SETD2,a frequently mutated epigenetic tumor suppressor gene in acute leukemia,is associated with chemotherapy resistance and poor patient outcomes.To explore potential therapeutics for SETD2-mutant leukemia,we employe...SETD2,a frequently mutated epigenetic tumor suppressor gene 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 the 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 predictions 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.展开更多
Predictive analytics is crucial in precision medicine for personalized patient care.To aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classi...Predictive analytics is crucial in precision medicine for personalized patient care.To aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease types.To achieve this,experiments were performed on diseased tissues obtained from the L1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical variables.In this study,the k-means technique was used for clustering the diseased tissue types,and the multinomial logistic regression(MLR)technique was applied for classifying the diseased tissue types.Dimensionality reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical variables.The results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 non-landmark genes,and the difference is statistically significant.Furthermore,it was evident that both clinical and genetic variables were important in predicting the diseased tissue types.The top three clinical predictors for predicting diseased tissue types were identified as morphology,gender,and age of diagnosis.Additionally,this study explored the possibility of using the latent representations of the clusters of landmark and non-landmark genes as predictors for an MLR classifier.The classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical variables.This study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine,enhancing diagnostic accuracy.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA0460403)the Scientific and Technological Innovation Project of China Academy of Chinese Medical Sciences(Grant No.CI2023C027YL).
文摘SETD2,a frequently mutated epigenetic tumor suppressor gene 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 the 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 predictions 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.
文摘Predictive analytics is crucial in precision medicine for personalized patient care.To aid in precision medicine,this study identifies a subset of genetic and clinical variables that can serve as predictors for classifying diseased tissues/disease types.To achieve this,experiments were performed on diseased tissues obtained from the L1000 dataset to assess differences in the functionality and predictive capabilities of genetic and clinical variables.In this study,the k-means technique was used for clustering the diseased tissue types,and the multinomial logistic regression(MLR)technique was applied for classifying the diseased tissue types.Dimensionality reduction techniques including principal component analysis and Boruta are used extensively to reduce the dimensionality of genetic and clinical variables.The results showed that landmark genes performed slightly better in clustering diseased tissue types compared to any random set of 978 non-landmark genes,and the difference is statistically significant.Furthermore,it was evident that both clinical and genetic variables were important in predicting the diseased tissue types.The top three clinical predictors for predicting diseased tissue types were identified as morphology,gender,and age of diagnosis.Additionally,this study explored the possibility of using the latent representations of the clusters of landmark and non-landmark genes as predictors for an MLR classifier.The classification models built using MLR revealed that landmark genes can serve as a subset of genetic variables and/or as a proxy for clinical variables.This study concludes that combining predictive analytics with dimensionality reduction effectively identifies key predictors in precision medicine,enhancing diagnostic accuracy.
