Safety and efficacy of tisagenlecleucel plus pembrolizumab in patients with r/r DLBCL: phase 1b PORTIA study results
Bibliographic record
Abstract
Tisagenlecleucel demonstrated high response rates and a manageable safety profile in adults with relapsed/refractory diffuse large B-cell lymphoma (r/r DLBCL) in the JULIET trial. However, lack of response and chimeric antigen receptor (CAR) T-cell exhaustion were observed in patients with programmed cell death protein 1 (PD-1) overexpression. Hence, pembrolizumab, a PD-1 inhibitor, was hypothesized to improve efficacy and cellular expansion of CAR T-cells in vivo. Here, we report the final analysis of the PORTIA trial in adult patients with r/r DLBCL who had ≥2 prior lines of therapy and had an Eastern Cooperative Oncology Group performance status of ≤1. Patients received 1 tisagenlecleucel infusion on day 1. Pembrolizumab (200 mg) was given every 21 days, for up to 6 doses. Three cohorts initiated pembrolizumab on days 15 (n = 4), 8 (n = 4), or -1 (n = 4). Safety, efficacy, cellular kinetics, and biomarker analyses were included. Tisagenlecleucel plus pembrolizumab was feasible and showed a manageable safety profile, without dose-limiting toxicities. Emerging efficacy with tisagenlecleucel was observed when pembrolizumab was given the day before tisagenlecleucel; however, the limited patient sample and short follow-up do not allow for definitive conclusions. Adding pembrolizumab to tisagenlecleucel did not augment the cellular expansion of tisagenlecleucel but delayed peak expansion if given the day before tisagenlecleucel (NCT03630159).
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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) | 0.002 | 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".