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Oncotarget. 2018 May 18;9(38):25166-25180. doi: 10.18632/oncotarget.25382. eCollection 2018 May 18.

Aberration hubs in protein interaction networks highlight actionable targets in cancer.

Author information

1
Department of Human Genetics, McGill University, Montreal, QC H3A 1B1, Canada.
2
McGill University and Genome Quebec Innovation Centre, Montreal, QC H3A 0G1, Canada.
3
Department of Biochemistry, The Rosalind and Morris Goodman Cancer Centre, McGill University, Montreal, QC H3G 1Y6, Canada.
4
Leeds Institute of Cancer and Pathology, University of Leeds, Cancer Research Building, St James's University Hospital, Leeds, LS9 7TF, UK.
5
European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Trust Genome Campus, Hinxton, CB10 1SD, UK.
6
International Agency for Research on Cancer (IARC), Lyon, 69008, France.

Abstract

Despite efforts for extensive molecular characterization of cancer patients, such as the international cancer genome consortium (ICGC) and the cancer genome atlas (TCGA), the heterogeneous nature of cancer and our limited knowledge of the contextual function of proteins have complicated the identification of targetable genes. Here, we present Aberration Hub Analysis for Cancer (AbHAC) as a novel integrative approach to pinpoint aberration hubs, i.e. individual proteins that interact extensively with genes that show aberrant mutation or expression. Our analysis of the breast cancer data of the TCGA and the renal cancer data from the ICGC shows that aberration hubs are involved in relevant cancer pathways, including factors promoting cell cycle and DNA replication in basal-like breast tumors, and Src kinase and VEGF signaling in renal carcinoma. Moreover, our analysis uncovers novel functionally relevant and actionable targets, among which we have experimentally validated abnormal splicing of spleen tyrosine kinase as a key factor for cell proliferation in renal cancer. Thus, AbHAC provides an effective strategy to uncover novel disease factors that are only identifiable by examining mutational and expression data in the context of biological networks.

KEYWORDS:

cancer; computational biology; genomics; systems biology; target discovery

Conflict of interest statement

CONFLICTS OF INTEREST The authors declare no competing financial interest.

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