A Novel PD-L1 Antibody Promotes Antitumor Function of Peripheral Cytotoxic Lymphocytes after Radical Nephrectomy in Patients with Renal Cell Carcinoma
Bibliographic record
Abstract
The intrinsic and acquired resistance to PD-1/PD-L1 immune checkpoint blockade is an important challenge for patients and clinicians because no reliable tool has been developed to predict individualized response to immunotherapy. In this study, we demonstrate the translational relevance of an ex vivo functional assay that measures the tumor cell killing ability of patient-derived CD8 T and NK cells (referred to as "cytotoxic lymphocytes," or CLs) isolated from the peripheral blood of patients with renal cell carcinoma. Patient-derived PBMCs were isolated before and after nephrectomy from patients with renal cell carcinoma. We compared the efficacy of U.S. Food and Drug Administration (FDA)-approved PD-1/PD-L1 inhibitors (pembrolizumab, nivolumab, atezolizumab) and a newly developed PD-L1 inhibitor (H1A Ab) in eliciting cytotoxic function. CL activity was improved at 3 mo after radical nephrectomy compared with baseline, and it was associated with higher circulating levels of tumor-reactive effector CD8 T cells (CD11ahighCX3CR1+GZMB+). Treatment of PBMCs with FDA-approved PD-1/PD-L1 inhibitors enhanced tumor cell killing activity of CLs, but a differential response was observed at the individual-patient level. H1A demonstrated superior efficacy in promoting CL activity compared with FDA-approved PD-1/PD-L1 inhibitors. PBMC immunophenotyping by mass cytometry revealed enrichment of effector CD8 T and NK cells in H1A-treated PBMCs and immunosuppressive regulatory T cells in atezolizumab-treated samples. Our study lays the ground for future investigation of the therapeutic value of H1A as a next-generation immune checkpoint inhibitor and the potential of measuring CTL activity in PBMCs as a tool to predict individual response to immune checkpoint inhibitors in patients with advanced renal cell carcinoma.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".