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PLoS One. 2017 Jan 24;12(1):e0170339. doi: 10.1371/journal.pone.0170339. eCollection 2017.

MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development.

Author information

1
Department of Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America.
2
Graduate Program in Biomedical Informatics, Emory University, Atlanta, Georgia, United States of America.
3
Winship Cancer Institute, Emory University, Atlanta, Georgia, United States of America.
4
Department of Biomedical Engineering, Emory University, Atlanta, Georgia, United States of America.
5
Graduate Program in Cancer Biology, Emory University, Atlanta, Georgia, United States of America.
6
Department of Pathology and Laboratory Medicine, Emory University, Atlanta, Georgia, United States of America.
7
Medical Scientist Training Program, Emory University, Atlanta, Georgia, United States of America.
8
Department of Pharmacology, Emory University, Atlanta, Georgia, United States of America.
9
Department of Hematology & Medical Oncology, Emory University, Atlanta, Georgia, United States of America.

Abstract

Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.

PMID:
28118365
PMCID:
PMC5261804
DOI:
10.1371/journal.pone.0170339
[Indexed for MEDLINE]
Free PMC Article

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