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Al B. Benson III, MD, FACP Professor of Medicine Associate Director for Clinical Investigations

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

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Al B. Benson III, MD, FACP Professor of Medicine Associate Director for Clinical Investigations

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

  2. Prognostic Markers versus Predictive Markers • Markers may have both prognostic and predictive value • This can complicate assessment 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 www.cancerdiagnosis.nci.nih.gov

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

  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. 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 Ann Thor, ECOG, 2002

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

  9. Prevalence of Alterations Prevalence (%95%CI) 18qL0H p53 overexpr. p21waf1 expr. 8pL0H 17pL0H

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

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

  12. All patients with same diagnosis

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

  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 IV 22% Stage III 29% Eligible for Adjuvant Chemotherapy N=83,000 (55%)

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

  19. Estimates of 5 Year DFS (%) with Surgery Plus Adjuvant Therapy Nodal T stage Low Grade High Grade Status S+AT S+AT 0 nodes T3 73776570 T4 606651 57 T1-T2 62755368 1-4 nodes T3 49653856 T4 33512340 T1-T2 39572846 > 5 nodes T3 24431532 T4 1127 517 Adapted from Cill et al.. J Clin Oncol 22 :1801, 2004

  20. Disease-free Survival: ITT 1.0 0.9 0.8 5.9% 0.7 0.6 0.5 Probability Events FOLFOX4 304/1123 (27.1%) LV5FU2 360/1123 (32.1%) HR [95% CI]: 0.80 [0.68–0.93] 0.4 FOLFOX4 LV5FU2 0.3 0.2 0.1 0 0 6 12 18 24 30 36 42 48 54 60 Disease-free survival (months) p=0.003 Data cut-off: June 2006

  21. Disease-free Survival: Stage II and Stage III Patients 1.0 0.9 0.8 0.7 0.6 Probability 0.5 0.4 0.3 FOLFOX4 stage II LV5FU2 stage II FOLFOX4 stage III LV5FU2 stage III 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 0.2 0.1 0 0 6 12 18 24 30 36 42 48 54 60 66 72 Months p=0.258 3.8% p=0.005 7.5% Data cut-off: June 2006

  22. Disease-free Survival: High-risk Stage II Patients 1.0 0.9 0.8 7.2% 0.7 FOLFOX4 n=286 LV5FU2 n=290 0.6 0.5 Probability 3-year5-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 0.4 0.3 0.2 0.1 0 0 6 12 18 24 30 36 42 48 54 60 66 72 Disease-free survival (months) Exploratory analysis

  23. 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

  24. 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

  25. 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

  26. Intergroup Adjuvant Colon CancerINT 0035 (E 2284) S U R G E R Y Observation Levamisole 5-FU / levamisole

  27. Intergroup Adjuvant Colon CancerINT 0089 (E 2288) S U R G E R Y 5-FU / leucovorin (Mayo) 5-FU / leucovorin (Roswell) 5-FU / levamisole 5-FU / levamisole / leucovorin

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

  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. 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 Stratify: Disease stage (IIA or IIB) Microsatellite stability (stable vs MSI) 18q LOH E5202 Trial Schema MSI-L = low-level microsatellite instability MSI-H = high-level microsatellite instability *Bevacizumab continued for an additional 6 months

  31. 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

  32. 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

  33. 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

  34. 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 David Kerr1, Richard Gray2, Philip Quirke3, Drew Watson4, Greg Yothers5, Ian Lavery6, Mark Lee4, Michael O'Connell5, Steven Shak4, Norman Wolmark5 and the Genomic Health & QUASAR Colon Teams 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

  35. 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

  36. Development and Validation of a Multi-Gene RT-PCR Colon Cancer Assay Colon Cancer Technical Feasibility • 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 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

  37. Group Risk (by Kaplan-Meier) 22% 12% 18% QUASAR RESULTS: Colon Cancer Recurrence Score Predicts Recurrence Following Surgery 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

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

  39. 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

  40. G2 M S G1 EGF-induced Signal Transduction and Tumorigenesis EGF • 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 (+) EGFR X Anti-EGFR RAS RAF SOS K K PI3-K pY pY GRB2 MEK pY STAT PTEN AKT MAPK Gene transcription Cell-cycle progression p27 X X X X Proliferation Invasion/ metastasis Survival/ anti-apoptosis Angiogenesis Perez-Soler R. Oncologist. 2004;9:58-67.

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

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

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

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

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

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

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

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