Genes commonly deleted in childhood B-cell precursor acute lymphoblastic leukemia: association with cytogenetics and clinical features
Bibliographic record
Abstract
In childhood B-cell precursor acute lymphoblastic leukemia, cytogenetics is important in diagnosis and as an indicator of response to therapy, thus playing a key role in risk stratification of patients for treatment. Little is known of the relationship between different cytogenetic subtypes in B-cell precursor acute lymphoblastic leukemia and the recently reported copy number abnormalities affecting significant leukemia associated genes. In a consecutive series of 1427 childhood B-cell precursor acute lymphoblastic leukemia patients, we have determined the incidence and type of copy number abnormalities using multiplex ligation-dependent probe amplification. We have shown strong links between certain deletions and cytogenetic subtypes, including the novel association between RB1 deletions and intrachromosomal amplification of chromosome 21. In this study, we characterized the different copy number abnormalities and show heterogeneity of PAX5 and IKZF1 deletions and the recurrent nature of RB1 deletions. Whole gene losses are often indicative of larger deletions, visible by conventional cytogenetics. An increased number of copy number abnormalities is associated with NCI high risk, specifically deletions of IKZF1 and CDKN2A/B, which occur more frequently among these patients. IKZF1 deletions and rearrangements of CRLF2 among patients with undefined karyotypes may point to the poor risk BCR-ABL1-like group. In conclusion, this study has demonstrated in a large representative cohort of children with B-cell precursor acute lymphoblastic leukemia that the pattern of copy number abnormalities is highly variable according to the primary genetic abnormality.
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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 | 0.001 |
| 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 | 0.000 | 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".