Prognostic Role of Gene Mutations in Chronic Myelomonocytic Leukemia Patients Treated With Hypomethylating Agents
Bibliographic record
Abstract
Somatic mutations contribute to the heterogeneous prognosis of chronic myelomonocytic leukemia (CMML).Hypomethylating agents (HMAs) are active in CMML, but analyses of small series failed to identify mutations predicting response or survival.We analyzed a retrospective multi-center cohort of 174 CMML patients treated with a median of 7 cycles of azacitidine (n = 68) or decitabine (n = 106).Sequencing data before treatment initiation were available for all patients, from Sanger (n = 68) or next generation (n = 106) sequencing.Overall response rate (ORR) was 52%, including complete response (CR) in 28 patients (17%).In multivariate analysis, ASXL1 mutations predicted a lower ORR (Odds Ratio [OR] = 0.85, p = 0.037), whereas TET2 mut /ASXL1 wt genotype predicted a higher CR rate (OR = 1.18, p = 0.011) independently of clinical parameters.With a median follow-up of 36.7 months, overall survival (OS) was 23.0 months.In multivariate analysis, RUNX1 mut (Hazard Ratio [HR] = 2.00, p = .011),CBL mut (HR = 1.90, p = 0.03) genotypes and higher WBC (log 10 (WBC) HR = 2.30, p = .005)independently predicted worse OS while the TET2 mut /ASXL1 wt predicted better OS (HR = 0.60, p = 0.05).CMMLspecific scores CPSS and GFM had limited predictive power.Our results stress the need for robust biomarkers of HMA activity in CMML and for novel treatment strategies in patients with myeloproliferative features and RUNX1 mutations.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".