A pragmatic adaptive enrichment design for selecting the right target population for cancer immunotherapies

TL;DR

This paper introduces a pragmatic adaptive enrichment design for cancer immunotherapy trials, enabling data-driven selection of biomarker subpopulations to improve target population identification.

Contribution

It presents a novel AED framework with a binary endpoint tailored for cancer immunotherapy, including practical operating characteristics and application to triple-negative breast cancer.

Findings

Design successfully identified PD-L1 as a predictive biomarker

Operates effectively with minimal detectable difference concept

Applicable to dynamic, real-world cancer trial settings

Abstract

One of the challenges in the design of confirmatory trials is to deal with uncertainties regarding the optimal target population for a novel drug. Adaptive enrichment designs (AED) which allow for a data-driven selection of one or more pre-specified biomarker subpopulations at an interim analysis have been proposed in this setting but practical case studies of AEDs are still relatively rare. We present the design of an AED with a binary endpoint in the highly dynamic setting of cancer immunotherapy. The trial was initiated as a conventional trial in early triple-negative breast cancer but amended to an AED based on emerging data external to the trial suggesting that PD-L1 status could be a predictive biomarker. Operating characteristics are discussed including the concept of a minimal detectable difference, that is, the smallest observed treatment effect that would lead to a statistically significant result in at least one of the target populations at the interim or the final analysis, respectively, in the setting of AED.