Acute myeloid leukemia (AML) is a cancer of myeloid-lineage cells with limited therapeutic options. We previously combined ex vivo drug sensitivity with genomic, transcriptomic, and clinical annotations for a large cohort of AML patients, which facilitated discovery of functional genomic correlates. Here, we present a dataset that has been harmonized with our initial report to yield a cumulative cohort of 805 patients (942 specimens). We show strong cross-cohort concordance and identify features of drug response. Further, deconvoluting transcriptomic data shows that drug sensitivity is governed broadly by AML cell differentiation state, sometimes conditionally affecting other correlates of response. Finally, modeling of clinical outcome reveals a single gene, PEAR1, to be among the strongest predictors of patient survival, especially for young patients. Collectively, this report expands a large functional genomic resource, offers avenues for mechanistic exploration and drug development, and reveals tools for predicting outcome in AML. Display omitted •Acute myeloid leukemia patient cohort with clinical, molecular, drug response data•Validation and discovery of diverse biological features of drug response•Broad mapping of tumor cell differentiation state affecting response to drugs•Modeling reveals a strong and targetable determinant of clinical outcome Bottomly et al. present a functional genomic resource composed of molecular, clinical, and drug response data on acute myeloid leukemia patient specimens. Through integration of all of these data, they identify genetic and cell differentiation state features that predict drug response, and they utilize modeling to identify targetable determinants of clinical outcome.