TAP expression reduces IL‐10 expressing tumor infiltrating lymphocytes and restores immunosurveillance against melanoma
Bibliographic record
Abstract
Many immune therapeutic strategies are under development for melanoma to treat metastatic disease and prevent disease reoccurrence. However, human melanoma cells are often deficient in antigen processing and this appears to play a role in their expansion and escape from immunosurveillance. For example, expression of the transporters associated with antigen processing (TAP1 and TAP2) is down-regulated in the mouse melanoma cell line B16F10. This results in a lack of tumor-associated antigen processing, low surface expression of MHC Class I molecules and low immunogenicity. We observe that restoration of TAP1 expression by transfection resurrects the processing and presentation of viral antigens, and the melanoma-associated antigen, TRP-2. Immunization with irradiated B16F10/rTAP1 transfected cells generates CTLs that are capable of killing B16F10/rTAP1 transfected targets and B16F10 targets deficient in TAP1. Furthermore, B16F10/rTAP1 transfectants grow at a significantly slower rate in mice than B16F10 cells. In an experimental model that closely recapitulates the clinical situation, treatment of B16F10 tumors in mice with a vaccinia virus vector expressing TAP1 also significantly decreases tumor growth in vivo. Furthermore, tumors treated with vaccinia TAP1 had significantly reduced numbers of immunosuppressive, CD3(+)/IL-10 positive, tumor infiltrating lymphocytes. Therefore, TAP1 expression restores both antigen presentation and immunogenicity in B16F10 melanoma cells and concomitantly reduces immunosuppressive IL-10 production at the local tumor site, thereby increasing immunosurveillance mechanisms against 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) | 0.000 | 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".