A Proof-of-Concept Clinical Trial Design for Evolutionary Guided Precision Medicine for Cancer

Area of Science:

• Oncology
• Mathematical Biology
• Clinical Trial Design

Background:

• Current Precision Medicine (CPM) relies on static molecular profiles, but cancer's subclonal heterogeneity drives dynamic evolution under therapy.
• This evolution can lead to treatment resistance and relapse, limiting the efficacy of CPM.
• Optimizing therapy timing and sequencing through mathematical modeling may overcome these limitations.

Purpose of the Study:

• To introduce and evaluate a novel clinical trial design for Evolutionary Guided Precision Medicine (EGPM).
• To demonstrate the potential of Dynamic Precision Medicine (DPM), a type of EGPM, in preventing or delaying cancer relapse.
• To present a proof-of-concept design distinct from traditional biomarker-driven trials.

Main Methods:

• Simulated a stratified randomized clinical trial design to test Dynamic Precision Medicine (DPM).
• Utilized an evolutionary classifier to stratify patients based on predicted benefit from DPM.
• Evaluated the design's performance in terms of statistical power and control of false positive rates.

Main Results:

• The proposed DPM trial design demonstrated high statistical power.
• The design effectively controlled false positive rates.
• Simulations showed robust performance, anticipating challenges in clinical translation.

Conclusions:

• EGPM strategies, exemplified by DPM, hold promise for improving cancer treatment outcomes by addressing tumor evolution.
• The presented stratified randomized trial design provides a robust framework for evaluating EGPM.
• This approach offers a distinct and potentially more effective method for personalized cancer therapy compared to CPM.