Unveiling the Complexity of KMT2A Rearrangements in Acute Myeloid Leukemias with Optical Genome Mapping
Bibliographic record
Abstract
Background: KMT2A rearrangements are major genetic entities in the classification of acute myeloid leukemias (AMLs), but their diverse and frequently cryptic nature makes their detection and characterization challenging. Karyotypic anomalies at the KMT2A locus and/or abnormal KMT2A Fluorescence in situ hybridization (FISH) results strongly indicate a KMT2A fusion, but the identification of the translocation partner gene often requires further investigation. KMT2A partial tandem duplications (PTDs), on the other hand, are undetectable by standard cytogenetics methods. Methods: We herein report the optical genome mapping (OGM) analysis of 38 AML samples: 12 cryptic/hard-to-characterize KMT2A fusions, 20 KMT2A-PTDs and 6 cases with no KMT2A anomaly. Results: In all the fusion cases, the rearrangement between 5’KMT2A and the 3’partner gene was identified as a translocation t(v;11q23.3)(v;118479068), and the analysis of co-occurring variants elucidated the formation of the rearrangement. The KMT2A variants detected in the KMT2A-PTD cases were surprisingly diverse. Combined with RNAseq data, OGM analysis identified 9 distinct in-frame KMT2A-PTD variants among the 20 cases analyzed. Conclusions: With the clinical development of menin inhibitors for the treatment of patients with KMT2A-rearranged acute leukemias, the characterization of these rearrangements is of utmost importance. Our results suggest that OGM is a promising tool for accurate genetic diagnosis in this context.
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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 | 0.002 | 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) | 0.001 | 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".