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. 2022 Dec 14;15(12):1562.
doi: 10.3390/ph15121562.

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

Zulfan Zazuli  1 , Lalu Muhammad Irham  2 , Wirawan Adikusuma  3 , Nur Melani Sari  4
Affiliations

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

Zulfan Zazuli et al. Pharmaceuticals (Basel). .

Abstract

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.

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

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
(A) List of genes with the score following application of functional annotations criteria. (B) The number of genes for each score following application of functional annotations criteria. (C) The number of genes overlapped with each functional annotations criterion. Most of the genes overlapped with the molecular function data.
Figure 2
Figure 2
37 drugs overlapped with 15 candidate drug target genes. Four out of these 15 genes (DDC, KCNMB2, PDE4B, and RYR2) were included in the list of the previously defined “biological ALL risk genes” (Figure 2). The data visualization in this figure was produced using RAWGraphs visualization (https://app.rawgraphs.io/ accessed on 12 July 2022).
Figure 3
Figure 3
The candidate drugs for acute lymphoblastic leukemia (ALL) are based on CMap analysis.
Figure 4
Figure 4
Workflow of the study by leveraging the genomic variants from GWAS and prioritizing it by several functional annotations. Through the genomic variants−driven drug repurposing concept, we finally obtained the drug candidates to be repurposed for ALL according to a drug database.
See this image and copyright information in PMC

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