Interaction between myelodysplasia-related gene mutations and ontogeny in acute myeloid leukemia
Bibliographic record
Abstract
Accurate classification and risk stratification are critical for clinical decision making in patients with acute myeloid leukemia (AML). In the newly proposed World Health Organization and International Consensus classifications of hematolymphoid neoplasms, the presence of myelodysplasia-related (MR) gene mutations is included as 1 of the diagnostic criteria for AML, AML-MR, based largely on the assumption that these mutations are specific for AML with an antecedent myelodysplastic syndrome. ICC also prioritizes MR gene mutations over ontogeny (as defined in the clinical history). Furthermore, European LeukemiaNet (ELN) 2022 stratifies these MR gene mutations into the adverse-risk group. By thoroughly annotating a cohort of 344 newly diagnosed patients with AML treated at the Memorial Sloan Kettering Cancer Center, we show that ontogeny assignments based on the database registry lack accuracy. MR gene mutations are frequently observed in de novo AML. Among the MR gene mutations, only EZH2 and SF3B1 were associated with an inferior outcome in the univariate analysis. In a multivariate analysis, AML ontogeny had independent prognostic values even after adjusting for age, treatment, allo-transplant and genomic classes or ELN risks. Ontogeny also helped stratify the outcome of AML with MR gene mutations. Finally, de novo AML with MR gene mutations did not show an adverse outcome. In summary, our study emphasizes the importance of accurate ontogeny designation in clinical studies, demonstrates the independent prognostic value of AML ontogeny, and questions the current classification and risk stratification of AML with MR gene mutations.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".