Advancing Radiobiology: Investigating the Effects of Photon, Proton, and Carbon-Ion Irradiation on PANC-1 Cells in 2D and 3D Tumor Models
Bibliographic record
Abstract
Introduction: Pancreatic cancer (PC) is one of the most aggressive and lethal malignancies, calling for enhanced research. Pancreatic ductal adenocarcinoma (PDAC) represents 70–80% of all cases and is known for its resistance to conventional therapies. Carbon-ion radiotherapy (CIRT) has emerged as a promising approach due to its ability to deliver highly localized doses and unique radiobiological properties compared to X-rays. In vitro radiobiology has relied on two-dimensional (2D) cell culture models so far; however, these are not sufficient to replicate the complexity of the in vivo tumor architecture. Three-dimensional (3D) models become a paradigm shift, surpassing the constraints of traditional models by accurately re-creating morphological, histological, and genetic characteristics as well as the interaction of tumour cells with the microenvironment. Materials and Methods: This study investigates the survival of pancreatic cancer cells in both 2D and spheroids, a 3D model, following photon, proton, and carbon-ion irradiation by means of clonogenic, MTT, spheroid growth, and vitality assays. Results: Our results demonstrate that carbon ions are more efficient in reducing cancer cell survival compared to photons and protons. In 2D cultures, carbon-ion irradiation reduced cell survival to approximately 15%, compared to 45% with photons and 30% with protons. In the 3D culture model, spheroid growth was similarly inhibited by carbon-ion irradiation; however, the overall survival rates were higher across all irradiation modalities compared to the 2D cultures. Carbon ions consistently showed the highest efficacy in reducing cell viability in both models. Conclusions: Our research highlights the pivotal role of 3D models in unraveling the complexities of pancreatic cancer radiobiology, offering new avenues for designing more effective and precise treatment protocols.
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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".