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.
Description
Rationalizing Drug Response in Cancer Cell Lines - ScienceDirect
%0 Journal Article
%1 juanblanco2018rationalizing
%A Juan-Blanco, Teresa
%A Duran-Frigola, Miquel
%A Aloy, Patrick
%D 2018
%J Journal of Molecular Biology
%K cancer drug-response machine-learning
%N 18, Part A
%P 3016 - 3027
%R https://doi.org/10.1016/j.jmb.2018.03.021
%T Rationalizing Drug Response in Cancer Cell Lines
%U http://www.sciencedirect.com/science/article/pii/S0022283618301700
%V 430
%X 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.
@article{juanblanco2018rationalizing,
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.},
added-at = {2019-10-22T10:16:58.000+0200},
author = {Juan-Blanco, Teresa and Duran-Frigola, Miquel and Aloy, Patrick},
biburl = {https://www.bibsonomy.org/bibtex/2d3bf94de9dd53b283413c71d30a0d589/karthikraman},
description = {Rationalizing Drug Response in Cancer Cell Lines - ScienceDirect},
doi = {https://doi.org/10.1016/j.jmb.2018.03.021},
interhash = {dcf1cf87b43f1053b71198fa57333b99},
intrahash = {d3bf94de9dd53b283413c71d30a0d589},
issn = {0022-2836},
journal = {Journal of Molecular Biology},
keywords = {cancer drug-response machine-learning},
note = {Theory and Application of Network Biology Toward Precision Medicine},
number = {18, Part A},
pages = {3016 - 3027},
timestamp = {2019-10-22T10:16:58.000+0200},
title = {Rationalizing Drug Response in Cancer Cell Lines},
url = {http://www.sciencedirect.com/science/article/pii/S0022283618301700},
volume = 430,
year = 2018
}