Recurrent pre-leukemic deletions in myeloid malignancies are the result of DNA double-strand breaks followed by microhomology-mediated end joining
Bibliographic record
Abstract
The mechanisms underlying myeloid malignancies deletions are not well understood, nor is it clear why specific genomic hotspots are predisposed to particular deletions. In the current study we inspected the genomic regions around recurrent deletions in myeloid malignancies, and identified microhomology-mediated end-joining (MMEJ) signatures in recurrent deletions in CALR, ASXL1 and SRSF2 loci . Since MMEJ deletions are the result of DNA double-strand breaks (DSBs), we introduced CRISPR Cas9 DSBs into exon 12 of ASXL1, successfully generating recurrent ASXL1 deletion in human hematopoietic stem and progenitor cells (HSPCs). A systematic search of COSMIC dataset for MMEJ deletions in all cancers revealed that recurrent deletions enrich myeloid malignancies. Despite this myeloid predominance, we provide evidence that MMEJ deletions occur in multipotent HSCs. An analysis of DNA repair pathway gene expression in single human adult bone marrow HSPCs could not identify a subpopulation of multipotent HSPCs with increased MMEJ expression, however exposed differences between myeloid and lymphoid biased progenitors. Our data indicate an association between MMEJ-repaired DSBs and recurrent MMEJ deletions in human HSCs and in myeloid leukemia. A better understanding of the source of these DSBs and the regulation of the HSC MMEJ repair pathway might aid with preventing recurrent deletions in human pre-leukemia.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".