Dataset Information

Association analysis using somatic mutations.

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 recently-thanks 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 gene-level analyses are often more appropriate for oncogene and tumor-suppressor gene, respectively.

SUBMITTER: Liu Y

PROVIDER: S-EPMC6235399 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Association analysis using somatic mutations.

Liu Yang Y He Qianchuan Q Sun Wei W

PLoS genetics 20181102 11

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 ...[more]

PMID: 30388102

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Project description:BackgroundThe relationship between body mass index (BMI) and the prognosis or treatment response in patients with breast cancer has been demonstrated in previous studies, but the somatic mutation profiles in breast cancer patients with different BMIs have not been explored.MethodsIn the present study, the somatic mutation profiles in 421 female breast cancer patients who were stratified into three subgroups based on BMI (normal weight, overweight/obese, and underweight) were investigated. Capture-based targeted sequencing was performed using a panel comprising 520 cancer-related genes.ResultsA total of 3547 mutations were detected in 390 genes. In breast cancer patients with different BMI statuses, the tumors exhibited high mutation frequency and burden. TP53 was the most common gene in the three groups, followed by PIK3CA, ERBB2, and CDK12. Meanwhile, the mutation hotspots in TP53 and PIK3CA were the same in the three BMI groups. More JAK1 mutations were identified in underweight patients than those in normal patients. Except for JAK1, differentially mutated genes in postmenopausal patients were completely different from those in premenopausal patients. The distribution of mutation types was significantly different among BMI groups in the postmenopausal group. Underweight patients in the postmenopausal group harbored more TP53 mutations, more amplifications, and more mutations in genes involved in the WNT signaling pathway.ConclusionsOur next-generation sequencing (NGS)-based gene panel analysis revealed the gene expression profiles of breast cancer patients with different BMI statuses. Although genes with high mutation frequency and burden were found in different BMI groups, some subtle differences could not be ignored. JAK1 mutations might play a vital role in the progression of breast cancer in underweight patients, and this needs further analysis. Postmenopausal underweight patients with breast cancer have more aggressive characteristics, such as TP53 mutations, more amplifications, and more mutations in genes involved in the WNT signaling pathway. This study provides new evidence for understanding the characteristics of breast cancer patients with different BMIs.

Dataset Information

Association analysis using somatic mutations.

Publications

Association analysis using somatic mutations.

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