Mechanisms of resistance to intermittent androgen deprivation in prostate cancer patients identified by a novel computational method
A partir de données portant sur 7 patients atteints d'un cancer de la prostate, cette étude présente un modèle mathématique permettant de prédire l'apparition d'une résistance à un traitement antiandrogénique intermittent
For progressive prostate cancer, intermittent androgen deprivation (IAD) is one of the most common and effective treatments. Although this treatment is usually initially effective at regressing tumors, most patients eventually develop castration-resistant prostate cancer (CRPC), for which there is no effective treatment and is generally fatal. Although several biological mechanisms leading to CRPC development and their relative frequencies have been identified, it is difficult to determine which mechanisms of resistance are developing in a given patient. Personalized therapy that identifies and targets specific mechanisms of resistance developing in individual patients is likely one of the most promising methods of future cancer therapy. Prostate-specific antigen (PSA) is a biomarker for monitoring tumor progression. We incorporated a cell-death rate function into a previous dynamical PSA model that was highly accurate at fitting clinical PSA data for 7 patients. The mechanism of action of IAD is largely induction of apoptosis, and each mechanism of resistance varies in its cell-death rate dynamics. Thus, we analyze the cell-death rate levels and their time-dependent oscillations to identify mechanisms of resistance to IAD developing in individual patients. Major Findings. Here, we introduce a novel computational method called cell-death rate analysis that uses a mathematical model validated by clinical data to identify the major mechanism of treatment resistance developing in a given patient. Although cell-death rate analysis may be applicable to other cancers, we use it here to predictively diagnose the major mechanism of castration resistance developing in individual prostate cancer patients after a period of IAD. Our results are consistent with biological literature, and this mathematical method using cell-death rate analysis may significantly improve personalized treatment of CRPC and other cancers.
Cancer Research , article en libre accès, 2014