Speaker
Description
Clonal Haematopoiesis of Indeterminate Potential (CHIP) is characterised by expansion of mutated Haematopoietic Stem and Progenitor Cells (HSPCs), resulting in overrepresentation of their progeny in peripheral blood. CHIP occurs in 15–20% of individuals over 60 years of age and is linked to haematologic malignancy, age-related disease, and increased all-cause mortality. Mathematical modelling has been successfully used to infer mutant fitness from longitudinal variant allele frequency (VAF) data, but extending these approaches to Myelodysplastic Syndromes (MDS), a form of blood cancer, requires relaxing assumptions.
First, we investigate how loss of heterozygosity (LoH) alters inferred clonal dynamics in expanding mutant populations and assess whether minimal models are sufficient for reliable fitness inference. We develop a methodology to estimate het/homozygous mutant cell proportions when these are not known from data. Secondly, we develop clonal growth models incorporating mutant-dependent growth, competition, and alternative carrying-capacity and a variable bias for mutants to displace healthy cells show when commonly used exponential growth models fail for fitness inference.
Our results demonstrate that modelling assumptions substantially alter inferred clonal dynamics and predicted VAF trajectories, highlighting how clonal expansion may transform during disease progression and how modelling choices impact interpretation of longitudinal sequencing data in CHIP and MDS.
