Cargando…

Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis

The advancement of high-throughput sequencing and genomic analysis revealed that acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease. The abundance of such genetic data in ALL can also be utilized to identify potential targets for drug discovery and even drug repurposing. We ai...

Descripción completa

Detalles Bibliográficos
Autores principales: Zazuli, Zulfan, Irham, Lalu Muhammad, Adikusuma, Wirawan, Sari, Nur Melani
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9786277/
https://www.ncbi.nlm.nih.gov/pubmed/36559013
http://dx.doi.org/10.3390/ph15121562
_version_ 1784858253574799360
author Zazuli, Zulfan
Irham, Lalu Muhammad
Adikusuma, Wirawan
Sari, Nur Melani
author_facet Zazuli, Zulfan
Irham, Lalu Muhammad
Adikusuma, Wirawan
Sari, Nur Melani
author_sort Zazuli, Zulfan
collection PubMed
description The advancement of high-throughput sequencing and genomic analysis revealed that acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease. The abundance of such genetic data in ALL can also be utilized to identify potential targets for drug discovery and even drug repurposing. We aimed to determine potential genes for drug development and further guide the identification of candidate drugs repurposed for treating ALL through integrated genomic network analysis. Genetic variants associated with ALL were retrieved from the GWAS Catalog. We further applied a genomic-driven drug repurposing approach based on the six functional annotations to prioritize crucial biological ALL-related genes based on the scoring system. Lastly, we identified the potential drugs in which the mechanisms overlapped with the therapeutic targets and prioritized the candidate drugs using Connectivity Map (CMap) analysis. Forty-two genes were considered biological ALL-risk genes with ARID5B topping the list. Based on potentially druggable genes that we identified, palbociclib, sirolimus, and tacrolimus were under clinical trial for ALL. Additionally, chlorprothixene, sirolimus, dihydroergocristine, papaverine, and tamoxifen are the top five drug repositioning candidates for ALL according to the CMap score with dasatinib as a comparator. In conclusion, this study determines the practicability and the potential of integrated genomic network analysis in driving drug discovery in ALL.
format Online
Article
Text
id pubmed-9786277
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-97862772022-12-24 Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis Zazuli, Zulfan Irham, Lalu Muhammad Adikusuma, Wirawan Sari, Nur Melani Pharmaceuticals (Basel) Article The advancement of high-throughput sequencing and genomic analysis revealed that acute lymphoblastic leukemia (ALL) is a genetically heterogeneous disease. The abundance of such genetic data in ALL can also be utilized to identify potential targets for drug discovery and even drug repurposing. We aimed to determine potential genes for drug development and further guide the identification of candidate drugs repurposed for treating ALL through integrated genomic network analysis. Genetic variants associated with ALL were retrieved from the GWAS Catalog. We further applied a genomic-driven drug repurposing approach based on the six functional annotations to prioritize crucial biological ALL-related genes based on the scoring system. Lastly, we identified the potential drugs in which the mechanisms overlapped with the therapeutic targets and prioritized the candidate drugs using Connectivity Map (CMap) analysis. Forty-two genes were considered biological ALL-risk genes with ARID5B topping the list. Based on potentially druggable genes that we identified, palbociclib, sirolimus, and tacrolimus were under clinical trial for ALL. Additionally, chlorprothixene, sirolimus, dihydroergocristine, papaverine, and tamoxifen are the top five drug repositioning candidates for ALL according to the CMap score with dasatinib as a comparator. In conclusion, this study determines the practicability and the potential of integrated genomic network analysis in driving drug discovery in ALL. MDPI 2022-12-14 /pmc/articles/PMC9786277/ /pubmed/36559013 http://dx.doi.org/10.3390/ph15121562 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
Zazuli, Zulfan
Irham, Lalu Muhammad
Adikusuma, Wirawan
Sari, Nur Melani
Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis
title Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis
title_full Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis
title_fullStr Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis
title_full_unstemmed Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis
title_short Identification of Potential Treatments for Acute Lymphoblastic Leukemia through Integrated Genomic Network Analysis
title_sort identification of potential treatments for acute lymphoblastic leukemia through integrated genomic network analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9786277/
https://www.ncbi.nlm.nih.gov/pubmed/36559013
http://dx.doi.org/10.3390/ph15121562
work_keys_str_mv AT zazulizulfan identificationofpotentialtreatmentsforacutelymphoblasticleukemiathroughintegratedgenomicnetworkanalysis
AT irhamlalumuhammad identificationofpotentialtreatmentsforacutelymphoblasticleukemiathroughintegratedgenomicnetworkanalysis
AT adikusumawirawan identificationofpotentialtreatmentsforacutelymphoblasticleukemiathroughintegratedgenomicnetworkanalysis
AT sarinurmelani identificationofpotentialtreatmentsforacutelymphoblasticleukemiathroughintegratedgenomicnetworkanalysis