The molecular landscape of pediatric acute myeloid leukemia reveals recurrent structural alterations and age-specific mutational interactions
Bibliographic record
Abstract
Abstract We present the molecular landscape of pediatric acute myeloid leukemia (AML), characterizing nearly 1,000 participants in Children’s Oncology Group (COG) AML trials. The COG/NCI TARGET AML initiative assessed cases by whole-genome, targeted DNA, mRNA, miRNA sequencing and CpG methylation profiling. Validated DNA variants revealed diverse, infrequent mutations with fewer than 40 genes mutated in >2% of cases. In contrast, somatic structural variants, including novel gene fusions and focal MBNL1 , ZEB2 , and ELF1 deletions, were disproportionately prevalent in young as compared to adult patients. Conversely, DNMT3A and TP53 mutations, common in adults, are conspicuously absent from virtually all pediatric cases. Novel GATA2 , FLT3 , and CBL mutations, recurrent MYC-ITD, NRAS, KRAS , and WT1 mutations are frequent in pediatric AML. Deletions, mutations, and promoter DNA hypermethylation convergently impact Wnt signaling, Polycomb repression, innate immune cell interactions, and a cluster of zinc finger genes associated with KMT2A rearrangements. These results highlight the need for, and facilitate the development of age-tailored targeted therapies for the treatment of pediatric AML.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".