and comparisons –Robust clinical data –Many trials now include correlatives

16. Marker Analyses from Clinical Trials Marker-driven Treatment Strategy –Stratification –Treatment assignment

17. Incidence of Colorectal Cancer U.S. 2003 N=152,000 Stage I 24% Stage II 26% Stage III 29% Stage IV 22% Eligible for Adjuvant Chemotherapy N=83,000 (55%)

18. AJCC 6 th Edition: Colorectal Cancer IIIA (T1-2N1M0) IIIB (T3-4N1M0) IIIC (TanyN2M0) - Stage III divided into IIA (T3N0M0) IIB (T4N0M0) - Stage II divided into

19. Estimates of 5 Year DFS (%) with Surgery Plus Adjuvant Therapy NodalT stageLow GradeHigh Grade Status S +AT S +AT 0 nodes T3 73 77 65 70 T4 60 66 51 57 T1-T2 62 75 53 68 1-4 nodes T3 49 65 38 56 T4 33 51 23 40 T1-T2 39 57 28 46 > 5 nodes T3 24 43 15 32 T4 11 27 5 17 Adapted from Cill et al.. J Clin Oncol 22 :1801, 2004

21. Disease-free Survival: ITT Data cut-off: June 2006 Disease-free survival (months) FOLFOX4 LV5FU2 Probability 1.0 0.8 0.6 0.4 0.2 0 0.9 0.7 0.5 0.3 0.1 06121824603036424854 Events FOLFOX4 304/1123 (27.1%) LV5FU2 360/1123 (32.1%) HR [95% CI]: 0.80 [0.68–0.93] 5.9% p=0.003

22. Disease-free Survival: Stage II and Stage III Patients Data cut-off: June 2006 HR [95% CI] p-value Stage II 0.84 [0.62–1.14] 0.258 Stage III 0.78 [0.65–0.93] 0.005 FOLFOX4 stage II LV5FU2 stage II FOLFOX4 stage III LV5FU2 stage III Months Probability 1.0 0.8 0.6 0.4 0.2 0 0.9 0.7 0.5 0.3 0.1 061218246030364248546672 3.8% 7.5% p=0.258 p=0.005

23. Disease-free Survival: High-risk Stage II Patients Disease-free survival (months) FOLFOX4 n=286 LV5FU2 n=290 Probability 1.0 0.8 0.6 0.4 0.2 0 0.9 0.7 0.5 0.3 0.1 061218246030364248546672 3-year 5-year FOLFOX4 85.4% 82.1% LV5FU2 80.4% 74.9% HR [95% CI]: 0.74 [0.52–1.06] High-risk stage II- defined as at least one of the following: T4, tumor perforation, bowel obstruction, poorly differentiated tumor, venous invasion, <10 lymph nodes examined; Data cut-off: June 2006 7.2% Exploratory analysis

24. Approximate Number of Patients Needed to Detect a Realistic Treatment Benefit* Dukes’ B Dukes’ C No. of No. of Survival ARR Patients Survival ARR Patients At 3 years 85% 2.5% 8,000 65% 5.2% 3,400 At 4 years 80% 3.3% 5,800 58% 6.0% 2,800 At 5 years 75% 4.0% 4,700 50% 6.6% 2,400 Abbreviation: ARR = absolute risk reduction For 90% power of detecting the treatment benefit using two-tailed significance tests at the 5% level, assuming the true relative risk reduction is 18% for both Dukes’ B and Dukes’ C. Buyse, Piedbois, 2001

25. Prognostic Factors in Colorectal Cancer COLLEGE OF AMERICAN PATHOLOGISTS CONSENSUS Category 1 – evidence from multiple statistically-robust published trials and used in pt. management Category IIA – extensively studied and sufficient for path reports, but needs validation Category IIB – promising Category III – insufficient study Category IV – well-studied and no prognostic significance

26. Prognostic Factors in Colorectal Cancer COLLEGE OF AMERICAN PATHOLOGISTS CONSENSUS Category I path-local extent of tumor = pT path-nodes = pN blood or lymphatic invasion post-op residual tumor = R (e.g., + margin) post-op  CEA Category IIA tumor grade radial margin status residual tumor s/p neoadjuvant tx

27. Intergroup Adjuvant Colon Cancer INT 0035 (E 2284) Observation Levamisole 5-FU / levamisole SURGERYSURGERY

28. Intergroup Adjuvant Colon Cancer INT 0089 (E 2288) 5-FU / leucovorin (Mayo) 5-FU / leucovorin (Roswell) 5-FU / levamisole 5-FU / levamisole / leucovorin SURGERYSURGERY

29. Analysis of Molecular Markers in Patients with Stage III Colon Cancer Watanbe T, et al. N Engl J Med 344(16);1196-1206, 2001

30. Analysis of Molecular Markers in Patients with Stage III Colon Cancer Watanbe T, et al. N Engl J Med 344(16);1196-1206, 2001

32. E5202 Trial Schema Low-Risk Patients MSS or MSI-L with retention of 18q alleles MSI-H Arm A: mFOLFOX6 q2w × 12 Arm B: mFOLFOX6 + bevacizumab* q2w × 12 Arm C: Observation only High-Risk Patients MSS/18q LOH or MSI-L/18q LOH are RANDOMIZED MSI-L = low-level microsatellite instability MSI-H = high-level microsatellite instability *Bevacizumab continued for an additional 6 months Stratify: Disease stage (IIA or IIB) Microsatellite stability (stable vs MSI) 18q LOH

33. E5202 Trial Design: Sample Submission Tumor and normal tissue sample required for enrollment –Samples must be formalin-fixed paraffin blocks or unstained histologic sections –Submission time points are crucial Received no later than 50 days following surgery Received within 5 days of trial registration –Surgeons at participating institutions should be aware of timeline in order to introduce patients to trial Critical given timeline of tissue collection

34. E5202 Correlative Studies Correlate tumor biologic characteristics with survival of patients treated with test regimens –Microsatellite stability –18q LOH All tissue from study to be archived by ECOG coordinating center and assessed for biologic characteristics by MD Anderson laboratories Tissue from studies will be archived for future assessment

35. Deficient Mismatch Repair as a Predictive Marker for Lack of Benefit from 5-FU based Chemotherapy in Adjuvant Colon Cancer DJ Sargent, S Marsoni, SN Thibodeau, R Labianca, SR Hamilton, V Torri, G Monges, C Ribic, A Grothey, S Gallinger ASCO 2008

36. David Kerr 1, Richard Gray 2, Philip Quirke 3, Drew Watson 4, Greg Yothers 5, Ian Lavery 6, Mark Lee 4, Michael O'Connell 5, Steven Shak 4, Norman Wolmark 5 and the Genomic Health & QUASAR Colon Teams A quantitative multi-gene RT-PCR assay for prediction of recurrence in stage II colon cancer: Selection of the genes in 4 large studies and results of the independent, prospectively-designed QUASAR validation study 1. University of Oxford, Oxford, UK; 2. Birmingham Clinical Trials Unit, Birmingham, UK; 3. Leeds Institute of Molecular Medicine, Leeds, UK; 4. Genomic Health, Inc., Redwood City, CA; 5. National Surgical Adjuvant Breast and Bowel Project, Pittsburgh, PA; 6. Cleveland Clinic Foundation, Cleveland, OH

37. The Need for Individualized Therapy in Stage II Colon Cancer The challenge: Which stage II colon cancer patients should be treated with adjuvant chemotherapy? –75-80% cured with surgery alone, but no method to identify them –Absolute benefit of chemotherapy is small and no consensus in guidelines on who to treat –Chemotherapy has significant toxicity Today, decision to give chemotherapy subjectively based on: –Clinical/pathologic markers of risk which are inadequate Not informative for majority of patients –Patient age, co-morbidities, preferences

38. Colon Cancer Technical Feasibility Development Studies Surgery Alone NSABP C-01/C-02 (n=270) CCF (n = 765) Selection of Final Gene List & Algorithm Development Studies Surgery + 5FU/LV NSABP C-04 (n=308) NSABP C-06 (n=508) Clinical Validation Study – Stage II Colon Cancer QUASAR (n=1,436) Test Prognosis and Treatment Benefit Development and Validation of a Multi-Gene RT- PCR Colon Cancer Assay Validation of Analytical Methods 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

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

40. QUASAR RESULTS: Recurrence Score, T Stage, and MMR Deficiency are Key Independent Predictors of Recurrence in Stage II Colon Cancer Multivariate Analysis

41. Summary and Conclusions The prospectively-defined continuous Recurrence Score has been validated as a predictor of recurrence in stage II colon cancer patients following surgery, and provides independent value beyond standard measures of risk A separate score, based on a distinct set of 6 genes, was not validated for prediction of differential 5FU/LV benefit The continuous RS provides individualized assessment of recurrence risk and will have the greatest clinical utility when used in conjunction with T stage and Mismatch Repair (MMR/MSI), particularly for the majority of patients for whom those markers are uninformative (~70% of pts) This is the first demonstration that a prospectively defined gene expression assay can independently predict recurrence in colon cancer Implications for Clinical Practice

42. EGF-induced Signal Transduction and Tumorigenesis Epidermal growth factor receptor (EGFR) – A large tyrosine kinase growth factor receptor Natural ligands – TGF- , EGF Potential to block multiple steps in the signal transduction process –Extracellular surface –Intracellular targets X Invasion/ metastasis Proliferation Survival/ anti-apoptosis Angiogenesis MAPK MEK Gene transcription Cell-cycle progression PI3-K RASRAF SOS GRB2 PTENAKT STAT pY KK M G1 S G2 EGF pY p27 X X X EGFR Anti-EGFR (+) X Perez-Soler R. Oncologist. 2004;9:58-67.

43. Potential Biomarkers: Methods of Testing EGFR protein expression EGFR gene copy number K-ras gene mutations EGFR ligands and phosphorylation

44. Amado, R. G. et al. J Clin Oncol; 26:1626-1634 2008 Fig 1. CONSORT diagram

45. Amado, R. G. et al. J Clin Oncol; 26:1626-1634 2008 Fig 2. Progression-free survival by treatment within KRAS groups

46. Amado, R. G. et al. J Clin Oncol; 26:1626-1634 2008 Fig 3. Subset analyses of progression-free survival in the KRAS wild-type group

47. Amado, R. G. et al. J Clin Oncol; 26:1626-1634 2008 Fig 4. Waterfall plots showing maximum percent decrease in target lesions (blinded central radiology)

48. Amado, R. G. et al. J Clin Oncol; 26:1626-1634 2008 Fig 5. Kaplan-Meier curves for overall survival by treatment and KRAS status

49. Amado, R. G. et al. J Clin Oncol; 26:1626-1634 2008 Fig A1. (A) Progression-free survival and (B) overall survival by KRAS status among patients receiving panitumumab after progression on best supportive care alone