Comprehensive cross cancer analyses reveal mutational signature cancer specificity  

作　　者：Rui Xin Limin Jiang Hui Yu Fengyao Yan Jijun Tang Yan Guo 

机构地区：[1]Department of Computer Science,University of South Carolina,Columbia,South Carolina,USA [2]Department of Public Health and Sciences,Sylvester Comprehensive Cancer Center,University of Miami,Miami,Florida,USA

出　　处：《Quantitative Biology》2024年第3期245-254,共10页定量生物学（英文版）

基　　金：Division of Cancer Prevention,National Cancer Institute,Grant/Award Number:P30CA240139。

摘　　要：Mutational signatures refer to distinct patterns of DNA mutations that occur in a specific context or under certain conditions.It is a powerful tool to describe cancer etiology.We conducted a study to show cancer heterogeneity and cancer specificity from the aspect of mutational signatures through collinearity analysis and machine learning techniques.Through thorough training and independent validation,our results show that while the majority of the mutational signatures are distinct,similarities between certain mutational signature pairs can be observed through both mutation patterns and mutational signature abundance.The observation can potentially assist to determine the etiology of yet elusive mutational signatures.Further analysis using machine learning approaches demonstrated moderate mutational signature cancer specificity.Skin cancer among all cancer types demonstrated the strongest mutational signature specificity.

关 键 词：cancer specificity collinearity analysis DNA mutational signatures machine learning 

分 类 号：R730[医药卫生—肿瘤]