Biomarker‐based precision dose finding for immunotherapy combined with radiotherapy

Author:

Lin Li‐Hsiang1,Han Yan2,Zhang Rui34,Guo Beibei1ORCID

Affiliation:

1. Department of Experimental Statistics Louisiana State University Baton Rouge Louisiana USA

2. Department of Biostatistics and Health Data Science School of Medicine, Indiana University Indianapolis Indiana USA

3. Department of Physics and Astronomy Louisiana State University Baton Rouge Louisiana USA

4. Department of Radiation Oncology Mary Bird Perkins Cancer Center Baton Rouge Louisiana USA

Abstract

AbstractRecent success of sequential administration of immunotherapy following radiotherapy (RT), often referred to as immunoRT, has sparked the urgent need for novel clinical trial designs to accommodate the unique features of immunoRT. For this purpose, we propose a Bayesian phase I/II design for immunotherapy administered after standard‐dose RT to identify the optimal dose that is personalized for each patient according to his/her measurements of PD‐L1 expression at baseline and post‐RT. We model the immune response, toxicity, and efficacy as functions of dose and patient's baseline and post‐RT PD‐L1 expression profile. We quantify the desirability of the dose using a utility function and propose a two‐stage dose‐finding algorithm to find the personalized optimal dose. Simulation studies show that our proposed design has good operating characteristics, with a high probability of identifying the personalized optimal dose.

Funder

American Cancer Society

Louisiana Board of Regents

National Institutes of Health

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,General Medicine,Statistics and Probability

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