Computational discovery of transcription factors associated with drug response
This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (...
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Nature Publishing Group
2016
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| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848185/ |
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pubmed-48481852016-11-17 Computational discovery of transcription factors associated with drug response Hanson, C Cairns, J Wang, L Sinha, S Original Article This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (Gene Expression iN the Middle), postulates that single-nucleotide polymorphisms within TF-binding sites putatively modulate its regulatory activity, and the resulting variation in gene expression leads to variation in drug response. Analysis of 161 TFs and 24 treatments revealed 334 significantly associated TF–treatment pairs. Investigation of 20 selected pairs yielded literature support for 13 of these associations, often from studies where perturbation of the TF expression changes drug response. Experimental validation of significant GENMi associations in taxanes and anthracyclines across two triple-negative breast cancer cell lines corroborates our findings. The method is shown to be more sensitive than an alternative, genome-wide association study-based approach that does not use gene expression. These results demonstrate the utility of GENMi in identifying TFs that influence drug response and provide a number of candidates for further testing. Nature Publishing Group 2016-11 2015-10-27 /pmc/articles/PMC4848185/ /pubmed/26503816 http://dx.doi.org/10.1038/tpj.2015.74 Text en Copyright © 2015 Macmillan Publishers Limited http://creativecommons.org/licenses/by-nc-sa/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ |
| repository_type |
Open Access Journal |
| institution_category |
Foreign Institution |
| institution |
US National Center for Biotechnology Information |
| building |
NCBI PubMed |
| collection |
Online Access |
| language |
English |
| format |
Online |
| author |
Hanson, C Cairns, J Wang, L Sinha, S |
| spellingShingle |
Hanson, C Cairns, J Wang, L Sinha, S Computational discovery of transcription factors associated with drug response |
| author_facet |
Hanson, C Cairns, J Wang, L Sinha, S |
| author_sort |
Hanson, C |
| title |
Computational discovery of transcription factors associated with drug response |
| title_short |
Computational discovery of transcription factors associated with drug response |
| title_full |
Computational discovery of transcription factors associated with drug response |
| title_fullStr |
Computational discovery of transcription factors associated with drug response |
| title_full_unstemmed |
Computational discovery of transcription factors associated with drug response |
| title_sort |
computational discovery of transcription factors associated with drug response |
| description |
This study integrates gene expression, genotype and drug response data in lymphoblastoid cell lines with transcription factor (TF)-binding sites from ENCODE (Encyclopedia of Genomic Elements) in a novel methodology that elucidates regulatory contexts associated with cytotoxicity. The method, GENMi (Gene Expression iN the Middle), postulates that single-nucleotide polymorphisms within TF-binding sites putatively modulate its regulatory activity, and the resulting variation in gene expression leads to variation in drug response. Analysis of 161 TFs and 24 treatments revealed 334 significantly associated TF–treatment pairs. Investigation of 20 selected pairs yielded literature support for 13 of these associations, often from studies where perturbation of the TF expression changes drug response. Experimental validation of significant GENMi associations in taxanes and anthracyclines across two triple-negative breast cancer cell lines corroborates our findings. The method is shown to be more sensitive than an alternative, genome-wide association study-based approach that does not use gene expression. These results demonstrate the utility of GENMi in identifying TFs that influence drug response and provide a number of candidates for further testing. |
| publisher |
Nature Publishing Group |
| publishDate |
2016 |
| url |
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4848185/ |
| _version_ |
1613572113361272832 |