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

Area of Science:

• Genomics
• Hematology
• Computational Biology

Background:

• Large-scale genomic data aids disease characterization and therapeutic strategy development.
• Hematologic malignancies represent a diverse group of blood cancers requiring tailored treatment approaches.

Purpose of the Study:

• To stratify hematologic malignancy subtypes using transcriptomic data.
• To identify novel biomarkers and prioritize drug targets for hematologic cancers.
• To integrate molecular phenotypes with drug target expression for predicting drug responsiveness.

Main Methods:

• Analysis of 9,544 transcriptomes from hematologic malignancies, normal blood cells, and cell lines.
• Data-driven stratification of disease types and identification of cluster-specific pathway activity.
• In silico drug target prioritization using drug databases and integration with proteomics data.

Main Results:

• Successful stratification of hematologic malignancy subtypes.
• Identification of BCL2 expression as a predictor of venetoclax response in leukemia and multiple myeloma.
• Linking polycomb group proteins (SFMBT1, CBX7, EZH1) to chronic lymphocytic leukemia and CDK6 to acute myeloid leukemia.
• Characterization of DPEP1 as a potential immunotherapy target in pre-B leukemia.

Conclusions:

• The study provides a data-driven resource (Hemap) for exploring molecular phenotypes and drug targets in hematologic malignancies.
• Integration of molecular data with drug screens enhances in silico prediction of drug responsiveness.
• Identified specific molecular targets and biomarkers that can accelerate therapeutic innovations for blood cancers.