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Functional Dependency Analysis Identifies Potential Druggable Targets in Acute Myeloid Leukemia
Yujia Zhou1, Gregory P Takacs1, Jatinder K Lamba2
1Division of Hematology and Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, FL 32610-0278, USA.
Cancers
|December 16, 2020
Summary
Researchers identified 94 essential and druggable gene targets for acute myeloid leukemia (AML) treatment. This compendium includes 44 targets with existing drugs (deKO) and 50 novel targets for drug discovery (disKO).
Area of Science:
- Hematology
- Cancer Biology
- Genomics
Background:
- Refractory acute myeloid leukemia (AML) remains a significant clinical challenge.
- Despite recent therapeutic advancements, long-term survival in AML patients is not yet guaranteed.
- Novel therapeutic targets are crucial for improving AML treatment outcomes.
Purpose of the Study:
- To identify novel, druggable gene targets specifically essential for acute myeloid leukemia (AML) cell survival.
- To expand the therapeutic arsenal for AML by discovering new drug targets.
Main Methods:
- Genome-wide CRISPR-Cas9 screening was performed on 15 AML cell lines to identify gene knockout (KO) dependencies.
- Genes essential for AML cell survival but not for non-AML cells were selected.
- Druggability assessment was conducted using 3D modeling and ligand-based scoring.
Main Results:
- Ninety-four gene KOs met the criteria for essentiality and druggability in AML.
- Forty-four targets (deKO list) have associated approved drugs, while 50 targets (disKO list) represent novel drug discovery opportunities.
- disKO targets are enriched in metabolic pathways (e.g., UMP biosynthesis) and regulation of hematopoiesis.
Conclusions:
- This study provides a validated compendium of 94 potential AML drug targets.
- The identified targets, particularly the novel disKO targets, offer promising avenues for developing new AML therapeutics.
- Further validation and translation of these targets may lead to improved treatment strategies for AML patients.
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