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Curr Opin Genet Dev. 2019 Jul 6;54:110-117. doi: 10.1016/j.gde.2019.04.005. [Epub ahead of print]

Mapping the protein-protein and genetic interactions of cancer to guide precision medicine.

Author information

1
Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States.
2
Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States.
3
Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States. Electronic address: minkyu.kim@ucsf.edu.
4
Department of Medicine, University of California, San Diego, California, United States. Electronic address: tideker@ucsd.edu.
5
Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States. Electronic address: nevan.krogan@ucsf.edu.

Abstract

Massive efforts to sequence cancer genomes have compiled an impressive catalogue of cancer mutations, revealing the recurrent exploitation of a handful of 'hallmark cancer pathways'. However, unraveling how sets of mutated proteins in these and other pathways hijack pro-proliferative signaling networks and dictate therapeutic responsiveness remains challenging. Here, we show that cancer driver protein-protein interactions are enriched for additional cancer drivers, highlighting the power of physical interaction maps to explain known, as well as uncover new, disease-promoting pathway interrelationships. We hypothesize that by systematically mapping the protein-protein and genetic interactions in cancer-thereby creating Cancer Cell Maps-we will create resources against which to contextualize a patient's mutations into perturbed pathways/complexes and thereby specify a matching targeted therapeutic cocktail.

PMID:
31288129
DOI:
10.1016/j.gde.2019.04.005

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