Evaluating PSA dynamics for predicting androgen deprivation failure with a patient specific prostate cancer model

Prostate cancer is the second leading cause of cancer-related death among American men, with a new diagnosis made every 2 min in the United States. Advanced cases are commonly treated with androgen deprivation therapy (ADT). Despite its effectiveness, treatment failure remains inevitable for many patients, necessitating better predictive tools for clinical management of disease. This study presents a data-driven mathematical modeling approach that integrates patient-specific prostate-specific antigen (PSA) time-course data with experimentally measured PSA expression rates to improve the prediction of ADT failure. Our findings suggest that post-nadir PSA dynamics, rather than initial decline, hold greater prognostic value and can inform PSA monitoring schedules. By employing virtual clones of individual patients, our model integrates routinely collected PSA measurements to dynamically predict ADT failure probabilities at future clinic visits. If implemented in clinical practice, this personalized framework could empower oncologists to make proactive, informed treatment decisions and guide timely interventions.