Large clones of clonal hematopoiesis affect outcome in mantle cell lymphoma: results from the FIL MCL0208 clinical trial
Bibliographic record
Abstract
ABSTRACT: Although recent evidence suggests that myeloid clonal hematopoiesis (M-CH) may influence lymphoma clinical outcome, its impact in mantle cell lymphoma (MCL) remains unclear. Here, we report a comprehensive next-generation sequencing-based analysis of the M-CH mutational landscape at baseline and follow-up in patients enrolled in the Fondazione Italiana Linfomi MCL0208 phase 3 trial, evaluating lenalidomide maintenance vs observation after chemoimmunotherapy and autologous stem cell transplantation (ASCT) in untreated young patients with MCL. Overall, 254 of 300 (85%) enrolled patients (median age, 57 years [range, 32-66]) had a baseline sample available for CH analysis. Using stringent criteria, at least 1 mutation involving M-CH candidate genes was described in 34 patients (13%), with DNMT3A being the most frequently mutated gene (54%). After a median follow-up of 7 years, the presence of large CH clones (variant allele frequency of ≥10%) predicted worse progression-free survival (hazard ratio [HR], 2.93; 95% confidence interval [CI] 1.36-6.31; P = .006) and overall survival (HR, 3.02 [1.21-7.55]; P = .018) compared with patients with CH. Importantly, the competing risks analysis demonstrates that the worse clinical outcome associated with M-CH large clones is linked to MCL progression (P < .05). Moreover, large M-CH clones showed longer time to hematological recovery after ASCT than the remaining cohort (P = .026). In conclusion, we showed for the first time that large CH clones might associate with unfavorable clinical impact in patients with MCL. This trial was registered at www.clinicaltrialsregister.eu as EudraCT (2009-012807-25) and www.ClinicalTrials.gov as #NCT02354313.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".