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Genome Biol. 2017 Aug 11;18(1):153. doi: 10.1186/s13059-017-1282-3.

Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance.

Author information

1
Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA.
2
Department of Molecular Pharmacology and Experimental Therapeutics, Gonda 19, Mayo Clinic Rochester, 200, 1st St. SW, Rochester, MN, 55905, USA.
3
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA.
4
Department of Molecular Pharmacology and Experimental Therapeutics, Gonda 19, Mayo Clinic Rochester, 200, 1st St. SW, Rochester, MN, 55905, USA. Wang.Liewei@mayo.edu.
5
Department of Computer Science and Institute of Genomic Biology, University of Illinois at Urbana-Champaign, 2122 Siebel Center, 201N. Goodwin Ave, Urbana, IL, 61801, USA. sinhas@illinois.edu.

Abstract

BACKGROUND:

Identification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance.

RESULTS:

We developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel, and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% of our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group.

CONCLUSIONS:

Our results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.

KEYWORDS:

Chemoresistance; Chemotherapy; Drug sensitivity; Gene interaction network; Gene prioritization; Network-based algorithm

PMID:
28800781
PMCID:
PMC5554409
DOI:
10.1186/s13059-017-1282-3
[Indexed for MEDLINE]
Free PMC Article

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