A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.

中文翻译：

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使用蛋白质系统的多尺度图解释癌症突变

癌症研究的一个主要目标是了解分布在不同基因中的突变如何影响常见的细胞系统，包括多蛋白复合物和组装体。两个挑战——如何全面绘制此类系统以及如何识别哪些系统处于突变选择之下——阻碍了这种理解。因此，我们创建了癌症蛋白质系统的综合图谱，在多个分析尺度上整合了新的和已发表的多组学相互作用数据。然后，我们开发了一个统一的统计模型，可以精确定位 13 种癌症类型中突变选择下的 395 个特定系统。这张名为 NeST（肿瘤嵌套系统）的图谱融合了规范过程和重大发现，包括抑制磷脂酰肌醇 3 激酶信号传导的 PIK3CA-肌动球蛋白复合物以及促进肿瘤增殖的胶原蛋白复合物中的反复突变。这些系统可用作临床生物标志物，涉及癌症进化和进展中总共 548 个基因。这项工作展示了不同的肿瘤突变如何在不同规模的蛋白质组装上聚合。