Resolving Leukemia Heterogeneity and Lineage Aberrations with HematoMap

Area of Science:

• Hematology
• Computational Biology
• Genomics

Background:

• Mapping leukemic cells to the normal hematopoietic hierarchy is crucial for understanding leukemia origins and development.
• Leukemia cell heterogeneity and differences from normal cells present significant challenges in this mapping process.

Purpose of the Study:

• To construct a comprehensive reference hierarchy of normal hematopoiesis using single-cell RNA sequencing (scRNA-seq) data.
• To develop a computational tool for mapping leukemic cells to their normal counterparts and identifying lineage aberrations.

Main Methods:

• Curated clustering and annotation of over 100,000 bone marrow mononuclear cells from healthy donors to build a normal hematopoiesis reference.
• Cosine distance algorithm for a likelihood score to assess leukemic cell similarity to normal cells.
• Development of a LASSO score model for bulk RNA sequencing data to detect lineage aberrancy in leukemia.

Main Results:

• Established a detailed reference hierarchy of normal hematopoiesis.
• Successfully mapped acute myeloid leukemia (AML) and B cell precursor acute lymphoblastic leukemia (BCP-ALL) cells to normal counterparts.
• Developed the HematoMap R package for analyzing scRNA-seq and bulk RNA-seq data to visualize lineage aberrations.

Conclusions:

• The HematoMap tool provides a user-friendly platform for analyzing leukemia lineage aberrations.
• This approach aids in understanding leukemogenesis and has potential for improved leukemia risk stratification and personalized treatment strategies.