Molecular risk markers define risk of relapse in myeloid leukemia of Down syndrome beyond measurable residual disease
Bibliographic record
Abstract
ABSTRACT: Myeloid leukemia of Down syndrome (ML-DS) is a distinct form of pediatric acute myeloid leukemia (AML) that responds to reduced-intensity chemotherapy, as compared with non-DS AML that requires intensive chemotherapy and often stem cell transplant. While most patients with ML-DS have a favorable prognosis, outcomes for those with refractory or relapsed disease are dismal. Children's Oncology Group study AAML1531 introduced the use of measurable residual disease by multiparameter flow cytometry at the end of the first course of induction therapy (EOI-1 MRD) for risk stratification of treatment intensity. Of 280 patients with ML-DS who were enrolled, 41 were classified as high risk (HR) due to positive EOI-1 MRD, and treated with intensified chemotherapy similar to that used for pediatric non-DS AML. Treatment intensification did not improve the 2-year event-free survival compared with patients who were MRD-positive treated with reduced-intensity therapy in the predecessor study AAML0431 (80.5% ± 12.4% vs 76%; P = .247) or overall survival (80.5% ± 12.4% vs 76.2% ± 18.6%; P = .819), but significantly increased the frequency of febrile neutropenia and sepsis events. While stratification of treatment intensity based on MRD was not beneficial, molecular markers of relapse risk proposed by the Japan Children's Cancer Group for ML-DS (alterations of CDKN2A, ZBTB7A, JAK2, TP53) proved prognostic. Relapse risk was 50% in patients who were HR from AAML1531 with any high-risk molecular marker compared with 6.7% in those without. Similar relapse results were obtained in the MRD-negative AAML1531 group, suggesting molecular risk markers can predict outcome and thus be used to stratify therapy in ML-DS. This trial was registered at www.clinicaltrials.gov as #NCT02521493.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".