  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 single-arm phase II trials. And yet, in the absence of randomization,
patient outcomes 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 in single
arm studies often arise as population averages, lacking specificity to the
targeted subgroup. For example, a recent phase III trial of atezolizumab in
patients with metastatic urothelial carcinoma found a 21.6% response rate to
standard of care chemotherapy in the biomarker subgbroup of interest, much
higher than the historical control rate of 10% that had been used to declare
success in the preceding phase II trial. Innovations in design methodology are
needed 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.
