Spotlight on nuclear PD-L1 in ovarian cancer chemoresistance: hidden but mighty
Bibliographic record
Abstract
Introduction: Ovarian cancer (OVCA) has a five-year survival rate of approximately 45%, with little improvement over recent decades. Although anti-PD-L1 therapies have shown substantial efficacy in other solid tumors, their effectiveness in OVCA has been limited. These treatments target only membranous and soluble forms of PD-L1, without addressing nuclear-localized PD-L1. The role of nuclear PD-L1 in OVCA chemoresistance, however, remains largely unexplored. In this study, we examined the prognostic significance of nuclear PD-L1 and its interactions with plasma gelsolin (pGSN) and CD8+ T cells within the tumor microenvironment. Methods: Using immunofluorescence, we quantified nuclear PD-L1, pGSN, and additional markers in OVCA samples. Statistical analyses and machine learning approaches were employed to assess associations between marker expression, patient outcomes, and chemoresistance. Results: Increased nuclear PD-L1 was associated with disease recurrence, chemoresistance and poor overall survival. Although CD8+ T cells provided survival benefits to patients, elevated PD-L1 hindered these benefits resulting in shortened disease free (DFS) and overall survival (OS). Co-expression of PD-L1 and pGSN was also associated with shortened DFS, OS and chemoresistance. Discussion: These findings indicate that nuclear PD-L1 serves as a poor prognostic marker in OVCA, being associated with tumor recurrence, chemoresistance, and reduced overall survival. Targeting nuclear PD-L1 may represent a novel therapeutic strategy to improve outcomes in patients with OVCA.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".