Statistical approach to immunotherapy trials

Transcription

1 Statistical approach to immunotherapy trials Urania Dafni National and Kapodistrian University of Athens Frontier Science Foundation-Hellas SAMO Interdisciplinary Workshop on Immunotherapy Lucerne Suisse 19 March 2016

2 Immunotherapy has truly blossomed Cancer Treatment Chemotherapy TARGETED therapies IMMUNOTHERAPY Breakthrough of the Year for 2013 Cancer immunotherapy (Science Editors) Clinical trials need to be modernized for this new era Designs and appropriate statistical tools that take into account special features of immunotherapy

3 Immunotherapeutic approaches Stahel, R., ESMO Preceptorship on Immuno-Oncology Zurich 2015

4 Immunotherapy has truly blossomed Data extraction: 17March16 clinicaltrials.gov Dafni, Tsourti & Peters to appear Lung Cancer 2016

5 NSCLC immunotherapy trials : Randomized/non Tremelimumab: 9 trials 3 Ph1 (2NR-1R), 1 Ph1 2 (NR), 2 Ph2 + 3 Ph3 (R) Avelumab: 5 trials; 2 Ph1 (NR), 1 Ph Ph3 (R) All since 2011, except 6 Number of clinical trials by Phase and patient allocation Dafni, Tsourti & Peters to appear Lung Cancer 2016

6 Immune checkpoint inhibitors FDA approvals PD-1 checkpoint inhibitors Nivolumab Dec-14: unresectable or metastatic melanoma, not responding to other drugs (ipilimumab and BRAF inhibitor) (Ph-III CheckMate-037) Mar-15: advanced Sq NSCLC, no longer responding to chemo (Ph-II CheckMate-063, Ph-III CheckMate-017) Oct-15: extension to non-sq NSCLC, no longer responding to chemo (Ph-III CheckMate-057) Nov-15: metastatic renal cell carcinoma (Ph-III CheckMate-025) Pembrolizumab (MK-3475) (Ph-I Keynote-001, 010) Sep-14: advanced melanoma following treatment with ipilimumab Oct-15: for metastatic PD-L1+ NSCLC, failed treatment with other chemo agents

7 Immune checkpoint inhibitors FDA approvals PD-L1 checkpoint inhibitors Atezolizumab (MPDL-3280A) Feb-15: Breakthrough Therapy designation for PD-L1+ NSCLC, progressed after chemotherapy CTLA-4 checkpoint inhibitors Ipilimumab 2011: metastatic melanoma Combination of inhibitors Nivo+Ipi: Oct-15: BRAF V600 wild-type unresectable or metastatic melanoma (pivotal Ph-II CheckMate -069) Jan-16: Unresectable or metastatic melanoma across BRAF status (Ph-III CheckMate-067)

8 Clinical trials in the new era of therapies: Goals (targetedimmunotherapy) - Test the methods of assessment of marker alteration in normal and tumor tissue samples - Get guidance in the determination of cut points - Get a preliminary assessment of efficacy within molecularly defined subsets Establish clinical value by careful retrospective assessment of the marker Validate the marker and companion diagnostic Modified from Sumithra et al. (2015) ASCO Educational Book

9 Adaptive Designs Treatment effect independent adaptive designs Covariate-Adaptive Randomization Minimization Re-estimation of sample size based on lower event rate They do not inflate the type I error - Regulatory approved Other types of adaptive designs Continual reassessment method (CRM) for phase I trials and Seamless phase I/II & II/III designs Well understood and recommended Treatment effect dependent adaptive designs One or more of the design features can be adapted, depending on the observed treatment effect: sample size, patient inclusion criteria, treatment groups, treatment allocation ratio, or even the primary endpoint Limitations of Adaptive Clinical Trials By Marc Buyse, ScD Educational Book Manuscript ASCO 2012

10 About Phase I Phase I design modifications Soria (ECCO/ESMO 2015)

11 Example: KEYNOTE-001 NCT Garon et al ( NEJM 2015) Large multi-cohort international study with 8 protocol amendments Overall sample size 1260 pts It consists of 6 parts: Part A: the dose-escalation phase of the study, Next parts: cohorts of patient by cancer type/histology exploring safety, tolerability, and efficacy of pembro It includes: dose randomization efficacy endpoints biomarker evaluation and cutoff determination (training & validation set) several design adaptations

12 Example: KEYNOTE-001 flowchart NCT Khoja et al (Immunother Cancer. 2015)

13 Example: KEYNOTE-001 NCT Garon et al ( NEJM 2015) The 8 protocol amendments include, among others: Modification of the primary endpoint (from irrc to RECIST 1.1), Addition or abandoning of specific cohorts, Increasing sample size for certain cohorts It would be a typical fully adaptive design, except that there does not seem that corresponding adjustments in Type I & II errors, or care for multiple testing are incorporated in these amendments A lot more than a typical Phase I study

14 KEYNOTE-001: NSCLC Cohort Garon et al ( NEJM 2015)

15 KEYNOTE-001: NSCLC Cohort Garon et al ( NEJM 2015)

16 KEYNOTE-010: Seamless Phase II/III Herbst et al (The Lancet 2015)

17 KEYNOTE-010: Seamless Phase II/III Adaptive design (enrichment) Co-primary endpoints (OS & PFS for all and strong PD-L1+) Total number of pts randomized: depends on 1 st interim analysis (sufficient ORs in a pembro arm of strong PD-L1+) Endpoint of 1st interim analysis: ORR labeling as a Phase II/III trial The study was successful in reaching statistically significant conclusions for both pembro doses: in the full cohort regarding the OS benefit in the strongly positive cohort regarding both OS/PFS benefit.

18 KEYNOTE-010: Seamless Phase II/III Herbst et al (2015). The Lancet

19 KEYNOTE-010: Seamless Phase II/III The design addressed successfully several multiplicity issues: arm comparisons, interim analyses with different endpoints, multiple primary endpoints in multiple cohorts. To accommodate multiplicity: type I error is for OS for PFS Need of a relatively big sample size and/or study duration, to be able to demonstrate statistical significance, BUT the solicited benefit is substantial (HRs of 0.55 or 0.60). The design accommodated all these questions in a way that didn t impact duration of study and number of patients.

20 KEYNOTE-010: Seamless Phase II/III Herbst et al (The Lancet 2015)

21 Adequacy of such an adaptive design Fortunate position of expecting from immunotherapy agents substantial benefits (HRs of 0.55 or 0.60) and more importantly we do observe such a size of improvement. This fact allows us to answer multiple questions on multiple endpoints and cohorts and at the same time have reasonable study size and duration. Questions Is the risk taken by thinning the alpha level from to worth it in a slightly less favorable scenario of benefit? What if the p-value for the final analysis for OS had ended been 0.009? Answer: Under examination through simulations

22 Multi-component Clinical trial Design: Courtesy of Solange Peters (ESMO Workshop-Vienna 2015)

23 List of Umbrella Trials Presented By Sumithra Mandrekar at 2015 ASCO Annual Meeting

24 Lung-MAP Phase II/III Biomarker-Driven Master Protocol for Second Line Therapy of Squamous Cell Lung Cancer

25 National Lung Matrix Trial NCT Multi-drug, Genetic Marker-directed, Non-comparative, Multicentre, Multi-arm Phase II Trial in NSCLC UMBRELLA Series of parallel multi-centre single arm phase II trial arms Primary objective: evaluate whether there is a signal of activity in each drug-(putative) biomarker cohort separately. Primarily, an enrichment putative biomarker design Bayesian adaptive design Adaptive umbrella trial design: As the trial goes on, the research team may identify other drugs that they think could work for people with certain genetic changes. If that happens, new groups are added. Middleton et al. (2015). Annals of Oncology

26 National Lung Matrix Trial NCT Patients are allocated to the appropriate targeted therapy according to the molecular genotype of their cancer Anticipated screening of 2,000 pts to enroll approx. 620 pts Group A AZD4547 Group B AZD2014 Group C palbociclib Group D crizotinib Group E selumetinib/docetaxel Group F AZD5363 Group G AZD9291 Don t have a gene change that can be matched to a drug tremelimumamb (MEDI4736) Middleton et al. (2015). Annals of Oncology

27 Statistical issues and challenges in immunooncology Both efficacy and safety profiles differ from previously characterized cytotoxic and pathway-specific agents. Issues to be addressed by Study Design Predictive Biomarker PD-L1 expression? Endpoint irrc vs RECIST, milestone survival Different response/survival patterns Delayed clinical effect Long-term survival Prolongation of study duration Fine (Drug Inf 2007) Chen (J ImmunTherapy C 2013) Chen (AACR Education book 2015)

28 Evaluation of biomarkers Correlation between biomarker and true clinical endpoint makes a prognostic marker. Correlation between biomarker and true clinical endpoint does not make a predictive biomarker. Interaction between biomarker status and treatment Quantitative interaction: one treatment is always better, but by varying degrees Qualitative heterogeneity: one treatment is better in a particular subgroup but worse in another subgroup Predictive

29 Prognostic & predictive marker Prognostic Clinical Endpoint <- Worse Better -> Clinical Endpoint <- Worse Better -> Std Treatment Exp Std Treatment Exp 60% 50% 40% 30% 20% 10% 0% Control Factor present Factor absent Treated Clinical Endpoint <- Worse Better -> Predictive Prognostic and predictive Clinical Endpoint <- Worse Better -> Std Treatment Exp Std Treatment Exp

30 Estimating the magnitude of treatment effect Heterogeneity: varying effect across levels of a biomarker

31 Estimating the magnitude of treatment effect: Qualitative heterogeneity

32 Estimating the magnitude of treatment effect: Quantitative heterogeneity

33 Heterogeneity and interaction Evaluating heterogeneity requires a test of interaction Separate tests within subgroups cannot be used to determine heterogeneity Interaction tests are often underpowered; leading to false negative conclusions Predictive Factor: Only if treatment by factor level interaction is significant

34 Universitätsklinik für Medizinische Onkologie, Inselspital Bern Predictors for Response to PD-1 Blockade Antigen Mutational burden predictive for neoantigens presented on MHC-class I T cells T cell infiltration in tumors (IFNg) clonal T cell expansion PD-L1 expression dynamic (archieval tissue?) on immune cells / on tumor cells / or both which antibody / test treshold role of PD-L2? Adrian Ochsenbein

35 Evaluation of Biomarkers/PD-L1 expression Issues for consideration Lack of established biomarker assay characteristics (detection antibody for staining, IHC criteria, cutoff level for positivity) Variability of PD-L1 expression at different sites & different time points during the treatment course Dynamic (discordance between primary & metastatic) archived tissue questionable especially if 1L post-biopsy treatment Archived tissue is used in almost all biomarker examining studies (except KEYNOTE-001 and KEYNOTE-010) Co-localization of PD-L1-expressing cells with TILs Responses in PD-L1 IHC negative patients can be observed and may be related to biopsy site selection as well as timing of biopsy Goldberg (Immunotherapy 2015); Patel (Transl Cancer Res 2015); Socinski; Garon (Seminars in Oncology 2015)

36 Evaluation of Biomarkers/PD-L1 expression Garon (Seminars in Oncology 2015) KEYNOTE- 010 II III Advanced NSCLC, PD-L1+ Randomized study of 2 doses of pembrol vs Docetaxel OS+PFS by RECIST (overall, for PD-L1+ strong) Archived or Fresh

37 KEYNOTE-001: NSCLC Cohort Garon et al (2015) Pembrolizumab for the treatment of NSCLC. NEJM, 372;21

38 KEYNOTE-001: NSCLC Cohort Garon et al ( NEJM 2015)

39 KEYNOTE-010: Seamless Phase II/III Herbst et al (The Lancet 2015)

40 CheckMate-017 In this study, the efficacy of nivo was observed regardless of PD-L1 expression levels, Results showed that PD-L1 expression was neither prognostic nor predictive of efficacy in the population of patients with squamous-cell NSCLC. Authors think that the lack of an association between PD-L1 expression and efficacy is probably not related to the performance of the PD-L1 assay but is rather a function of complex interactions between tumors and the immune system

41 CheckMate-017

42 CheckMate-057 Presented By Luis Paz-Ares at 2015 ASCO Annual Meeting Trial stopped early because the study met its primary endpoint of superior OS It also showed a predictive association between PD-L1 expression and benefit from nivo

43 CheckMate-057 Presented By Luis Paz-Ares at 2015 ASCO Annual Meeting

44 OS and PFS Hazard Ratios by Baseline PD-L1 Expression Presented By Luis Paz-Ares at 2015 ASCO Annual Meeting

45 Pembrolizumab Pembrolizumab vs Placebo PEARLS (KEYNOTE-091) Early stage NSCLC after resection with or without standard adjuvant therapy NCT Garon (Seminars in Oncology 2015)

46 KEYNOTE-091 (PEARLS) N=1380 Stratification factors: - Stage: IB vs II vs IIIA - Adjuvant chemotherapy: no adjuvant vs adjuvant - PD-L1 status: negative (PS=0) vs weak positive (PS=1-49%) vs strong positive (PS 50) - Region: Western Europe vs Eastern Europe vs Rest of the world vs Asia

47 Slide 6 Presented By Richard Simon at 2015 ASCO Annual Meeting

48 Evaluation of Biomarkers/PD-L1 expression All previously mentioned limitations (variability -sites& time, discordance in primary & metastatic, lack of established assay, Archived tissue, co-localization with TILs) Some benefit in PD-L1 negative patients Contradicting evidence on the predictivity of PD-L1 expression CheckMate-017 (and all others) vs CheckMate-057 Further studies are required, with appropriate design for the evaluation of PD-L1 biomarker IMPLICATION Under the circumstances we should NOT use enrichment design

49 Statistical issues and challenges in immunooncology Both efficacy and safety profiles differ from previously characterized cytotoxic and pathway-specific agents. Issues to be addressed by Study Design Predictive Biomarker PD-L1 expression? Endpoint irrc vs RECIST, milestone survival Different response/survival patterns Delayed clinical effect Long-term survival Prolongation of study duration Fine (Drug Inf 2007) Chen (J ImmunTherapy C 2013) Chen (AACR Education book 2015)

50 Patterns of response to Cancer Immunotherapy Patterns of response to ipilimumab observed in advanced melanoma Wolchock et al, 2009 CCR

51 Measurement of Antitumor Response: irrc Based on the conventional WHO or RECIST criteria Use of TOTAL MEASURABLE TUMOR BURDEN Response and Progression are confirmed by a second assessment at least 4 weeks apart Wolchock et al, 2009; Hoos et all, JNCI 2010

52 Delayed clinical effect & long-term survival Chen, JITC 2013

53 Statistical issues and challenges Delayed clinical effect Long-term survival Delayed separation of survival curves: Violation of proportional hazards assumption Loss of statistical power to differentiate between treatment arms Adverse impact on conventional interim analyses (i.e., increase the chance of stopping early due to futility at interim analysis) Prolongation of study duration Hoos et all, JNCI 2010; Chen, JITC 2013; Chen, AACR Education Book 2015

54 Alternative clinical endpoints Brookings Conference on Clinical Cancer Research (2013) & Cancer Immunotherapy Consortium Workshop (2014) Milestone Survival: cross-sectional assessment of the OS data at prespecified time points (using KM survival probabilities), e.g., 2-year OS rate Disease control rate: the composite rate of objective response and stable disease Immune-related response rate: response rate based on criteria accounting for pseudoprogression (including the presence of new target lesions and the confirmation of progression at subsequent scans) (Immune-related) progression-free survival: time from randomization to progression or immune-related progression Chen, AACR Education Book 2015

55 1 st prospective randomized study with irpfs as primary endpoint. Response rates per irrc were generally similar to those per modified WHO criteria.

56 Statistical issues and challenges Currently irrc are mainly used for patient management, or as secondary endpoints while RECIST 1.1 are still used as primary Recommendations to address the challenge of different survival patterns: Extensive Simulations to explore design characteristics Use of different methodology for design and analysis: Cure rate models Weighting procedures (weighted log-rank; Fine, DIJ 2007) Use Milestone Survival Landmark analysis Use of piecewise constant hazard rates CHECKMATE 057 Chen, JITC 2013; Chen, AACR Education Book 2015; Dafni et al 2016

57 Conclusions Advantage of using new designs developed and tested on targeted therapies (basket, umbrella, multiple endpoints/cohorts, adaptive) Change in the paradigm of Phase I expansion cohorts efficacy evaluation Adaptation (co-primary endpoints; interim) possible due to substantial improvements targeted AND OBSERVED No verdict yet on PD-L1 as predictive marker Favored primary endpoints: ORR (RECIST 1.1) & OS, milestone survival

58 Very exciting times CAUTION High-stakes competition for the combination of therapies in registration studies The accelerated approach has the potential to validate a novel combination more quickly, BUT the potential for negative consequences is real. Garon,comment on Antonia et al, Lancet Oncol, 2016

59 Thank you for your attention