Combining the Antigen Processing Components TAP and Tapasin Elicits Enhanced Tumor-Free Survival
Bibliographic record
Abstract
PURPOSE: Tpn is a member of the MHC class I loading complex and functions to bridge the TAP peptide transporter to MHC class I molecules. Metastatic human carcinomas often express low levels of the antigen-processing components Tapasin and TAP and display few functional surface MHC class I molecules. As a result, carcinomas are unrecognizable by effector CTLs. The aim of this study is to examine if Tapasin (Tpn) plays a critical role in the escape of tumors from immunologic recognition. EXPERIMENTAL DESIGN: To test our hypothesis, a nonreplicating adenovirus vector encoding human Tpn (AdhTpn) was constructed to restore Tpn expression in vitro and in vivo in a murine lung carcinoma cell line (CMT.64) that is characterized by down-regulation of surface MHC class I due to deficiency in antigen-processing components. RESULTS: Ex vivo, Tpn expression increased surface MHC class I and restored susceptibility of tumor cells to antigen-specific CTL killing, and AdhTpn infection of dendritic cells also significantly increased cross-presentation and cross-priming. Furthermore, tumor-bearing animals inoculated with AdhTpn demonstrated a significant increase in CD8(+) and CD4(+) T cells and CD11c(+) dendritic cells infiltrating the tumors. Provocatively, whereas syngeneic mice bearing tumors that were inoculated with AdhTpn a significant reduction in tumor growth and increased survival compared with vector controls, combining AdhTpn inoculation with AdhTAP1 resulted in a significant augmentation of protection from tumor-induced death than either component alone. CONCLUSIONS: This is the first demonstration that Tpn alone can enhance survival and immunity against tumors but additionally suggests that Tpn and TAP should be used together as components of immunotherapeutic vaccine protocols to eradicate tumors.
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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) | 0.001 | 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".