美国麻省理工学院Bonnie Berger、哈佛医学院Po-Ru Loh等研究人员合作利用体细胞突变率的全基因组图谱揭示癌症的驱动因素。该项研究成果于2022年6月20日在线发表在《自然—生物技术》杂志上。

研究人员提出了Dig，一种在基因组中任何地方搜索驱动元素和突变的方法。研究人员使用深度神经网络在整个基因组范围内以kb级的分辨率绘制了癌症特定突变率。然后，通过比较观察到的突变数和预期突变数，对这些估计进行改进，进而搜索整个基因组中正向选择下的驱动突变的证据。研究人员绘制了37种癌症类型的突变率图，并应用这些图来识别隐性剪接区、5′非翻译区和不经常突变的基因中的假定驱动因素。这个高分辨率突变率图谱可供网络探索，并且是在全基因组范围内发现驱动因素的一种资源。

据悉，识别赋予增殖优势的癌症驱动突变是理解癌症的核心；然而，搜索往往局限于蛋白质编码序列和特定的非编码元素（例如启动子），因为在整个肿瘤基因组中观察到的高度可变的体细胞突变率的建模存在挑战。

附：英文原文

Title: Genome-wide mapping of somatic mutation rates uncovers drivers of cancer

Author: Sherman, Maxwell A., Yaari, Adam U., Priebe, Oliver, Dietlein, Felix, Loh, Po-Ru, Berger, Bonnie

Issue&Volume: 2022-06-20

Abstract: 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.

DOI: 10.1038/s41587-022-01353-8

Source: https://www.nature.com/articles/s41587-022-01353-8