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SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology

Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here...

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Autores principales: Seo, Heewon, Tkachuk, Denis, Ho, Chantal, Mammoliti, Anthony, Rezaie, Aria, Madani Tonekaboni, Seyed Ali, Haibe-Kains, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319572/
https://www.ncbi.nlm.nih.gov/pubmed/32442307
http://dx.doi.org/10.1093/nar/gkaa421
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author Seo, Heewon
Tkachuk, Denis
Ho, Chantal
Mammoliti, Anthony
Rezaie, Aria
Madani Tonekaboni, Seyed Ali
Haibe-Kains, Benjamin
author_facet Seo, Heewon
Tkachuk, Denis
Ho, Chantal
Mammoliti, Anthony
Rezaie, Aria
Madani Tonekaboni, Seyed Ali
Haibe-Kains, Benjamin
author_sort Seo, Heewon
collection PubMed
description Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here we developed a cloud-based pharmacogenomics portal called SYNERGxDB (http://SYNERGxDB.ca/) that integrates multiple high-throughput drug-combination studies with molecular and pharmacological profiles of a large panel of cancer cell lines. This portal enables the identification of synergistic drug combinations through harmonization and unified computational analysis. We integrated nine of the largest drug combination datasets from both academic groups and pharmaceutical companies, resulting in 22 507 unique drug combinations (1977 unique compounds) screened against 151 cancer cell lines. This data compendium includes metabolomics, gene expression, copy number and mutation profiles of the cancer cell lines. In addition, SYNERGxDB provides analytical tools to discover effective therapeutic combinations and predictive biomarkers across cancer, including specific types. Combining molecular and pharmacological profiles, we systematically explored the large space of univariate predictors of drug synergism. SYNERGxDB constitutes a comprehensive resource that opens new avenues of research for exploring the mechanism of action for drug synergy with the potential of identifying new treatment strategies for cancer patients.
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spelling pubmed-73195722020-07-01 SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology Seo, Heewon Tkachuk, Denis Ho, Chantal Mammoliti, Anthony Rezaie, Aria Madani Tonekaboni, Seyed Ali Haibe-Kains, Benjamin Nucleic Acids Res Web Server Issue Drug-combination data portals have recently been introduced to mine huge amounts of pharmacological data with the aim of improving current chemotherapy strategies. However, these portals have only been investigated for isolated datasets, and molecular profiles of cancer cell lines are lacking. Here we developed a cloud-based pharmacogenomics portal called SYNERGxDB (http://SYNERGxDB.ca/) that integrates multiple high-throughput drug-combination studies with molecular and pharmacological profiles of a large panel of cancer cell lines. This portal enables the identification of synergistic drug combinations through harmonization and unified computational analysis. We integrated nine of the largest drug combination datasets from both academic groups and pharmaceutical companies, resulting in 22 507 unique drug combinations (1977 unique compounds) screened against 151 cancer cell lines. This data compendium includes metabolomics, gene expression, copy number and mutation profiles of the cancer cell lines. In addition, SYNERGxDB provides analytical tools to discover effective therapeutic combinations and predictive biomarkers across cancer, including specific types. Combining molecular and pharmacological profiles, we systematically explored the large space of univariate predictors of drug synergism. SYNERGxDB constitutes a comprehensive resource that opens new avenues of research for exploring the mechanism of action for drug synergy with the potential of identifying new treatment strategies for cancer patients. Oxford University Press 2020-07-02 2020-05-22 /pmc/articles/PMC7319572/ /pubmed/32442307 http://dx.doi.org/10.1093/nar/gkaa421 Text en © The Author(s) 2020. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Web Server Issue
Seo, Heewon
Tkachuk, Denis
Ho, Chantal
Mammoliti, Anthony
Rezaie, Aria
Madani Tonekaboni, Seyed Ali
Haibe-Kains, Benjamin
SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology
title SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology
title_full SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology
title_fullStr SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology
title_full_unstemmed SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology
title_short SYNERGxDB: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology
title_sort synergxdb: an integrative pharmacogenomic portal to identify synergistic drug combinations for precision oncology
topic Web Server Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7319572/
https://www.ncbi.nlm.nih.gov/pubmed/32442307
http://dx.doi.org/10.1093/nar/gkaa421
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