ArCH: improving the performance of clonal hematopoiesis variant calling and interpretation.

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.

Impact of Cancer Therapy on Clonal Hematopoiesis Mutations and Subsequent Clinical Outcomes.

Exposure to cancer therapies is associated with an increased risk of clonal hematopoiesis (CH). The objective of our study was to investigate the genesis and evolution of CH following cancer therapy. In this prospective study, we undertook error-corrected duplex DNA sequencing in blood samples collected prior to and at two timepoints following chemoradiation in patients with esophageal or lung cancer recruited from 2013-2018. We applied a customized workflow to identify the earliest changes in CH mutation count and clone size and determine their association with clinical outcomes. Our study included 29 patients (87 samples). Their median age was 67 years, 76% (n = 22) were male; the median follow-up period was 3.9 years. The most mutated genes were DNMT3A, TET2, TP53, and ASXL1. We observed a two-fold increase in the number of mutations from before to after treatment in TP53, which differed from all other genes examined (P < .001). Among mutations detected before and after treatment, we observed an increased clone size in 38% and a decreased clone size in 5% of TP53 mutations (odds ratio = 3.7; 95% CI = 1.75-7.84; P < .001). Changes in mutation count and clone size were not observed in other genes. Individuals with an increase in the number of TP53 mutations following chemoradiation experienced shorter overall survival (hazard ratio = 7.07; 95% CI = 1.50-33.46; P = .014). In summary, we found an increase in the number and size of TP53 CH clones following chemoradiation that were associated with clinical outcomes.

Plasma Proteomic Signature Predicts Myeloid Neoplasm Risk.

PURPOSE: 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 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: 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.

Genome and clonal hematopoiesis stability contrasts with immune, cfDNA, mitochondrial, and telomere length changes during short duration spaceflight.

Background: The Inspiration4 (I4) mission, the first all-civilian orbital flight mission, investigated the physiological effects of short-duration spaceflight through a multi-omic approach. Despite advances, there remains much to learn about human adaptation to spaceflight's unique challenges, including microgravity, immune system perturbations, and radiation exposure. Methods: To provide a detailed genetics analysis of the mission, we collected dried blood spots pre-, during, and post-flight for DNA extraction. Telomere length was measured by quantitative PCR, while whole genome and cfDNA sequencing provided insight into genomic stability and immune adaptations. A robust bioinformatic pipeline was used for data analysis, including variant calling to assess mutational burden. Result: Telomere elongation occurred during spaceflight and shortened after return to Earth. Cell-free DNA analysis revealed increased immune cell signatures post-flight. No significant clonal hematopoiesis of indeterminate potential (CHIP) or whole-genome instability was observed. The long-term gene expression changes across immune cells suggested cellular adaptations to the space environment persisting months post-flight. Conclusion: Our findings provide valuable insights into the physiological consequences of short-duration spaceflight, with telomere dynamics and immune cell gene expression adapting to spaceflight and persisting after return to Earth. CHIP sequencing data will serve as a reference point for studying the early development of CHIP in astronauts, an understudied phenomenon as previous studies have focused on career astronauts. This study will serve as a reference point for future commercial and non-commercial spaceflight, low Earth orbit (LEO) missions, and deep-space exploration.

What Clonal Hematopoiesis Can Teach Us About MDS.

Clonal hematopoiesis (CH), defined as the clonal expansion of mutated hematopoietic stem and progenitor cells (HSPCs), is a common aging process. CH is a risk factor for the development of hematologic malignancies, most commonly myeloid neoplasms (MNs) including acute myeloid leukemia (AML), myelodysplastic syndrome (MDS), and myeloproliferative neoplasm (MPN). Recent work has elucidated how the development and cellular fitness of CH is shaped by aging, environmental exposures, and the germline (inherited) genetic background of an individual. This in turn has provided valuable insights into the pathogenesis of MNs including MDS. Here, in this review, we discuss the genetic origins of CH, the environmental stressors that influence CH, and the implications of CH on health outcomes including MDS. Since MNs have shared risk factors and underlying biology, most of our discussion regarding the implications of CH surrounds MN in general rather than focusing specifically on MDS. We conclude with future directions and areas of investigation including how intervention studies of CH might inform future therapeutic approaches to MN including MDS.

Molecular Subclasses of Clear Cell Ovarian Carcinoma and Their Impact on Disease Behavior and Outcomes.

PURPOSE: To identify molecular subclasses of clear cell ovarian carcinoma (CCOC) and assess their impact on clinical presentation and outcomes. EXPERIMENTAL DESIGN: We profiled 421 primary CCOCs that passed quality control using a targeted deep sequencing panel of 163 putative CCOC driver genes and whole transcriptome sequencing of 211 of these tumors. Molecularly defined subgroups were identified and tested for association with clinical characteristics and overall survival. RESULTS: We detected a putative somatic driver mutation in at least one candidate gene in 95% (401/421) of CCOC tumors including ARID1A (in 49% of tumors), PIK3CA (49%), TERT (20%), and TP53 (16%). Clustering of cancer driver mutations and RNA expression converged upon two distinct subclasses of CCOC. The first was dominated by ARID1A-mutated tumors with enriched expression of canonical CCOC genes and markers of platinum resistance; the second was largely comprised of tumors with TP53 mutations and enriched for the expression of genes involved in extracellular matrix organization and mesenchymal differentiation. Compared with the ARID1A-mutated group, women with TP53-mutated tumors were more likely to have advanced-stage disease, no antecedent history of endometriosis, and poorer survival, driven by their advanced stage at presentation. In women with ARID1A-mutated tumors, there was a trend toward a lower rate of response to first-line platinum-based therapy. CONCLUSIONS: Our study suggests that CCOC consists of two distinct molecular subclasses with distinct clinical presentation and outcomes, with potential relevance to both traditional and experimental therapy responsiveness. See related commentary by Lheureux, p. 4838.