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Predictive biomarker validation in practice: Lessons from real trials

Predictive biomarker validation in practice: Lessons from real trials. Daniel Sargent, PhD Division of Biomedical Statistics and Informatics Mayo Clinic, Rochester MN U Penn Conference, April 29, 2009. Prognostic Marker.

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Predictive biomarker validation in practice: Lessons from real trials

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  1. Predictive biomarker validation in practice: Lessons from real trials Daniel Sargent, PhD Division of Biomedical Statistics and Informatics Mayo Clinic, Rochester MN U Penn Conference, April 29, 2009

  2. Prognostic Marker Single trait or signature of traits that separates different populations with respect to the risk of an outcome of interest in absence of treatment or despite non targeted “standard” treatment Prognostic No treatment or Standard, non targeted treatment Marker + Marker –

  3. Predictive Marker Single trait or signature of traits that separates different populations with respect to the outcome of interest in response to a particular (targeted) treatment Predictive No treatment Or Standard Targeted Treatment Marker + Marker –

  4. Predictive Goal: Curable Disease LOW RISK INT RISK HIGH RISK Control New Treatment

  5. Randomized Controlled Trial (RCT) for predictive marker validation • Goal: Determine which treatment will work for which patient • Vital: Patients treated with treatment choices in question must be comparable • Only true assurance: Patients randomized between treatments in question

  6. Example: Lack of randomization • Observational study of 656 consecutive patients • Tested association of biomarker with chemotherapy benefit • Appears pts with marker get big therapy benefit (compare dotted lines) • Problem: Non-randomized: Treated pts median 13 years younger than untreated! Elsaleh, Lancet 2000

  7. Phase III Trial Designs • Retrospective Validation • Prospective Validation • Enrichment Designs • All-comers or Unselected Designs • Adaptive “Analysis” Designs

  8. Retrospective/Prospective Validation • Test a marker by treatment interaction effect utilizing data collected from previously conducted randomized controlled trial (RCT) comparing therapies for which a marker is proposed to be predictive • Reasonable when: • a prospective RCT is ethically impossible based on results from previous trials, and/or • a prospective RCT is not logistically feasible (large trial and long time to complete). • Feasible and timely

  9. Retrospective/Prospective Validation • Samples must be available on a large majority of patients to avoid selection bias in the patients that have or do not have the samples. • Hypotheses, analyses techniques, patient population, and precise algorithm for assay techniques must be stated prospectively • All marker subgroup analyses have to be stated upfront, with appropriate sample size justification • If replicated, this should be considered acceptable for full marker validation

  10. Single-Arm Studies Support the Hypothesis for KRAS as a Biomarker for EGFr Inhibitors WT, wild type; MT, mutant; cmab, cetuximab; CT, chemotherapy; pmab, panitumumab

  11. R A N D O M I Z E Panitumumab PD Follow-up 6.0 mg/kg Q2W + BSC BSC PD Follow-up Optional Panitumumab Crossover Study KRAS Analysis of a Phase 3, Randomized, Controlled Trial Comparing Panitumumab vs Best Supportive Care (BSC) in Colorectal Cancer Hypothesis: The treatment effect of panitumumab monotherapy is larger in patients with wild-type KRAS compared to patients with mutant KRAS 1:1 • Randomization stratification • ECOG score: 0-1 vs. 2 • Geographic region: Western EU vs. Central & Eastern EU vs. Rest of World Van Cutsem, Peeters et al. JCO. 2007;25:1658-1664.

  12. Objectives and Analysis Methodology • Primary Objective • To assess if the effect of panitumumab on progression-free survival (PFS) was significantly greater in patients with wild-type KRAS compared to patients with mutant KRAS • Secondary Objectives • To assess whether panitumumab improves PFS compared with BSC alone in patients with wild-type KRAS • To assess whether panitumumab improves OS compared with BSC alone in patients with wild-type KRAS Test for a PFS effect among all randomized patients at a 5% level Test for quantitative PFS effect interaction, i.e., wild-type effect > mutant p ≤ 0.05 p > 0.05 Compare PFS in wild-type KRAS subset STOP p ≤ 0.05 p > 0.05 Compare OR & OS in wild-type KRAS subset STOP

  13. KRAS Evaluable Pts (92% of population):PFS by Treatment Median In Weeks Mean In Weeks 1 . 0 Events/N (%) 0 . 9 Pmab + BSC 191/208 (92) 8.0 15.4 9.6 BSC Alone 209/219 (95) 7.3 0 . 8 0 . 7 HR = 0.59 (95% CI: 0.48–0.72) 0 . 6 Proportion with PFS 0 . 5 0 . 4 0 . 3 0 . 2 0 . 1 0 . 0 0 2 4 6 8 1 0 1 2 1 4 1 6 1 8 2 0 2 2 2 4 2 6 2 8 3 0 3 2 3 4 3 6 3 8 4 0 4 2 4 4 4 6 4 8 5 0 5 2 Weeks Patients at Risk Pmab + BSC 2 0 8 1 9 7 1 8 8 1 7 8 1 0 6 7 9 7 1 6 4 5 5 5 0 4 9 4 9 3 7 2 9 2 5 2 4 1 9 1 5 1 5 1 5 1 2 9 9 7 6 6 2 1 9 2 0 0 1 6 8 1 4 2 7 5 4 2 3 4 2 5 2 3 1 9 1 6 1 4 1 4 1 0 1 0 1 0 1 0 9 8 6 6 5 4 4 4 3 BSC Alone

  14. Mutant KRAS Subgroup:PFS by Treatment Median In Weeks Mean In Weeks 1.0 Events/N (%) 0.9 Pmab + BSC 76/84 (90) 7.4 9.9 0.8 10.2 BSC Alone 95/100 (95) 7.3 0.7 HR = 0.99 (95% CI: 0.73–1.36) 0.6 Proportion with PFS 0.5 0.4 0.3 0.2 0.1 0.0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 Weeks Patients at Risk Pmab + BSC 84 78 76 72 26 10 8 6 5 5 5 5 4 4 4 4 2 2 2 2 2 2 2 1 1 1 100 91 77 61 37 22 19 10 9 8 6 5 5 4 4 4 4 4 4 3 3 3 2 2 2 2 BSC Alone

  15. Wild-type KRAS Subgroup: PFS by Treatment p < 0.0001 for quantitative-interaction test comparing PFS log-HR (pmab/BSC) between KRAS groups 1 1 . . 0 0 0 0 . . 9 9 Median In Weeks Mean In Weeks Events/N (%) 0 0 . . 8 8 Pmab + BSC 115/124 (93) 12.3 19.0 0 0 . . 7 7 9.3 BSC Alone 114/119 (96) 7.3 0 0 . . 6 6 HR = 0.45 (95% CI: 0.34–0.59) Stratified log-rank test, p < 0.0001 Proportion with PFS 0 0 . . 5 5 0 0 . . 4 4 0 0 . . 3 3 0 0 . . 2 2 0 0 . . 1 1 0 0 . . 0 0 0 2 4 6 8 10 12 14 16 18 2 0 2 2 2 4 2 6 2 8 3 0 3 2 3 4 3 6 3 8 4 0 4 2 4 4 4 6 4 8 5 0 5 2 Weeks Patients at Risk 124 119 112 106 80 69 63 58 50 45 44 44 33 25 21 20 17 13 13 13 10 7 7 6 5 5 Pmab + BSC 6 10 9 9 6 6 6 5 4 3 3 2 2 2 2 1 119 109 91 81 38 20 15 15 14 11 BSC Alone

  16. CRYSTAL trial: first-line mCRC Cetuximab + FOLFIRI Cetuximab IV 400 mg/m2 on day 1, then 250 mg/m2 weekly + irinotecan (180mg/m2) + 5-FU (400 mg/m2 bolus+ 2400 mg/m2 as 46-hr continuous infusion) + FA every 2 weeks EGFR-expressing metastatic CRC R FOLFIRI Irinotecan (180 mg/m2) + 5-FU (400 mg/m2 bolus+ 2400 mg/m2 as 46-hr continuous infusion) + FA every 2 weeks • Stratification factors • Regions • ECOG PS • Populations • Randomized patients n=1217 • Safety population n=1202 • ITT population n=1198 Van Cutsem E et al, ASCO 2007

  17. 1.0 0.9 0.8 0.7 0.6 Progression-free survival estimate 0.5 0.4 0.3 0.2 0.1 0.0 20 0 2 4 6 8 10 12 14 16 18 Months Cetuximab + FOLFIRI (n=599) FOLFIRI (n-599) CRYSTAL trial – Primary endpoint PFS Van Cutsem E et al, ASCO 2007

  18. KRAS evaluable population 1198 subjects (ITT) 587 subjects analyzed for KRAS mutation status 540 (45%) subjects: KRAS evaluable population 348 (64.4%) KRAS wild-type 192 (35.6%) KRAS mutant Group A: 172 (49.4%) Group B: 176 (50.6%) Group A: 105 (54.7%) Group B: 87 (45.3%) 171 subjects with events (49.1%) 101 subjects with events (52.6%) Cetuximab + FOLFIRI FOLFIRI

  19. KRAS wild-type (n=348) HR=0.68; p=0.017mPFS Cetuximab + FOLFIRI: 9.9 months mPFS FOLFIRI: 8.7 months 1.0 0.9 0.8 0.7 0.6 1-year PFS rate 25% vs 43% Progression-free survival estimate 0.5 0.4 0.3 0.2 0.1 0.0 0 2 4 6 8 10 12 14 16 18 Months Cetuximab + FOLFIRI FOLFIRI PFS – KRAS wild-type Van Cutsem, NEJM 2009

  20. KRAS mutant (n=192) HR=1.07; p=0.47mPFS Cetuximab + FOLFIRI: 7.6 months mPFS FOLFIRI: 8.1 months 1.0 0.9 0.8 0.7 0.6 Progression-free survival estimate 0.5 0.4 0.3 0.2 0.1 0.0 0 2 4 6 8 10 12 14 16 Months Cetuximab + FOLFIRI FOLFIRI PFS – KRAS mutant Van Cutsem, NEJM 2009

  21. KRAS conclusions • Marker identified in single arm trials after non-targeted phase III trials completed • Initial targeting marker wrong: EGFR expression • Prospective specification of KRAS analysis plan • Multiple retrospective trials provided very consistent results • Research & clinical communities convinced – all ongoing trials of EGRF inhibitors modified to enroll only KRAS WT patients

  22. Phase III Trial Designs • Retrospective Validation • Prospective Validation • Enrichment Designs • All-comers or Unselected Designs • Adaptive “Analysis” Designs

  23. Enrichment Designs • Screens patients for the presence or absence of a marker or a panel of markers, AND • Only includes patients in the clinical trial who either have or do not have a certain marker characteristic or profile • Paradigm: Not all patients will benefit from the study treatment under consideration • Understand the safety, tolerability and clinical benefit of a treatment in the subgroup of the patient population defined by a specific marker status

  24. Enrichment Designs Appropriate when: • Mechanism of drug action is known • Assay is reliable • Compelling preliminary evidence suggesting that patients with or without that marker profile do not benefit from the treatments in question • Needs fewer overall randomized patients compared to an “untargeted” design Simon, CCR 2005

  25. Enrichment Designs SIMPLE, BUT… • Need real time method for assessing patients who are / are not likely to respond • End up screening all patients anyway – so maybe not a real time saver! • Sample size for screening/randomization depends on: • Accuracy of the assay • Prevalence of the marker

  26. Enrichment Design - Example HER2 as a marker for Herceptin in Breast Cancer (BC) • Trastuzumab (Herceptin) is currently approved for treatment of HER2 positive BC patients in the adjuvant setting • Based on improvement in disease free survival from a combined analysis of 2 national intergroup adjuvant BC trials (NSABP B-31, NCCTG N9831) • Both trials utilized an enrichment design strategy of allowing only HER2 positive BC patients, based on preliminary evidence • Enrichment strategy was advantageous here: • only approximately 20% of women are HER2 positive • if truly no benefit of Herceptin in 80% of women deemed HER2 negative, a much larger sample size would have been required to establish statistically significant results in an unselected study

  27. Using markers to restrict trial eligibility: success – Her 2+ Breast Cancer Romond, NEJM 2005

  28. Using markers to restrict trial eligibility: beware • What about Herceptin in Her2- breast cancer? • New Data: No difference in benefit based on strength of HER2+ • After 10 years, may need new study of Herceptin in Her2- patients Paik, ASCO 2007

  29. Enrichment Design - Example • Enrichment strategy MAYBE not so successful?  • High degree of discordance between central and local testing for FISH and IHC • Post-hoc central testing for HER2 expression suggests patients with tumors negative for FISH and less than IHC 3+ staining also derived benefit from Herceptin • Patients deemed HER2 negative not enrolled onto the trials, so cannot fully establish the predictive utility of HER2

  30. Enrichment Design - Example While the enrichment strategy did - Clearly and quickly define an effective treatment for a subset of patients It did not answer - Questions regarding the predictive utility of HER2 due to the issues of assay reproducibility and inclusion of only biomarker defined subgroups An unselected design, allowing for both HER2 positive and negative patients, may have helped provide these answers in a definitive and ultimately more timely manner.

  31. Semi-Enrichment Design: N0147 R A N D O M I Z E mFOLFOX6 Wild type K-ras P R E R E G I S T E R mFOLFOX6 + Cetuximab Stage 3 Colon Cancer (N = 3768) Centralized K-ras analysis R E G I S T E R Adjuvant therapy per primary oncologist Report therapy given Annual status through year 8 Mutant K-ras

  32. Unselected Designs • Subset Analysis, if overall effect is not significant • Marker based strategy design • Randomize subjects to treatment either based on or independent of the marker status • Marker by treatment interaction design • Use the marker status as a stratification factor when randomizing subjects to treatment All patients of a specific disease type and stage are eligible for the clinical trial, regardless of their actual marker status

  33. Unselected Design: Marker Based Strategy Marker Level (-) Treatment A Marker Based Strategy Marker Level (+) Treatment B Register Randomize Test Marker Treatment A Non Marker Based Strategy Randomize Treatment B Sargent et al., JCO 2005

  34. Unselected Design: Upfront Stratification by Marker status Treatment A Marker Level (-) Randomize Treatment B Register Test Marker Treatment A Marker Level (+) Randomize Power trial separately within marker groups Treatment B Sargent et al., JCO 2005

  35. Marker by Treatment Interaction DesignEGFR as a marker for Erlotinib in Lung Cancer • Randomized trials in unselected patients with advanced non small cell lung cancer (NSCLC) have demonstrated: • A small survival advantage for erlotinib-treated patients and a trend toward improved survival for gefitinib-treated patients (two epidermal growth factor receptor (EGFR) tyrosine kinase (TK) inhibitors) • Patients with EGFR+ tumors by IHC, FISH and mutations appear to derive more benefit from erlotinib than patients with EGFR- tumors • So, why not use an enrichment strategy design for validation?

  36. FISH May Predict Survival Benefit of EGFR-TKIs – Subset Analyses Gefitinib Erlotinib Placebo Placebo Gefitinib Erlotinib Placebo Placebo ISEL FISH + BR.21 FISH + 100 100 80 80 60 60 Survival, % Survival, % 40 40 HR=0.61 (0.36, 1.04) P=.07 HR=0.44 (0.23, 0.82) P=.008 20 20 0 0 6 12 18 24 30 4 8 12 16 Months Months ISEL FISH - BR.21 FISH - 100 100 80 80 60 60 Survival, % Survival, % HR=1.16 (0.81, 1.64) P=.42 HR=0.85 (0.48, 1.51) P=.59 40 40 20 20 0 0 24 4 8 12 16 6 12 18 30 Months Months • BR.21 FISH Interaction p=0.10 • ISEL FISH interaction p=0.04

  37. FISH May NOT? Predict Survival Benefit of EGFR-TKIs– INTEREST Subset Analysis (Trial N = 1466) Probability of progression-free survival 1.00 EGFR FISH+ n=158 EGFR FISH- n=179 0.8 0.6 0.4 0.2 0.00 32 36 40 0 4 8 12 16 20 24 28 32 36 40 0 4 8 12 16 20 24 28 At risk : Months Months Gefitinib 77 27 10 4 3 3 2 2 0 0 0 80 21 7 3 1 1 0 0 0 0 0 Docetaxel 81 28 5 3 1 0 0 0 0 0 0 99 32 8 2 1 0 0 0 0 0 0 Gefitinib Docetaxel Gefitinib Docetaxel N Events 80 73 99 83 N Events 77 68 81 74 HR (95% CI) = 0.84 (0.59, 1.19) p=0.3343 HR (95% CI) = 1.30 (0.93, 1.83) p=0.1229

  38. MARVEL - Marker Validation for Erlotinib in Lung Cancer Initial Registration Strata Randomize EGFR FISH + (~ 30%) Erlotinib 2nd line NSCLC with specimen FISH Testing Pemetrexed EGFR FISH − (~ 70%) Erlotinib Pemetrexed 1196 patients 957 patients Primary: To evaluate whether there are differences in progression free survival between erlotinib and pemetrexed within the FISH positive and FISH negative subgroups Secondary: To evaluate whether there are differences in overall survival between erlotinib and pemetrexed within the FISH positive and FISH negative subgroups

  39. Summary: Predictive Biomarkers • Proof of principle: established • Translation to clinical utility will require • Prospective planning • Independent validation • Data from both retrospective and in some cases prospective trials • Decision between targeted vs. unselected eligibility is trial specific

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