NLRC5 expression in tumor cells is critical to activate adaptive and innate antitumor immune responses
Bibliographic record
Abstract
Abstract Tumors evade cytotoxic T lymphocyte (CTL)-mediated killing by downregulating MHC class-I, mainly resulting from the loss of its transcriptional activator NLRC5. Expressing full-length NLRC5 (NLRC5-FL) or a shorter NLRC5-CIITA fusion protein termed NLRC5 super-activator (NLRC5-SA) in cancer cells upregulates MHC-I expression and promotes antitumor immunity. To distinguish the role of NLRC5 expressed within tumor cells and antigen presenting cells, we studied B16-F10 melanoma expressing NLRC5-FL (B16-N-FL) or NLRC5-SA (B16-N-SA) in Nlrc5 +/+ and Nlrc5 −/− mice. Both tumors were efficiently controlled in both Nlrc5 +/+ and Nlrc5 −/− hosts with abundant immune cell infiltration, enriched for activated and differentiated CD8 + and CD4 + T cells, NK, NKT and iNKT cells. B16-N-FL and B16-N-SA tumors showed increased collagen deposition and vascularization, with upregulation of CCL4 and CXCL9 chemokine genes in B16-N-SA tumors. Depletion of either CD8 + T cells or NK1.1 + cells increased the growth of B16-N-FL and B16-N-SA tumors in Nlrc5 +/+ mice, and that of B16-N-SA tumors in Nlrc5 -/- hosts. Proteomes of B16-N-FL and B16-N-SA cells showed downmodulation of dominant tumor antigens and upregulation of ubiquitination and protein processing pathway proteins. Differentially expressed proteins shared between B16-N-FL and B16-N-SA cells showed enrichment in phagosome and autophagy pathways. We conclude that tumor cell-intrinsic NLRC5 expression is critical for the activation of adaptive and innate immune cells, and establishment of an immune-supportive tumor microenvironment to permit immune cell infiltration and their effector functions and achieve tumor control. NLRC5 expression in APCs is dispensable to mediate these effects. Delivering NLRC5-SA is a promising approach to restore antitumor immune responses in MHC-I-low immune evasive tumors. Graphical abstract
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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) | 0.002 | 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".