Generative AI - Assisted Adaptive Cancer Therapy.

Abstract

Adaptive combination therapy is deemed the most intuitive strategy to thwart therapeutic resistance through dynamic treatment tuning that accounts for cancer evolutionary dynamics. However, higher accuracy and reliability of treatment response predictions would be needed, in addition to the need for clinically feasible models of adaptive combination therapy that consider newly approved therapeutics and the growing multimodal data being available about cancer. Grounded in nonlinear system control theory, this review offers a perspective on exploiting GenAI learning and inferencing capabilities to predict treatment response and recommend treatments in the context of adaptive cancer therapy. Results from nonlinear system identification, control theory and deep learning are integrated within an adaptive cancer control framework to leverage the continuously expanding data about cancer and its treatment towards GenAI-enhanced adaptive therapy. The resulting models and their analysis contribute to a much-needed conceptual clarity about the research and translational pathways that would be needed to realize GenAI-assisted cancer treatments. In particular, they underscore that access to clinical data, deep learning opacity, and clinical validation present critical challenges that require adequate attention to pave the way towards acceptance and integration of GenAI in real-world oncology workflows.