Update in childhood acute myeloid leukemia: recent developments in the molecular basis of disease and novel therapies
Bibliographic record
Abstract
Childhood acute myeloid leukemia is a heterogeneous group of disorders that remains challenging to treat. There are multiple common genetic alterations in childhood acute myeloid leukemia. These include chromosomal translocations affecting RUNX1-CBFbeta, RARalpha, and MLL. There are known activating mutations in the genes for the receptor tyrosine kinases FLT3, KIT, and FMS. As these abnormalities are better understood, they are providing important insights into the pathogenesis of disease as well as information about prognosis. Although intensive chemotherapy remains the mainstay of acute myeloid leukemia therapy, long-term cure rates with chemotherapy alone remain approximately 50%, creating an urgent need for better therapies. Multiple avenues are being explored in the design of new treatments for pediatric acute myeloid leukemia. Targeted therapies include targeted antibody therapy; inhibitors of FLT3, KIT, and farnesyltransferase; diphtheria toxin conjugated to the granulocyte-macrophage colony-stimulating factor; and antisense oligonucleotides. Another area of interest is chromatin remodeling and differentiation therapy, including agents such as all- retinoic acid, arsenic trioxide, and inhibitors of DNA methylation and histone deacetylation. There are also ongoing trials of antiangiogenesis agents. Another avenue for novel therapies is immunotherapy with agents such as interleukin-2 and tumor vaccines. This article reviews recent advances in understanding of the molecular basis for childhood acute myeloid leukemia and the design of novel therapies for the treatment of childhood acute myeloid leukemia.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".