Researchers at Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and EMBL-EBI have reconstructed the lineage history of individual hematopoietic stem cells from cancer patients, revealing that the first mutation occurs decades before diagnosis.
Myeloproliferative neoplasms (MPNs) are a group of rare chronic blood cancers. Early studies have indicated that the JAK2-V617F mutation underlies the molecular pathogenesis of the majority of Philadelphia chromosome-negative MPNs. More recently, JAK2 was found to be one of the most commonly mutated genes in clonal haematopoiesis. The JAK2-V617F mutation results in activated JAK2 signalling. This leads to increased production of mature blood cells of the myeloid lineage, resulting in MPNs. Although the effects of this mutation on HSCs and all mature cell lineages has been shown, how the mutation affects HSC differentiation and proliferation in their native bone marrow microenvironment is still unclear.
Reconstructing lineage histories
In this study, published in Cell Stem Cell, the team developed a method to simultaneously measure both the full transcriptome and somatic mutations of single haematopoietic stem and progenitor cells (HSPCs). The team obtained the cells from the bone marrow of MPN patients in order to investigate how the JAK2-V617F mutation impacts HSC differentiation and proliferation in vivo in each individual. They used this information to reconstruct the lineage history of individuals’ HSCs to show how the diseases expanded in each patient over time.
The team found that JAK2-V617F mutations occurred in single HSC several decades before MPN diagnosis. They also found that mutant HSCs have a selective advantage. This population of JAK2 mutant stem cells subsequently grow exponentially but may exhibit large fluctuations and even stochastic extinction.
These findings provide a compelling motivation for the development of JAK2-V617F mutant-specific inhibitors for more effective and less toxic treatment of MPN in the clinic. Additionally, the technology platforms and computational frameworks developed in this study could be broadly applicable for use in future studies looking at other cancer types.
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