Structural variants involving <i>MLLT10</i> fusion are associated with adverse outcomes in pediatric acute myeloid leukemia
Bibliographic record
Abstract
ABSTRACT: MLLT10 gene rearrangements with KMT2A occur in pediatric acute myeloid leukemia (AML) and confer poor prognosis, but the prognostic impact of MLLT10 in partnership with other genes is unknown. We conducted a retrospective study with 2080 children and young adults with AML registered on the Children's Oncology Group AAML0531 (NCT00372593) and AAML1031 trials (NCT01371981). Transcriptome profiling and/or karyotyping were performed to identify leukemia-associated fusions associated with prognosis. Collectively, 127 patients (6.1%) were identified with MLLT10 fusions: 104 (81.9%) with KMT2A::MLLT10, 13 (10.2%) with PICALM::MLLT10, and 10 (7.9%) X::MLLT10: (2 each of DDX3X and TEC), with 6 partners (DDX3Y, CEP164, SCN2B, TREH, NAP1L1, and XPO1) observed in single patients. Patients with MLLT10 (n = 127) demonstrated adverse outcomes, with 5-year event-free survival (EFS) of 18.6% vs 49% in patients without MLLT10 (n = 1953, P < .001), inferior 5-year overall survival (OS) of 38.2% vs 65.7% (P ≤ .001), and a higher relapse risk of 76% vs 38.6% (P < .001). Patients with KMT2A::MLLT10 had an EFS from study entry of 19.5% vs 12.7% (P = .628), and an OS from study entry of 40.4% vs 27.6% (P = .361) in those with other MLLT10 fusion partners. Patients with PICALM::MLLT10 had an EFS of 9.2% vs 20% in other MLLT10- without PICALM (X::MLLT10; P = .788). Patients with PICALM::MLLT10 and X::MLLT10 fusions exhibit a DNA hypermethylation signature resembling NUP98::NSD1 fusions, whereas patients with KMT2A::MLLT10 bear aberrations primarily affecting distal regulatory elements. Regardless of the fusion partner, patients with AML harboring MLLT10 fusions exhibit very high-risk features and should be prioritized for alternative therapeutic interventions.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".