Trogocytosis of MHC-I/Peptide Complexes Derived from Tumors and Infected Cells Enhances Dendritic Cell Cross-Priming and Promotes Adaptive T Cell Responses
Bibliographic record
Abstract
The transporter associated with antigen processing (TAP) and the major histocompatibility complex class I (MHC-I), two important components of the MHC-I antigen presentation pathway, are often deficient in tumor cells. The restoration of their expression has been shown to restore the antigenicity and immunogenicity of tumor cells. However, it is unclear whether TAP and MHC-I expression in tumor cells can affect the induction phase of the T cell response. To address this issue, we expressed viral antigens in tumors that are either deficient or proficient in TAP and MHC-I expression. The relative efficiency of direct immunization or immunization through cross-presentation in promoting adaptive T cell responses was compared. The results demonstrated that stimulation of animals with TAP and MHC-I proficient tumor cells generated antigen specific T cells with greater killing activities than those of TAP and MHC-I deficient tumor cells. This discrepancy was traced to differences in the ability of dendritic cells (DCs) to access and sample different antigen reservoirs in TAP and MHC-I proficient versus deficient cells and thereby stimulate adaptive immune responses through the process of cross-presentation. In addition, our data suggest that the increased activity of T cells is caused by the enhanced DC uptake and utilization of MHC-I/peptide complexes from the proficient cells as an additional source of processed antigen. Furthermore, we demonstrate that immune-escape and metastasis are promoted in the absence of this DC 'arming' mechanism. Physiologically, this novel form of DC antigen sampling resembles trogocytosis, and acts to enhance T cell priming and increase the efficacy of adaptive immune responses against tumors and infectious pathogens.
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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 | 0.001 |
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".