Hemap: An nteractive online resource for characterizing molecular phenotypes across hematologic malignancies

Large collections of genome-wide data can facilitate the characterization of disease states and subtypes, permitting pan-cancer analysis of molecular phenotypes and evaluation of disease contexts for new therapeutic approaches. We analyzed 9,544 transcriptomes from over 30 hematologic malignancies, normal blood cell types and cell lines, and show that the disease types can be stratified in a data-driven manner. We utilized the obtained molecular clustering for discovery of cluster-specific pathway activity, new biomarkers and in silico drug target prioritization through integration with drug target databases. Using known vulnerabilities and available drug screens in benchmarking, we highlight the importance of integrating the molecular phenotype context and drug target expression for in silico prediction of drug responsiveness. Our analysis implicates BCL2 expression level as
important indicator of venetoclax responsiveness and provides a rationale for its targeting in specific leukemia subtypes and multiple myeloma, links several polycomb group proteins that could be targeted by small molecules (SFMBT1, CBX7 and EZH1) with CLL, and supports CDK6 as disease-specific target in AML. Through integration with proteomics data, we characterized target protein expression for pre-B leukemia immunotherapy candidates, including DPEP1. These molecular data can be explored using our freely available interactive resource, Hemap, for expediting therapeutic innovations in hematologic malignancies.