Identification of cancer driver mutations that confer a proliferative advantage is central to understanding cancer; however, searches have often been limited to protein-coding sequences and specific non-coding elements (for example, promoters) because of the challenge of modeling the highly variable somatic mutation rates observed across tumor genomes. Here we present Dig, a method to search for driver elements and mutations anywhere in the genome. We use deep neural networks to map cancer-specific mutation rates genome-wide at kilobase-scale resolution. These estimates are then refined to search for evidence of driver mutations under positive selection throughout the genome by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and applied these maps to identify putative drivers within intronic cryptic splice regions, 5′ untranslated regions and infrequently mutated genes. Our high-resolution mutation rate maps, available for web-based exploration, are a resource to enable driver discovery genome-wide.

鉴定赋予增殖优势的癌症驱动突变对于理解癌症至关重要；然而，由于对在肿瘤基因组中观察到的高度可变的体细胞突变率进行建模存在挑战，因此搜索通常仅限于蛋白质编码序列和特定的非编码元件（例如，启动子）。在这里，我们介绍 Dig，这是一种在基因组中的任何位置搜索驱动元素和突变的方法。我们使用深度神经网络以千碱基级分辨率在全基因组范围内绘制癌症特异性突变率。然后通过将观察到的突变计数与预期突变计数进行比较，对这些估计进行细化，以在整个基因组中寻找正选择下驱动突变的证据。我们绘制了 37 种癌症类型的突变率图，并应用这些图谱来识别内含子隐蔽剪接区、5' 非翻译区和不常突变基因中的推定驱动因素。我们的高分辨率突变率图可用于基于网络的探索，是实现全基因组驱动程序发现的资源。