The impact of comparative genomic hybridization/single‐nucleotide polymorphism microarray in risk stratification of pediatric acute lymphoblastic leukemia
Bibliographic record
Abstract
Abstract Background The objective of this study is to assess the concordance and added value of combined comparative genomic hybridization plus single‐nucleotide polymorphism microarray (CGH/SNP) analyses in pediatric acute lymphoblastic leukemia (ALL) risk stratification compared to conventional cytogenetic methods. Procedure This is a retrospective study that included patients aged 1–18 years diagnosed with de novo ALL at Sainte‐Justine Hospital between 2016 and 2021. Results from conventional cytogenetic and molecular analyses were collected and compared to those of CGH/SNP. Results A total of 135 ALL patients were included. Sample failures or non‐diagnostic analyses occurred in 17.8% cases with G‐banding karyotypes versus 1.5% cases with CGH/SNP. The mean turnaround time for results was significantly faster for CGH/SNP than karyotype with 5.8 versus 10.7 days, respectively. The comparison of ploidy assessment by CGH/SNP and G‐banding karyotype showed strong concordance (r = .82, p < .001, r2 = .68). Furthermore, G‐banding karyotype did not detect additional clinically relevant aberrations that were missed by the combined analysis of CGH/SNP and fluorescence in situ hybridization. The most common gene alterations detected by CGH/SNP were deletions involving CDKN2A (35.8%), ETV6 (31.3%), CDKN2B (28.4%), PAX5 (20.1%), IKZF1 (12.7%), and copy‐neutral loss of heterozygosity (CN‐LOH) of 9p (9.0%). Among these, only ETV6 deletion was found to have a significant prognostic impact with superior event‐free survival in both univariate and multivariate analyses (adjusted hazard ratio 0.08, 95% confidence interval: 0.01–0.50, p = .02). Conclusion CGH/SNP provided faster, reliable, and highly concordant results than those obtained by conventional cytogenetics. CGH/SNP identified recurrent gene deletions in pediatric ALL, of which ETV6 deletion conferred a favorable prognosis.
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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 | 0.003 | 0.004 |
| 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".