Integration of Efficacy Biomarkers Together with Toxicity Endpoints in Immune-Oncology Dose Finding Studies

TL;DR

This paper introduces a flexible multivariate Gaussian model that integrates efficacy biomarkers with toxicity endpoints in immune-oncology dose finding, improving dose selection by considering multiple outcomes.

Contribution

It presents a novel, easily extendable model for combining efficacy and toxicity data to better identify optimal doses in immunotherapy studies.

Findings

The model effectively identifies biologically optimal doses.

Simulation results show desirable operating characteristics.

The method is implemented in an accessible R Shiny tool.

Abstract

The primary objective of phase I oncology studies is to establish the safety profile of a new treatment and determine the maximum tolerated dose (MTD). This is motivated by the development of cytotoxic agents based on the underlying assumption that the higher the dose, the greater the likelihood of efficacy and toxicity. However, evidence from the recent development of cancer immunotherapies that aim to stimulate patients' immune systems to fight cancer challenges this assumption, particularly further escalation after a certain dose level might not necessarily increase the efficacy. Dose escalation study of molecular targeted agents (MTA) often does not only rely on the safety profile. In this paper, we propose a simple and flexible model that uses multivariate Gaussian latent variables to integrate toxicity endpoint and efficacy biomarker. This model can be easily extended to incorporate additional immune biomarkers. By simultaneously considering multiple outcomes, the proposed method is better at identifying the biologically optimal dose, which results in better decision-making. Simulation studies showed that the proposed method has desirable operating characteristics by determining the target dose with an optimal risk-benefit trade-off. We have also implemented our proposed method in a user-friendly R Shiny tool.