Selenocompounds mediated upregulation of HLA class I expression enhanced mammaglobin-A peptide-specific cytotoxic T lymphocyte responses against breast cancer cells
Bibliographic record
Abstract
Abstract Recent phase I DNA vaccine clinical trials with Mammaglobin-A (Mam-A), a human breast tumor-associated antigen (TAA), have shown to be safe and efficient. However, the success of cancer vaccines is limited by the diminished expression of HLA class I molecules on cancer cells. In our current communication, we studied the impact of various selenocompounds towards the expression of HLA class I molecules on breast cancer cells and their impact on the cytotoxicity of MamA2.1 (HLA-A2 immunodominant epitope of Mam-A) activated human CD8+T lymphocytes (hCTLs). We noted an enhanced HLA-A2 expression along with upregulation of components involved in antigen presentation machinery in all four breast cancer cell lines, namely AU565, UACC-812, MCF-7, and MDA-MB-231, following treatment with methyselenol producing methylseninic acid (MSA) and dimethylselenide (DMDSe). Furthermore, we have demonstrated enhanced cytotoxicity of MamA2.1 activated CTLs on HLA-A2+/Mam-A+ AU565 and UACC-812 cell lines following pre-treatment with MSA and DMDSe, while no significant toxicity was noted under similar conditions on HLA-A2+/Mam-A− MCF-7 and MDA-MB-231 breast cancer cell lines. Taken together, our data demonstrated that MSA and DMDSe potentiate effector cytotoxic responses following TAA vaccine specific activation of CD8+T lymphocytes, and thus suggesting their futuristic role as vaccine adjuvants in cancer immunotherapy.
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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) | 0.002 | 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".