Natural killer or natural killer/T cell lineage large granular lymphocytosis associated with dasatinib therapy for Philadelphia chromosome positive leukemia
Bibliographic record
Abstract
Dasatinib, a dual tyrosine kinase inhibitor, is known to modulate or suppress T-cell activation and proliferation. We report a series of 8 patients who developed chronic peripheral lymphocytosis, identified as natural killer cells or natural killer/T-cells based on their large granular lymphocyte morphologies and CD16(+), CD56(+), CD3(-) or CD3(+) immunophenotypic profiles, out of 18 patients receiving dasatinib therapy. All cases that developed large granular lymphocyte lymphocytosis achieved optimal molecular response (8/8 in large granular lymphocyte(+) patients vs. 3/10 in large granular lymphocyte(-) patients, p=0.002). A (51)Cr release assay demonstrated that natural killer cell cytotoxicity has been enhanced in a case of large granular lymphocyte lymphocytosis compared to normal healthy donors, and that natural killer cell cytotoxicity in dasatinib-responders was superior to that in non-responders. In summary, the present study suggests that natural killer or natural killer/T cell lineage large granular lymphocyte lymphocytosis develops in association with dasatinib therapy and that large granular lymphocyte might have a therapeutic effect on Ph(+) leukemic cells.
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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 | 0.000 | 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".