4.6 Article

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

期刊

PHARMACEUTICALS
卷 15, 期 12, 页码 -

出版社

MDPI
DOI: 10.3390/ph15121562

关键词

acute lymphoblastic leukemia; bioinformatics; leukemia; drug repurposing; genetic variants; genomic network analysis

资金

  1. Research Program Fund
  2. Bandung Institute of Technology [223/IT1.B07.1/TA.00/2022]

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This study utilizes high-throughput sequencing and genomic analysis to uncover the genetic heterogeneity of ALL and proposes potential drug development genes and drug repurposing candidates through genomic network analysis, providing guidance for the treatment of ALL.
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.

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