MPN Fitness: A Single-Cell, Single-Tube Measurement of the Pathobiology of MPNs
Authors: Castillo Tokumori Franco , Choi Daniel C , Abu-Zeinah Ghaith , Messali Nassima , Yabut Maria Mia , Scandura Joseph M. 
Published: December 2024

Introduction:

Myeloproliferative neoplasms (MPNs) are characterized by shortened survival due to thrombosis and progression to myelofibrosis and acute leukemia. Meaningful improvement in the care of patients requires therapies that prolong event-free survival (EFS); however, validated monitoring biomarkers that can serve as surrogate measures do not currently exist. Our group developed an MPN fitness model by quantifying the JAK2V617F mutant allele frequency (MAF) in 11 defined and validated immunophenotypic MPN-hematopoietic stem cell and progenitor cells (HSPCs) and mature cell populations using fluorescence-activated cell sorting (FACS) and droplet digital PCR (ddPCR). We used this approach to develop a risk model prognostic for near-term EFS (within 3 years), proving to be a reliable monitoring biomarker.1 This approach directly measures the competitive advantage (MPN Fitness) of malignant MPN-HSPCs over normal blood-forming cells. To advance the use of MPN Fitness as an endpoint in MPN care, we've adapted to single-cell genotyping and immunophenotyping of peripheral blood using the MissionBio Tapestri Platform. This platform enables the analysis of individual cells in a single-tube assay, reducing processing time and minimizing sample manipulation.

Methods:

The Weill Cornell Silver MPN Center has established workflows to collect fresh patient samples. We are processing paired fresh specimens using the Tapestri and original FACS/ddPCR methods. Re-proportion of nucleated cells by CD34 separation ensures sufficient representation of HSPCs (median 43%, IQR 34-56%), CD34-depleted mononuclear cells (median 45%, IQR 34-56%), and polymorphonucleated cells (median 4%, IQR 3-7%) in a single-tube cell suspension. We then label the cells with a 45-gene (Myeloid DNA panel) and a 45 oligo-labeled antibody panel (Biolegend TotalSeq-D) and process the suspension using the Tapestri instrument for single-cell emulsification. The sequenced DNA and protein libraries are analyzed downstream in Python and R.

Results:

We performed a pooled analysis of 69,000 individual cells from multiple samples to characterize the immunophenotype of different cell populations in MPN. Using dimension reduction methods, we clustered the cells in 36 different cell populations. We then select the clusters representing the 11 cell populations used for our original MPN fitness model. By integrating the genomic data, we accurately identify the wild-type, monoallelic, and biallelic status ofJAK2V617F in each individual cell. By combining both layers, we easily calculate the MAF of each cell population.

We performed a correlation analysis in paired samples to evaluate the concordance between workflows. We found a Pearson correlation of 0.74 (95% CI 0.60-0.85, p=0.01) between both methods. By decreasing the number of antibodies to only the ones used in the FACS/ddPCR workflow, we can still identify the 11 defined cell populations in each individual sample. Also, we found that as few as 3,000 cells per sample are enough to make this correlation analysis between workflows (Pearson correlation of 0.85, 95% CI 0.66-0.93, p &lt; 0.001).

Additionally, by separately assessing the JAK2V617F monoallelic and biallelic clones, we can independently quantify their fitness advantage through the hematopoietic hierarchy. While monoallelic clones have a fitness advantage in differentiation towards neutrophils compared to WT cells (log2 fold change 1.12), we found that biallelic clones have a 2-fold fitness advantage in the neutrophil lineage (log2 fold change 2.66). We only noticed this fitness in the biallelic clones towards erythroid precursors (log2FC 1.97) and monocytes (log2FC 1.30). JAK2 homozygous clones fail to differentiate towards mature lymphoid cells (median log2FC –7.25 to -3.44). These findings offer new insights into the myeloid bias towards specific lineages and the role of lymphopenia in MPNs (see Abstract #212169).

Conclusion:

Our results with this new platform are consistent with our established model and demonstrate a feasible and potentially cost-effective method to measure MPN fitness, a surrogate for short-term outcomes. At the same time, our data shows insights into the impact of the heterozygous and homozygous JAK2V617F clones in clonal propagation through hematopoiesis and proves a powerful platform to generate new insights in MPN biology.