Autologous and allogeneic hematopoietic cell transplantation for diffuse large B-cell lymphoma–type Richter syndrome
Bibliographic record
Abstract
Richter syndrome (RS) represents a transformation from chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) to aggressive lymphoma, most commonly diffuse large B-cell lymphoma (DLBCL), which is associated with a dismal prognosis. Patients with DLBCL-RS have poor outcomes with DLBCL-directed therapy; thus, consolidation with hematopoietic cell transplantation (HCT) has been used, with durable remissions observed. Studies reporting HCT outcomes in patients with DLBCL-RS have been small, have not evaluated the prognostic impact of cytogenetic risk factors, and were conducted prior to the era of novel targeted therapy of CLL/SLL. We performed a Center for International Blood and Transplant Research registry study evaluating outcomes after autologous HCT (auto-HCT; n = 53) and allogeneic HCT (allo-HCT; n = 118) in patients with DLBCL-RS treated in the modern era. More auto-HCT recipients were in complete response (CR) at HCT relative to allo-HCT recipients (66% vs 34%), whereas a higher proportion of allo-HCT recipients had 17p deletion (33% vs 7%) and had previously received novel agents (39% vs 10%). In the auto-HCT cohort, the 3-year relapse incidence, progression-free survival (PFS), and overall survival (OS) were 37%, 48%, and 57%, respectively. Among allo-HCT recipients, the 3-year relapse incidence, PFS, and OS were 30%, 43%, and 52%, respectively. In the allo-HCT cohort, deeper response at HCT was associated with outcomes (3-year PFS/OS, 66%/77% CR vs 43%/57% partial response vs 5%/15% resistant; P < .0001 for both), whereas cytogenetic abnormalities and prior novel therapy did not impact outcomes. In our study, HCT resulted in durable remissions in therapy-sensitive patients with DLBCL-RS treated in the era of targeted CLL/SLL therapy, including patients with high-risk features.
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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 | 0.000 |
| 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".