Rapid proliferation is a hallmark of cancer, associated with sensitivity to therapeutics that cause DNA replication stress (RS). Many tumors exhibit drug resistance, however, via molecular pathways that are incompletely understood. Here, we develop an ensemble of predictive models that elucidate how cancer mutations impact the response to common RS-inducing (RSi) agents. The models implement recent advances in deep learning to facilitate multi-drug prediction and mechanistic interpretation. Initial studies in tumor cells identify 41 molecular assemblies that integrate alterations in hundreds of genes for accurate drug response prediction. These cover roles in transcription, repair, cell-cycle checkpoints, and growth signaling, of which 30 are shown by loss-of-function genetic screens to regulate drug sensitivity or replication restart. The model translates to cisplatin-treated cervical cancer patients, highlighting an RTK (receptor tyrosine kinase)-JAK-STAT assembly governing resistance. This study defines a compendium of mechanisms by which mutations affect therapeutic responses, with implications for precision medicine.

中文翻译：

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癌症突变集中在蛋白质组装体的集合上，以预测对复制压力的抵抗力

快速增殖是癌症的一个标志，与引起 DNA 复制应激 (RS) 的治疗方法的敏感性有关。然而，许多肿瘤通过不完全了解的分子途径表现出耐药性。在这里，我们开发了一组预测模型，阐明癌症突变如何影响对常见 RS 诱导 (RSi) 药物的反应。这些模型应用了深度学习的最新进展，以促进多药物预测和机制解释。肿瘤细胞的初步研究确定了 41 个分子组装体，这些组装体整合了数百个基因的改变，以实现准确的药物反应预测。这些涵盖了转录、修复、细胞周期检查点和生长信号传导中的作用，其中 30 种通过功能丧失遗传筛选显示，可调节药物敏感性或复制重启。该模型适用于接受顺铂治疗的宫颈癌患者，强调了控制耐药性的 RTK（受体酪氨酸激酶）-JAK-STAT 组合。这项研究定义了突变影响治疗反应的机制概要，对精准医学具有影响。