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Integrated genomic analysis to identify druggable targets for pancreatic cancer
According to the National Comprehensive Cancer Network and the American Society of Clinical Oncology, the standard treatment for pancreatic cancer (PC) is gemcitabine and fluorouracil. Other chemotherapeutic agents have been widely combined. However, drug resistance remains a huge challenge, leading...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9752886/ https://www.ncbi.nlm.nih.gov/pubmed/36531045 http://dx.doi.org/10.3389/fonc.2022.989077 |
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author | Mugiyanto, Eko Adikusuma, Wirawan Irham, Lalu Muhammad Huang, Wan-Chen Chang, Wei-Chiao Kuo, Chun-Nan |
author_facet | Mugiyanto, Eko Adikusuma, Wirawan Irham, Lalu Muhammad Huang, Wan-Chen Chang, Wei-Chiao Kuo, Chun-Nan |
author_sort | Mugiyanto, Eko |
collection | PubMed |
description | According to the National Comprehensive Cancer Network and the American Society of Clinical Oncology, the standard treatment for pancreatic cancer (PC) is gemcitabine and fluorouracil. Other chemotherapeutic agents have been widely combined. However, drug resistance remains a huge challenge, leading to the ineffectiveness of cancer therapy. Therefore, we are trying to discover new treatments for PC by utilizing genomic information to identify PC-associated genes as well as drug target genes for drug repurposing. Genomic information from a public database, the cBio Cancer Genomics Portal, was employed to retrieve the somatic mutation genes of PC. Five functional annotations were applied to prioritize the PC risk genes: Kyoto Encyclopedia of Genes and Genomes; biological process; knockout mouse; Gene List Automatically Derived For You; and Gene Expression Omnibus Dataset. DrugBank database was utilized to extract PC drug targets. To narrow down the most promising drugs for PC, CMap Touchstone analysis was applied. Finally, ClinicalTrials.gov and a literature review were used to screen the potential drugs under clinical and preclinical investigation. Here, we extracted 895 PC-associated genes according to the cBioPortal database and prioritized them by using five functional annotations; 318 genes were assigned as biological PC risk genes. Further, 216 genes were druggable according to the DrugBank database. CMap Touchstone analysis indicated 13 candidate drugs for PC. Among those 13 drugs, 8 drugs are in the clinical trials, 2 drugs were supported by the preclinical studies, and 3 drugs are with no evidence status for PC. Importantly, we found that midostaurin (targeted PRKA) and fulvestrant (targeted ESR1) are promising candidate drugs for PC treatment based on the genomic-driven drug repurposing pipelines. In short, integrated analysis using a genomic information database demonstrated the viability for drug repurposing. We proposed two drugs (midostaurin and fulvestrant) as promising drugs for PC. |
format | Online Article Text |
id | pubmed-9752886 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97528862022-12-16 Integrated genomic analysis to identify druggable targets for pancreatic cancer Mugiyanto, Eko Adikusuma, Wirawan Irham, Lalu Muhammad Huang, Wan-Chen Chang, Wei-Chiao Kuo, Chun-Nan Front Oncol Oncology According to the National Comprehensive Cancer Network and the American Society of Clinical Oncology, the standard treatment for pancreatic cancer (PC) is gemcitabine and fluorouracil. Other chemotherapeutic agents have been widely combined. However, drug resistance remains a huge challenge, leading to the ineffectiveness of cancer therapy. Therefore, we are trying to discover new treatments for PC by utilizing genomic information to identify PC-associated genes as well as drug target genes for drug repurposing. Genomic information from a public database, the cBio Cancer Genomics Portal, was employed to retrieve the somatic mutation genes of PC. Five functional annotations were applied to prioritize the PC risk genes: Kyoto Encyclopedia of Genes and Genomes; biological process; knockout mouse; Gene List Automatically Derived For You; and Gene Expression Omnibus Dataset. DrugBank database was utilized to extract PC drug targets. To narrow down the most promising drugs for PC, CMap Touchstone analysis was applied. Finally, ClinicalTrials.gov and a literature review were used to screen the potential drugs under clinical and preclinical investigation. Here, we extracted 895 PC-associated genes according to the cBioPortal database and prioritized them by using five functional annotations; 318 genes were assigned as biological PC risk genes. Further, 216 genes were druggable according to the DrugBank database. CMap Touchstone analysis indicated 13 candidate drugs for PC. Among those 13 drugs, 8 drugs are in the clinical trials, 2 drugs were supported by the preclinical studies, and 3 drugs are with no evidence status for PC. Importantly, we found that midostaurin (targeted PRKA) and fulvestrant (targeted ESR1) are promising candidate drugs for PC treatment based on the genomic-driven drug repurposing pipelines. In short, integrated analysis using a genomic information database demonstrated the viability for drug repurposing. We proposed two drugs (midostaurin and fulvestrant) as promising drugs for PC. Frontiers Media S.A. 2022-12-01 /pmc/articles/PMC9752886/ /pubmed/36531045 http://dx.doi.org/10.3389/fonc.2022.989077 Text en Copyright © 2022 Mugiyanto, Adikusuma, Irham, Huang, Chang and Kuo https://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 Mugiyanto, Eko Adikusuma, Wirawan Irham, Lalu Muhammad Huang, Wan-Chen Chang, Wei-Chiao Kuo, Chun-Nan Integrated genomic analysis to identify druggable targets for pancreatic cancer |
title | Integrated genomic analysis to identify druggable targets for pancreatic cancer |
title_full | Integrated genomic analysis to identify druggable targets for pancreatic cancer |
title_fullStr | Integrated genomic analysis to identify druggable targets for pancreatic cancer |
title_full_unstemmed | Integrated genomic analysis to identify druggable targets for pancreatic cancer |
title_short | Integrated genomic analysis to identify druggable targets for pancreatic cancer |
title_sort | integrated genomic analysis to identify druggable targets for pancreatic cancer |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9752886/ https://www.ncbi.nlm.nih.gov/pubmed/36531045 http://dx.doi.org/10.3389/fonc.2022.989077 |
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