The B subunit of E. coli helps control the in vivo growth of solid tumors expressing the Epstein-Barr virus latent membrane protein 2A
Bibliographic record
Abstract
The latent membrane protein 2A (LMP2A), of Epstein Barr Virus (EBV), is expressed on a number of EBV-associated cancers and is a potential target for immunotherapeutic intervention and vaccination. The E. coli enterotoxin subunit B (EtxB), has previously been shown to enhance antigen processing and presentation of LMP2A leading to increased killing by LMP2A-specific cytotoxic T lymphocytes (CTLs) in vitro. To test the potential of EtxB to enhance CTL targeting of LMP2A expressed in solid tumors in vivo, we generated a murine tumor model (Renca-LMP2A), in which LMP2A is expressed as a transgenic neo-antigen on a renal carcinoma (Renca) cell line and forms solid tumors when injected s.c into BALB/c mice. The data clearly demonstrate that despite expression of LMP2A, Renca-LMP2A tumors do not trigger immune responses sufficient to control tumor growth in vivo. However, prior immunization with a recombinant vaccinia virus vector expressing LMP2A (Vac-LMP2A) led to significantly lower tumor growth rate in mice compared to un-immunized controls. Importantly, the rate of growth of Renca-LMP2A in mice previously immunized with Vac-LMP2A was significantly less compared with mice injected with vector-control transgenic Renca cells that do not express LPM2A (Renca-VC). Furthermore, we found that amongst Vac-LMP2A-immunized mice given Renca-LMP2A, daily injection of EtxB into the tumor site resulted in a significant delay in the onset of tumor growth and lower tumor volumes compared with Vac-LMP2A-immunized mice that were not treated with EtxB. Critically, the immunomodulatory effect caused by EtxB was significant; despite the progressive down-regulation of LMP2A and MHC class I expression by RencaLMP2A. Taken together these data clearly demonstrate the potential efficacy of using EtxB as a novel therapeutic agent in controlling the growth of EBV-associated 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".