Background
Chronological age is the dominant risk factor for cancers and cardiovascular disease – the leading causes of death worldwide [
1]. Aging is also associated with a higher prevalence of acquired somatic mutations, especially in frequently regenerating cells, such as hematopoietic stem cells (HSC). Clonal hematopoiesis of indeterminate potential (CHIP) is the age-related expansion (defined as variant allele fraction, VAF ≥2%) of cancer-associated somatic mutations (typically in
DNMT3A,
TET2,
ASXL1,
JAK2) in hematopoietic stem cells in the absence of unexplained cytopenia, dysplasia, or neoplasia [
2]. Recent whole exome sequence (WES) and whole genome sequence (WGS) analyses of blood-derived DNA have shown that CHIP is increasingly common with advancing age (i.e., approximately 10% of asymptomatic adults older than 70 years of age) [
3‐
6]. While CHIP is a risk factor for hematologic malignancy and all-cause mortality [
3,
7,
8], a number of analyses have shown an association with atherosclerotic cardiovascular disease [
4,
9,
10]. CHIP is also associated with heightened risk of therapy-related myeloid malignancies [
11‐
14]. These studies underline the importance of CHIP as a novel biomarker for early detection and monitoring of multiple age-related diseases [
15,
16]. However, further longitudinal studies are needed for a better understanding of the root causes of CHIP, surveillance strategies, and how CHIP dynamics influence the development of chronic diseases.
Sensitivity for the detection of driver mutations is highly dependent on sequencing depth. Both WGS and WES are suitable for the detection of larger clones (e.g., VAF > 5% in WGS [
6,
7], and VAF > 3% in WES [
3,
4]). By comparison, deeper coverage, error-corrected targeted sequencing techniques are capable of detecting very small clones [
8,
15], which are nearly ubiquitous in healthy adults [
17]. Additional studies of apparently healthy adults characterizing longitudinal changes in clone size over time may reveal genetic and environmental factors promoting clonal stability versus progression and yield new insights into mechanisms underlying somatic mutagenesis and aging as well as resultant disease pathogenesis and disease prediction.
Here we present a single-molecule molecular inversion probe sequencing (smMIPS) assay [
18], that leverages a cost-effective, ultrasensitive, high-throughput targeted sequencing technique, for the detection of CHIP. We apply this assay to a set of longitudinal peripheral blood DNA samples obtained over a median range of 16 years from 182 post-menopausal women from the Women’s Health Initiative to compare to whole genome sequence analysis and evaluate clonal dynamics.
Discussion
Here we describe a rapid and cost-effective smMIPS-based assay that enables detection of CHIP in large scale longitudinal populations. Applying this assay to multi-time point samples from WHI participants demonstrates robust real-world performance in a large collection of longitudinal samples and reveals novel insights on clonal dynamics in a population without hematologic malignancy.
Most studies of CHIP to date rely upon either WGS or WES, or commercial capture kits which have high sequencing or library preparation costs, respectively, ranging from $150–$1000 per sample. The smMIPS approach offers a sensitive alternative at much lower per-sample cost ($30 per sample). Previous work using smMIPS for CHIP detection has been focused on individual hotspots [
24] with full gene tiling of only
DNMT3A [
22]. Our results demonstrate that smMIPS can scale to fully tile gene sets which cumulatively account for nearly 90% of CHIP as determined by WGS.
Our application of the smMIPS assay to WHI reveals several important insights. We observe a significant burden of driver mutations below the conventional CHIP definition (VAF ≥ 2%) [
4], enabled by deep sequencing coverage this assay provides. Indeed, for 97 individuals sequenced by both smMIPS and WGS, smMIPS detected 75 of the 81 driver mutations found by WGS, and an even greater number (
n = 103) of driver mutations missed by WGS. Although these smMIPS-only clones tended to be less abundant, as expected, a subset nevertheless exceeded the working VAF ≥ 2% definition of CHIP. While these lower-VAF driver mutations may be less likely to have a clinical impact, in another WHI study, somatic mutations with any detectable VAF > 1% were associated with increased risk of acute myeloid leukemia [
8]. Likewise, driver mutations in
DNMT3A and
TET2 at frequencies as low as 1% have been associated with poor prognosis in chronic ischemic heart failure [
25]. Thus, the clinical implications of these small clones (VAF range 0.1–2%) remain to be determined in future work, enabled through cost-efficient sequencing via assays like the one described here.
Our results add to recent observations regarding the longitudinal dynamics of clonal hematopoiesis suggesting driver gene-specific differences in clonal fitness. We find that CHIP clones detected among individuals without cancer do not inexorably grow: just over half of those observed did expand, with the remaining, mostly low-frequency clones divided roughly evenly between static and shrinking trajectories. Once mutations reached appreciable frequency, they tended to continue growing. Our results showing that
DNMT3A mutant clones are less likely to be in growing trajectories are consistent with those of Fabre et al. [
21] who found that clonal growth rate varies according to both age and driver gene mutation, with
DNMT3A having a comparatively slow clonal growth rate in older aged adults. Similarly, using longitudinal targeted error-corrected sequence analysis in the Lothian Birth Cohorts, Robertson et al. [
23] showed that clonal growth and fitness can differ substantially by gene, with splicing genes (such as
SF3B1) having higher growth rates and clonal fitness compared to mutations in common genes such as
DNMT3A, TET2 or
ASXL1.
In a longitudinal study of ultra-sensitive smMIP-based targeted gene sequencing of obese individuals, Van Deuren et al. [
22] reported that metabolic factors such as insulin resistance and high density lipoprotein cholesterol may accelerate expansion of CHIP clones. While we did not detect any association of other baseline participant characteristics such as race/ethnicity, BMI, or smoking on clonal growth, larger sample sizes with serial sampling will be required to identify the genetic and environmental factors contributing to the differing outcomes of clonal competition and growth. This area of investigation has important clinical implications because mutations driving faster clonal growth, as reflected by a more rapid rise in VAF, carry a higher risk of malignant progression [
21] and shorter time to development of AML [
8].
Our study has several limitations. First, our assay robustly targets genes which account for ~ 90% of CHIP present in the population, so we may be misclassifying ~ 10% of CHIP-positive individuals due to omission of minor CHIP genes from the sequencing panel. This tradeoff was required to make the platform highly cost effective. However, a key benefit of the assay is that it is simple to extend to cover new targets, or to optimize coverage at existing ones, by spiking in new probes. In the present study, we leveraged this capability to add additional probes targeting a highly G + C-rich mutational hotspot in SRSF2, which increased its mean coverage from < 1 to 508. A second limitation of our study is that the availability of multi-time point samples was not uniform due to differences in the WHI study protocol. Third, there are other kinds of clonal hematopoiesis, such as mosaic chromosomal abnormalities (e.g. structural variants) that are not detected with our CHIP assay. These limitations are balanced by the significant strengths of the novel CHIP detection assay applied to one of the largest sample sizes studied to date.
Methods
Samples
The Women’s Health Initiative (WHI) is a multicenter prospective study of risk factors for CVD, cancer, osteoporotic fractures, and other causes of morbidity and mortality among postmenopausal women [
26]. Between 1993 and 1998, women aged 50–79 years from forty WHI clinical centers throughout the United States (US) were enrolled. All WHI participants completed a baseline screening visit at the time of enrollment which included blood sample collection. WHI participants have been followed prospectively for over 25 years. A subset of participants had blood collected at annual visits (AV) occurring at one, three, six, and 9 years after enrollment (AV1, AV3, AV6, AV9). An additional visit occurred between 2012 and 2013 (mean 15.4 years; range from 14 to 19 years after enrollment) as part of the WHI Long Life Study (LLS), which recruited a subset of 7875 surviving women ranging in age from 63 to 99 years at the time of LLS recruitment [
27]. At each visit (baseline, AV1, AV3, AV6, AV9, LLS) genomic DNA was extracted from peripheral blood leukocytes using the 5 Prime DNA extraction kit.
A total of 182 WHI participants (without known prevalent hematological malignancy) were included in the current smMIPS-based sequencing study. These 182 individuals were selected either on the basis of either (a) having previously undergone WGS-based or targeted sequencing -based CHIP determination through the NHLBI TOPMed project (sample set A;
N = 100) or having DNA samples at 3 or more time points (sample set B;
N = 86). Sample set A was used to compare the detection of driver mutations between WGS and our new smMIPS capture panel. Therefore, we intentionally over-sampled WHI TOPMed participants who were previously determined to have CHIP (driver mutations at VAF ≥ 2% based on WGS or targeted sequencing) in order to directly compare intra-subject CHIP detection and VAF as determined by different assays using the same blood sample at the same time point. Sample set B was primarily to maximize our ability to assess longitudinal CHIP trajectories over time, and therefore includes mainly individuals who had DNA samples available at 4, 5 or 6 different time points. A detailed breakdown of number of samples at each time point (baseline, AV1, AV3, AV6, AV9, LLS) is provided in Table S
1. Median age of participants was 62 years at baseline (range: 50–78 years) and 81 years (range: 66–95 years) at the LLS visit, respectively.
Single molecule molecular inversion probe sequencing (smMIPS) assay
A smMIPS capture panel was designed to tile coding exons (+/− 5 bp) of the 11 most common CHIP genes [
9] and recurrent mutational hotspots in four others (Table
1). Probe sequences were selected as previously described [
28], with adjustments to eliminate the need for custom sequencing primers. Briefly, probe libraries were synthesized as a 12 k oligo pool by CustomArray (Bothell, WA) Inc., and subjected to bulk PCR amplification using flanking primers jklab0255_2019mipsPrep1f (GAGATCGGCGCGTTAGAAGAC) and jklab0256_2019mipsPrep1r (TGCAGGATCTAGGGCGAAGAC). PCR product was cleaned with 2.5X SPRI beads and eluted in 1X NEB cut smart buffer. To generate capture-ready probe pools, flanking adaptors were removed by BbsI-HF (#R3539L, NEB; Ipswitch, MA) digestion, overnight at 37 °C. Digested probes were cleaned by incubating with 1x volume SPRI beads (supplemented with 5 volumes isopropanol for 20 minutes), followed by washes in 70% ethanol and elution in Tris-EDTA pH 8. Poorly captured regions were tiled with additional probes (
N = 112), synthesized as an oPool library by Integrated DNA Technologies (Coralville, IA) lacking flanking amplification adaptors and with 5′ phosphates. Original and make-up probes were combined into a single pool before use.
Capture reactions were assembled in a 96-well format, in 20 ul volume containing: probes (150:1 M excess to genomic DNA targets), 1X Ampligase buffer, 1 U Ampligase (Lucigen; Madison, WI), dNTPs at 0.4 uM each and 0.32 ul Hemo KlenTaq polymerase (NEB). Plates were incubated in a thermocycler at 95 °C for 10 minutes, 95 °C → 60 °C at − 0.1 °C/sec, followed by a hold at 60 °C for 18–24 hours. Exonuclease treatment was continued immediately after capture by adding 2 ul of mix containing 1X Ampligase buffer, 5 U Exonuclease I (NEB), and 25 U Exonuclease III (NEB) to each sample. Reactions were incubated at 37 °C for 45 minutes and 95 °C for 2 minutes. Dual indexed sequencing libraries were constructed by PCR amplification using indexing primers directed against common sequences on the probe backbone. Libraries were pooled at equal volumes, purified by 0.9X SPRI beads, and sequenced in batches of 196 on Hiseq 4000 or Novaseq instruments with paired-end 150-bp reads. Reagent, consumable, and sequencing costs total approximately $30 USD/sample.
Sequencing reads were aligned to the human reference genome (build 37) with bwa mem [
29], and a custom sequencing pipeline (
https://github.com/kitzmanlab/mimips) was used for post-alignment processing to remove probe arm sequences from each alignment and filter reads with duplicate unique molecular identifiers (UMIs).
smMIPS assay validation and reliability
To validate the clone size detection limit of the smMIPS method, we prepared mixtures of gDNAs from five lymphoblastoid cell lines (GM06994, GM12878, GM20847, GM12877 and GM18507) with known genotypes, combined at 78.8, 16, 4, 1, 0.25%. Within the target region, these cell lines have 152 known variants as defined by the 1000 Genome Project (1000G) WGS genotypes and by detecting germline variants by sequencing cell lines individually. In the resulting mixture, their expected VAFs range from 0.125 to 100%. These variants constituted the true positive variant set. We also defined as ‘true negative’ sites 13 common polymorphism SNVs absent from all of the five cell lines, and those sites (+/− 50 bp) were defined as true negative variants. The positive control mixture was included with each sequencing batch for a total of 27 replicates. The between-variant reliability of VAF estimated as an intraclass correlation coefficient was 0.998 (95% confidence interval 0.998 to 0.999) (see
Supplementary Note).
Variant calling
Somatic SNPs and indels were called using LoFreq 2.1.3.1 [
30], requiring minimum coverage 40, with ≥5 reads supporting the alternate allele and a variant allele frequency (VAF) ≥ 0.1%. Variants present in ≥5% of samples at a VAF of 1–10% were discarded as likely recurrent artifacts.
CHIP calling
Variants were annotated using ANNOVAR software [
31]. Variant calls processed using an existing filtering pipeline based upon gene name, variant functional class, and populational allele frequency [
6]; workflow is available at available at
https://app.terra.bio/#workspaces/terra-outreach/CHIP-Detection-Mutect2/notebooks. For
ZBTB33 and
ZNF318, two genes not listed in [
6], we included variants annotated as frameshift/splice-site/nonsense or nonsynonymous [
32]. The full list of specific mutations queried is presented in Table S
5. We manually reviewed alignments for selected CHIP variant calls using Integrative Genomics Viewer (IGV) [
33].
CHIP clone trajectories
To characterize the longitudinal trajectory of each CHIP clone over time, we restricted our analysis to individuals who (a) underwent smMIPS sequencing at least 3 time points and (b) had at least one driver mutation detectable at VAF > 1% at any of the timepoints. We excluded any variants with alternate read count < 2 or total read depth < 200. For each driver mutation meeting these criteria, we modeled the trajectory by fitting a linear regression: log10(VAF) = C + β * age; VAFs of zero were set to a minimum of 10− 4 (reflecting a conservative limit of detection for smMIPS), and each observation was weighted by the square root of the read depth. To further characterize clonal dynamics, we classified each trajectory based on linear trajectory, as (a) growing (β > 0, P < 0.5), (b) shrinking (β ≤ 0, P < 0.5), or (c) static (P ≥ 0.5). For trajectory analysis, we excluded CHIP clones with starting VAF > 10%, for which an exponential growth assumption may not fit.
Association between participant characteristics and CHIP VAF and growth rate
For cross-sectional analyses at a single time point, we fit linear and logistic regression models to assess the relationship of either CHIP prevalence (total clones or large clones only) or log-transformed VAF to age at blood draw, race/ethnicity, smoking status, or BMI. We used the first visit time point for each subject. To assess the relationship of these same participant characteristics, to clone growth over time, we utilized longitudinal data from all individuals with sequencing data from at least two time points with positive VAF observations (
N = 148 individuals). For each participant, we first selected a single driver clone, prioritizing as the predominant clone those with the highest VAF at any follow-up timepoint. We used a linear regression to determine the effect of age, race/ethnicity, smoking, or BMI on the difference (after log10 transformation) between the first non-zero VAF value and the last non-zero VAF. All statistical analyses were adjusted for the first visit time of year and performed in R version 4.2 (R Core Team, URL
https://www.R-project.org/).
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