Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA.
Nat Commun. 2018 Oct 8;9(1):4159. doi: 10.1038/s41467-018-06464-y.
Many recent efforts to analyze cancer genomes involve aggregation of mutations within reference maps of molecular pathways and protein networks. Here, we find these pathway studies are impeded by molecular interactions that are functionally irrelevant to cancer or the patient's tumor type, as these interactions diminish the contrast of driver pathways relative to individual frequently mutated genes. This problem can be addressed by creating stringent tumor-specific networks of biophysical protein interactions, identified by signatures of epistatic selection during tumor evolution. Using such an evolutionarily selected pathway (ESP) map, we analyze the major cancer genome atlases to derive a hierarchical classification of tumor subtypes linked to characteristic mutated pathways. These pathways are clinically prognostic and predictive, including the TP53-AXIN-ARHGEF17 combination in liver and CYLC2-STK11-STK11IP in lung cancer, which we validate in independent cohorts. This ESP framework substantially improves the definition of cancer pathways and subtypes from tumor genome data.
许多最近分析癌症基因组的努力都涉及在分子途径和蛋白质网络的参考图谱内聚合突变。在这里，我们发现这些途径研究受到与癌症或患者肿瘤类型无关的功能分子相互作用的阻碍，因为这些相互作用相对于个体经常突变的基因降低了驱动途径的对比度。通过创建由肿瘤进化过程中上位性选择特征识别的严格的肿瘤特异性生物物理蛋白质相互作用网络，可以解决这个问题。使用这样一个进化选择的途径 (ESP) 图谱，我们分析了主要的癌症基因组图谱，以得出与特征性突变途径相关的肿瘤亚型的层次分类。这些途径具有临床预后和预测价值，包括肝癌中的 TP53-AXIN-ARHGEF17 组合和肺癌中的 CYLC2-STK11-STK11IP，我们在独立队列中进行了验证。这个 ESP 框架从肿瘤基因组数据中大大提高了癌症途径和亚型的定义。
