Modeling study of mathematical biomarkers of adaptive therapy outcomes in prostate cancer

Adaptive therapy is an evolution-based treatment paradigm that has been shown to delay resistance in prostate cancer through treatment breaks that control, rather than minimzie, tumor burden. However, patient responses are highly heterogeneous, and there is a significant unmet clinical need for biomarkers to personalize treatment scheduling. A retrospective modeling and validation study evaluated mathematical biomarkers that predict time to progression (TTP) under adaptive therapy from first-cycle PSA dynamics. The analysis used clinical trial data from two independent cohorts: 40 patients with castrate-sensitive prostate cancer (CSPC) receiving intermittend androgen deprivation therapy, and 13 patients with metastatic castrate-resistant prostate cancer (mCRPC) receiving adaptive abiraterone acetate. In the CSPC cohort, the adaptive therapy score derived from first-cycle data was prognostic for prolonged clinical TTP (HR 0.49; p=0.002). In the mCRPC cohort, the adaptive therapy score exhibited a strong rank correlation with clinical TTP (Spearman ρ=0.76; p=0.002) and was associted with prolonged TTP (HR 0.41; p=0.07). Analysis of long-term survival in the mCRPC cohort demonstrated that both the adaptive therapy score and expected TTP were significantly associated with prolonged overall surviva, whereas standard empirical PSA metrics were not. The authors conclude that in this study, mechanism-based mathematical biomarkers derived from initial-cycle PSA dynamics accurately predicted patient-specific outcomes and survival, outperforming traditional phenomenological PSA monitoring.