The immune suppressive factors CD155 and PD-L1 show contrasting expression patterns and immune correlates in ovarian and other cancers
Bibliographic record
Abstract
OBJECTIVE: We recently showed that tumors with an immunologically 'cold' phenotype are enriched for expression of stemness-associated genes and PVR/CD155, the ligand of the immunosuppressive molecule TIGIT. To explore the therapeutic implications of this finding, we investigated the relationship between PVR/CD155 expression, tumor-infiltrating lymphocytes (TIL), and prognosis in high-grade serous ovarian cancer (HGSC) and other cancers. METHODS: Expression of CD155, TIGIT, PD-1, PD-L1, and other immune markers in HGSC was assessed by high-dimensional flow cytometry, multi-color histological imaging, and/or gene expression profiling. The prognostic significance of PVR/CD155 and CD274/PD-L1 expression was assessed bioinformatically in HGSC and 32 other cancers in The Cancer Genome Atlas. RESULTS: T cells from HGSC frequently co-expressed TIGIT and PD-1, and the ratio of TIGIT to PD-1 expression increased markedly after in vitro expansion with a clinically relevant protocol. CD155 was commonly expressed on malignant epithelium in HGSC and showed a negative or non-significant association with TIL. In contrast, PD-L1 was predominantly expressed by tumor-associated macrophages and positively associated with TIL. These contrasts between CD155 and PD-L1 were seen across HGSC patients, across metastatic sites within individual patients, and even within individual tumor deposits. PVR/CD155 and CD274/PD-L1 exhibited divergent prognostic associations across diverse cancer types in TCGA, including HGSC. CONCLUSIONS: CD155 and PD-L1 exhibit contrasting expression patterns, TIL associations and prognostic significance, suggesting they represent non-redundant immunosuppressive mechanisms. The CD155/TIGIT pathway represents a compelling immunotherapeutic target for HGSC and for immunologically cold tumors in general.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".