Increased tumour dihydroceramide production after Photofrin-PDT alone and improved tumour response after the combination with the ceramide analogue LCL29. Evidence from mouse squamous cell carcinomas
Bibliographic record
Abstract
Photodynamic therapy (PDT) has been proven effective for treatment of several types of cancer. Photodynamic therapy alone, however, attains limited cures with some tumours and there is need for its improved efficacy in such cases. Sphingolipid (SL) analogues can promote tumour response in combination with anticancer drugs. In this study, we used mouse SCCVII squamous cell carcinoma tumours to determine the impact of Photofrin-PDT on the in vivo SL profile and the effect of LCL29, a C6-pyridinium ceramide, on PDT tumour response. Following PDT, the levels of dihydroceramides (DHceramides), in particular C20-DHceramide, were elevated in tumours. Similarly, increases in DHceramides, in addition to C20:1-ceramide, were found in PDT-treated SCCVII cells. These findings indicate the importance of the de novo ceramide pathway in Photofrin-PDT response not only in cells but also in vivo. Notably, co-exposure of SCCVII tumours to Photofrin-PDT and LCL29 led to enhanced tumour response compared with PDT alone. Thus, we show for the first time that Photofrin-PDT has a distinct signature effect on the SL profile in vitro and in vivo, and that the combined treatment advances PDT therapeutic gain, implying translational significance of the combination.
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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) | 0.002 | 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".