Spectrum of somatic mutation dynamics in chronic myeloid leukemia following tyrosine kinase inhibitor therapy
Bibliographic record
Abstract
Somatic mutations commonly detected in a variety of myeloid neoplasms have not been systematically investigated in chronic myeloid leukemia (CML). We performed targeted deep sequencing on a total of 300 serial samples from 100 CML patients; 37 patients carried mutations. Sixteen of these had evidence of mutations originating from preleukemic clones. Using unsupervised hierarchical clustering, we identified 5 distinct patterns of mutation dynamics arising following tyrosine kinase inhibitor (TKI) therapy. This study demonstrates that patterns of mutation acquisition, persistence, and clearance vary but have a number of interesting correlations with clinical outcomes. Mutation burden often persisted despite successful TKI response (pattern 1), providing indirect evidence that these mutations also originated from preleukemic mutations, whereas patients exhibiting mutation clearance (pattern 3) showed mixed clinical outcomes. Unsurprisingly, patients acquiring new mutations during treatment failed TKI therapy (pattern 2). These patterns show that CML mutation dynamics following TKI therapy are markedly distinct from other myeloid neoplasms. In summary, clinical implications of mutation profiles and dynamics in CML should be interpreted with caution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 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".