Leukemia-Associated Somatic Mutations Drive Distinct Patterns of Age-Related Clonal Hemopoiesis

Abstract

SummaryClonal hemopoiesis driven by leukemia-associated gene mutations can occur without evidence of a blood disorder. To investigate this phenomenon, we interrogated 15 mutation hot spots in blood DNA from 4,219 individuals using ultra-deep sequencing. Using only the hot spots studied, we identified clonal hemopoiesis in 0.8% of individuals under 60, rising to 19.5% of those ≥90 years, thus predicting that clonal hemopoiesis is much more prevalent than previously realized. DNMT3A-R882 mutations were most common and, although their prevalence increased with age, were found in individuals as young as 25 years. By contrast, mutations affecting spliceosome genes SF3B1 and SRSF2, closely associated with the myelodysplastic syndromes, were identified only in those aged >70 years, with several individuals harboring more than one such mutation. This indicates that spliceosome gene mutations drive clonal expansion under selection pressures particular to the aging hemopoietic system and explains the high incidence of clonal disorders associated with these mutations in advanced old age.

Clonal haematopoiesis is not prevalent in survivors of childhood cancer

“…This phenomenon, termed clonal haematopoiesis of indeterminate potential (CHIP), is associated with an increased risk of haematological malignancies and all‐cause mortality (Link & Walter, 2016). Recent empirical evidence and computational models suggest that mutation acquisition may not be the major rate‐limiting factor in the emergence of CHIP (Altrock et al , 2015; McKerrell et al , 2015; Link & Walter, 2016; Young et al , 2016). Instead, clonal expansion of mutant haematopoietic stem cells (HSCs) probably reflects the interaction between the effects of driver mutations and selection pressures prevailing in the bone marrow microenvironment (Link & Walter, 2016).…”

“…We performed targeted next generation sequencing (NGS) using multiplex polymerase chain reaction to amplify 32 regions of 14 genes frequently mutated in CHIP or t‐MN (Table 1) (McKerrell et al , 2015; Link & Walter, 2016; Gibson et al , 2017). For this we extended a previously validated assay that detected clonal haemopoiesis in 2·6% of unselected adults (McKerrell et al , 2015), to include all coding exons of TP53 and PPM1D , genes implicated in t‐MN pathogenesis (Rowland & Bellizzi, 2014; Link & Walter, 2016; Gibson et al , 2017).…”

“…For this we extended a previously validated assay that detected clonal haemopoiesis in 2·6% of unselected adults (McKerrell et al , 2015), to include all coding exons of TP53 and PPM1D , genes implicated in t‐MN pathogenesis (Rowland & Bellizzi, 2014; Link & Walter, 2016; Gibson et al , 2017). Primer design and sequencing was performed as described previously (McKerrell et al , 2015); see Table SII for primer sequences. Reads were aligned to human genome build GRCh37 using the Burrows‐Wheeler Aligner (Li & Durbin, 2010) and analysed for somatic single nucleotide variants.…”

“…Allele counts were generated using an in‐house script (https://github.com/cancerit/alleleCount), considering only loci with ≥1000 reads and minimum base and mapping quality of 25 and 35, respectively. Somatic mutations with variant allele frequency (VAF) ≥0·008 (McKerrell et al , 2015) were sought and examined visually and by interrogation with the Shearwater algorithm (https://github.com/mg14/deepSNV) (Gerstung et al , 2014). …”

“…Studies of larger numbers of paediatric cancer survivors are needed to identify rare individuals with CHIP after chemoradiotherapy, whose particular characteristics may offer insights into factors facilitating clonal outgrowth of mutated HSCs. Furthermore, in view of the shifting patterns of mutations driving CHIP in different adult age groups (McKerrell et al , 2015), selective pressures particular to a less mature bone marrow environment may confer clonal advantage on a distinct spectrum of somatic variants in the very young. Although a much broader screening approach is required to identify such mutations, the potential role for CHIP as a biomarker for patient risk‐stratification (Gibson et al , 2017) may render this a worthwhile endeavour.…”