By Jane Cook
Cooperation in Cancer Mutations
How do cellular mutations cooperate to drive cancer formation? In a paper published last week in Nature Communications, Liu et. al. answered this question using single-cell transcriptomic profiling of healthy and diseased mouse bone marrow during leukemia development.
The group produced a mouse model with two common oncogenic mutations: NRasG12D and EZH2. Mice harboring only one mutation or the other had minimal health issues, but mice expressing both mutations developed acute myeloid leukemia (AML). Could it be that these two mutations cooperated to drive cancer progression?
Single-Cell Resolution of Cancer Evolution
Transcriptomic profiling using scRNA-seq analysis was performed on blood progenitor cells from distinct stages of leukemia development in mice with either of the individual mutations and with both mutations.
The distinct transcriptomic profiles of each sample revealed the mechanism by which these two mutations cooperate to drive leukemia progression. NRasG12D skews blood cell differentiation towards the myeloid lineage, resulting in too few other cell lineages like erythroid and megakaryocytic cells. EZH2, on the other hand, impairs myeloid cell maturation, creating a large population of immature myeloid cells.
Together, these create a profound effect on blood cell differentiation, dysregulating the balance of blood cell types and thus driving leukemia progression.
Genetic Signatures of Leukemia Progression
The researchers further identified individual genes in their transcriptomic data whose expression was dysregulated by the oncogenic mutations in their mouse models. NRasG12D and EZH2 have independent gene targets but also share gene targets, like those required for platelet and B cell differentiation, further explaining their synergistic effect on leukemia development.
A New Approach to In Vivo Cancer Evolution Analysis
Using a script-based pipeline of high-resolution single-cell transcriptomics, this research group was able to uncover the complex intertwining mechanisms of leukemia progression in vivo.
This bioinformatic analysis pipeline not only provided great insight into cellular evolution during leukemia but the principles can be applied to many kinds of oncogenic mutations across cancer types. It could even illuminate the foundations of cancer cell evolution.
Jane Cook, Journalist & Content Writer, Bridge Informatics
Jane is a Content Writer at Bridge Informatics, a professional services firm that helps biotech customers implement advanced techniques in the management and analysis of genomic data. Bridge Informatics focuses on data mining, machine learning, and various bioinformatic techniques to discover biomarkers and companion diagnostics. If you’re interested in reaching out, please email [email protected] or [email protected].