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.

Methods: Lower-risk MDS (according to IPSS-R) bone marrow (BM) samples from patients harboring an SF3B1 mutation (n = 5) or higher-risk MDS BM samples from patients with an ASXL1 mutation (n = 5) were chosen for analysis to limit heterogeneity and allow for better comparability. Healthy age-matched control BM samples (n = 5) were obtained from patients undergoing routine hip replacement surgery. Distribution of stem and progenitor cell compartments (HSC, MPP, MLP, CMP, GMP, MEP) in BM on day 0 were determined by multiparameter flow cytometry. Cells were labeled with violet cell trace and HSC (Lin-CD34+CD38-CD90+CD45RA-) were sorted by flow cytometry. A fixed number of HSC as well single HSC cell cultures were set up in vitro in serum-free medium with defined growth factors (FL, KL, TPO, IL-6, G-CSF, GM-CSF and EPO). Cell divisions, proliferation rates, cell death and lineage maturation were determined by flow cytometry on days 1, 3, 5, 7 and 9. Clonogenic capacity was probed by standard CFU assays in methylcellulose on day 0 and day 7. Additional integration of genomic sequencing analyses of bulk BM and HSC was used to assess the contribution of the malignant clone to the population of proliferating cells and to each hematopoietic lineage.

Results: While CFU numbers at day 0 were significantly reduced for all MDS samples compared to healthy controls, ASXL1 mutated MDS BM samples contained significantly higher numbers of HSC than SF3B1 MDS or healthy control BM samples. SF3B1 MDS samples had significantly larger CMP and GMP compartments compared to healthy controls or ASXL1 mutated samples. In cultures started with a fixed number of HSC, no apparent differences in proliferation kinetics were observed between MDS and healthy samples over time. However, cell trace labeling and sorting of single HSC into 96 well plates revealed clear differences between MDS and healthy HSC. Overall, MDS HSC were less robust, with fewer wells of the 96 plate containing cells after sorting than for healthy HSC samples. In contrast, the remaining HSC from both both ASXL1 and SF3B1 mutated MDS showed a significantly higher cell division rate than healthy HSC in vitro, suggesting MDS HSC are more active than their normal counterparts. Genotyping analyses to assess single MDS HSC contributions to proliferation are ongoing and data are currently being fitted to mathematical models.

Conclusion: Detailed analysis of hematopoietic stem and progenitor cell compartments as well as time-resolved tracking of HSC revealed significant differences between MDS and healthy HSC kinetics in vitro. Integration of this data into a comprehensive mathematical model of MDS hematopoiesis may aid in dissecting the mechanisms of progression of MDS towards acute myeloid leukaemia (AML).