Nivolumab plus relatlimab for patients with relapsed or progressed B-cell malignancies in RELATIVITY-022
Bibliographic record
Abstract
ABSTRACT: Despite high response rates, anti-programmed death 1 (anti-PD-1) monotherapy eventually fails in most patients with relapsed/refractory (R/R) Hodgkin lymphoma (HL) and is ineffective in most other B-cell malignancies. The lymphocyte activation gene 3 (LAG-3) cell-surface receptor represents another immune checkpoint that can be targeted to induce remissions in these diseases; dual inhibition of PD-1 and LAG-3 is approved in advanced melanoma. We performed a multicenter phase 1/2a open-label study of the anti-LAG-3 antibody relatlimab (RELATIVITY-022) administered as monotherapy or in combination with nivolumab in patients with R/R B-cell malignancies. We treated 106 patients and no dose-limiting toxicities were observed during escalation. The recommended phase 2 dose was relatlimab 240 mg as monotherapy or nivolumab 240 mg plus relatlimab 160 mg, administered every 2 weeks. No unexpected safety signals were observed compared with anti-PD-1 monotherapy. In the HL expansion cohorts, objective response rate (ORR) was 62% and complete response rate (CRR) was 19% in anti-PD-1/anti-programmed death ligand 1 (anti-PD-[L]1)-naive patients (n = 21), with a median progression-free survival (PFS) of 19 months; ORR was 15% and CRR 0%, with median PFS of 6 months in anti-PD-(L)1-progressed patients (n = 20). In diffuse large B-cell lymphoma, ORR was 7% with no CRs (n = 15), and median PFS was 2 months. Nivolumab plus relatlimab appeared to be safe and tolerable. Responses in patients with anti-PD-(L)1-naive HL was encouraging, although the contribution of relatlimab to overall efficacy of the combination needs to be further evaluated. This trial was registered at www.ClinicalTrials.gov as #NCT02061761.
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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 | 0.000 |
| 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 | 0.000 | 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".