Efficacy and safety of bendamustine for lymphodepletion before lisocabtagene maraleucel
Bibliographic record
Abstract
Bendamustine has been retrospectively shown to be an effective and safe lymphodepletion regimen prior to the anti-CD19 chimeric antigen receptor T cell (CART) products tisagenlecleucel and axicabtagene ciloleucel, as well as the anti-BCMA CART products idecabtagene vicleucel and ciltacabtagene autoleucel. However, bendamustine as lymphodepletion prior to lisocabtagene maraleucel (liso-cel), a 4-1BB co-stimulated, fixed CD4:CD8 ratio anti-CD19 CART product, has not been described yet. Thus, we studied a cohort of sequentially-treated patients with large B-cell lymphomas who received bendamustine lymphodepletion before liso-cel at the University of Pennsylvania between 5/2021 and 12/2023 (n = 31). Patients were evaluated for toxicities and responses. Of note, 7 patients (22.6%) would have dnot met the inclusion criteria for the registrational liso-cel clinical trials, mostly due to older age. Overall and complete response rates were 76.9% and 73.1%, respectively. At a median follow-up of 6.3 months, the 6-month progression-free and overall survival were 59.9% and 91.1%, respectively. Rates of cytokine-release syndrome (CRS) and neurotoxicity (ICANS) of any grade were 9.7% and 9.7%, respectively, with no grade ≥ 3 events. No infections were reported during the first 30 days following liso-cel infusion. Neutropenia ≥ grade 3 was observed in 29.0% of patients; thrombocytopenia ≥ grade 3 occurred in 9.7%. In conclusion, bendamustine lymphodepletion before liso-cel appears to be a strategy that can drive tumor responses while ensuring a mild toxicity profile.
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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 | 0.001 |
| 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 | 0.001 |
| 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".