Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that NetSig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Our study presents a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

整合分子网络信息和肿瘤基因组数据的方法可以补充基于基因的统计测试，以识别可能的新癌症基因；但这些方法很难大规模验证，而且它们的预测价值仍不清楚。我们开发了一项强大的统计数据 (NetSig)，它将蛋白质相互作用网络与来自 4,742 个肿瘤外显子组的数据集成起来。 NetSig 可以对 60% 的测试肿瘤类型中的已知驱动基因进行准确分类，并预测 62 个新的候选驱动基因。使用定量实验框架来确定小鼠体内致瘤潜力，我们发现 NetSig 候选物诱发肿瘤的速率与已知癌基因相当，并且比随机基因高十倍。通过重新分析 242 名癌基因阴性肺腺癌患者的 9 种肿瘤诱导 NetSig 候选者，我们发现其中两种（ AKT2和TFDP2 ）显着扩增。我们的研究提出了一个可扩展的集成计算和实验工作流程，以扩大癌症基因组的发现。