Integrated Computational and Functional Screening Identify G9a Inhibitors for SETD2-Mutant Leukemia

SETD2, a frequently mutated epigenetic tumor suppressor in acute leukemia, is associated with chemotherapy resistance and poor patient outcomes. To explore potential therapeutics for SETD2-mutant leukemia, we employed an integrated approach combining computational prediction with epigenetic compound library screening. This approach identified G9a inhibitors as promising candidates, capable of reversing gene expression signatures associated with Setd2 deficiency and selectively inhibiting SETD2-deficient cells. RNA-sequencing analysis revealed that G9a inhibitor significantly downregulated Myc and Myc-regulated genes involved in translation, DNA replication, and G1/S transition in Setd2-mutant cells. Further chromatin immunoprecipitation sequencing analysis showed that G9a inhibition reduced H3K9me2 levels at the long non-coding RNA Mir100hg locus, coinciding with specific upregulation of the embedded microRNA let-7a-2 in Setd2-mutant cells. Given the established role of let-7a in MYC suppression, these findings suggest a potential mechanism by which G9a inhibitors induce MYC downregulation in SETD2-mutant leukemia. Additionally, correlation analysis between computational prediction and phenotypic outcomes highlighted the MYC signature as a key predictor of drug efficacy. Collectively, our study identifies G9a inhibitors as a promising therapeutic avenue for SETD2-mutant leukemia and provides novel insights into refining drug prediction strategies.