PROSTATE CANCER HORMONAL THERAPY
Predicting Time to Castration Resistance in Hormone Sensitive Prostate Cancer by a Personalization Algorithm Based on a Mechanistic Model Integrating Patient Data
Background: Prostate 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.
Methods: A 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. Regression analysis evaluated the potential use of the model parameters as prognostic markers for time to CRPC and survival.
Results: The 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 an initial period after ADT onset (i.e. 6 months), and given the full ADT regimen, the personalization algorithm accurately predicted the individual time to BF of ADT in 90 percent of patients in the retrospective cohort (R2=0.98). Time-to-event analysis revealed that PSA growth rate and ADT clearance rate parameters were good predictive and prognostic factors for early progression to CRPC and poor survival, respectively.
Conclusions: The 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.
In summary, a small number of pretreatment and initial in–treatment PSA measurements are sufficient for constructing patient–specific PCa immunotherapy models, which accurately predicted the subsequent PSA dynamics. While PSA levels are of limited predictive value themselves, their analysis by the demonstrated approach can guide the design of treatment schedules, when the projected responses of initial regimens are predicted to be ineffective.
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