Generalized Evolutionary Classifier for Evolutionary Guided Precision Medicine

作者：Matthew McCoy, Chen‐Hsiang Yeang, Shaymaa Bahnassy, Stanley Tam, Rebecca B. Riggins, Deepak Parashar, Robert A. Beckman · 发表于：JCO Precision Oncology · 年份：2025 · DOI：10.1200/po.23.00714 · 被引用次数：6 · 研究领域：Cancer Genomics and Diagnostics、Evolution and Genetic Dynamics、Genetic Associations and Epidemiology

PURPOSE: Current precision medicine (CPM) matches patients to therapies using traditional biomarkers, but inevitably resistance develops. Dynamic precision medicine (DPM) is a new evolutionary guided precision medicine (EGPM) approach undergoing translational development. It tracks intratumoral genetic heterogeneity and evolutionary dynamics, adapts as frequently as every 6 weeks, plans proactively for future resistance development, and incorporates multiple therapeutic agents. Simulations indicated DPM can significantly improve long-term survival and cure rates in a cohort of 3 million virtual patients representing a variety of clinical scenarios. Given the cost and invasiveness of monitoring subclones frequently, we sought to determine the value of a short DPM window of only two 6-week adaptations (moves). METHODS: In a new simulation, nearly 3 million virtual patients, differing in DPM input parameters of initial subclone compositions, drug sensitivities, and growth and mutational kinetics, were simulated as previously described. Each virtual patient was treated with CPM, DPM, and DPM for two moves followed by CPM. RESULTS: The first two DPM moves provide similar average benefit to a 5-year, 40-move sequence in the full virtual population. If the first two moves are identical for DPM and CPM, patients will not benefit from DPM (65% negative predictive value). A patient subset (20%) in which 2-move DPM and 40-move DPM provide closely similar outcomes has extraordinary predi...