Integrating biomarker-derived individual treatment response assessment into Bayesian trial design for personalized cancer treatment

作者：Pavlos Kolias, Ali Ajdari, A. Cicchetti, Robert Jeraj, Iuliana Toma-Dasu, Daniela Thorwarth, Cihan Gani, Christian Thieke, Nils H. Nicolay, C. Fiorino, Thomas Bortfeld, Maximilian Niyazi · 发表于：Radiotherapy and Oncology · 年份：2026 · DOI：10.1016/j.radonc.2026.111484 · 被引用次数：2 · 研究领域：Statistical Methods in Clinical Trials、Cancer Genomics and Diagnostics、Gene expression and cancer classification

INTRODUCTION: Biomarker-driven strategies are central to personalized oncology, yet local treatments such as radiotherapy still lack validated stratification frameworks. Conventional frequentist trial designs often require prohibitively large patient cohorts without incorporating previously validated data. We therefore propose a Bayesian trial framework, which utilizes information of a patient's biomarker and historical information of the biomarker effect to optimize the assigned dose. The aim of this work is to develop and illustrate such a framework for radiotherapy in rectal cancer organ preservation. METHODS: We designed a prospective two-arm Bayesian response-adaptive trial concept incorporating the principle of optimal stopping. Biomarker measurements obtained during treatment guided dose adaptation, illustrated here using the imaging-derived early regression index (ERI). Feasibility and statistical performance were evaluated through a fully simulated trial in rectal cancer organ preservation. Sensitivity analysis was applied to investigate the effect of prior information on the posterior distribution. RESULTS: Simulations modeled the patients' response using biomarker-dependent tumor control and biomarker-independent toxicity curves. One subgroup of patients would benefit most from moderate dose escalation, achieving improved tumor control without excessive toxicity. In contrast, poor and excellent responders gained limited additional benefit at clinically acceptable d...