Longitudinal dynamics of clonal haematopoiesis identifies gene-specific fitness effects

The prevalence of clonal haematopoiesis of indeterminate potential (CHIP) in healthy individuals increases rapidly from age 60 onwards and has been associated with increased risk for malignancy, heart disease and ischemic stroke. CHIP is driven by somatic mutations in stem cells that are also drivers of myeloid malignancies. Since mutations in stem cells often drive leukaemia, we hypothesised that stem cell fitness substantially contributes to transformation from CHIP to leukaemia. Stem cell fitness is defined as the proliferative advantage over cells carrying no or only neutral mutations. It is currently unknown whether mutations in different CHIP genes lead to distinct fitness advantages that could form the basis for patient stratification. We set out to quantify the fitness effects of CHIP drivers over a 12-year timespan in older age, using longitudinal error-corrected sequencing data. Two key results support the possibility for individualised clinical monitoring of CHIP: (i) We developed a new filtering method to extract fitness effects from longitudinal data using Bayesian inference, while taking into account individual mutational context and co-occurrence of mutations, and thus quantify the growth potential of variants within each individual. (ii) We show that gene-specific fitness differences can outweigh inter-individual variation and therefore could form the basis for personalised clinical management in the future. The Lothian Birth Cohort 1921 (LBC1921) contains a total of 550 participants at Wave 1 of their testing (done between 1999 and 2001) with a gender ratio of 234/316 (m/f) and a mean age at Wave 1 of 79.1 (SD=0.6) (Table 1) 17. The Lothian Birth Cohort 1936 (LBC1936), contains a total of 1091 participants at Wave1 of their testing (done between 2004 and 2007) with a gender ratio of 548/543 (m/f) and a mean age at Wave 1 of 69.5 (SD=0.8) (Table 1, (Taylor et al., 2018)). We previously identified 73 participants with CHIP at Wave 1 18. We sequenced DNA from those 73 LBC participants using a targeted gene panel (Table 2) and added 16 LBC participants with previously unidentified CHIP and 4-5 time-points. We have accepted 85 of 89 participants for inclusion in our study, removing 4 participants for failing to meet quality criteria (low library complexity), with a total of 248 samples together with 14 “Genome in a Bottle” (GIAB) controls - two per sequencing batch. In addition, two individuals carrying the JAK2V617F mutation received treatment for leukaemia after the first respective time point available - potentially driving the observed reductions in clone size. Those patients were omitted from further analysis after sequencing.