Year: 2018
Type: article
Abstract: Somatic mutations drive the growth of tumor cells and are pivotal biomarkers for many cancer treatments.Genetic association analysis using somatic mutations is an effective approach to study the functional impact of somatic mutations.However, standard regression methods are not appropriate for somatic mutation association studies because somatic mutation calls often have non-ignorable false positive rate and/or false negative rate.While large scale association analysis using somatic mutations becomes feasible recentlythanks for the improvement of sequencing techniques and the reduction of sequencing cost-there is an urgent need for a new statistical method designed for somatic mutation association analysis.We propose such a method with computationally efficient software implementation: Somatic mutation Association test with Measurement Errors (SAME).SAME accounts for somatic mutation calling uncertainty using a likelihood based approach.It can be used to assess the associations between continuous/dichotomous outcomes and individual mutations or gene-level mutations.Through simulation studies across a wide range of realistic scenarios, we show that SAME can significantly improve statistical power than the naive generalized linear model that ignores mutation calling uncertainty.Finally, using the data collected from The Cancer Genome Atlas (TCGA) project, we apply SAME to study the associations between somatic mutations and gene expression in 12 cancer types, as well as the associations between somatic mutations and colon cancer subtype defined by DNA methylation data.SAME recovered some interesting findings that were missed by the generalized linear model.In addition, we demonstrated that mutation-level and genelevel analyses are often more appropriate for oncogene and tumor-suppressor gene, respectively. Author summaryCancer is a genetic disease that is driven by the accumulation of somatic mutations.Association studies using somatic mutations is a powerful approach to identify the potential impact of somatic mutations on molecular or clinical features.One challenge for such tasks is the non-ignorable somatic mutation calling errors.We have developed a statistical method to address this challenge and applied our method to study the gene expression traits associated with somatic mutations in 12 cancer types.Our results show that some
Source: PLoS Genetics
Authors: Yang Liu, Qianchan He, Wei Sun
Institutions: Wright State University, Fred Hutch Cancer Center
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: G1209886030, G1584997690, G3887731537, G4258672507, G4554055148, Statistical Methods for Cancer Clinical Trials, G5255084490, Methods for Analyzing Cancer Somatic Mutation Data, G5944368067, G6012682559, G736634319, G8882569155, Bayesian Approaches to Model Selection for Survival Data, G9000108611, G9000255759, Cancer Center Support Grant
OpenAlex ID: W2898649334