Intratumoral expression levels of <i>PD-L1</i>, <i>GZMA</i>, and <i>HLA-A</i> along with oligoclonal T cell expansion associate with response to nivolumab in metastatic melanoma
Bibliographic record
Abstract
Immune checkpoint inhibitors blocking the interaction between programmed death-1 (PD-1) and PD-1 ligand-1 (PD-L1) are revolutionizing the cancer immunotherapies with durable clinical responses. Although high expression of PD-L1 in tumor tissues has been implicated to correlate with the better response to the anti-PD-1 therapies, this association has been controversial. In this study, to characterize immune microenvironment in tumors, we examined mRNA levels of immune-related genes and characterized T cell repertoire in the tumors of 13 melanoma patients before and after nivolumab treatment. We found that, in addition to the PD-L1 (p = 0.03), expression levels of PD-1 ligand-2 (PD-L2), granzyme A (GZMA) and human leukocyte antigen-A (HLA-A) in the pre-treatment tumors were significantly higher (p = 0.04, p = 0.01 and p = 0.006, respectively) in responders (n = 5) than in non-responders (n = 8). With nivolumab treatment, tumors in responders exhibited a substantial increase of CD8, GZMA and perforin 1 (PRF1) expression levels as well as increased ratio of TBX21/GATA3, suggesting dominancy of helper T cell type 1 (Th1) response to type 2 (Th2) response. T cell receptor β (TCR-β) repertoire analysis revealed oligoclonal expansion of tumor-infiltrating T lymphocytes (TILs) in the tumor tissues of the responders. Our findings suggest that melanoma harboring high PD-1 ligands (PD-L1 and PD-L2), GZMA and HLA-A expression may respond preferentially to nivolumab treatment, which can enhance Th1-skewed cellular immunity with oligoclonal expansion of TILs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".