NLRC5 overexpression in ovarian tumors remodels the tumor microenvironment and increases T-cell reactivity toward autologous tumor-associated antigens
Bibliographic record
Abstract
Introduction: Epithelial ovarian cancer (OC) stands as one of the deadliest gynecologic malignancies, urgently necessitating novel therapeutic strategies. Approximately 60% of ovarian tumors exhibit reduced expression of major histocompatibility complex class I (MHC I), intensifying immune evasion mechanisms and rendering immunotherapies ineffective. NOD-like receptor CARD domain containing 5 (NLRC5) transcriptionally regulates MHC I genes and many antigen presentation machinery components. We therefore explored the therapeutic potential of NLRC5 in OC. Methods: We generated OC cells overexpressing NLRC5 to rescue MHC I expression and antigen presentation and then assessed their capability to respond to PD-L1 blockade and an infected cell vaccine. Results: Analysis of microarray datasets revealed a correlation between elevated NLRC5 expression and extended survival in OC patients; however, NLRC5 was scarcely detected in the OC tumor microenvironment. OC cells overexpressing NLRC5 exhibited slower tumor growth and resulted in higher recruitment of leukocytes in the TME with lower CD4/CD8 T-cell ratios and increased activation of T cells. Immune cells from peripheral blood, spleen, and ascites from these mice displayed heightened activation and interferon-gamma production when exposed to autologous tumor-associated antigens. Finally, as a proof of concept, NLRC5 overexpression within an infected cell vaccine platform enhanced responses and prolonged survival in comparison with control groups when challenged with parental tumors. Discussion: These findings provide a compelling rationale for utilizing NLRC5 overexpression in "cold" tumor models to enhance tumor susceptibility to T-cell recognition and elimination by boosting the presentation of endogenous tumor antigens. This approach holds promise for improving antitumoral immune responses in OC.
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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) | 0.001 | 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".