小柯机器人

研究人员利用新技术揭示了癌基因的突变特征
2021-07-31 22:06

西班牙巴塞罗那科学技术学院Lopez-Bigas, Nuria、Abel Gonzalez-Perez和Ferran Muiños研究组合作宣布他们利用硅饱和技术揭示了癌基因的突变特征。相关论文在线发表在2021年7月28日出版的《自然》杂志上。

研究人员认为可以利用在数千个肿瘤中观察到的突变-利用实验测试它们在个体和组织复制中的致癌潜力-来解决癌基因特异性突变的问题。利用这些突变可以计算每个癌症基因和组织肿瘤发生机制的特征,并便于构建利用这些机制的机器学习模型。

研究人员通过构建和验证185个基因组织特异性机器学习模型来证明该解决方案的可行性,这些模型在识别驱动突变和乘客突变方面表现优于实验性的优势。该模型及其对每个突变的评估是可解释的,从而避免了使用黑盒预测设备。使用这些模型,研究人员概述了癌症基因中潜在驱动突变的蓝图,并证明了这些突变在重塑驱动突变景观中的作用。这些图谱有利于对新测序肿瘤的解释以及对跨组织癌症基因促进肿瘤发生机制的研究。

研究人员表示,尽管存在完善的癌症基因目录,但在很大程度上识别那些在不同肿瘤类型中诱导肿瘤发生的基因特异性突变仍具有挑战性。因此,在不同肿瘤癌症基因中鉴定的大多数突变对肿瘤发生具有巨大的意义。

附:英文原文

Title: In silico saturation mutagenesis of cancer genes

Author: Muios, Ferran, Martnez-Jimnez, Francisco, Pich, Oriol, Gonzalez-Perez, Abel, Lopez-Bigas, Nuria

Issue&Volume: 2021-07-28

Abstract: Despite the existence of good catalogues of cancer genes1,2, identifying the specific mutations of those genes that drive tumorigenesis across tumour types is still a largely unsolved problem. As a result, most mutations identified in cancer genes across tumours are of unknown significance to tumorigenesis3. We propose that the mutations observed in thousands of tumours—natural experiments testing their oncogenic potential replicated across individuals and tissues—can be exploited to solve this problem. From these mutations, features that describe the mechanism of tumorigenesis of each cancer gene and tissue may be computed and used to build machine learning models that encapsulate these mechanisms. Here we demonstrate the feasibility of this solution by building and validating 185 gene–tissue-specific machine learning models that outperform experimental saturation mutagenesis in the identification of  driver and passenger mutations. The models and their assessment of each mutation are designed to be interpretable, thus avoiding a black-box prediction device. Using these models, we outline the blueprints of potential driver mutations in cancer genes, and demonstrate the role of mutation probability in shaping the landscape of observed driver mutations. These blueprints will support the interpretation of newly sequenced tumours in patients and the study of the mechanisms of tumorigenesis of cancer genes across tissues.

DOI: 10.1038/s41586-021-03771-1

Source: https://www.nature.com/articles/s41586-021-03771-1

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html


本期文章:《自然》:Online/在线发表

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