Pathologic, cytogenetic, and molecular features of acute myeloid leukemia with megakaryocytic differentiation: A report from the Children's Oncology Group
Bibliographic record
Abstract
BACKGROUND: Acute myeloid leukemia (AML) with megakaryocytic differentiation (AMkL) is a rare subtype of AML more common in children. Recent literature has identified multiple fusions associated with this type of leukemia. METHODS: Morphology, cytogenetics, and genomic sequencing were assessed in patients from Children's Oncology Group trials AAML0531 and AAML1031 with central-pathology review confirmed non-Down syndrome AMkL. The 5-year event-free survival (EFS), overall survival (OS), and RR were evaluated in these AMkL subcategories. RESULTS: A total of 107 cases of AMkL (5.5%) were included. Distinct fusions were identified in the majority: RBM15::MRTFA (20%), CBFA2T3::GLIS2 (16%), NUP98 (10%), KMT2A (7%), TEC::MLLT10 (2%), MECOM (1%), and FUS::ERG (1%); many of the remaining cases were classified as AMkL with (other) myelodysplasia-related changes (MRC). Very few cases had AML-associated somatic mutations. Cases with CBFA2T3::GLIS2 were enriched in trisomy 3 (p = .015) and the RAM phenotype, with associated high CD56 expression (p < .001). Cases with NUP98 fusions were enriched in trisomy 6 (p < .001), monosomy 13/del(13q) (p < .001), trisomy 21 (p = .026), and/or complex karyotypes (p = .026). While different 5-year EFS and OS were observed in AMkL in each trial, in general, those with CBFA2T3::GLIS2 or KMT2A rearrangements had worse outcomes compared to other AMkL, while those with RBM15::MRTFA or classified as AMkl-MRC fared better. AMkL with NUP98 fusions also had poor outcomes in the AAML1031 trial. CONCLUSION: Given the differences in outcomes, AMkL classification by fusions, cytogenetics, and morphology may be warranted to help in risk stratification and therapeutic options.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".