Elishmereni, Moran

BACKGROUNDProstate cancer (PCa) is a leading cause of cancer death of men worldwide. In hormone‐sensitive prostate cancer (HSPC), androgen deprivation therapy (ADT) is widely used, but an eventual failure on ADT heralds the passage to the castration‐resistant prostate cancer (CRPC) stage. Because predicting time to failure on ADT would allow improved planning of personal treatment strategy, we aimed to develop a predictive personalization algorithm for ADT efficacy in HSPC patients.METHODSA mathematical mechanistic model for HSPC progression and treatment was developed based on the underlying disease dynamics (represented by prostate‐specific antigen; PSA) as affected by ADT. Following fine‐tuning by a dataset of ADT‐treated HSPC patients, the model was embedded in an algorithm, which predicts the patient's time to biochemical failure (BF) based on clinical metrics obtained before or early in‐treatment.RESULTSThe mechanistic model, including a tumor growth law with a dynamic power and an elaborate ADT‐resistance mechanism, successfully retrieved individual time‐courses of PSA (R2 = 0.783). Using the personal Gleason score (GS) and PSA at diagnosis, as well as PSA dynamics from 6 months after ADT onset, and given the full ADT regimen, the personalization algorithm accurately predicted the individual time to BF of ADT in 90% of patients in the retrospective cohort (R2 = 0.98).CONCLUSIONSThe algorithm we have developed, predicting biochemical failure based on routine clinical tests, could be especially useful for patients destined for short‐lived ADT responses and quick progression to CRPC. Prospective studies must validate the utility of the algorithm for clinical decision‐making. Prostate 76:48–57, 2016. © 2015 Wiley Periodicals, Inc.

Abstract Although therapeutic vaccination often induces markers of tumor-specific immunity, therapeutic responses remain rare. An improved understanding of patient-specific dynamic interactions of immunity and tumor progression, combined with personalized application of immune therapeutics would increase the efficacy of immunotherapy. Here, we developed a method to predict and enhance the individual response to immunotherapy by using personalized mathematical models, constructed in the early phase of treatment. Our approach includes an iterative real-time in-treatment evaluation of patient-specific parameters from the accruing clinical data, construction of personalized models and their validation, model-based simulation of subsequent response to ongoing therapy, and suggestion of potentially more effective patient-specific modified treatment. Using a mathematical model of prostate cancer immunotherapy, we applied our model to data obtained in a clinical investigation of an allogeneic whole-cell therapeutic prostate cancer vaccine. Personalized models for the patients who responded to treatment were derived and validated by data collected before treatment and during its early phase. Simulations, based on personalized models, suggested that an increase in vaccine dose and administration frequency would stabilize the disease in most patients. Together, our findings suggest that application of our method could facilitate development of a new paradigm for studies of in-treatment personalization of the immune agent administration regimens (P-trials), with treatment modifications restricted to an approved range, resulting in more efficacious immunotherapies. Cancer Res; 72(9); 2218–27. ©2012 AACR.