Precision medicine for high-risk gene fusions in pediatric AML: a focus on <i>KMT2A</i>, <i>NUP98</i>, and <i>GLIS2</i> rearrangements
Bibliographic record
Abstract
ABSTRACT: Robust genetic characterization of pediatric acute myeloid leukemia (AML) has demonstrated that fusion oncogenes are highly prevalent drivers of AML leukemogenesis in young children. Identification of fusion oncogenes associated with adverse outcomes has facilitated risk stratification of patients, although successful development of precision medicine approaches for most fusion-driven AML subtypes have been historically challenging. This knowledge gap has been in part due to difficulties in targeting structural alterations involving transcription factors and in identification of a therapeutic window for selective inhibition of the oncofusion without deleterious effects upon essential wild-type proteins. Herein, we discuss the current molecular landscape and functional characterization of 3 of the most lethal childhood AML fusion-oncogene driven subtypes harboring KMT2A, NUP98, or CBFA2T3::GLIS2 rearrangements. We further review early-phase clinical trial data of novel targeted inhibitors and immunotherapies that have demonstrated initial success specifically for children with these poor-prognosis genetic subtypes of AML and provide appreciable optimism to improve clinical outcomes in the future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".