Decoding Clonal Dynamics of MDS By Adapting a CHIP Model

Myelodysplastic Syndromes (MDS) are a group of disorders, characterised by low blood counts, that arise from clonal hematopoiesis driven by somatic mutations. Our goal is to stratify the risks of mutations found in MDS patients and generate hypotheses about the conditions in which these mutations thrive or fail to expand. While mathematical models have been successfully applied to infer clonal fitness of gene specific mutations in Clonal Hematopoiesis of Indeterminate Potential (CHIP), a precursor to MDS, applying these models directly to MDS requires accounting for additional biological factors such as loss of heterozygosity (LOH), higher clonal fitness, and increased mutational burden. In this work, we adapt a previously published CHIP model to better capture the clonal dynamics of MDS. To this end, we introduce several modifications, with a primary focus on incorporating mixed heterozygous and homozygous cell populations into the model. In CHIP, all cells were previously assumed to be heterozygous; however, LOH is common in MDS, and we have samples where LOH must have occurred. We therefore explore scenarios where both homozygosity and heterozygosity are necessary to explain observed clonal dynamics, enabling more accurate inference of mutation-specific fitness. Using synthetic longitudinal Variant Allele Frequency (VAF) data, we assess the impact of these model refinements on fitness estimation and predicted growth trajectories, both of which are important for patient stratification. Our results demonstrate that zygosity significantly influences fitness estimates, and that the inference pipeline can be extended to estimate the fraction of homozygous cells when this information is not directly available from sequencing. This highlights the importance of MDS-specific modeling in clonal inference tasks.

Decoding Clonal Dynamics of MDS By Adapting a CHIP Model

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Cameron Kerr

"Decoding Clonal Dynamics of MDS By Adapting a CHIP Model"

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