Enhancing CAR-T Efficacy in Large B-Cell Lymphoma with Radiation Bridging Therapy: A Real-World Single-Center Experience
Bibliographic record
Abstract
One challenge of chimeric antigen receptor T-cell therapy (CAR-T) for relapsed or refractory large B-cell lymphoma (LBCL) is achieving disease control during manufacturing. We report real-word outcomes of 100 patients treated with axicabtagene ciloleucel (axi-cel, n = 50) or tisagenlecleucel (tisa-cel, n = 50) at our center. Most patients received bridging therapy (BT) with 48 undergoing radiation BT (RBT) and 32 receiving systemic BT (SBT). The best overall response rate (ORR) was 84% (78% complete response (CR)) for axi-cel and 60% (42% CR) for tisa-cel. At a median follow-up of 16 months, 12-month progression-free survival (PFS) and overall survival (OS) were 72% and 82% for axi-cel, compared to 35% and 57% for tisa-cel. By the bridging approach, 12-month PFS was 60% with RBT, 59% without BT and 35% with SBT (p = 0.06). Notably, axi-cel patients without lymphoma progression during manufacturing (n = 24) achieved 12-month PFS and OS rates of 91% and 96%, respectively. Axi-cel was associated with more cytokine release syndrome (92% vs. 66%, p = 0.003) and neurotoxicity (all-grade 56% vs. 10%, p < 0.001, grade ≥ 328% vs. 4%, p = 0.002). Multivariate analysis identified RBT as independently associated with improved PFS (HR 0.46, 95% CI 0.22–0.96). Pending prospective validation, RBT shows promise for improving CAR-T outcomes in LBCL.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".