Biomarker-Driven Oncology Clinical Trials: Key Design Elements, Types, Features, and Practical Considerations

Abstract

In this precision oncology era, where molecular profiling at the individual patient level becomes increasingly accessible and affordable, more and more clinical trials are now driven by biomarkers, with an overarching objective to optimize and personalize disease management. As compared with the conventional clinical development paradigms, where the key is to evaluate treatment effects in histology-defined populations, the choices of biomarker-driven clinical trial designs and analysis plans require additional considerations that are heavily dependent on the nature of biomarkers (eg, prognostic or predictive, integral or integrated) and the credential of biomarkers' performance and clinical utility. Most recently, another major paradigm change in biomarker-driven trials is to conduct multi-agent and/or multihistology master protocols or platform trials. These trials, although they may enjoy substantial infrastructure and logistical advantages, also face unique operational and conduct challenges. Here we provide a concise overview of design options for both the setting of single-biomarker/single-disease and the setting of multiple-biomarker/multiple-disease types. We focus on explaining the trial design and practical considerations and rationale of when to use which designs, as well as how to incorporate various adaptive design components to provide additional flexibility, enhance logistical efficiency, and optimize resource allocation. Lessons learned from real trials are also presented for illustration.

FIG 1.

Schemas for single integral biomarker-driven trial designs. (A) Enrichment design. (B) Biomarker-stratified design. (C) Biomarker-strategy design. (D) Biomarker-directed design. M-negative, biomarker-negative; M-positive, biomarker-positive; Rx, treatment.

FIG 2.

Schemas for master protocols and adaptive design elements. (A) Basket trial. (B) Umbrella trial. (C) Seamless phase II/III design. (D) Outcome-adaptive randomization. IE, intermediate end point.