NLRC5-CIITA Fusion Protein as an Effective Inducer of MHC-I Expression and Antitumor Immunity
Bibliographic record
Abstract
Aggressive tumors evade cytotoxic T lymphocytes by suppressing MHC class-I (MHC-I) expression that also compromises tumor responsiveness to immunotherapy. MHC-I defects strongly correlate to defective expression of NLRC5, the transcriptional activator of MHC-I and antigen processing genes. In poorly immunogenic B16 melanoma cells, restoring NLRC5 expression induces MHC-I and elicits antitumor immunity, raising the possibility of using NLRC5 for tumor immunotherapy. As the clinical application of NLRC5 is constrained by its large size, we examined whether a smaller NLRC5-CIITA fusion protein, dubbed NLRC5-superactivator (NLRC5-SA) as it retains the ability to induce MHC-I, could be used for tumor growth control. We show that stable NLRC5-SA expression in mouse and human cancer cells upregulates MHC-I expression. B16 melanoma and EL4 lymphoma tumors expressing NLRC5-SA are controlled as efficiently as those expressing full-length NLRC5 (NLRC5-FL). Comparison of MHC-I-associated peptides (MAPs) eluted from EL4 cells expressing NLRC5-FL or NLRC5-SA and analyzed by mass spectrometry revealed that both NLRC5 constructs expanded the MAP repertoire, which showed considerable overlap but also included a substantial proportion of distinct peptides. Thus, we propose that NLRC5-SA, with its ability to increase tumor immunogenicity and promote tumor growth control, could overcome the limitations of NLRC5-FL for translational immunotherapy applications.
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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 | 0.001 |
| 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".