Distinctive genotypes in infants with T‐cell acute lymphoblastic leukaemia
Bibliographic record
Abstract
Infant T-cell acute lymphoblastic leukaemia (iT-ALL) is a very rare and poorly defined entity with a poor prognosis. We assembled a unique series of 13 infants with T-ALL, which allowed us to identify genotypic abnormalities and to investigate prenatal origins. Matched samples (diagnosis/remission) were analysed by single nucleotide polymorphism-array to identify genomic losses and gains. In three cases, we identified a recurrent somatic deletion on chromosome 3. These losses result in the complete deletion of MLF1 and have not previously been described in T-ALL. We observed two cases with an 11p13 deletion (LMO2-related), one of which also harboured a deletion of RB1. Another case presented a large 11q14·1-11q23·2 deletion that included ATM and only five patients (38%) showed deletions of CDKN2A/B. Four cases showed NOTCH1 mutations; in one case FBXW7 was the sole mutation and three cases showed alterations in PTEN. KMT2A rearrangements (KMT2A-r) were detected in three out of 13 cases. For three patients, mutations and copy number alterations (including deletion of PTEN) could be backtracked to birth using neonatal blood spot DNA, demonstrating an in utero origin. Overall, our data indicates that iT-ALL has a diverse but distinctive profile of genotypic abnormalities when compared to T-ALL in older children and adults.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 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".