NLRC5 elicits antitumor immunity by enhancing processing and presentation of tumor antigens to CD8<sup>+</sup> T lymphocytes
Bibliographic record
Abstract
Cancers can escape immunesurveillance by diminishing the expression of MHC class-I molecules (MHC-I) and components of the antigen-processing machinery (APM). Developing new approaches to reverse these defects could boost the efforts to restore antitumor immunity. Recent studies have shown that the expression of MHC-I and antigen-processing molecules is transcriptionally regulated by NOD-like receptor CARD domain containing 5 (NLRC5). To investigate whether NLRC5 could be used to improve tumor immunogenicity, we established stable lines of B16-F10 melanoma cells expressing NLRC5 (B16-5), the T cell co-stimulatory molecule CD80 (B16-CD80) or both (B16-5/80). Cells harboring NLRC5 constitutively expressed MHC-I and LMP2, LMP7 and TAP1 genes of the APM. The B16-5 cells efficiently presented the melanoma antigenic peptide gp10025-33 to Pmel-1 TCR transgenic CD8(+) T cells and induced their proliferation. In the presence of CD80, B16-5 cells stimulated Pmel-1 cells even without the addition of gp100 peptide, indicating that NLRC5 facilitated the processing and presentation of endogenous tumor antigen. Upon subcutaneous implantation, B16-5 cells showed markedly reduced tumor growth in C57BL/6 hosts but not in immunodeficient hosts, indicating that the NLRC5-expressing tumor cells elicited antitumor immunity. Following intravenous injection, B16-5 and B16-5/80 cells formed fewer lung tumor foci compared to control cells. In mice depleted of CD8(+) T cells, B16-5 cells formed large subcutaneous and lung tumors. Finally, immunization with irradiated B16-5 cells conferred protection against challenge by parental B16 cells. Collectively, our findings indicate that NLRC5 could be exploited to restore tumor immunogenicity and to stimulate protective antitumor immunity.
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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".