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

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Pölönen , P , Mehtonen , J , Lin , J , Liuksiala , T , Häyrynen , S , Teppo , S , Mäkinen , A , Kumar , A , Malani , D , Pohjolainen , V , Porkka , K , Heckman , C A , May , P , Hautamäki , V , Granberg , K J , Lohi , O , Nykter , M & Heinäniemi , M 2019 , ' Hemap : An Interactive Online Resource for Characterizing Molecular Phenotypes across Hematologic Malignancies ' , Cancer Research , vol. 79 , no. 10 , pp. 2466-2479 . https://doi.org/10.1158/0008-5472.CAN-18-2970

Title: Hemap : An Interactive Online Resource for Characterizing Molecular Phenotypes across Hematologic Malignancies
Author: Pölönen, Petri; Mehtonen, Juha; Lin, Jake; Liuksiala, Thomas; Häyrynen, Sergei; Teppo, Susanna; Mäkinen, Artturi; Kumar, Ashwini; Malani, Disha; Pohjolainen, Virva; Porkka, Kimmo; Heckman, Caroline A.; May, Patrick; Hautamäki, Ville; Granberg, Kirsi J.; Lohi, Olli; Nykter, Matti; Heinäniemi, Merja
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Hematologian yksikkö
University of Helsinki, Institute for Molecular Medicine Finland
Date: 2019-05-15
Language: eng
Number of pages: 14
Belongs to series: Cancer Research
ISSN: 0008-5472
URI: http://hdl.handle.net/10138/303526
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.
Subject: ACUTE MYELOID-LEUKEMIA
THERAPEUTIC TARGET
GENE-EXPRESSION
DATABASE
REVEALS
MODELS
VISUALIZATION
CELLS
BCL2
3122 Cancers
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