Prostate cancer is the most common cancer in men in the Western world. One-third of the patients with prostate cancer will develop resistance to hormonal therapy and progress into metastatic castration-resistant prostate cancer (mCRPC). Currently, docetaxel is a preferred treatment for mCRPC. However, about 20% of the patients will undergo early therapeutic failure owing to adverse events induced by docetaxel-based chemotherapy. There is an emergent need for a computational model that can accurately stratify patients into docetaxel-tolerable and docetaxel-intolerable groups. Here we present the best-performing algorithm in the Prostate Cancer DREAM Challenge for predicting adverse events caused by docetaxel treatment. We integrated the survival status and severity of adverse events into our model, which is an innovative way to complement and stratify the treatment discontinuation information. Critical stratification biomarkers were further identified in determining the treatment discontinuation. Our model has the potential to improve future personalized treatment in mCRPC.

前列腺癌是西方世界男性最常见的癌症。三分之一的前列腺癌患者会对激素治疗产生耐药性，并进展为转移性去势抵抗性前列腺癌（mCRPC）。目前，多西他赛是 mCRPC 的首选治疗方法。然而，约20%的患者会因多西紫杉醇化疗引起的不良事件而出现早期治疗失败。迫切需要一种能够准确地将患者分为多西他赛耐受组和多西他赛不耐受组的计算模型。在这里，我们在前列腺癌梦想挑战赛中展示了性能最佳的算法，用于预测多西紫杉醇治疗引起的不良事件。我们将生存状态和不良事件的严重程度整合到我们的模型中，这是补充和分层治疗中断信息的创新方法。在确定治疗停止时进一步确定了关键分层生物标志物。我们的模型有潜力改善未来 mCRPC 的个性化治疗。