AUTHOR=Zabor Emily C. , Kaizer Alexander M. , Pennell Nathan A. , Hobbs Brian P. 

TITLE=Optimal predictive probability designs for randomized biomarker-guided oncology trials

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.955056

DOI=10.3389/fonc.2022.955056

ISSN=2234-943X

ABSTRACT=Efforts to develop biomarker-targeted anti-cancer therapies have progressed rapidly in recent years. Six antibodies acting on programmed death ligand 1 or programmed death 1 pathways were approved in 75 cancer indications between 2015 and 2021. With efforts to expedite regulatory reviews of promising therapies, several targeted cancer therapies have been granted accelerated approval on the basis of evidence acquired in single-arm phase II clinical trials. And yet, in the absence of randomization, patient prognosis for progression-free survival and overall survival may not have been studied under standard of care chemotherapies for emerging biomarker subpopulations prior to the submission of an accelerated approval application. Historical control rates used to design and evaluate emerging targeted therapies often arise as population averages, lacking specificity to the targeted genetic or immunophenotypic profile. Thus, historical trial results are inherently limited for inferring the potential “comparative efficacy” of novel targeted therapies. A recent phase III trial of atezolizumab in patients with locally advanced or metastatic urothelial carcinoma who had disease progression following platinum-containing chemotherapy found a 21.6% response rate to standard of care chemotherapy in the biomarker subgroup of interest, much higher than the historical control rate of 10% that had been used to declare success in the preceding phase II trial. Consequently, randomization may be unavoidable in this setting. Innovations in design methodology are needed, however, to enable efficient implementation of randomized trials for agents that target biomarker subpopulations. This article proposes three randomized designs for early phase biomarker-guided oncology clinical trials. Each design utilizes the optimal efficiency predictive probability method to monitor multiple biomarker subpopulations for futility. A simulation study motivated by the results reported in the atezolizumab trial is used to evaluate the operating characteristics of the various designs. Our findings suggest that efficient statistical design can be conducted with randomization and futility stopping to effectively acquire more evidence pertaining to comparative efficacy before deciding to conduct a phase III confirmatory trial.