Axicabtagene ciloleucel as first-line therapy in high-risk large B-cell lymphoma: the phase 2 ZUMA-12 trial
Bibliographic record
Abstract
High-risk large B-cell lymphoma (LBCL) has poor outcomes with standard first-line chemoimmunotherapy. In the phase 2, multicenter, single-arm ZUMA-12 study (ClinicalTrials.gov NCT03761056) we evaluated axicabtagene ciloleucel (axi-cel), an autologous anti-CD19 chimeric antigen receptor (CAR) T-cell therapy, as part of first-line treatment in 40 patients with high-risk LBCL. This trial has completed accrual. The primary outcome was complete response rate (CRR). Secondary outcomes were objective response rate (ORR), duration of response (DOR), event-free survival (EFS), progression-free survival (PFS), overall survival (OS), assessment of safety, central nervous system (CNS) relapse and blood levels of CAR T cells and cytokines. The primary endpoint in efficacy-evaluable patients (n = 37) was met, with 78% CRR (95% confidence interval (CI), 62-90) and 89% ORR (95% CI, 75-97). As of 17 May 2021 (median follow-up, 15.9 months), 73% of patients remained in objective response; median DOR, EFS and PFS were not reached. Grade ≥3 cytokine release syndrome (CRS) and neurologic events occurred in three patients (8%) and nine patients (23%), respectively. There were no treatment-related grade 5 events. Robust CAR T-cell expansion occurred in all patients with a median time to peak of 8 days. We conclude that axi-cel is highly effective as part of first-line therapy for high-risk LBCL, with a manageable safety 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".