Faculty Opinions recommendation of Absence of biallelic TCRgamma deletion predicts early treatment failure in pediatric T-cell acute lymphoblastic leukemia.
Bibliographic record
Abstract
PurposeTo identify children with T-cell acute lymphoblastic leukemia (T-ALL) at high risk of induction chemotherapy failure by using DNA copy number analysis of leukemic cells collected at diagnosis. Patients and MethodsArray comparative genomic hybridization (CGH) was performed on genomic DNA extracted from diagnostic lymphoblasts from 47 children with T-ALL treated on Children's Oncology Group Study P9404 or Dana-Farber Cancer Institute Protocol 00-01.These samples represented nine patients who did not achieve an initial complete remission, 13 who relapsed, and 25 who became long-term, event-free survivors.The findings were confirmed in an independent cohort of patients by quantitative DNA polymerase chain reaction (DNA-PCR), an assay that is well suited for clinical application. ResultsAnalysis of the CGH findings in patients in whom induction chemotherapy failed compared with those in whom induction chemotherapy was successful identified the absence of biallelic TCR␥ locus deletion (ABD), a characteristic of early thymocyte precursors before V(D)J recombination, as the most robust predictor of induction failure (P Ͻ .001).This feature was also associated with markedly inferior event-free (P ϭ .002)and overall survival (P Ͻ .001)rates: 25% versus 58% and 25% versus 72%, respectively.Using a rapid and inexpensive quantitative DNA-PCR assay, we validated ABD as a predictor of a poor response to induction chemotherapy in an independent series of patients. ConclusionLymphoblasts from children with T-ALL should be evaluated at diagnosis for deletion within the TCR␥ locus.Patients lacking biallelic deletion, which confers a high probability of induction failure with contemporary therapy, should be assigned to alternative therapy in the context of a prospective clinical trial.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.028 |
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".