[Dobutamine Enhances the Targeted Inhibitory Effect of Quizartinib on FLT3-ITD Mutant Acute Myeloid Leukemia].

OBJECTIVE: To observe the inhibitory effect of dobutamine on proliferation of FLT3-ITD mutated acute myeloid leukemia (AML) cells and explore the feasibility of dobutamine as a monotherapy or in combination with quizartinib for the treatment of this type of AML. METHODS: FLT3-ITD mutant cell lines MOLM13 and MV4-11 were cultured in vitro and divided into control group, dobutamine treatment group, quizartinib treatment group, and dobutamine combined with quizartinib treatment group. Cell viability, ROS levels, and apoptosis rate were detected by CCK-8, Flow cytometry, respectively, as well as the expression of YAP1 protein by Western blot. RESULTS: Both dobutamine and quizartinib inhibited the proliferation of FLT3-ITD mutant AML cell lines. Compared with the control group, the dobutamine group exhibited a significant increase in ROS levels (P < 0.01), an increase in apoptosis rates (P < 0.05), and a decrease in YAP1 protein expression (P < 0.01), and decreased YAP1 expression (P < 0.05). CONCLUSION: Dobutamine as a monotherapy can inhibit theproliferation of FLT3-ITD mutated AML cells, inducing apoptosis. Additionally, the combination of quizartinib enhances the targeted inhibitory effect on FLT3-ITD mutated AML. The mechanism may involve the inhibition of YAP1 protein expression in AML cells of this type, leading to an increase in ROS levels and exerting its anti-tumor effects.

CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection.

Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.