Background: Clonal hematopoiesis (CH) is associated an increased risk of hematologic malignancy and numerous other adverse events. While there are no established therapeutic approaches to treat CH, clinical trials are underway, many of which use targeted therapeutic approaches for specific CH genes or genetic pathways. Since CH screening is generally not part of routine clinical testing, the identification of CH-positive individuals is a challenge to recruitment. While age is the most important risk factor for CH, CH is also known to be associated with other demographic and clinical risk factors. Blood count and blood parameters are also known to be influenced by CH with gene-specific patterns. Here we sought to understand whether commonly available clinical predictors and blood counts could be used to develop a gene-specific CH risk prediction tool with the goal of facilitating CH research screening strategies. Methods: We constructed a prediction model for CH across three cohorts of participants using LASSO regression. Clinical predictors included clinical/demographic characteristics (smoking history, gender, race) and blood count parameters. The UK Biobank served as the development cohort, consisting of 452,547 participants. We used two separate cohorts for validation, The All of Us (AoU) Research database, consisting of 143,850 participants, and MSK-IMPACT cohort, including 8,150 patients with non-hematologic cancers. We compared models with age alone to models including blood count and other clinical/demographic parameters. The predictive performance was determined based on 2 criteria: discrimination by calculating the area under the curve (AUC) receiver operating characteristic (ROC) and calibration by calculating the calibration slope (slope of 1 indicates perfect calibration) and the intercept. Results: A total of 604,547 participants were included in the study. We observed strong associations between clinical features and gene-specific CH including platelet count with DNMT3A and JAK2, neutrophil count and IDH1/2 mutations, and a strong association between spliceosome CH and age. Overall our model showed excellent discrimination (AUC>0.8) for risk JAK2, ASXL1, PPM1D, SF3B1, SRF2, U2AF1 and modest discrimination (AUC>0.7) for DNMT3A, IDH1/2, TP53 and TET2. Compared to a model with age alone, the addition of blood count and clinical parameters improved the model's performance most notably for JAK2 (AUC = 0.72 vs 0.82) and IDH1/2 (AUC = 0.75 vs 0.78). The calibration slopes for gene-specific models ranged from 0.35-1.65 and were highest for JAK2 (slope=0.9; intercept=0.02 ) and TP53 (slope=0.89; intercept=-0.02) . To better determine how our risk prediction model could be used to inform CH screening strategies, we determined the number of patients that would be required to screen using our CH risk prediction model and the number needed to sequence to identify 100 CH positive individuals across 10 CH genes. Application of our risk prediction model to identify individuals at high risk of CH for screening reduced the number of samples needed to sequence by 4-19 fold. Conclusion: We developed and validated a model for gene-specific CH prediction using blood count parameters and demographic factors with strong discriminative performance. These findings highlight the potential of commonly available clinical data to improve CH prediction, aiding in efficient identification of individuals with CH to facilitate clinical trial design.

AbstractPurpose: Clonal hematopoiesis (CH) is thought to be the origin of myeloid neoplasms (MN). Yet, our understanding of the mechanisms driving CH progression to MN and clinical risk prediction of MN remains limited. The human proteome reflects complex interactions between genetic and epigenetic regulation of biological systems. We hypothesized that the plasma proteome might predict MN risk and inform our understanding of the mechanisms promoting MN development. Experimental Design: We jointly characterized CH and plasma proteomic profiles of 46,237 individuals in the UK Biobank at baseline study entry. During 500,036 person-years of follow-up, 115 individuals developed MN. Cox proportional hazard regression was used to test for an association between plasma protein levels and MN risk. Results: We identified 115 proteins associated with MN risk, of which 30% (N = 34) were also associated with CH. These were enriched for known regulators of the innate and adaptive immune system. Plasma proteomics improved the prediction of MN risk (AUC = 0.85; P = 5×10–9) beyond clinical factors and CH (AUC = 0.80). In an independent group (N = 381,485), we used inherited polygenic risk scores (PRS) for plasma protein levels to validate the relevance of these proteins toMNdevelopment. PRS analyses suggest that most MN-associated proteins we identified are not directly causally linked toMN risk, but rather represent downstream markers of pathways regulating the progression of CH to MN. Conclusions: These data highlight the role of immune cell regulation in the progression of CH to MN and the promise of leveraging multi-omic characterization of CH to improveMN risk stratification. See related commentary by Bhalgat and Taylor, p. 3095

Association between plasma protein levels and individual clonal hematopoiesis (CH) genes with frequency greater than 20 (N=13 genes).

Association between plasma protein levels and biologically informed categories of clonal hematopoiesis (CH) genes.

Supplemental Table 5. Comparison of CH Models with Significant Associations Excluding Hematological and All Cancers

Supplemental Table 6. Comparison of CH Models with Significant Associations Excluding all Ever-Smokers

Significantly enriched Reactome gene sets after multiple hypothesis testing correction (pFDR <= 0.05).

MOTIVATION:The acquisition of somatic mutations in hematopoietic stem and progenitor stem cells with resultant clonal expansion, termed clonal hematopoiesis (CH), is associated with increased risk of hematologic malignancies and other adverse outcomes. CH is generally present at low allelic fractions, but clonal expansion and acquisition of additional mutations leads to hematologic cancers in a small proportion of individuals. With high depth and high sensitivity sequencing, CH can be detected in most adults and its clonal trajectory mapped over time. However, accurate CH variant calling is challenging due to the difficulty in distinguishing low frequency CH mutations from sequencing artifacts. The lack of well-validated bioinformatic pipelines for CH calling may contribute to lack of reproducibility in studies of CH. RESULTS:Here, we developed ArCH, an Artifact filtering Clonal Hematopoiesis variant calling pipeline for detecting single nucleotide variants and short insertions/deletions by combining the output of four variant calling tools and filtering based on variant characteristics and sequencing error rate estimation. ArCH is an end-to-end cloud-based pipeline optimized to accept a variety of inputs with customizable parameters adaptable to multiple sequencing technologies, research questions, and datasets. Using deep targeted sequencing data generated from six acute myeloid leukemia patient tumor: normal dilutions, 31 blood samples with orthogonal validation, and 26 blood samples with technical replicates, we show that ArCH improves the sensitivity and positive predictive value of CH variant detection at low allele frequencies compared to standard application of commonly used variant calling approaches. AVAILABILITY AND IMPLEMENTATION:The code for this workflow is available at: https://github.com/kbolton-lab/ArCH.

Abstract Background: Risk factors including smoking, alcohol intake, physical activity (PA), and sleep patterns have been associated with cancer risk. Clonal hematopoiesis (CH), including mosaic chromosomal alterations and clonal hematopoiesis of indeterminate potential, is linked to increased hematopoietic cancer risk and could be used as common preclinical intermediates for the better understanding of associations of risk factors with rare hematologic malignancies. Methods: We analyzed cross-sectional data from 478,513 UK Biobank participants without hematologic malignancies using multivariable-adjusted analyses to assess the associations between lifestyle factors and CH types. Results: Smoking was reinforced as a potent modifiable risk factor for multiple CH types, with dose-dependent relationships persisting after cessation. Males in socially deprived areas of England had a lower risk of mosaic loss of chromosome Y (mLOY), females with moderate/high alcohol consumption (2–3 drinks/day) had increased mosaic loss of the X chromosome risk [OR = 1.17; 95% confidence interval (CI), 1.09–1.25; P = 8.31 × 10−6] compared with light drinkers, active males (moderate-high PA) had elevated risks of mLOY (PA category 3: OR = 1.06; 95% CI, 1.03–1.08; P = 7.57 × 10−6), and men with high body mass index (≥40) had reduced risk of mLOY (OR = 0.57; 95% CI, 0.51–0.65; P = 3.30 × 10−20). Sensitivity analyses with body mass index adjustment attenuated the effect in the mLOY–PA associations (IPAQ2: OR = 1.03; 95% CI, 1.00–1.06; P = 2.13 × 10−2 and IPAQ3: OR = 1.03; 95% CI, 1.01–1.06; P = 7.77 × 10−3). Conclusions: Our study reveals associations between social deprivation, smoking, and alcohol consumption and CH risk, suggesting that these exposures could contribute to common types of CH and potentially rare hematologic cancers. Impact: This study underscores the impact of lifestyle factors on CH frequency, emphasizing social, behavioral, and clinical influences and the importance of sociobehavioral contexts when investigating CH risk factors.

Association between plasma proteins and common CH genes.

Association between protein levels (N=1,463), myeloid neoplasm (MN) risk and clonal hematopoesisis (CH).

Supplemental Table 4. Comparison of CH Models with Significant Associations with and without BMI Adjustment

Supplemental Table 1. UK Biobank Field IDs for Single Metric and Summary Measure Logistic Regression Models

Context Clonal hematopoiesis (CH) is thought to be the origin of myeloid neoplasms (MN), yet our understanding of the mechanisms driving CH progression to MN and clinical risk prediction of MN remains limited. The human proteome reflects complex interactions between genetic and epigenetic regulation of biological systems. We hypothesized that the plasma proteome might predict MN risk and inform our understanding of the mechanisms promoting MN development. Design We jointly characterized CH and plasma proteomic profiles of 46,237 individuals in the UK Biobank at baseline study entry. During 500,036 person-years of follow-up, 115 individuals developed MN. Cox proportional hazard regression was used to test for an association between plasma protein levels and MN risk. The relative contribution of clinical, CH, and proteomics features to prediction of incident MN was assessed using lasso regression with 100-fold cross validation. We performed protein quantitative trait loci mapping to identify independent germline autosomal genetic loci that significantly regulate plasma proteomic levels (P < 5×10-8) and used them to calculate genetically predicted risk scores for plasma protein levels by linear regression analysis. Results We identified 115 proteins associated with MN risk, of which 30% (N = 34) were also associated with CH. These were enriched for known regulators of the innate and adaptive immune system. Plasma proteomics improved the prediction of MN risk (AUC = 0.85; P = 5×10–9) beyond clinical factors and CH (AUC = 0.80). In an independent group (N = 381,485), we used inherited polygenic risk scores (PRS) for plasma protein levels to validate the relevance of these proteins to MN development. PRS analyses suggest that most MN-associated proteins we identified are not directly causally linked to MN risk, but rather represent downstream markers of pathways regulating the progression of CH to MN. Conclusions We show that plasma proteomic markers predict risk of MN and can improve MN risk prediction beyond clinical and CH features. These data highlight the role of immune cell regulation in the progression of CH to MN and the promise of leveraging multi-omic characterization of CH to improve MN risk stratification.

Abstract Clonal hematopoiesis (CH) is the age-related clonal expansion of hematopoietic cells as a result of acquired mutations in driver genes, commonly referred to as clonal hematopoiesis of indeterminate potential (CHIP), or due to large-scale mosaic chromosomal alterations (mCAs). While the type and genomic location of CH exhibits varying associations with hematological parameters as well as lymphoid and myeloid malignancy risk, few studies have examined the co-occurrence of CH types. We characterized the frequency of co-occurring CH and examined the association of co-occurring CH with various cancer-related phenotypes to identify potential high-risk clones associated with hematologic cancer risk. We analyzed sequencing and genotyping array data from 453,807 participants in the UK Biobank to detect CHIP and mCAs. In total, 83,240 (18.3%) individuals had at least one detectable type of CH, with increasing age strongly associated with increasing frequency of CH (P < 2 × 10−16). The most common type of CH was mCAs (N= 67,081 (14.7%)), with mosaic loss of the Y (mLOY) and X (mLOX) chromosomes being most frequently observed in males and females, respectively. We noted inverse associations between mLOY or mLOX with autosomal mCAs, but positive associations between some autosomal mCAs (e.g., chr3 and chr18 mCAs). CHIP was detected in 20,354 (4.5%) individuals, with mutations in DNMT3A, TET2, and ASXL1 most common. Similar to mCAs, some forms of CHIP were inversely associated (e.g., DNMT3A CHIP with most other CHIP mutations) while others displayed positive associations (e.g., JAK2 CHIP with NFE2 CHIP), suggesting both instances of mutual exclusivity as well as cooperation. We likewise noted several instances of CHIP and mCAs co-occurring (e.g., MYD88 and chr18 mCAs) and overlapping (e.g., JAK2 and 9p24 mCAs) more frequently than expected. Compared to those with no CH, participants with CH had notable alterations in blood cell counts, leukocyte telomere length (LTL), and elevated hematologic cancer risk even after adjusting for age, sex, smoking, and genetic ancestry. Furthermore, co-occurring CH had more pronounced associations, with the greatest alterations and hematologic cancer risk generally noted in participants with overlapping CHIP and mCAs (e.g., MYD88 and 3p22 mCAs). These findings highlight substantial enrichment in CH co-occurrences, particularly in the positional overlap of CHIP and mCAs, and indicate an increased risk of hematological malignancies when CH types overlap (HR = 29.5, 95% CI [24.0, 36.3], P < 2 × 10−16). Overall, this study details the landscape of co-occurring CH and nominates high risk co-occurrences with strong implications for future hematological malignancy risk. Citation Format: Kara M. Barnao, Aubrey K. Hubbard, Weiyin Zhou, Irenaeus Chan, Duc Tran, Yin Cao, Stephen J. Chanock, Kelly L. Bolton, Mitchell J. Machiela. Characterization of co-occurring clonal hematopoiesis to identify high risk clones associated with hematologic cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3433.

Supplemental Table 3. Relationship Between Alcohol Autosomal Mosaic Chromosomal Alterations (mCAs) and CHIP in Females

Two-sample Mendelian randomization (MR) analyses of the causal effect of plasma protein levels on myeloid neoplasm (MN) risk in the UKBB (N=381,485).

Abstract Observational studies have implicated modifiable factors like smoking, alcohol intake, physical activity (PA), and sleep to cancer risk. Clonal hematopoiesis (CH), marked by clonal expansions of mutated hematopoietic progenitors with mosaic chromosomal alterations (mCAs) or clonal hematopoiesis of indeterminate potential (CHIP), has been associated with the risk of serval cancers and may shed light the interplay between these factors and early preneoplastic hematopoietic expansion.Leveraging genotyping array and whole-exome sequencing data of leukocyte-derived DNA from 485,028 participants without hematologic malignancies in the UK Biobank (UKBB), we performed genome-wide characterization of two common forms of CH—mCAs and CHIP. To assess associations between social deprivation, self-reported modifiable risk factors, and CH risk, we employed multivariable logistic regression models adjusted for potential confounders including age, sex, smoking history, and genetic ancestry.We identified 11,826 (2.4%) individuals with autosomal mosaic chromosomal alterations (auto mCAs), 15,499 (3.2%) with loss of the X chromosome (mLOX), 43,044 (8.8%) with loss of the Y chromosome (mLOY) and 22,508 (4.6%) with CHIP. Individuals with any CH subtype were on average older than CH-free individuals (p < 9.11 × 10−52). Multivariable models identified a significant negative association between mLOY and social deprivation in England (1 SD change in score: OR = 0.968 [0.956-0.980], p = 4.88 × 10−7), suggesting modifiable risk factors could influence risk of mLOY. We observed a positive association with ever smoking and CH, which decreased with increased years since smoking cessation. Current smokers were at higher risk for auto mCAs (Odds ratio (OR) = 1.23, 95% Confidence Interval (CI):[1.16-1.31], P-value (p) = 2.63 × 10−11), mLOY (OR = 2.25, 95%CI: [2.17-2.33], p < 9.11 × 10−52), and CHIP (OR = 1.40,, 95%CI: [1.34-1.47] p < 9.11 × 10−52); and former smokers also exhibited significant associations for mLOY (OR = 1.16, 95%CI: [1.13-1.19], p = 2.97 × 10−32) and CHIP (OR = 1.11, 95%CI: [1.08-1.15], p = 2.75 × 10−13). Alcohol consumption was associated with a significant increase in the risk of mLOX in heavy drinkers (>2-3 drinks/day) compared to non-drinkers (OR = 1.16, 95%CI: [1.09-1.24], p = 1.21 × 10−5). Moderate and high PA levels (categorized as level 2 and 3 based on total activity), were positively associated with increased mLOY frequency (moderate: OR = 1.05, 95%CI: [1.02-1.08], p = 8.6 × 10−2, high: OR = 1.06, 95%CI: [1.03-1.08], p = 1.6 × 10−3). We observed no evidence for an association between sleep patterns and CH.Our investigation in a large cohort identified associations between CH and social deprivation, smoking, alcohol consumption and PA. As CH is an intermediate marker for hematologic cancer risk, our findings imply that modifiable exposures may contribute to hematologic cancer risk through clonal mechanisms. Citation Format: Corey D. Young, Aubrey K. Hubbard, Pedro Saint-Maurice, Irenaeus Chan, Kelly L. Bolton, Stephen J. Chanock, Charles Matthews, Steven C. Moore, Erikka Loftfield, Yin Cao, Mitchell J. Machiela, Duc Tran. Modifiable risk factors are associated with clonal hematopoiesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2205.

Supplemental Table 2. Model Output for Multivariate Analysis of Sleep, Physical Activity, Alcohol, and Social Indices

Asociation between polygenic risk score and risk of myeloid neoplasm (MN) with and without adjusting for CH.