BCR-ABL1 mutation development during first-line treatment with dasatinib or imatinib for chronic myeloid leukemia in chronic phase
Bibliographic record
Abstract
BCR-ABL1 mutations are a common, well-characterized mechanism of resistance to imatinib as first-line treatment of chronic myeloid leukemia in chronic phase (CML-CP). Less is known about mutation development during first-line treatment with dasatinib and nilotinib, despite increased use because of higher response rates compared with imatinib. Retrospective analyses were conducted to characterize mutation development in patients with newly diagnosed CML-CP treated with dasatinib (n=259) or imatinib (n=260) in DASISION (Dasatinib versus Imatinib Study in Treatment-Naive CML-CP), with 3-year minimum follow-up. Mutation screening, including patients who discontinued treatment and patients who had a clinically relevant on-treatment event (no confirmed complete cytogenetic response (cCCyR) and no major molecular response (MMR) within 12 months; fivefold increase in BCR-ABL1 with loss of MMR; loss of CCyR), yielded a small number of patients with mutations (dasatinib, n=17; imatinib, n=18). Dasatinib patients had a narrower spectrum of mutations (4 vs 12 sites for dasatinib vs imatinib), fewer phosphate-binding loop mutations (1 vs 9 mutations), fewer multiple mutations (1 vs 6 patients) and greater occurrence of T315I (11 vs 0 patients). This trial was registered at www.clinicaltrials.gov as NCT00481247.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".