Dataset Information

Functional characterization of somatic mutations in cancer using network-based inference of protein activity.

ABSTRACT: Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activity by enriched regulon analysis (VIPER), for accurate assessment of protein activity from gene expression data. We used VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all samples in The Cancer Genome Atlas (TCGA). In addition to accurately infer aberrant protein activity induced by established mutations, we also identified a fraction of tumors with aberrant activity of druggable oncoproteins despite a lack of mutations, and vice versa. In vitro assays confirmed that VIPER-inferred protein activity outperformed mutational analysis in predicting sensitivity to targeted inhibitors.

SUBMITTER: Alvarez MJ

PROVIDER: S-EPMC5040167 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Functional characterization of somatic mutations in cancer using network-based inference of protein activity.

Alvarez Mariano J MJ Shen Yao Y Giorgi Federico M FM Lachmann Alexander A Ding B Belinda BB Ye B Hilda BH Califano Andrea A

Nature genetics 20160620 8

Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, virtual inference of protein activ ...[more]

PMID: 27322546

Similar Datasets

Project description:BackgroundRecently, a number of large-scale cancer genome sequencing projects have generated a large volume of somatic mutations; however, identifying the functional consequences and roles of somatic mutations in tumorigenesis remains a major challenge. Researchers have identified that protein pocket regions play critical roles in the interaction of proteins with small molecules, enzymes, and nucleic acid. As such, investigating the features of somatic mutations in protein pocket regions provides a promising approach to identifying new genotype-phenotype relationships in cancer.MethodsIn this study, we developed a protein pocket-based computational approach to uncover the functional consequences of somatic mutations in cancer. We mapped 1.2 million somatic mutations across 36 cancer types from the COSMIC database and The Cancer Genome Atlas (TCGA) onto the protein pocket regions of over 5,000 protein three-dimensional structures. We further integrated cancer cell line mutation profiles and drug pharmacological data from the Cancer Cell Line Encyclopedia (CCLE) onto protein pocket regions in order to identify putative biomarkers for anticancer drug responses.ResultsWe found that genes harboring protein pocket somatic mutations were significantly enriched in cancer driver genes. Furthermore, genes harboring pocket somatic mutations tended to be highly co-expressed in a co-expressed protein interaction network. Using a statistical framework, we identified four putative cancer genes (RWDD1, NCF1, PLEK, and VAV3), whose expression profiles were associated with overall poor survival rates in melanoma, lung, or colorectal cancer patients. Finally, genes harboring protein pocket mutations were more likely to be drug-sensitive or drug-resistant. In a case study, we illustrated that the BAX gene was associated with the sensitivity of three anticancer drugs (midostaurin, vinorelbine, and tipifarnib).ConclusionsThis study provides novel insights into the functional consequences of somatic mutations during tumorigenesis and for anticancer drug responses. The computational approach used might be beneficial to the study of somatic mutations in the era of cancer precision medicine.

Dataset Information

Functional characterization of somatic mutations in cancer using network-based inference of protein activity.

Publications

Functional characterization of somatic mutations in cancer using network-based inference of protein activity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets