Protein interaction network underpins concordant prognosis among heterogeneous breast cancer signatures

  • Authors:

  • James Chen;Lee Sam;Yong Huang;Younghee Lee;Jianrong Li;Yang Liu;H. Rosie Xing;Yves A. Lussier

  • Affiliations:

  • Sections of Hematology/Oncology, The University of Chicago Cancer Research Center, The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL, USA;Genetic Medicine of the Department of Medicine, The University of Chicago Cancer Research Center, The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL, USA;Genetic Medicine of the Department of Medicine, The University of Chicago Cancer Research Center, The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL, USA;Genetic Medicine of the Department of Medicine, The University of Chicago Cancer Research Center, The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL, USA;Genetic Medicine of the Department of Medicine, The University of Chicago Cancer Research Center, The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL, USA;Genetic Medicine of the Department of Medicine, The University of Chicago Cancer Research Center, The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL, USA;Genetic Medicine of the Department of Medicine, The University of Chicago Cancer Research Center, The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL, USA and Departm ...;Genetic Medicine of the Department of Medicine, The University of Chicago Cancer Research Center, The Ludwig Center for Metastasis Research, The University of Chicago, Chicago, IL, USA and Institu ...

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2010

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Abstract

Characterizing the biomolecular systems' properties underpinning prognosis signatures derived from gene expression profiles remains a key clinical and biological challenge. In breast cancer, while different ''poor-prognosis'' sets of genes have predicted patient survival outcome equally well in independent cohorts, these prognostic signatures have surprisingly little genetic overlap. We examine 10 such published expression-based signatures that are predictors or distinct breast cancer phenotypes, uncover their mechanistic interconnectivity through a protein-protein interaction network, and introduce a novel cross-''gene expression signature'' analysis method using (i) domain knowledge to constrain multiple comparisons in a mechanistically relevant single-gene network interactions and (ii) scale-free permutation re-sampling to statistically control for hubness (SPAN - Single Protein Analysis of Network with constant node degree per protein). At adjusted p-values