Abstract 4905: Mosaic chromosomal alterations overlapping hotspot and coldspot sites of somatic structural variation are associated with increased odds of hematological malignancy
Bibliographic record
Abstract
Abstract BACKGROUND: Clonal hematopoiesis (CH) occurs when hematopoietic cells acquire somatic mutations and proliferate to generate clones in blood. CH can be driven by mosaic chromosomal alterations (mCAs), which are large somatic structural variants, and is associated with increased risk of blood malignancy. We conducted a genome-wide search for sites significantly more or less impacted by mCA events, termed hotspots and coldspots. We tested whether the odds of blood malignancy in participants harboring mCAs overlapping hotspots or coldspots differed from the overall association between mCAs and blood malignancy. METHODS: This study utilized two cohorts of the Canadian Partnership for Tomorrow’s Health, including the Ontario Health Study (OHS; n=7070) and CARTaGENE (n=28,639). Participants were genotyped with the UK Biobank Axiom or Infinium Global Screening Array and completed a baseline cancer questionnaire. All mCA calling was performed using MoChA software. We identified hotspot and coldspot sites of autosomal mCA accumulation using binomial tests scaled by chromosome and array type. We determined whether the number of overlapping mCAs at each query site was greater or less than would be expected under the null expectation. Then, we calculated the prevalence of cancer in participants with or without mCAs overlapping hotspots/coldspots and conducted Fisher’s exact tests to generate odds ratios. RESULTS: In OHS, participants with an mCA had a significantly greater odds of having a hematological malignancy at baseline (OR=7.29, 95% CI=3.72-13.38, p=7.84e-08). They were also more likely to have a cancer diagnosis of any type (OR=1.63, 95% CI=1.14-2.30, p=5.59e-03). Participants with an mCA overlapping a hotspot site had a further increased odds of harboring hematological malignancy (OR=9.79, 95% CI=3.94-21.29, p=5.05-e06). For participants with an mCA overlapping a coldspot site, the OR reached 20.52 (95% CI=3.68-76.85, p=7.36e-04). In CARTaGENE, participants with an mCA were also at increased odds of hematological malignancy (OR=11.31, 95% CI=6.78-18.17, p=9.77e-16), and any cancer diagnosis (OR=1.93, 95% CI=1.53-2.41, p=5.95e-08). Those carrying an mCA overlapping a hotspot site were 14.6 times more likely to have a hematological malignancy (95% CI=7.94-25.29, p=3.30e-13), and those with an mCA overlapping a coldspot site were 14.2 times more likely (95% CI=5.0-32.9, p=7.63e-06). CONCLUSIONS: These results suggest that there may be genomic locations at which somatic structural variation has a larger impact on the development of blood malignancy, relative to other regions. Further work is needed to characterize the functional consequences of somatic mutation at these key regions and explore whether these associations of hotspots, coldspots, and cancer incidence are conserved across a range of tissues. Citation Format: Jasmine Ryu Won Kang, Vanessa Bruat, Kimberly Skead, Mawusse Agbessi, June Kim, Elias Gbeha, Marie-Julie Fave, Philip Awadalla. Mosaic chromosomal alterations overlapping hotspot and coldspot sites of somatic structural variation are associated with increased odds of hematological malignancy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4905.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".