Lisocabtagene maraleucel in follicular lymphoma: the phase 2 TRANSCEND FL study
Bibliographic record
Abstract
An unmet need exists for patients with relapsed/refractory (R/R) follicular lymphoma (FL) and high-risk disease features, such as progression of disease within 24 months (POD24) from first-line immunochemotherapy or disease refractory to both CD20-targeting agent and alkylator (double refractory), due to no established standard of care and poor outcomes. Chimeric antigen receptor (CAR) T cell therapy is an option in R/R FL after two or more lines of prior systemic therapy, but there is no consensus on its optimal timing in the disease course of FL, and there are no data in second-line (2L) treatment of patients with high-risk features. Lisocabtagene maraleucel (liso-cel) is an autologous, CD19-directed, 4-1BB CAR T cell product. The phase 2 TRANSCEND FL study evaluated liso-cel in patients with R/R FL, including 2L patients who all had POD24 from diagnosis after treatment with anti-CD20 antibody and alkylator ≤6 months of FL diagnosis and/or met modified Groupe d'Etude des Lymphomes Folliculaires criteria. Primary/key secondary endpoints were independent review committee-assessed overall response rate (ORR)/complete response (CR) rate. At data cutoff, 130 patients had received liso-cel (median follow-up, 18.9 months). Primary/key secondary endpoints were met. In third-line or later FL (n = 101), ORR was 97% (95% confidence interval (CI): 91.6‒99.4), and CR rate was 94% (95% CI: 87.5‒97.8). In 2L FL (n = 23), ORR was 96% (95% CI: 78.1‒99.9); all responders achieved CR. Cytokine release syndrome occurred in 58% of patients (grade ≥3, 1%); neurological events occurred in 15% of patients (grade ≥3, 2%). Liso-cel demonstrated efficacy and safety in patients with R/R FL, including high-risk 2L FL. ClinicalTrials.gov identifier: NCT04245839 .
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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 | 0.001 |
| 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) | 0.003 | 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".