Data Driven Computational Modeling Of Hematopoiesis In Myelodysplastic Syndromes Unveils Differences In Hematopoietic Stem Cell Kinetics Compared To Age-Matched Healthy Controls

Background: Myelodysplastic syndromes (MDS) are clonal hematopoietic stem cell (HSC) disorders characterized by ineffective hematopoiesis, peripheral cytopenias and risk of transformation to acute myeloid leukemia (AML). Recently, HSC in MDS have been shown to carry acquired distinct genetic and epigenetic mutations, most frequently in epigenetic regulators or splicing machinery factors, which are considered disease-initiating events and are likely responsible for the dysplastic features observed in this disease. Hierarchies of hematopoiesis have been established for healthy HSC and their progeny but it is unclear whether MDS HSC follow the same patterns. Clonal hierarchies in MDS have been inferred on the basis of mutational data, these only constitute a snapshot of hematopoiesis in the patient at a given time and may not accurately reflect the kinetics of the disease. To understand how clonal dominance of MDS over healthy HSC is achieved, we sought to identify differences in proliferation kinetics and lineage fate decisions of MDS HSC in comparison to healthy hematopoiesis and to fit this real data into a mathematical model of MDS hematopoiesis.