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Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance
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 expressio...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5554409/ https://www.ncbi.nlm.nih.gov/pubmed/28800781 http://dx.doi.org/10.1186/s13059-017-1282-3 |
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author | Emad, Amin Cairns, Junmei Kalari, Krishna R. Wang, Liewei Sinha, Saurabh |
author_facet | Emad, Amin Cairns, Junmei Kalari, Krishna R. Wang, Liewei Sinha, Saurabh |
author_sort | Emad, Amin |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-017-1282-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5554409 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-55544092017-08-15 Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance Emad, Amin Cairns, Junmei Kalari, Krishna R. Wang, Liewei Sinha, Saurabh Genome Biol Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-017-1282-3) contains supplementary material, which is available to authorized users. BioMed Central 2017-08-11 /pmc/articles/PMC5554409/ /pubmed/28800781 http://dx.doi.org/10.1186/s13059-017-1282-3 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Emad, Amin Cairns, Junmei Kalari, Krishna R. Wang, Liewei Sinha, Saurabh Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance |
title | Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance |
title_full | Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance |
title_fullStr | Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance |
title_full_unstemmed | Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance |
title_short | Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance |
title_sort | knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5554409/ https://www.ncbi.nlm.nih.gov/pubmed/28800781 http://dx.doi.org/10.1186/s13059-017-1282-3 |
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