Precision medicine is an approach for disease treatment that defines treatment strategies based on the individual characteristics of the patients. Motivated by an open problem in cancer genomics, we develop a novel model that flexibly clusters patients with similar predictive characteristics and similar treatment responses; this approach identifies, via predictive inference, which one among a set of treatments is better suited for a new patient. The proposed method is fully model-based, avoiding uncertainty underestimation attained when treatment assignment is performed by adopting heuristic clustering procedures, and belongs to the class of product partition models with covariates, here extended to include the cohesion induced by the Normalized Generalized Gamma process. The method performs particularly well in scenarios characterized by considerable heterogeneity of the predictive covariates in simulation studies. A cancer genomics case study illustrates the potential benefits in terms of treatment response yielded by the proposed approach. Finally, being model-based, the approach allows estimating clusters' specific response probabilities and then identifying patients more likely to benefit from personalized treatment.

翻译：精准医疗是一种根据患者个体特征制定治疗策略的疾病治疗方法。受癌症基因组学中一个待解决问题启发，我们开发了一种新颖模型，该模型能够灵活地对具有相似预测特征和相似治疗反应的患者进行聚类；通过预测推断，该方法可识别一组治疗方案中哪种更适合新患者。所提出的方法完全基于模型，避免了采用启发式聚类程序进行治疗方案分配时产生的低估不确定性，并属于带协变量的乘积分割模型类别，此处扩展了由归一化广义伽马过程诱导的凝聚性。该方法在模拟研究中，当预测协变量存在显著异质性时表现尤为出色。一项癌症基因组学案例研究展示了所提方法在治疗反应方面的潜在获益。最后，由于基于模型，该方法可估计各聚类的特定反应概率，从而识别出更可能从个性化治疗中获益的患者。