The study presents a comprehensive functional genomic analysis of 672 AML tumor specimens from 562 patients, revealing novel mutational events and gene expression signatures associated with drug response. Whole exome sequencing, RNA sequencing, and ex vivo drug sensitivity assays were used to identify mutations, gene expression patterns, and drug sensitivity profiles. The data show that drug response is linked to specific mutational events, with some mutations showing unique sensitivity patterns. Integration of RNA sequencing data revealed gene expression signatures that predict the role of specific gene networks in drug response. The dataset is accessible via the Beat AML data viewer, enabling further research into AML biology. The study highlights the heterogeneity of AML, with mutations in genes such as SRSF2, IDH1, IDH2, RUNX1, and others showing distinct patterns of drug sensitivity and resistance. Mutations in TP53 and ASXL1 were associated with broad drug resistance, while mutations in IDH2 showed sensitivity to a wide range of drugs. FLT3-ITD mutations were linked to sensitivity to FLT3 inhibitors, and mutations in FLT3, NPM1, and DNMT3A were associated with sensitivity to ibrutinib. The study also identified co-occurring mutations that significantly influenced drug sensitivity, suggesting potential therapeutic targets. RNA sequencing data revealed gene expression signatures that correlated with AML subtypes and drug response. Analysis of drug sensitivity patterns showed that certain drugs were more effective in de novo cases than transformed cases. The study also identified gene expression clusters associated with drug sensitivity and resistance, including a 17-gene signature for ibrutinib sensitivity and a 110-gene subnetwork associated with ibrutinib resistance. The study provides a large-scale dataset of AML tumor biopsies, including clinical annotations, genomic and transcriptomic data, and ex vivo drug sensitivity results. These data are publicly available through the NIH/NCI dbGaP and Genomic Data Commons (GDC) resources, and the study offers tools for data integration and analysis. The findings suggest that certain mutational combinations may represent vulnerabilities in AML, and further research is needed to explore these associations for potential therapeutic strategies. The study also highlights the importance of integrating genomic and transcriptomic data to better understand AML biology and develop targeted therapies.The study presents a comprehensive functional genomic analysis of 672 AML tumor specimens from 562 patients, revealing novel mutational events and gene expression signatures associated with drug response. Whole exome sequencing, RNA sequencing, and ex vivo drug sensitivity assays were used to identify mutations, gene expression patterns, and drug sensitivity profiles. The data show that drug response is linked to specific mutational events, with some mutations showing unique sensitivity patterns. Integration of RNA sequencing data revealed gene expression signatures that predict the role of specific gene networks in drug response. The dataset is accessible via the Beat AML data viewer, enabling further research into AML biology. The study highlights the heterogeneity of AML, with mutations in genes such as SRSF2, IDH1, IDH2, RUNX1, and others showing distinct patterns of drug sensitivity and resistance. Mutations in TP53 and ASXL1 were associated with broad drug resistance, while mutations in IDH2 showed sensitivity to a wide range of drugs. FLT3-ITD mutations were linked to sensitivity to FLT3 inhibitors, and mutations in FLT3, NPM1, and DNMT3A were associated with sensitivity to ibrutinib. The study also identified co-occurring mutations that significantly influenced drug sensitivity, suggesting potential therapeutic targets. RNA sequencing data revealed gene expression signatures that correlated with AML subtypes and drug response. Analysis of drug sensitivity patterns showed that certain drugs were more effective in de novo cases than transformed cases. The study also identified gene expression clusters associated with drug sensitivity and resistance, including a 17-gene signature for ibrutinib sensitivity and a 110-gene subnetwork associated with ibrutinib resistance. The study provides a large-scale dataset of AML tumor biopsies, including clinical annotations, genomic and transcriptomic data, and ex vivo drug sensitivity results. These data are publicly available through the NIH/NCI dbGaP and Genomic Data Commons (GDC) resources, and the study offers tools for data integration and analysis. The findings suggest that certain mutational combinations may represent vulnerabilities in AML, and further research is needed to explore these associations for potential therapeutic strategies. The study also highlights the importance of integrating genomic and transcriptomic data to better understand AML biology and develop targeted therapies.