Cancers are caused by genomic alterations that may be inherited, induced by environmental carcinogens, or caused due to random replication errors. Postinduction of carcinogenicity, mutations further propagate and drastically alter the cancer genomes. Although a subset of driver mutations has been identified and characterized to date, most cancer-related somatic mutations are indistinguishable from germline variants or other noncancerous somatic mutations. Thus, such overlap impedes appreciation of many deleterious but previously uncharacterized somatic mutations. The major bottleneck arises due to patient-to-patient variability in mutational profiles, making it difficult to associate specific mutations with a given disease outcome. Here, we describe a newly developed technique Continuous Representation of Codon Switches (CRCS), a deep learning-based method that allows us to generate numerical vector representations of mutations, thereby enabling numerous machine learning-based tasks. We demonstrate three major applications of CRCS; first, we show how CRCS can help detect cancer-related somatic mutations in the absence of matched normal samples, which has applications in cell-free DNA–based assessment of tumor mutation burden. Second, the proposed approach also enables identification and exploration of driver genes; our analyses implicate DMD, RSK4, OFD1, WDR44, and AFF2 as potential cancer drivers. Finally, we used CRCS to score individual mutations in a tumor sample, which was found to be predictive of patient survival in bladder urothelial carcinoma, hepatocellular carcinoma, and lung adenocarcinoma. Taken together, we propose CRCS as a valuable computational tool for analysis of the functional significance of individual cancer mutations.

癌症是由基因组改变引起的，这些改变可能是遗传的、由环境致癌物引起的，或者是由于随机复制错误引起的。致癌性诱导后，突变进一步传播并彻底改变癌症基因组。尽管迄今为止已经鉴定和表征了驱动突变的一个子集，但大多数与癌症相关的体细胞突变与种系变异或其他非癌性体细胞突变无法区分。因此，这种重叠阻碍了对许多有害但以前未表征的体细胞突变的认识。主要瓶颈是由于突变谱的患者与患者之间的变异性而出现的，这使得很难将特定突变与给定的疾病结果联系起来。在这里，我们描述了一种新开发的密码子开关连续表示 (CRCS) 技术，一种基于深度学习的方法，允许我们生成突变的数值向量表示，从而实现大量基于机器学习的任务。我们展示了CRCS的三个主要应用；首先，我们展示了 CRCS 如何在没有匹配的正常样本的情况下帮助检测癌症相关的体细胞突变，这在基于无细胞 DNA 的肿瘤突变负荷评估中具有应用。其次，所提出的方法还可以识别和探索驱动基因；我们的分析暗示 它在基于无细胞 DNA 的肿瘤突变负荷评估中具有应用。其次，所提出的方法还可以识别和探索驱动基因；我们的分析暗示 它在基于无细胞 DNA 的肿瘤突变负荷评估中具有应用。其次，所提出的方法还可以识别和探索驱动基因；我们的分析暗示DMD、RSK4、OFD1、WDR44和AFF2作为潜在的癌症驱动因素。最后，我们使用 CRCS 对肿瘤样本中的个体突变进行评分，发现该突变可预测膀胱尿路上皮癌、肝细胞癌和肺腺癌的患者存活率。总之，我们建议将 CRCS 作为一种有价值的计算工具，用于分析个体癌症突变的功能意义。