The OncoPPi Portal: an integrative resource to explore and prioritize protein-protein interactions for cancer target discovery.

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ID: 51770
2018
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Abstract
As cancer genomics initiatives move toward comprehensive identification of genetic alterations in cancer, attention is now turning to understanding how interactions among these genes lead to the acquisition of tumor hallmarks. Emerging pharmacological and clinical data suggest a highly promising role of cancer-specific protein-protein interactions (PPIs) as druggable cancer targets. However, large-scale experimental identification of cancer-related PPIs remains challenging, and currently available resources to explore oncogenic PPI networks are limited.Recently, we have developed a PPI high-throughput screening platform to detect PPIs between cancer-associated proteins in the context of cancer cells. Here, we present the OncoPPi Portal, an interactive web resource that allows investigators to access, manipulate and interpret a high-quality cancer-focused network of PPIs experimentally detected in cancer cell lines. To facilitate prioritization of PPIs for further biological studies, this resource combines network connectivity analysis, mutual exclusivity analysis of genomic alterations, cellular co-localization of interacting proteins and domain-domain interactions. Estimates of PPI essentiality allow users to evaluate the functional impact of PPI disruption on cancer cell proliferation. Furthermore, connecting the OncoPPi network with the approved drugs and compounds in clinical trials enables discovery of new tumor dependencies to inform strategies to interrogate undruggable targets like tumor suppressors. The OncoPPi Portal serves as a resource for the cancer research community to facilitate discovery of cancer targets and therapeutic development.The OncoPPi Portal is available at http://oncoppi.emory.edu.andrey.ivanov@emory.edu or hfu@emory.edu.
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ivanov2018thebioinformatics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ivanov, Andrei A;Revennaugh, Brian;Rusnak, Lauren;Gonzalez-Pecchi, Valentina;Mo, Xiulei;Johns, Margaret A;Du, Yuhong;Cooper, Lee A D;Moreno, Carlos S;Khuri, Fadlo R;Fu, Haian;
Journal Bioinformatics
Year 2018
DOI
10.1093/bioinformatics/btx743
URL
Keywords
Machine learning energy prediction human energy expenditure

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