This article is part of the supplement: Fourth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBio) 2010
Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer
1 Computational Biology Division, Translational Genomics Research Institute, 445 N. Fifth Street, Phoenix, AZ, USA
2 Breast and Ovarian Cancer Unit, Computational Biology Division, Translational Genomics Research Institute, 445 N. Fifth Street, Phoenix, AZ, USA
3 Department of Biochemistry, University of Otago, New Zealand
4 School of Computing Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
BMC Bioinformatics 2011, 12(Suppl 2):S3 doi:10.1186/1471-2105-12-S2-S3Published: 29 March 2011
Breast 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.
This 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.
The 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.