Key role of glutamine metabolism in persistence of leukemic cells upon exposition to FLT3 tyrosine kinase inhibitors.
Raeeka KhamariClaire DegandQuentin FovezAnne TrinhAxel ChomyWilliam LaineSalim DekioukBart GhesquiereBruno QuesnelPhilippe MarchettiSalomon ManierJ KluzaPublished in: Experimental hematology (2024)
Acute myeloid leukemias are a group of hematological malignancies characterized by a poor prognosis for survival. The discovery of oncogenic mutations in the FMS-like tyrosine kinase 3 (FLT3) gene has led to the development of tyrosine kinase inhibitors such as quizartinib. However, achieving complete remission in patients remains challenging because these new tyrosine kinase inhibitors (TKIs) are unable to completely eradicate all leukemic cells. Residual leukemic cells persist during quizartinib treatment, leading to the rapid emergence of drug-resistant leukemia. Given that mitochondrial oxidative metabolism promotes the survival of leukemic cells after exposure to multiple anticancer drugs, we characterized the metabolism of leukemic cells that persisted during quizartinib treatment and developed metabolic strategies to eradicate them. In our study, employing biochemical and metabolomics approaches, we confirmed that the survival of leukemic cells treated with FLT3 inhibitors critically depends on maintaining mitochondrial metabolism, specifically through glutamine oxidation. We uncovered a synergistic interaction between the FLT3 inhibitor quizartinib and L-asparaginase, operating through antimetabolic mechanisms. Utilizing various models of persistent leukemia, we demonstrated that leukemic cells resistant to quizartinib are susceptible to L-asparaginase. This combined therapeutic strategy shows promise in reducing the development of resistance to FLT3 inhibitors, offering a potential strategy to enhance treatment outcomes.
Keyphrases
- acute myeloid leukemia
- tyrosine kinase
- induced apoptosis
- cell cycle arrest
- drug resistant
- poor prognosis
- oxidative stress
- endoplasmic reticulum stress
- intensive care unit
- epidermal growth factor receptor
- bone marrow
- cell death
- machine learning
- end stage renal disease
- signaling pathway
- cystic fibrosis
- long non coding rna
- hepatitis b virus
- gene expression
- hydrogen peroxide
- deep learning
- risk assessment
- mass spectrometry
- quantum dots
- dna methylation
- rheumatoid arthritis
- liver failure
- acinetobacter baumannii
- peritoneal dialysis
- drug induced
- respiratory failure
- patient reported outcomes