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Drepmel—A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment
Although substantial progress has been made in treating patients with advanced melanoma with targeted and immuno-therapies, de novo and acquired resistance is commonplace. After treatment failure, therapeutic options are very limited and novel strategies are urgently needed. Combination therapies ar...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497118/ https://www.ncbi.nlm.nih.gov/pubmed/36139469 http://dx.doi.org/10.3390/cells11182894 |
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author | Thompson, Zachary J. Teer, Jamie K. Li, Jiannong Chen, Zhihua Welsh, Eric A. Zhang, Yonghong Ayoubi, Noura Eroglu, Zeynep Tan, Aik Choon Smalley, Keiran S. M. Chen, Yian Ann |
author_facet | Thompson, Zachary J. Teer, Jamie K. Li, Jiannong Chen, Zhihua Welsh, Eric A. Zhang, Yonghong Ayoubi, Noura Eroglu, Zeynep Tan, Aik Choon Smalley, Keiran S. M. Chen, Yian Ann |
author_sort | Thompson, Zachary J. |
collection | PubMed |
description | Although substantial progress has been made in treating patients with advanced melanoma with targeted and immuno-therapies, de novo and acquired resistance is commonplace. After treatment failure, therapeutic options are very limited and novel strategies are urgently needed. Combination therapies are often more effective than single agents and are now widely used in clinical practice. Thus, there is a strong need for a comprehensive computational resource to define rational combination therapies. We developed a Shiny app, DRepMel to provide rational combination treatment predictions for melanoma patients from seventy-three thousand combinations based on a multi-omics drug repurposing computational approach using whole exome sequencing and RNA-seq data in bulk samples from two independent patient cohorts. DRepMel provides robust predictions as a resource and also identifies potential treatment effects on the tumor microenvironment (TME) using single-cell RNA-seq data from melanoma patients. Availability: DRepMel is accessible online. |
format | Online Article Text |
id | pubmed-9497118 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94971182022-09-23 Drepmel—A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment Thompson, Zachary J. Teer, Jamie K. Li, Jiannong Chen, Zhihua Welsh, Eric A. Zhang, Yonghong Ayoubi, Noura Eroglu, Zeynep Tan, Aik Choon Smalley, Keiran S. M. Chen, Yian Ann Cells Article Although substantial progress has been made in treating patients with advanced melanoma with targeted and immuno-therapies, de novo and acquired resistance is commonplace. After treatment failure, therapeutic options are very limited and novel strategies are urgently needed. Combination therapies are often more effective than single agents and are now widely used in clinical practice. Thus, there is a strong need for a comprehensive computational resource to define rational combination therapies. We developed a Shiny app, DRepMel to provide rational combination treatment predictions for melanoma patients from seventy-three thousand combinations based on a multi-omics drug repurposing computational approach using whole exome sequencing and RNA-seq data in bulk samples from two independent patient cohorts. DRepMel provides robust predictions as a resource and also identifies potential treatment effects on the tumor microenvironment (TME) using single-cell RNA-seq data from melanoma patients. Availability: DRepMel is accessible online. MDPI 2022-09-16 /pmc/articles/PMC9497118/ /pubmed/36139469 http://dx.doi.org/10.3390/cells11182894 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Thompson, Zachary J. Teer, Jamie K. Li, Jiannong Chen, Zhihua Welsh, Eric A. Zhang, Yonghong Ayoubi, Noura Eroglu, Zeynep Tan, Aik Choon Smalley, Keiran S. M. Chen, Yian Ann Drepmel—A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment |
title | Drepmel—A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment |
title_full | Drepmel—A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment |
title_fullStr | Drepmel—A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment |
title_full_unstemmed | Drepmel—A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment |
title_short | Drepmel—A Multi-Omics Melanoma Drug Repurposing Resource for Prioritizing Drug Combinations and Understanding Tumor Microenvironment |
title_sort | drepmel—a multi-omics melanoma drug repurposing resource for prioritizing drug combinations and understanding tumor microenvironment |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497118/ https://www.ncbi.nlm.nih.gov/pubmed/36139469 http://dx.doi.org/10.3390/cells11182894 |
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