Precision Combination Therapies Based on Recurrent Oncogenic Coalterations.
Precision Combination Therapies Based on Recurrent Oncogenic Coalterations.
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DOI：
10.1158/2159-8290.cd-21-0832
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发表时间：
2022-06-02
期刊：
影响因子：
28.2
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中科院分区：
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癌细胞依赖于多种驱动改变，其致癌作用可以通过药物组合来抑制。在这里，我们提供了针对患者队列中反复出现的致癌共同改变量身定制的精确组合疗法的全面资源。为了生成资源，我们开发了联合治疗利用的重复特征（REFLECT），它集成了机器学习和癌症信息学算法。使用多组学数据，该方法将患者群组中的复发性共改变特征映射到组合疗法。我们使用来自患者来源的异种移植物、体外药物筛选和联合治疗临床试验的数据验证了REFLECT管道。这些验证表明，REFLECT选择的联合治疗显著改善了疗效、协同作用和生存结局。在具有免疫治疗应答标志物、DNA修复畸变和HER2活化的患者队列中，我们已经鉴定了治疗上可行的和复发的共改变特征。REFLECT提供了一个资源和框架，用于在数据驱动的临床试验和临床前研究中设计针对肿瘤队列的联合治疗。
Cancer cells depend on multiple driver alterations whose oncogenic effects can be suppressed by drug combinations. Here, we provide a comprehensive resource of precision combination therapies tailored to oncogenic co-alterations that are recurrent across patient cohorts. To generate the resource, we developed Recurrent Features Leveraged for Combination Therapy (REFLECT), which integrates machine learning and cancer informatics algorithms. Using multi-omic data, the method maps recurrent co-alteration signatures in patient cohorts to combination therapies. We validated the REFLECT pipeline using data from patient-derived xenografts, in vitro drug screens, and a combination therapy clinical trial. These validations demonstrate that REFLECT-selected combination therapies have significantly improved efficacy, synergy, and survival outcomes. In patient cohorts with immunotherapy response markers, DNA repair aberrations, and HER2 activation, we have identified therapeutically actionable and recurrent co-alteration signatures. REFLECT provides a resource and framework to design combination therapies tailored to tumor cohorts in data-driven clinical trials and pre-clinical studies.