Potential of Gold Nanoparticles in Current Radiotherapy Using a Co-Culture Model of Cancer Cells and Cancer Associated Fibroblasts
Bibliographic record
Abstract
Many cancer therapeutics are tested in vitro using only tumour cells. However, the tumour promoting effect of cancer associated fibroblasts (CAFs) within the tumour microenvironment (TME) is thought to reduce cancer therapeutics' efficacy. We have chosen pancreatic ductal adenocarcinoma (PDAC) as our tumor model. Our goal is to create a co-culture of CAFs and tumour cells to model the interaction between cancer and stromal cells in the TME and allow for better testing of therapeutic combinations. To test the proposed co-culture model, a gold nanoparticle (GNP) mediated-radiation response was used. Cells were grown in co-culture with different ratios of CAFs to cancer cells. MIA PaCa-2 was used as our PDAC cancer cell line. Co-cultured cells were treated with 2 Gy of radiation following GNP incubation. DNA damage and cell proliferation were examined to assess the combined effect of radiation and GNPs. Cancer cells in co-culture exhibited up to a 23% decrease in DNA double strand breaks (DSB) and up to a 35% increase in proliferation compared to monocultures. GNP/Radiotherapy (RT) induced up to a 25% increase in DNA DSBs and up to a 15% decrease in proliferation compared to RT alone in both monocultured and co-cultured cells. The observed resistance in the co-culture system may be attributed to the role of CAFs in supporting cancer cells. Moreover, we were able to reduce the activity of CAFs using GNPs during radiation treatment. Indeed, CAFs internalize a significantly higher number of GNPs, which may have led to the reduction in their activity. One reason experimental therapeutics fail in clinical trials relates to limitations in the pre-clinical models that lack a true representation of the TME. We have demonstrated a co-culture platform to test GNP/RT in a clinically relevant environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".