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Cancer Res. 2019 May 15;79(10):2466-2479. doi: 10.1158/0008-5472.CAN-18-2970. Epub 2019 Apr 2.

Hemap: An Interactive Online Resource for Characterizing Molecular Phenotypes across Hematologic Malignancies.

Author information

1
Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland.
2
Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland.
3
Institute for Molecular Medicine Finland FIMM, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
4
Tampere Center for Child Health Research, Tampere University and Tampere University Hospital, Tampere, Finland.
5
Tampere University Hospital and Fimlab Laboratories, Tampere, Finland.
6
Hematology Research Unit Helsinki, University of Helsinki and Department of Hematology, Helsinki University Central Hospital Cancer Center, Helsinki, Finland.
7
Luxembourg Center for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg.
8
School of Computing, University of Eastern Finland, Kuopio, Finland.
9
Tampere Center for Child Health Research, Tampere University and Tampere University Hospital, Tampere, Finland. matti.nykter@uta.fi olli.lohi@uta.fi merja.heinaniemi@uef.fi.
10
Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland. matti.nykter@uta.fi olli.lohi@uta.fi merja.heinaniemi@uef.fi.
11
Institute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland. matti.nykter@uta.fi olli.lohi@uta.fi merja.heinaniemi@uef.fi.
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Contributed equally

Abstract

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 context for new therapeutic approaches. We analyzed 9,544 transcriptomes from more than 30 hematologic malignancies, normal blood cell types, and cell lines, and showed that disease types could be stratified in a data-driven manner. We then identified cluster-specific pathway activity, new biomarkers, and in silico drug target prioritization through interrogation of drug target databases. Using known vulnerabilities and available drug screens, we highlighted the importance of integrating molecular phenotype with drug target expression for in silico prediction of drug responsiveness. Our analysis implicated BCL2 expression level as an important indicator of venetoclax responsiveness and provided a rationale for its targeting in specific leukemia subtypes and multiple myeloma, linked several polycomb group proteins that could be targeted by small molecules (SFMBT1, CBX7, and EZH1) with chronic lymphocytic leukemia, and supported CDK6 as a disease-specific target in acute myeloid leukemia. 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 publicly available interactive resource, Hemap, for expediting therapeutic innovations in hematologic malignancies. SIGNIFICANCE: This study describes a data resource for researching derailed cellular pathways and candidate drug targets across hematologic malignancies.

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