Dataset Information

Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework.

ABSTRACT:

Motivation

Comprehensive catalogue of genes that drive tumor initiation and progression in cancer is key to advancing diagnostics, therapeutics and treatment. Given the complexity of cancer, the catalogue is far from complete yet. Increasing evidence shows that driver genes exhibit consistent aberration patterns across multiple-omics in tumors. In this study, we aim to leverage complementary information encoded in each of the omics data to identify novel driver genes through an integrative framework. Specifically, we integrated mutations, gene expression, DNA copy numbers, DNA methylation and protein abundance, all available in The Cancer Genome Atlas (TCGA) and developed iDriver, a non-parametric Bayesian framework based on multivariate statistical modeling to identify driver genes in an unsupervised fashion. iDriver captures the inherent clusters of gene aberrations and constructs the background distribution that is used to assess and calibrate the confidence of driver genes identified through multi-dimensional genomic data.

Results

We applied the method to 4 cancer types in TCGA and identified candidate driver genes that are highly enriched with known drivers. (e.g.: P Availability and implementationThe C?++?source code is freely available at https://medschool.vanderbilt.edu/cgg/ .

Contacts

hai.yang@vanderbilt.edu or bingshan.li@Vanderbilt.Edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Yang H

PROVIDER: S-EPMC6075201 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Publications

Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework.

Yang Hai H Wei Qiang Q Zhong Xue X Yang Hushan H Li Bingshan B

Bioinformatics (Oxford, England) 20170201 4

<h4>Motivation</h4>Comprehensive catalogue of genes that drive tumor initiation and progression in cancer is key to advancing diagnostics, therapeutics and treatment. Given the complexity of cancer, the catalogue is far from complete yet. Increasing evidence shows that driver genes exhibit consistent aberration patterns across multiple-omics in tumors. In this study, we aim to leverage complementary information encoded in each of the omics data to identify novel driver genes through an integrati ...[more]

PMID: 27797769

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Project description:Significant advances in biotechnology have allowed for simultaneous measurement of molecular data across multiple genomic, epigenomic and transcriptomic levels from a single tumor/patient sample. This has motivated systematic data-driven approaches to integrate multi-dimensional structured datasets, since cancer development and progression is driven by numerous co-ordinated molecular alterations and the interactions between them. We propose a novel multi-scale Bayesian approach that combines integrative graphical structure learning from multiple sources of data with a variable selection framework-to determine the key genomic drivers of cancer progression. The integrative structure learning is first accomplished through novel joint graphical models for heterogeneous (mixed scale) data, allowing for flexible and interpretable incorporation of prior existing knowledge. This subsequently informs a variable selection step to identify groups of co-ordinated molecular features within and across platforms associated with clinical outcomes of cancer progression, while according appropriate adjustments for multicollinearity and multiplicities. We evaluate our methods through rigorous simulations to establish superiority over existing methods that do not take the network and/or prior information into account. Our methods are motivated by and applied to a glioblastoma multiforme (GBM) dataset from The Cancer Genome Atlas to predict patient survival times integrating gene expression, copy number and methylation data. We find a high concordance between our selected prognostic gene network modules with known associations with GBM. In addition, our model discovers several novel cross-platform network interactions (both cis and trans acting) between gene expression, copy number variation associated gene dosing and epigenetic regulation through promoter methylation, some with known implications in the etiology of GBM. Our framework provides a useful tool for biomedical researchers, since clinical prediction using multi-platform genomic information is an important step towards personalized treatment of many cancers.

Dataset Information

Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework.

Motivation

Results

Contacts

Supplementary information

Publications

Cancer driver gene discovery through an integrative genomics approach in a non-parametric Bayesian framework.

Similar Datasets

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