Next‐generation sequencing‐based analysis to assess the pattern of relapse in patients with Philadelphia‐positive acute lymphoblastic leukemia
Bibliographic record
Abstract
Abstract In this study, we performed serial monitoring using targeted DNA sequencing to identify genetic alterations in adults with Philadelphia‐positive acute lymphoblastic leukemia (Ph‐ALL). Deep sequencing was performed by targeting the coding regions of 45 genes with recurrent driver mutations and 1129 single nucleotide polymorphism sites. Of the 43 patients that we examined, at least one case of genetic alterations was detected in 38 (88%) of the 43 patients at diagnosis (somatic mutations in 10 patients [23%] and copy number aberrations [CNA] in 36 patients [84%]). The most frequently detected CNA lesions were in IKZF1 ( n = 25, 58%) and the most frequently mutated gene was SETD2 ( n = 5). At least one genetic abnormality (loss, gain, or persistence) was observed in all the samples obtained at relapse that were available for analysis ( n = 15), compared with the samples obtained at diagnosis (disappearance of any previously detected genetic alterations: 11 patients [73%]; new genetic abnormalities: nine patients [60%]; and persistent genetic abnormalities: eight patients [53%]]. The most frequently deleted lesions were in IKZF1 ( n = 9, 60%), and the most frequently mutated gene was ABL1 (eight patients, 53%). Our data indicate that leukemic progression may be associated with complex genetic alterations in Ph‐ALL during the course of treatment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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 teacher head, 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".