Resolving driver events in MLL-r negative high-risk infant ALL.
Bibliographic record
Abstract
10030 Background: Infant acute lymphoblastic leukemia (ALL) is the only subtype of childhood ALL whose outcome has not improved over the past two decades. The most important prognosticator is the presence of rearrangements in the Mixed Lineage Leukemia gene (MLL-r), however, many patients present with high-risk clinical features but without MLL-r. We recently identified two cases of infant ALL with high-risk clinical features resembling MLL-r, but were negative for MLL-r by conventional diagnostics. RNA sequencing revealed a partial tandem duplication in MLL (MLL-PTD). We thus aimed to determine if MLL-PTD, other MLL abnormalities, or other genetic or transcriptomic features were driving this subset of high-risk infant ALL without MLL-r. Methods: We obtained 19 banked patient samples from the Children’s Oncology Group (COG) infant ALL trial (AALL0631) from MLL wildtype patients as determined by FISH and cytogenetics. Utilizing deep RNA-sequencing, we manually inspected the MLL gene for MLL-PTD, while also performing automated fusion detection and gene expression profiling in search of defining features of these tumors. Results: 3 additional MLL-PTDs were identified, all in patients with infant T-cell ALL, whereas both index cases were in patients with infant B-cell ALL. Gene expression profiling analysis revealed that all five MLL-PTD infants clustered together. Eight infants (7 with B-cell ALL) were found to have Ph-like expression. Five of these 8 infants were also found to have an IKZF1/JAK2 expression profile; one of these five had a PAX5-JAK2 fusion detected. Two infants (including the one noted above) had novel PAX5 fusions, known drivers of B-cell leukemia. Additional detected fusions included TCF3-PBX1 and TCF4-ZNF384. Conclusions: MLL-PTDs were found in both B- and T-cell infant ALL. Though Ph-like ALL has been described in adolescents and young adults, we found a substantial frequency of Ph-like expression among MLL-WT infants. Further characterization of these infants is ongoing. If replicated in other infant cohorts, these two findings may help explain the poor prognosis of MLL-WT ALL when compared to children with standard risk ALL, and offer the possibility of targeted therapy for select infants.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".