Targeted demethylation and activation of <i>NLRC5</i> augment cancer immunogenicity through MHC class I
Bibliographic record
Abstract
Impaired expression of MHC (major histocompatibility complex) class I in cancers constitutes a major mechanism of immune evasion. It has been well documented that the low level of MHC class I is associated with poor prognosis and resistance to checkpoint blockade therapies. However, there is lmited approaches to specifically induce MHC class I to date. Here, we show an approach for robust and specific induction of MHC class I by targeting an MHC class I transactivator (CITA)/NLRC5, using a CRISPR/Cas9-based gene-specific system, designated TRED-I (Targeted reactivation and demethylation for MHC-I). The TRED-I system specifically recruits a demethylating enzyme and transcriptional activators on the NLRC5 promoter, driving increased MHC class I antigen presentation and accelerated CD8+ T cell activation. Introduction of the TRED-I system in an animal cancer model exhibited tumor-suppressive effects accompanied with increased infiltration and activation of CD8+ T cells. Moreover, this approach boosted the efficacy of checkpoint blockade therapy using anti-PD1 (programmed cell death protein) antibody. Therefore, targeting NLRC5 by this strategy provides an attractive therapeutic approach for cancer.
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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".