Single-cell diploid Hi-C reveals the role of spatial aggregations in complex rearrangements and KMT2A fusions in leukemia
Bibliographic record
Abstract
BACKGROUND: Simple translocations and complex rearrangements are formed through illegitimate ligations of double-strand breaks of fusion partners and lead to generation of oncogenic fusion genes that affect cellular function. The contact first hypothesis states that fusion partners tend to colocalize prior to fusion in normal cells. Here we test this hypothesis at the single-cell level and explore the underlying mechanism. RESULTS: By analyzing published single-cell diploid Hi-C datasets, we find partner genes fused in leukemia exhibit smaller spatial distances than those fused in solid tumor and control gene pairs. Intriguingly, multiple partners tend to colocalize with KMT2A in the same cell. 3D genome architecture has little association with lineage decision of KMT2A fusion types in leukemia. Besides simple translocations, complex rearrangement-related KMT2A fusion genes (CRGs) also show closer proximity and belong to a genome-wide mutual proximity network. We find CRGs are co-expressed, co-localized, and enriched in the targets of the transcriptional factor RUNX1, suggesting they may be involved in RUNX1-mediated transcription factories. Knockdown of RUNX1 leads to significantly fewer contacts among CRGs. We also find CRGs are enriched in active transcriptional regions and loop anchors, and exhibit high levels of TOP2-mediated DNA breakages. Inhibition of transcription leads to reduced DNA breakages of CRGs. CONCLUSIONS: Our results demonstrate KMT2A partners and CRGs may form dynamic and multipartite spatial clusters in individual cells that may be involved in RUNX1-mediated transcription factories, wherein massive DNA damages and illegitimate ligations of genes may occur, leading to complex rearrangements and KMT2A fusions in 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 | 0.001 |
| 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) | 0.002 | 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".