Combination of PDT and a DNA demethylating agent produces anti-tumor immune response in a mouse tumor model
Bibliographic record
Abstract
Epigenetic mechanisms, which involve DNA methylation and histone modifications, result in the heritable silencing of genes without a change in their coding sequence. However, these changes must be actively maintained after each cell division rendering them a promising target for pharmacologic inhibition. DNA methyltransferase inhibitors like 5-aza-deoxycytidine (5-aza-dC) induce and/or up-regulate the expression of MAGE-type antigens in human and mice cancer cells. Photodynamic therapy (PDT) has been shown to be an effective locally ablative anti-cancer treatment that has the additional advantage of stimulating tumor-directed immune response. We studied the effects of a new therapy that combined the demethylating agent 5-aza-dC with PDT in the breast cancer model 4T1 syngenic to immunocompetent BALB/c mice. PDT was used as a locally ablating tumor treatment that is capable of eliciting strong and tumor directed immune response while 5-aza-dC pretreatment was used promote de novo induction of the expression of P1A.protein. This is the mouse homolog of human MAGE family antigens and is reported to function as a tumor rejection antigen in certain mouse tumors. This strategy led to an increase in PDT-mediated immune response and better treatment outcome. These results strongly suggest that the MAGE family antigens are important target for PDT mediated immune response but that their expression can be silenced by epigenetic mechanisms. Therefore the possibility that PDT can be combined with epigenetic strategies to elicit anti-tumor immunity in MAGE-positive tumor models is highly clinically significant and should be studied in detail.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".