Statistical Considerations for Novel Trial Designs: Biomarkers, Umbrellas and Baskets

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1 Statistical Considerations for Novel Trial Designs: Biomarkers, Umbrellas and Baskets Bibhas Chakraborty, PhD Centre for Quantitative Medicine, Duke-NUS March 29, 2015

2 Personalized or Precision Medicine

3 Targeted Cancer Therapy and Biomarkers Many cancer treatments benefit only a minority of patients to whom they are administered Particularly true for molecularly targeted drugs Being able to predict which patients are likely to benefit would Save patients from unnecessary toxicity, and enhance their chance of receiving a drug that helps them Help control medical costs Improve the success rate of clinical drug development This is where biomarkers play a role

4 Biomarker and therapy codevelopment is an iterative process Identify interesting biomarker Engineer therapeutic agent to target biomarker

5 Key issues in evaluation of a biomarker for therapy selection Be careful to distinguish prognostic effects of biomarker from treatment effects What must be established about treatment effect in the biomarker-negative subgroup?

6 First Instincts Biomarker is useful in identifying patients who will benefit from new therapy? Biomarker is not useful in identifying patients who will benefit from new therapy?

7 may deceive you in judging the value of biomarker for therapy selection

8 Prognostic and Predictive Biomarkers PROGNOSTIC: Biomarker-based test producing result associated with clinical outcome in absence of therapy (natural course) or with standard therapy all patients are likely to receive PREDICTIVE: Biomarker-based test producing result associated with benefit or lack of benefit from a particular therapy relative to other available therapy Alternatively called treatment-selection biomarker or treatment effect modifier

9 Prognostic vs. Predictive: Importance of Control Groups Prognostic but not predictive Prognostic and predictive

10 Prospective Phase II Trial Design Considerations: Role of Biomarker Biomarker Enrichment: Biomarker positivity required for eligibility Biomarker Adaptive: Trial design features adapted during course of the trial depending on early results within biomarker-positive and -negative subgroups All-comers with Biomarker Stratification: Consider results combined and separately within biomarker-positive and -negative subgroups

11 Single-arm Biomarker Enrichment Phase II Designs (One-stage Designs) Screening for Biomarker status Biomarker POSITIVE Receive new therapy Biomarker NEGATIVE Off Study Is success rate > B? No STOP: FAILURE Yes STOP: SUCCESS

12 Single-arm Biomarker Enrichment Phase II Designs (Two-stage Designs) Screening for Biomarker status Biomarker POSITIVE N 1 patients receive new therapy Biomarker NEGATIVE STOP: FAILURE Is success rate > B 1? No STOP: FAILURE Off Study STOP: SUCCESS Yes No Is success rate among N 1 + N 2 patients > B 2? Yes N 2 more patients receive new therapy

13 Single-arm Biomarker Enrichment Phase II Designs PROS: 1. Smaller, faster 2. Sometimes more feasible to enroll patients CONS: 1. How to find appropriate benchmark success rates if biomarker is prognostic? 2. No randomized comparison of efficacy and safety 3. Selection bias 4. What to do with the biomarker-negative group?

14 Prospective Phase II Trial Design: When Is A Randomized Trial Necessary? Is the biomarker prognostic? Is it possible for a patient s condition to improve and/or resolve with no treatment? Are other standard therapies available for the intended patient population? Will the new therapy be tested in combination with an existing standard therapy (standard therapy ± new agent)?

15 Randomized Biomarker Enrichment Designs New therapy Biomarker + R Screening for Biomarker status Control therapy Biomarker - Off Study Still can t do anything for, or can t say anything about the Biomarker-NEGATIVE group!

16 Randomized Biomarker-Stratified Designs New therapy Biomarker + R Screening for Biomarker status Control therapy Biomarker - R New therapy Control therapy This design maximizes information, and controls for the prognostic effect of the marker.

17 Challenges in Studying the Biomarker- Negative Subgroup When are preliminary data sufficiently convincing that biomarker negative patients should not be included in trials of the new therapy? If additional information about efficacy of new therapy in biomarker-negative subgroup is needed, must randomized trial be conducted in biomarker-negative subgroup prior to drug approval for biomarker-positive? o Should new therapy for biomarker-positive be held hostage? o Is post-marketing evaluation of therapy in biomarker-negative subgroup feasible?

18 Needs for more rapid and efficient biomarker and targeted therapy development Resources for pre-clinical work and assay development Broadly accessible trials to accrue sufficient numbers in small biomarker subgroups o Coordination & comparison of assays among multiple trials o Multi-arm master protocol trials ( basket, umbrella trials) give options for more patients/fewer biomarker-negative

19 Mater Protocol One overarching protocol that includes one or more of the following: o Multiple diseases o Multiple therapies o Multiple biomarkers Rationale: o Screening a large number of patients for multiple targets by a broad based platform reduces the screen failure rate o Provides a sufficient hit rate to engage patients and clinicians o Brings safe and effective drugs to patients faster

20 Mater Protocols: Avenues for Innovation o Establish a trial network with infrastructure in place to streamline trial logistics, improve data quality, and facilitate data sharing and new data collection o Develop a common protocol for the network that incorporates innovative statistical approaches to study design and data analysis

21 Mater Protocols: Infrastructure Advantages o Streamlined enrolment processes o Established systems in place to improve trial processes and to reduce time (e.g. centralized randomization, central electronic data capture, central IRB, common informed consent, etc.) o Data sharing within the network, when appropriate

22 Master Protocol: Umbrella vs. Basket Trial Test impact of different drugs on different mutations in a single type of cancer BATTLE I-SPY2 Umbrella Lung-MAP Squamous Lung Master Test the effect of a drug(s) on a single mutation(s) in a variety of cancer types Imatinib Basket BRAF+ Basket NCI MATCH 22

23 BATTLE 1 Trial Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) (Kim et al., Cancer Discovery, 2011) Phase II umbrella protocol patients with advanced NSCLC 5 biomarker groups (identified via needle biopsy) o EGFR o VEGF / VEGFR-2 o KRAS / BRAF o RxR / Cyclin D1 o None 4 targeted therapies o Erlotinib o Vandetanib o Erlotinib + Bexarotene o Sorafenib

24 BATTLE 1 Trial Primary endpoint: Disease Control Rate (DCR) at 8 weeks (i.e., complete or partial response or stable disease, via RECIST) Equal randomization, followed by adaptive randomization (n = 255) o First 97 patients went through equal randomization to the 4 therapy groups o Subsequently, patients went through adaptive randomization to the 4 therapy groups, according to their biomarker profile Adaptive randomization under Bayesian Hierarchical model Assign patients into therapies with greater potential for efficacy based on individual biomarker profile The design also had option of suspension of certain arms

25 Summary Oncology clinical trials are increasingly challenging Trial networks with established infrastructure and use of a common protocol can address many of the challenges o Optimize trial design and conduct to realize efficiencies and improve data quality through centralization of processes, systems, and training Innovative trial designs, e.g., umbrella or basket protocols, are likely to be increasingly common in future Overall objective is to reduce time and cost of developing promising drugs

26 Thank You