A memory-driven reinforcement learning model of phenotypic adaptation for anticipating therapeutic resistance in prostate cancer.

ABSTRACT: While contemporary cancer treatment strategies have significantly prolonged the lives of patients, therapeutic resistance remains a predominant cause of disease progression and cancer-related deaths. Cancer therapy often induces gene regulatory responses that promote cell survival in the face of this therapy. Herein, we sought to develop a stochastic model of the response to repeat therapeutic challenge. This model integrates reinforcement learning to account for environmental history-dependent cellular transitions and growth dynamics. When applied to prostate cancer, this memory-driven adaptive model successfully captures the experimentally-observed dynamics of drug-sensitive and drug-resistant LNCaP cells under varying dosing schedules of androgen receptor blockade with enzalutamide (enz

SUBMITTER: Ghoreyshi ZS 

PROVIDER: S-EPMC12637677 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature