The increase in available high-throughput molecular data creates computational challenges for the identification of cancer genes. Genetic as well as non-genetic causes contribute to tumorigenesis, and this necessitates the development of predictive models to effectively integrate different data modalities while being interpretable. We introduce EMOGI, an explainable machine learning method based on graph convolutional networks to predict cancer genes by combining multiomics pan-cancer data—such as mutations, copy number changes, DNA methylation and gene expression—together with protein–protein interaction (PPI) networks. EMOGI was on average more accurate than other methods across different PPI networks and datasets. We used layer-wise relevance propagation to stratify genes according to whether their classification was driven by the interactome or any of the omics levels, and to identify important modules in the PPI network. We propose 165 novel cancer genes that do not necessarily harbour recurrent alterations but interact with known cancer genes, and we show that they correspond to essential genes from loss-of-function screens. We believe that our method can open new avenues in precision oncology and be applied to predict biomarkers for other complex diseases.

可用高通量分子数据的增加为癌症基因的鉴定带来了计算挑战。遗传和非遗传原因导致肿瘤发生，这需要开发预测模型以有效整合不同的数据模式，同时可解释。我们介绍了 EMOGI，这是一种基于图卷积网络的可解释机器学习方法，通过将多组学泛癌数据（例如突变、拷贝数变化、DNA 甲基化和基因表达）与蛋白质-蛋白质相互作用 (PPI) 网络相结合来预测癌症基因. 在不同的 PPI 网络和数据集中，EMOGI 平均比其他方法更准确。我们使用分层相关传播根据基因的分类是由相互作用组还是任何组学水平驱动来对基因进行分层，并识别 PPI 网络中的重要模块。我们提出了 165 个新的癌症基因，这些基因不一定具有反复发生的改变，但与已知的癌症基因相互作用，我们表明它们对应于功能丧失筛选的必需基因。我们相信我们的方法可以开辟精准肿瘤学的新途径，并应用于预测其他复杂疾病的生物标志物。