Dataset Information

High-throughput Phenotyping of Lung Cancer Somatic Mutations.

ABSTRACT: Recent genome sequencing efforts have identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood. Here we characterize 194 somatic mutations identified in primary lung adenocarcinomas. We present an expression-based variant-impact phenotyping (eVIP) method that uses gene expression changes to distinguish impactful from neutral somatic mutations. eVIP identified 69% of mutations analyzed as impactful and 31% as functionally neutral. A subset of the impactful mutations induces xenograft tumor formation in mice and/or confers resistance to cellular EGFR inhibition. Among these impactful variants are rare somatic, clinically actionable variants including EGFR S645C, ARAF S214C and S214F, ERBB2 S418T, and multiple BRAF variants, demonstrating that rare mutations can be functionally important in cancer.

SUBMITTER: Berger AH

PROVIDER: S-EPMC5003022 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Publications

High-throughput Phenotyping of Lung Cancer Somatic Mutations.

Berger Alice H AH Brooks Angela N AN Wu Xiaoyun X Shrestha Yashaswi Y Chouinard Candace C Piccioni Federica F Bagul Mukta M Kamburov Atanas A Imielinski Marcin M Hogstrom Larson L Zhu Cong C Yang Xiaoping X Pantel Sasha S Sakai Ryo R Watson Jacqueline J Kaplan Nathan N Campbell Joshua D JD Singh Shantanu S Root David E DE Narayan Rajiv R Natoli Ted T Lahr David L DL Tirosh Itay I Tamayo Pablo P Getz Gad G Wong Bang B Doench John J Subramanian Aravind A Golub Todd R TR Meyerson Matthew M Boehm Jesse S JS

Cancer cell 20160728 2

Recent genome sequencing efforts have identified millions of somatic mutations in cancer. However, the functional impact of most variants is poorly understood. Here we characterize 194 somatic mutations identified in primary lung adenocarcinomas. We present an expression-based variant-impact phenotyping (eVIP) method that uses gene expression changes to distinguish impactful from neutral somatic mutations. eVIP identified 69% of mutations analyzed as impactful and 31% as functionally neutral. A ...[more]

PMID: 27478040

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Project description:ObjectiveHigh-throughput electronic phenotyping algorithms can accelerate translational research using data from electronic health record (EHR) systems. The temporal information buried in EHRs is often underutilized in developing computational phenotypic definitions. This study aims to develop a high-throughput phenotyping method, leveraging temporal sequential patterns from EHRs.Materials and methodsWe develop a representation mining algorithm to extract 5 classes of representations from EHR diagnosis and medication records: the aggregated vector of the records (aggregated vector representation), the standard sequential patterns (sequential pattern mining), the transitive sequential patterns (transitive sequential pattern mining), and 2 hybrid classes. Using EHR data on 10 phenotypes from the Mass General Brigham Biobank, we train and validate phenotyping algorithms.ResultsPhenotyping with temporal sequences resulted in a superior classification performance across all 10 phenotypes compared with the standard representations in electronic phenotyping. The high-throughput algorithm's classification performance was superior or similar to the performance of previously published electronic phenotyping algorithms. We characterize and evaluate the top transitive sequences of diagnosis records paired with the records of risk factors, symptoms, complications, medications, or vaccinations.DiscussionThe proposed high-throughput phenotyping approach enables seamless discovery of sequential record combinations that may be difficult to assume from raw EHR data. Transitive sequences offer more accurate characterization of the phenotype, compared with its individual components, and reflect the actual lived experiences of the patients with that particular disease.ConclusionSequential data representations provide a precise mechanism for incorporating raw EHR records into downstream machine learning. Our approach starts with user interpretability and works backward to the technology.

Dataset Information

High-throughput Phenotyping of Lung Cancer Somatic Mutations.

Publications

High-throughput Phenotyping of Lung Cancer Somatic Mutations.

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