Prevalence and significance of cytoses in clonal hematpoiesis
Bibliographic record
Abstract
Abstract Introduction Clonal hematopoiesis (CH) is the age-related clonal expansion of hematopoietic progenitor cells, often caused by mutations in genetic drivers of myeloid neoplasia (MN). Clonal hematopoiesis of indeterminate potential (CHIP) and clonal cytopenia of uncertain significance (CCUS) are formally defined CH subtypes, acknowledged by the World Health Organization (WHO) and the International Consensus Classification (ICC) as MN precursors. CHIP is defined by the presence of mutations in MN driver genes at a variant allele fraction ≥2% in individuals without cytopenias. CCUS is used when a person possessing the molecular characteristics of CHIP also has persistent and unexplained cytopenias without clinical or morphological features of MN. At diagnosis, MNs may exist with various combinations of cytopenias and cytoses. However, cytoses are not considered in commonly used CH nosology and remain largely underexplored in the context of CH. We utilized the UK Biobank to determine the prevalence of cytoses among individuals with CHIP and CCUS and evaluated how cytoses influence incident MN risk. An independent clinical cohort from the Dana-Farber Hematologic Malignancies Data Repository (HMDR) was used for validation. Methods In a cohort of 470,960 UK Biobank participants without prior history of hematologic malignancy, 29,385 were classified as having CHIP or CCUS based upon variant calls from whole exome sequencing and blood counts. Cytoses were defined based on the presence of neutrophilia (neutrophils ≥ 6K/uL), monocytosis (monocytes ≥ 1K/uL), eosinophilia (eosinophils ≥ 0.5K/uL), basophilia (basophils ≥ 0.3K/uL), thrombocytosis (platelets ≥ 450K/uL), and erythrocytosis (hemoglobin ≥ 17g/dL). Prevalences were summarized descriptively and age- and sex- adjusted binomial logistic regression was used for statistical comparisons. Using a competing risk approach, with death as the competing event, we compared the cumulative incidences of MN between groups with and without cytoses subclassified by CH status or clonal hematopoiesis risk score (CHRS)-defined risk groups. Hazard ratios for MN were determined by age- and sex- adjusted cox proportional hazard models. Results Cytoses were more common in CHIP/CCUS (n = 4,549, 15.5%) compared to individuals without CHIP or CCUS (n = 58,619, 13.3%), OR 1.2 (95% confidence interval 1.15-1.23), p < 0.0001. Most CHIP/CCUS cytoses cases only involved 1 lineage (n = 4041, 88.8%). The prevalence of individuals with ≥ 1 cytosis was similar in CHIP (n = 4160, 15.1%) and CCUS (389, 16.6%), p = 0.084. Neutrophilia was the most common cytosis, detected in 11.5% of all individuals with CHIP/CCUS (n = 3391) and representing 74.5% of CHIP/CCUS cytoses cases. Cytoses were associated with a higher likelihood of mutations in JAK2 (OR 11.5, 95%CI 8.69-15.3, p < 0.0001), SRSF2 (OR 1.79, 95% CI 1.4-2.3, p < 0.0001), SF3B1 (OR 1.59, 95% CI 1.16-2.16, p = 0.0035), and ASXL1 (1.32, 95% CI 1.20-1.46, p <0.0001). The 10-year cumulative incidence of MN was higher for CHIP/CCUS with cytosis compared to without (4.6% vs 1.2%, hazard ratio = 5.86, 95% CI 5.32-6.45, p < 0.0001). This was driven by incident MPNs (51.4% of incident MNs), though 30.5% and 18.1% of incident MNs were AML and MDS. As 47% of CHRS-defined high-risk CHIP/CCUS had ≥1 cytosis, we investigated the impact of cytoses on CHRS risk estimates. For all CHRS risk strata, the cumulative incidence of MN in CHIP/CCUS with cytoses was significantly higher compared to without (10-year risk of MN: 66.3% vs 44.3% in high-risk CHIP/CCUS, 9.36% vs 3.78% in intermediate-risk CHIP/CCUS, and 4.98% vs 1.17% in low-risk CHIP/CCUS (p<0.001). Findings in the HMDR validated these trends, with unexplained cytoses being present in 27.1% of clinical CHIP/CCUS cases and similarly associated with an increased risk of MNs, particularly MPN and MDS/MPN overlap syndromes. Conclusion Cytoses are common among individuals labeled as CHIP or CCUS using current classification strategies. CHIP/CCUS with cytoses has a distinct molecular distribution driven by JAK2, splicing factors, and ASXL1 and is associated with a greater risk of incident MN, driven by MPNs, than estimated using the CHRS. These observations suggest clonal cytosis is a clinical entity distinct from CHIP and CCUS. Precise classification of myeloid precursor states is essential to refine risk stratification tools and improve selection of at-risk populations for early interception clinical trials to prevent MN.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".