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Int J Cancer. 2018 Apr 1;142(7):1427-1439. doi: 10.1002/ijc.31158. Epub 2017 Nov 27.

Accurate prediction and elucidation of drug resistance based on the robust and reproducible chemoresponse communities.

Dai E1,2,3,4,5, Wang J5, Yang F5, Zhou X5, Song Q5, Wang S5, Yu X5, Liu D5, Yang Q5, Dai H6, Jiang W5,7, Ling H1,2,3,4.

Author information

1
Department of Microbiology, Wu Lien-Teh Institute, Harbin Medical University, Harbin, 150081, People's Republic of China.
2
Department of Parasitology, Harbin Medical University, Harbin, 150081, People's Republic of China.
3
Heilongjiang Provincial Key Laboratory of Infection and Immunity, Harbin, 150081, People's Republic of China.
4
Key Laboratory of Pathogen Biology, Harbin, 150081, People's Republic of China.
5
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, People's Republic of China.
6
The 2nd Affiliated Hospital, Harbin Medical University, Harbin, 150081, People's Republic of China.
7
Department of Biomedical Engineering, College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, People's Republic of China.

Abstract

Selecting the available treatment for each cancer patient from genomic context is a core goal of precision medicine, but innovative approaches with mechanism interpretation and improved performance are still highly needed. Through utilizing in vitro chemotherapy response data coupled with gene and miRNA expression profiles, we applied a network-based approach that identified markers not as individual molecules but as functional groups extracted from the integrated transcription factor and miRNA regulatory network. Based on the identified chemoresponse communities, the predictors of drug resistance achieved high accuracy in cross-validation and were more robust and reproducible than conventional single-molecule markers. Meanwhile, as candidate communities not only enriched abundant cellular process but also covered a variety of drug enzymes, transporters, and targets, these resulting chemoresponse communities furnished novel models to interpret multiple kinds of potential regulatory mechanism, such as dysregulation of cancer cell apoptosis or disturbance of drug metabolism. Moreover, compounds were linked based on the enrichment of their common chemoresponse communities to uncover undetected response patterns and possible multidrug resistance phenotype. Finally, an omnibus repository named ChemoCommunity (http://www.jianglab.cn/ChemoCommunity/) was constructed, which furnished a user-friendly interface for a convenient retrieval of the detailed information on chemoresponse communities. Taken together, our method, and the accompanying database, improved the performance of classifiers for drug resistance and provided novel model to uncover the possible regulatory mechanism of individual response to drug.

KEYWORDS:

chemoresponse; classification; drug resistance; miRNA; transcription factor and miRNA regulatory network

PMID:
29143332
DOI:
10.1002/ijc.31158
[Indexed for MEDLINE]

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