Statistical Design and Considerations of a Phase 3 Basket Trial for Simultaneous Investigation of Multiple Tumor Types in One Study

ASBTRACT The discovery of numerous molecular subtypes of common cancers leads to the investigation of biomarkers potentially predictive of treatment effect of an experimental treatment in multiple histologies. However, the prevalence of a putative predictive biomarker within a histology is often low, which makes it challenging to enroll adequate number of patients in a conventional histology-based confirmatory trial. An alternative approach is to study patients with a common biomarker signature in a “basket” trial across multiple histologies. This study design has previously been used to explore experimental therapies with potentially transformative effects. We present a general design concept of a Phase 3 basket trial broadly applicable to any effective therapy. The trial is designed with scientific and statistical rigor to enable the approval of an experimental treatment in multiple tumor indications based on the outcome from a single study. Given the difficulty in indication selection, the basic idea is to prune the inactive indications at an interim analysis and pool the active indications in the final analysis. A critical statistical issue of the basket design is Type I error control for the pooled analysis after pruning. While pruning may be seen as cherry-picking which tends to inflate the Type I error, it also shares similarity with a binding futility analysis which tends to deflate the Type I error if all indications are pruned. The net impact of pruning is complicated. The use of different endpoints for pruning and pooling further complicates the issue. This paper will provide statistical details on Type I error control for the general basket design concept under three sample size adjustment strategies after pruning. Power and sample size calculations are also provided. Comparisons are made to a straightforward design without pruning. Supplementary materials for this article are available online.

[1]  A. Hauschild,et al.  Improved survival with vemurafenib in melanoma with BRAF V600E mutation. , 2011, The New England journal of medicine.

[2]  Baldur P Magnusson,et al.  Group sequential enrichment design incorporating subgroup selection , 2013, Statistics in medicine.

[3]  Robert A Beckman,et al.  Maximizing Return on Socioeconomic Investment in Phase II Proof-of-Concept Trials , 2014, Clinical Cancer Research.

[4]  Jan Bogaerts,et al.  Designing transformative clinical trials in the cancer genome era. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[5]  Alternative Trial Designs Based on Tumor Genetics / Pathway Characteristics Instead of Histology , 2022 .

[6]  D. Berry,et al.  I‐SPY 2: An Adaptive Breast Cancer Trial Design in the Setting of Neoadjuvant Chemotherapy , 2009, Clinical pharmacology and therapeutics.

[7]  Donald A Berry,et al.  The Brave New World of clinical cancer research: Adaptive biomarker‐driven trials integrating clinical practice with clinical research , 2015, Molecular oncology.

[8]  P. Thall,et al.  Accounting for patient heterogeneity in phase II clinical trials , 2008, Statistics in medicine.

[9]  Denis Lacombe,et al.  The dream and reality of histology agnostic cancer clinical trials , 2014, Molecular oncology.

[10]  Linda Sun,et al.  Evaluation of Early Efficacy Endpoints for Proof-of-Concept Trials , 2013, Journal of biopharmaceutical statistics.

[11]  R. Bernards,et al.  Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR , 2012, Nature.

[12]  Heikki Joensuu,et al.  Phase II, Open-Label Study Evaluating the Activity of Imatinib in Treating Life-Threatening Malignancies Known to Be Associated with Imatinib-Sensitive Tyrosine Kinases , 2008, Clinical Cancer Research.

[13]  L. Siu,et al.  Surrogate end points for median overall survival in metastatic colorectal cancer: literature-based analysis from 39 randomized controlled trials of first-line chemotherapy. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[14]  D. Berry,et al.  Bayesian hierarchical modeling of patient subpopulations: Efficient designs of Phase II oncology clinical trials , 2013, Clinical trials.

[15]  David Wholley,et al.  Lung Master Protocol (Lung-MAP)—A Biomarker-Driven Protocol for Accelerating Development of Therapies for Squamous Cell Lung Cancer: SWOG S1400 , 2015, Clinical Cancer Research.

[16]  Robert A. Beckman,et al.  Optimal Cost-Effective Go-No Go Decisions in Late-Stage Oncology Drug Development , 2009 .

[17]  Anne Whitehead,et al.  Meta-Analysis of Controlled Clinical Trials , 2002 .

[18]  Daniel J Sargent,et al.  Disease-free survival versus overall survival as a primary end point for adjuvant colon cancer studies: individual patient data from 20,898 patients on 18 randomized trials. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[19]  Satrajit Roychoudhury,et al.  Robust exchangeability designs for early phase clinical trials with multiple strata , 2016, Pharmaceutical statistics.

[20]  R. Simon,et al.  The Bayesian basket design for genomic variant-driven phase II trials. , 2016, Seminars in oncology.

[21]  Christine M. Micheel,et al.  Beyond Histology: Translating Tumor Genotypes into Clinically Effective Targeted Therapies , 2014, Clinical Cancer Research.

[22]  P. Thall,et al.  Hierarchical Bayesian approaches to phase II trials in diseases with multiple subtypes , 2003, Statistics in medicine.

[23]  R. Labianca,et al.  Disease-free survival (DFS) vs. overall survival (OS) as a primary endpoint for adjuvant colon cancer studies: Individual patient data from 12,915 patients on 15 randomized trials. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[24]  Robert A. Beckman,et al.  Integrating predictive biomarkers and classifiers into oncology clinical development programmes , 2011, Nature Reviews Drug Discovery.

[25]  Edward S. Kim,et al.  The BATTLE trial: personalizing therapy for lung cancer. , 2011, Cancer discovery.

[26]  Caroline McNeil,et al.  NCI-MATCH launch highlights new trial design in precision-medicine era. , 2015, Journal of the National Cancer Institute.

[27]  W. Brannath,et al.  Selection and bias—Two hostile brothers , 2009, Statistics in medicine.

[28]  E. C. Moseley,et al.  The right drug for the right patient. , 1965, Journal of consulting psychology.