Literature DB >> 29626539

Rationalizing Drug Response in Cancer Cell Lines.

Teresa Juan-Blanco1, Miquel Duran-Frigola1, Patrick Aloy2.   

Abstract

Cancer cell lines (CCLs) play an important role in the initial stages of drug discovery allowing, among others, for the screening of drug candidates. As CCL panels continue to grow in size and diversity, many polymorphisms in genes encoding drug-metabolizing enzymes, transporters and drug targets, as well as disease-related genes have been linked to altered drug sensitivity. However, identifying the correlation between this variability and pharmacological responses remains challenging due to the heterogeneity of cancer biology and the intricate interplay between cell lines and drug molecules. Here, we propose a network-based strategy that exploits information on gene expression and somatic mutations of CCLs to group cells according to their molecular similarity. We then identify genes that are characteristic of each cluster and correlate their status with drug response. We find that CCLs with similar characteristic active network regions present specific responses to certain drugs, and identify a limited set of genes that might be directly involved in drug sensitivity or resistance.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  antineoplastic drugs; cancer cell lines; drug response; molecular signatures; network-based stratification

Mesh:

Substances:

Year:  2018        PMID: 29626539     DOI: 10.1016/j.jmb.2018.03.021

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  2 in total

1.  Encircling the regions of the pharmacogenomic landscape that determine drug response.

Authors:  Adrià Fernández-Torras; Miquel Duran-Frigola; Patrick Aloy
Journal:  Genome Med       Date:  2019-03-26       Impact factor: 15.266

2.  Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.

Authors:  Liang-Chin Huang; Wayland Yeung; Ye Wang; Huimin Cheng; Aarya Venkat; Sheng Li; Ping Ma; Khaled Rasheed; Natarajan Kannan
Journal:  BMC Bioinformatics       Date:  2020-11-12       Impact factor: 3.169

  2 in total

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