Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer

BMC Bioinformatics

Proceedings

Heather E Cunliffe¹  Sara Nasser²  Seungchan Kim³  Michael A Black⁴ 

[1] Breast and Ovarian Cancer Unit, Computational Biology Division, Translational Genomics Research Institute, 445 N. Fifth Street, Phoenix, AZ, USA;Computational Biology Division, Translational Genomics Research Institute, 445 N. Fifth Street, Phoenix, AZ, USA;Computational Biology Division, Translational Genomics Research Institute, 445 N. Fifth Street, Phoenix, AZ, USA;School of Computing Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA;Department of Biochemistry, University of Otago, New Zealand;

关键词: Breast Cancer;    Estrogen Receptor;    Driver Gene;    Intrinsic Subtype;    Markov Cluster;   

DOI  :  10.1186/1471-2105-12-S2-S3

来源: Springer

【 摘 要 】

BackgroundBreast cancer is a highly heterogeneous disease with respect to molecular alterations and cellular composition making therapeutic and clinical outcome unpredictable. This diversity creates a significant challenge in developing tumor classifications that are clinically reliable with respect to prognosis prediction.ResultsThis paper describes an unsupervised context analysis to infer context-specific gene regulatory networks from 1,614 samples obtained from publicly available gene expression data, an extension of a previously published methodology. We use the context-specific gene regulatory networks to classify the tumors into clinically relevant subgroups, and provide candidates for a finer sub-grouping of the previously known intrinsic tumors with a focus on Basal-like tumors. Our analysis of pathway enrichment in the key contexts provides an insight into the biological mechanism underlying the identified subtypes of breast cancer.ConclusionsThe use of context-specific gene regulatory networks to identify biological contexts from heterogenous breast cancer data set was able to identify genomic drivers for subgroups within the previously reported intrinsic subtypes. These subgroups (contexts) uphold the clinical relevant features for the intrinsic subtypes and were associated with increased survival differences compared to the intrinsic subtypes. We believe our computational approach led to the generation of novel rationalized hypotheses to explain mechanisms of disease progression within sub-contexts of breast cancer that could be therapeutically exploited once validated.

【 授权许可】

Unknown   
© Nasser et al; licensee BioMed Central Ltd. 2011. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.