Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Discovery of effective combination therapies is challenging due to the complexity of the biomolecular landscape of drug responses. Here, we developed the method REFLECT (REcurrent Features Leveraged for Combination Therapies), which integrates machine learning and cancer informatics algorithms. The method maps recurrent co-alteration signatures from multi-omic data across patient cohorts to combination therapies. Using the REFLECT framework, we generated a precision therapy resource matching 2,201 drug combinations to co-alteration signatures across 201 cohorts stratified from 10,392 patients and 33 cancer types. We validated that REFLECT-predicted combinations introduce significantly higher therapeutic benefit through analysis of independent data from comprehensive drug screens. In patient cohorts with immunotherapy response markers, HER2 activation and DNA repair aberrations, we identified therapeutically actionable co-alteration signatures shared across patient sub-cohorts. REFLECT provides a framework to design combination therapies tailored to patient cohorts in data-driven clinical trials.

中文翻译：

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基于复发性致癌联合替代的精密组合疗法

癌细胞取决于多种驱动因素的改变，其致癌作用可以被药物组合抑制。由于药物反应的生物分子格局的复杂性，发现有效的联合疗法具有挑战性。在这里，我们开发了方法REFLECT（用于组合疗​​法的循环特征），该方法集成了机器学习和癌症信息学算法。该方法将跨患者群的多组学数据中的复发性联合替代特征映射到联合疗法。使用REFLECT框架，我们生成了一个精确的治疗资源，将2,201种药物组合与来自10,392名患者和33种癌症类型的201个队列的共替代特征相匹配。我们验证了REFLECT预测的组合通过对来自全面药物筛选的独立数据进行分析，可带来更高的治疗益处。在具有免疫治疗反应标记，HER2激活和DNA修复异常的患者队列中，我们确定了在患者亚队列之间共享的具有治疗作用的共替代特征。REFLECT提供了一个框架，可设计针对数据驱动的临床试验中针对患者人群的联合疗法。