Dataset Information

Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma.

ABSTRACT: The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations within this geneset was predictive of relapse in lung cancer patients that have undergone surgery. Since it is the density of mutations within this module that is informative, rather than the status of any individual gene, these data are in keeping with a 'mini-driver' model of tumorigenesis in which multiple mutations, each with a weak effect, combine to form a polygenic driver with sufficient power to significantly alter cell behaviour and ultimately patient outcome. These polygenic mini-drivers therefore provide a means by which heterogeneous mutation patterns can generate the consistent hallmark changes in phenotype observed across tumours.

SUBMITTER: Bennett L

PROVIDER: S-EPMC6172282 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma.

Bennett Laura L Howell Matthew M Memon Danish D Smowton Chris C Zhou Cong C Miller Crispin J CJ

Scientific reports 20181004 1

The genomic lesions found in malignant tumours exhibit a striking degree of heterogeneity. Many tumours lack a known driver mutation, and their genetic basis is unclear. By mapping the somatic mutations identified in primary lung adenocarcinomas onto an independent coexpression network derived from normal tissue, we identify a critical gene network enriched for metastasis-associated genes. While individual genes within this module were rarely mutated, a significant accumulation of mutations with ...[more]

PMID: 30287876

Similar Datasets

Project description:BackgroundLocal relapses and metastases are primary causes of death in lung cancer patients. In the present study, we aimed to develop a prognostic signature based on metastasis-associated lncRNAs in patients with lung adenocarcinoma (LUAD).MethodsFirstly, the potential metastasis-associated lncRNAs were identified by analyzing high-throughput data from The Cancer Genome Atlas (TCGA), and based on which, an lncRNA signature was constructed for prediction of relapse in LUAD patients using Cox proportional hazards regression analysis. Moreover, the prognostic performance of the lncRNA signature was evaluated using Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curve and Cox analysis, respectively. In addition, the potential metastasis-associated function of these six lncRNAs was confirmed by lncRNA over-expression or depletion and in vitro transwell assays in LUAD cells.ResultsAn lncRNA signature consisting of six most important prognostic factors (LINC01819, ZNF649-AS1, HNF4A-AS1, FAM222A-AS1, LINC02323 and LINC00672) was developed. The signature was an independent predictor for patients' relapse-free survival (RFS), which could provide higher tumor relapse prediction capability compared with the TNM staging system at three years and five years, respectively (P = 0.0209 and P = 0.0468). Furthermore, the combination of this lncRNA signature and TNM stage had better prognostic value than TNM stage alone at three and five years, respectively (P = 0.0006 and P = 0.0096). Additionally, all the lncRNAs of the signature had a regulatory role in the LUAD cell mobility.ConclusionsThis novel six-lncRNA signature had considerable prognostic value for prediction of relapse in LUAD patients.Key pointsSignificant findings of the study The unique metastasis-associated lncRNA signature was related to tumor metastasis and prognosis in LUAD patients. What this study adds This signature had considerable prognostic value for prediction of relapse in LUAD patients.

Dataset Information

Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma.

Publications

Mutation pattern analysis reveals polygenic mini-drivers associated with relapse after surgery in lung adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets