Management of Down Syndrome–Associated Leukemias
Bibliographic record
Abstract
Importance: Down syndrome (DS), caused by an extra copy of material from chromosome 21, is one of the most common genetic conditions. The increased risk of acute leukemia in DS (DS-AL) has been recognized for decades, consisting of an approximately 150-fold higher risk of acute myeloid leukemia (AML) before age 4 years, and a 10- to 20-fold higher risk of acute lymphoblastic leukemia (ALL), compared with children without DS. Observations: A recent National Institutes of Health-sponsored conference, ImpacT21, reviewed research and clinical trials in children, adolescents, and young adults (AYAs) with DS-AL and are presented herein, including presentation and treatment, clinical trial design, and ethical considerations for this unique population. Between 10% to 30% of infants with DS are diagnosed with transient abnormal myelopoiesis (TAM), which spontaneously regresses. After a latency period of up to 4 years, 20% to 30% develop myeloid leukemia associated with DS (ML-DS). Recent studies have characterized somatic mutations associated with progression from TAM to ML-DS, but predicting which patients will progress to ML-DS remains elusive. Clinical trials for DS-AL have aimed to reduce treatment-related mortality (TRM) and improve survival. Children with ML-DS have better outcomes compared with non-DS AML, but outcomes remain dismal in relapse. In contrast, patients with DS-ALL have inferior outcomes compared with those without DS, due to both higher TRM and relapse. Management of relapsed leukemia poses unique challenges owing to disease biology and increased vulnerability to toxic effects. Late effects in survivors of DS-AL are an important area in need of further study because they may demonstrate unique patterns in the setting of chronic medical conditions associated with DS. Conclusions and Relevance: Optimal management of DS-AL requires specific molecular testing, meticulous supportive care, and tailored therapy to reduce TRM while optimizing survival. There is no standard approach to treatment of relapsed disease. Future work should include identification of biomarkers predictive of toxic effects; enhanced clinical and scientific collaborations; promotion of access to novel agents through innovative clinical trial design; and dedicated studies of late effects of treatment.
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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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".