Clinical significance of clonal hematopoiesis in the setting of autologous stem cell transplantation for lymphoma
Bibliographic record
Abstract
Autologous stem cell transplantation (ASCT) remains a key therapeutic strategy for treating patients with relapsed or refractory non-Hodgkin and Hodgkin lymphoma. Clonal hematopoiesis (CH) has been proposed as a major contributor not only to the development of therapy-related myeloid neoplasms but also to inferior overall survival (OS) in patients who had undergone ASCT. Herein, we aimed to investigate the prognostic implications of CH after ASCT in a cohort of 420 lymphoma patients using ultra-deep, highly sensitive error-correction sequencing. CH was identified in the stem cell product samples of 181 patients (43.1%) and was most common in those with T-cell lymphoma (72.2%). The presence of CH was associated with a longer time to neutrophil and platelet recovery. Moreover, patients with evidence of CH had inferior 5-year OS from the time of first relapse (39.4% vs. 45.8%, p = .043) and from the time of ASCT (51.8% vs. 59.3%, p = .018). The adverse prognostic impact of CH was not due to therapy-related myeloid neoplasms, the incidence of which was low in our cohort (10-year cumulative incidence of 3.3% vs. 3.0% in those with and without CH, p = .445). In terms of specific-gene mutations, adverse OS was mostly associated with PPM1D mutations (hazard ratio (HR) 1.74, 95% confidence interval (CI) 1.13-2.67, p = .011). In summary, we found that CH is associated with an increased risk of non-lymphoma-related death after ASCT, which suggests that lymphoma survivors with CH may need intensified surveillance strategies to prevent and treat late complications.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".