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

  • Authors:

  • Sara Nasser;Heather Cunliffe;Michael A. Black;Seungchan Kim

  • Affiliations:

  • Translational Genomics Research Institute, Phoenix, AZ, USA;Translational Genomics Research Institute, Phoenix, AZ, USA;University of Otago, Dunedin, New Zealand;Translational Genomics Research Institute, Phoenix, AZ, USA

  • Venue:
  • DTMBIO '10 Proceedings of the ACM fourth international workshop on Data and text mining in biomedical informatics
  • Year:
  • 2010

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Abstract

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 previously published method. The main focus of the paper, however, is to use the context specific gene regulatory networks to classify the tumors into clinically relevant sub-groups and providing candidates for a finer sub-grouping of the previously known intrinsic tumors with focus on basal-like tumors. Further analysis of pathway enrichment of the key contexts provides an understanding of the biological mechanism for identified subtypes of breast cancers.