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Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer

Purpose: Most prostate cancers (PCs) initially respond to androgen deprivation therapy (ADT), but eventually many PC patients develop castration resistant PC (CRPC). Currently, available drugs that have been approved for the treatment of CRPC patients are limited. Computational drug repositioning me...

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Autores principales: Kim, In-Wha, Kim, Jae Hyun, Oh, Jung Mi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6664029/
https://www.ncbi.nlm.nih.gov/pubmed/31396486
http://dx.doi.org/10.3389/fonc.2019.00661
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author Kim, In-Wha
Kim, Jae Hyun
Oh, Jung Mi
author_facet Kim, In-Wha
Kim, Jae Hyun
Oh, Jung Mi
author_sort Kim, In-Wha
collection PubMed
description Purpose: Most prostate cancers (PCs) initially respond to androgen deprivation therapy (ADT), but eventually many PC patients develop castration resistant PC (CRPC). Currently, available drugs that have been approved for the treatment of CRPC patients are limited. Computational drug repositioning methods using public databases represent a promising and efficient tool for discovering new uses for existing drugs. The purpose of the present study is to predict drug candidates that can treat CRPC using a computational method that integrates publicly available gene expression data of tumors from CRPC patients, drug-induced gene expression data and drug response activity data. Methods: Gene expression data from tumoral and normal or benign prostate tissue samples in CRPC patients were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in CRPC were determined with a meta-signature analysis by a metaDE R package. Additionally, drug activity data were downloaded from the ChEMBL database. Furthermore, the drug-induced gene expression data were downloaded from the LINCS database. The reversal relationship between the CRPC and drug gene expression signatures as the Reverse Gene Expression Scores (RGES) were computed. Drug candidates to treat CRPC were predicted using summarized scores (sRGES). Additionally, synergic effects of drug combinations were predicted with a Target Inhibition interaction using the Minimization and Maximization Averaging (TIMMA) algorithm. Results: The drug candidates of sorafenib, olaparib, elesclomol, tanespimycin, and ponatinib were predicted to be active for the treatment of CRPC. Meanwhile, CRPC-related genes, in this case MYL9, E2F2, APOE, and ZFP36, were identified as having gene expression data that can be reversed by these drugs. Additionally, lenalidomide in combination with pazopanib was predicted to be most potent for CRPC. Conclusion: These findings support the use of a computational reversal gene expression approach to identify new drug and drug combination candidates that can be used to treat CRPC.
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spelling pubmed-66640292019-08-08 Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer Kim, In-Wha Kim, Jae Hyun Oh, Jung Mi Front Oncol Oncology Purpose: Most prostate cancers (PCs) initially respond to androgen deprivation therapy (ADT), but eventually many PC patients develop castration resistant PC (CRPC). Currently, available drugs that have been approved for the treatment of CRPC patients are limited. Computational drug repositioning methods using public databases represent a promising and efficient tool for discovering new uses for existing drugs. The purpose of the present study is to predict drug candidates that can treat CRPC using a computational method that integrates publicly available gene expression data of tumors from CRPC patients, drug-induced gene expression data and drug response activity data. Methods: Gene expression data from tumoral and normal or benign prostate tissue samples in CRPC patients were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in CRPC were determined with a meta-signature analysis by a metaDE R package. Additionally, drug activity data were downloaded from the ChEMBL database. Furthermore, the drug-induced gene expression data were downloaded from the LINCS database. The reversal relationship between the CRPC and drug gene expression signatures as the Reverse Gene Expression Scores (RGES) were computed. Drug candidates to treat CRPC were predicted using summarized scores (sRGES). Additionally, synergic effects of drug combinations were predicted with a Target Inhibition interaction using the Minimization and Maximization Averaging (TIMMA) algorithm. Results: The drug candidates of sorafenib, olaparib, elesclomol, tanespimycin, and ponatinib were predicted to be active for the treatment of CRPC. Meanwhile, CRPC-related genes, in this case MYL9, E2F2, APOE, and ZFP36, were identified as having gene expression data that can be reversed by these drugs. Additionally, lenalidomide in combination with pazopanib was predicted to be most potent for CRPC. Conclusion: These findings support the use of a computational reversal gene expression approach to identify new drug and drug combination candidates that can be used to treat CRPC. Frontiers Media S.A. 2019-07-23 /pmc/articles/PMC6664029/ /pubmed/31396486 http://dx.doi.org/10.3389/fonc.2019.00661 Text en Copyright © 2019 Kim, Kim and Oh. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Kim, In-Wha
Kim, Jae Hyun
Oh, Jung Mi
Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer
title Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer
title_full Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer
title_fullStr Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer
title_full_unstemmed Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer
title_short Screening of Drug Repositioning Candidates for Castration Resistant Prostate Cancer
title_sort screening of drug repositioning candidates for castration resistant prostate cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6664029/
https://www.ncbi.nlm.nih.gov/pubmed/31396486
http://dx.doi.org/10.3389/fonc.2019.00661
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