Research Papers: Chromosome:
OncoBinder facilitates interpretation of proteomic interaction data by capturing coactivation pairs in cancer
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Samya Van Coillie1,2,*, Lunxi Liang1,*, Yao Zhang1, Huanbin Wang1, Jing-Yuan Fang1, Jie Xu1
1State Key Laboratory for Oncogenes and Related Genes, Key Laboratory of Gastroenterology & Hepatology, Ministry of Health, Division of Gastroenterology and Hepatology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive Disease, Shanghai 200001, China
2Faculty of Medicine, Catholic University Leuven, Leuven, B-3000, Belgium
*These authors have contributed equally to this work
Jie Xu, e-mail: email@example.com
Keywords: Chromosome Section, protein-protein interaction, cancer genome, copy number alteration, gene mutation
Received: November 01, 2015 Accepted: January 29, 2016 Published: February 10, 2016
High-throughput methods such as co-immunoprecipitationmass spectrometry (coIP-MS) and yeast 2 hybridization (Y2H) have suggested a broad range of unannotated protein-protein interactions (PPIs), and interpretation of these PPIs remains a challenging task. The advancements in cancer genomic researches allow for the inference of “coactivation pairs” in cancer, which may facilitate the identification of PPIs involved in cancer. Here we present OncoBinder as a tool for the assessment of proteomic interaction data based on the functional synergy of oncoproteins in cancer. This decision tree-based method combines gene mutation, copy number and mRNA expression information to infer the functional status of protein-coding genes. We applied OncoBinder to evaluate the potential binders of EGFR and ERK2 proteins based on the gastric cancer dataset of The Cancer Genome Atlas (TCGA). As a result, OncoBinder identified high confidence interactions (annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) or validated by low-throughput assays) more efficiently than co-expression based method. Taken together, our results suggest that evaluation of gene functional synergy in cancer may facilitate the interpretation of proteomic interaction data. The OncoBinder toolbox for Matlab is freely accessible online.
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