Outline Background and Definitions Bayesian Clinical Trials Umbrella Protocol Example – BATTLE

1. Case Study 10Precision Medicine Trials in Cancer via Umbrellas and Baskets: the BATTLE and MATCH StudiesMaterialsBATTLE protocol (umbrella): Design (technical) - Lee, Gue, Li, Clinical Trials 5: 584-96 (2010); Results – Kim, et al., Cancer Research, published online on 4/3/2011.MATCH protocol (basket): https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/nci-matchBayesian clinical trials: - Article in Encyclopedia of Biostatistics (Ashby)- Berry, Nature Reviews 5 2006; 27-36.Response adaptive randomization warning: - Karrison, Huo and Chappell. Controlled Clinical Trials24 2003; 506-522.

2. Key Words: Umbrella protocol, Basket protocol, Bayesian clinical trials, Biomarkers, Precision medicine, Response adaptive randomization. Rick Chappell, Ph.D. Professor, Department of Biostatistics and Medical Informatics University of Wisconsin Medical School Stat 542 – Spring 2018

3. Outline Background and Definitions Bayesian Clinical Trials Umbrella Protocol Example – BATTLE Basket Protocol Example – MATCH

4. Background Precision Medicine: The tailoring of medical treatment to the individual characteristics of each patient. This is not a new concept. Researchers have been tailoring our questions to: - Patient demographics (age, sex …) - Surgical pathology - Tumor biopsy pathology - Many other risk factors, or predictive markers (In cancer, and increasingly other fields, a “prognostic” marker forecasts disease outcome; a “predictive” marker forecasts treatment success.) However, rarely do we have so many simultaneous markers as in a genome.

5. Umbrella Protocol: Designed to test the impact of different drugs on different mutations in a single cancer. The primary features of umbrella trials are: (i) the inclusion of multiple treatments and multiple biomarkers within the same protocol; (ii) a design that allows for randomized comparisons; (iii) a design that can have flexible biomarker cohorts; and (iv) a design that can add/drop biomarker subgroups (“adaptive”) – R. Simon, 2015 ASCO talk. “Adaptive” is an over-general term referring to designs which allow interim changes in dose, sample size, treatment arms, in/exclusion, and randomization ratio.

6. Alternative definition: “Umbrella Protocol” is also an IRB term can covering a variety of relatively unspecified (usually low-risk) research. E.g.  Steven Kecskemeti, PhD / Andrew Alexander, PhD (Co-Principal Investigators) had a UW ICTR pilot grant entitled “Emerging MRI Techniques for Imaging Non-Sedated Children”. This was for a series of strictly observational case studies wherein they just wanted to record basic information about images which they were taking as part of standard medical care. It is completely distinct from the phrase as used her.

7. Basket Protocol: (i) studies designed to test the effect of a single drug on a single mutation; (ii) uses a variety of cancer types; (iii) may also screen multiple drugs across many cancer types. “Basket trials provide a unique way of merging the traditional clinical trial design with rapidly evolving genomic data that facilitate the molecular classification of tumors” – Richard Simon, 2015 ASCO talk. Of course, these designs are not completely new. They are also not limited to cancer.

8. Basket Protocol: A basket design provides evidence for pairing a drug with a validated biomarker in a range of tumors. It is one departure from the usual practice in cancer and most other clinical trials to use a single disease or group of diseases. Another such example is Phase I dose-escalation toxicity trials.

9. Berry paper: “… in the Bayesian approach all uncertainty is measured by probability. Anything that is unknown has a probability distribution. Everything that is known is taken as given and all probabilities are calculated conditionally on known values.” • This makes it ideal for sequential (“adaptive”). • We must start with a prior guess of these probabilities; • We then update the prior using data to make a posterior guess; • The posterior can then be used to change the trial’s structure (dose, treatment arms, sample size …): B. Bayesian Clinical Trialssee Encyclopedia of Biostatistics and Berry articles

10. Create Prior Probabilities of Success for Each Biomarker/Treatment Combination Randomize Each Patient Based on these Probabilities B. Bayesian Clinical Trialssee Encyclopedia of Biostatistics and Berry articles Use Posterior Probabilities to Change the Randomization Ratios Use Results to Update Probabilities Repeat until Sample Size is Attained; Report Current Posterior Estimates of Success

11. Bayesian analyses can be used to progressively update conclusions, see the BMS analysis of Pravastatin and aspirin in preventing MIs (Berry).

12. Disadvantages of Bayesian analysis: The prior is subjective; thus it is usually used for early-phase trials. Examples like the pravastatin licensure are unusual. Unlike frequentist analyses, which focus on limiting type I error to probability a, Bayesian methods make no formal adjustments for multiple comparisons. They can be complex to implement. A DSMB-member for one publicized adaptive trial asked the algorithm for determining randomization probabilities and was told it was unavailable “because the programmer quit.” Note that the BATTLE paper doesn’t give any algorithms.

13. Umbrella Protocol Example – BATTLE From the Kim paper’s Abstract: “The Biomarker-integrated Approaches of Targeted Therapy for Lung Cancer Elimination (BATTLE) trial [is a] ... biopsy-mandated, biomarker-based,adaptively randomized study in 255 pretreated lung cancer patients.” “Following an initial equal randomization period, chemorefractory non–small cell lung cancer (NSCLC) patients were adaptively randomized to erlotinib, vandetanib, erlotinib plus bexarotene, or sorafenib, based on relevant molecular biomarkers analyzed in fresh core needle biopsy specimens.”

15. Umbrella Protocol Example – BATTLE From the Kim paper’s Abstract: “Overall results include a 46% 8-week disease control rate (primary end point), confirm pre-specified hypotheses, and show an impressive benefit from sorafenib among mutant-KRAS patients. BATTLE establishes the feasibility of a new paradigm for a personalized approach to lung cancer clinical trials.” "Significance: The BATTLE study is the first completed prospective, adaptively randomized study in heavily pretreated NSCLC patients that mandated tumor profiling with “real-time” biopsies, taking a substantial step toward realizing personalized lung cancer therapy by integrating real-time molecular laboratory findings in delineating specific patient populations for individualized treatment.” .”

16. From the Kim paper’s small print: Study Design "BATTLE was a randomized phase II, single-center, open-label study in patients with advanced NSCLC refractory to prior chemo-therapy (Fig. 1). “Following molecular tumor-biomarker assessments, patients were randomly assigned to oral treatment with erlotinib ...;vandetanib ...; erlotinib ... plus bexarotene . .. or sorafenib ... .” “The primary end point was the DCR [disease control rate] at 8 weeks.Secondary end points included response rate, PFS, OS, and toxicity. ...” “We planned to randomly assign at least the initial 80 patients equally to the 4 treatments, to allow at least 1 patient in each marker group to complete treatment, …”

17. Study Design, cont. "Subsequent randomization switched to an adaptive algorithm, which incorporated the data of each patient evaluated at the 8-week time point (treatment, biomarker group, and 8-week DCR) into recalculations of the posterior probability of efficacy for treatments in relation to biomarker groups.“ "Patients enrolled after the initial cohort were randomly assigned to treatment according to a Bayesian adaptive algorithm, which incorporated the prior probability and DC data into a “posterior” probability of the DCR for a given treatment; the resulting posterior probability was continually updated per accumulating data on the associations between the DC and biomarkers of patients.”

18. Study Design, cont. This scheme raised or lowered randomization probabilities for each of the 4 treatments from an equal chance away from 25% depending on their prior success. These probabilities depend on each patient's biomarker values [my words].

20. Study Design, cont. Discussion “The BATTLE study is important in demonstrating several key points: 1) establishing the feasibility of performing biopsies and real-time bio-marker analyses in previously treated lung cancer patients; 2) identifying interactions between the treatments and markers(e.g., DCR of 79% with sorafenib but only 14% with erlotinibin the KRAS/BRAF marker group) for guiding adaptive randomization; and 3) confirming the pre-specified hypotheses of treatment efficacy in the presence of individual markers related to the treatments’ mechanism of action."

21. Study Design, cont. 1. Logistics: "The BATTLE approach requires a highly integrated team of multidisciplinary investigators and should be implemented at specialized centers in carefully designed clinical trials.” 2. Bias in Response Adaptive Randomization (RAR): The BATTLE statistical design was based on adaptive randomization under a Bayesian hierarchical model that would increasingly assign patients into treatments with the greatest potential for efficacy based on individual biomarker profiles. This confounds the effects of treatment and patient order. The latter can be large - see slides on RAR. The short version: to avoid bias, keep randomization ratios constant except for allowing old treatments to be stopped and new treatments started - the "all or nothing" approach.

22. Study Design, cont. Limitations 3. Ethical: The chairman of biostatistics at MD Anderson at the time this study was conducted owns an expanding consulting company with a specialty in RAR. This was not here or in many other venues in which RAR was endorsed, and is a conflict of interest.

23. Basket Protocol Example – MATCH MATCH [Molecular Analysis for Therapy Choice] is a precision medicine randomized clinical trial in which patients are assigned treatment based on tumor (almost any tumor – “advanced solid tumors, lymphomas, or myeloma”) genetic mutations. There are 30 treatment arms, each specific to a single genetic change (not a single tumor). Each mutation-arm will have 35 (rare) or “up to 70” (common) patients.

24. Treatment Arms that Are Open and Enrolling Patients Each mutation-arm will have 35 (rare) or “up to 70” (common) patients.

25. These are nonrandomized allocations and so the design and analysis of the results are various simple. Enrollment continues until accrual goal is met. The primary objective is to evaluate the proportion of patients with objective response (OR) to targeted study agent(s). Secondary objectives include time until death or progression and exploratory aims.

26. STEP 0 (Screening): tumor biopsy and sequencing. STEPS 1, 3, 5, 7 (Treatment): Patients are assigned to 1 of 30 treatment subprotocols based on molecularly-defined subgroup. STEPS 2, 4, 6 (Screening): Patients experiencing disease progression on the prior Step treatment or who could not tolerate the assigned treatment undergo review of their previous biopsy results to determine if another treatment is available or undergo another biopsy.

27. STEP 0 (Screening): tumor biopsy and sequencing. STEP 8 (Optional Research): Consenting patients undergo end-of-treatment biopsy and collection of blood samples for research purposes. Followup: After completion of study treatment, patients are followed up every 3 months for 2 years and then every 6 months for 1 year.