Cellular Impact of Local Sub-ablative Radiotherapy on Pleural Mesothelioma
Bibliographic record
Abstract
In this study, we optimized a subcutaneous mouse model of pleural mesothelioma and demonstrated that local radiotherapy (LRT) can suppress tumor growth for up to eight days post-treatment. Single-cell RNA sequencing of tumor samples identified diverse tumor microenvironment cell types, including T cells, natural killer cells, dendritic cells, macrophages, fibroblasts, and endothelial cells. LRT-treated tumors exhibited major compositional shifts toward a pro-inflammatory state, with increased infiltration of CD8⁺ T cells and Ly6Chigh monocytes/macrophages. After treatment, endothelial cells upregulated CXCL10 expression, generating a chemotactic gradient to enhance immune cell recruitment. Evidence further suggested activation of the non-canonical STING (stimulator of interferon genes) pathway in fibroblasts and endothelial cells eight days post-treatment, potentially contributing to tumor regrowth. Concurrently, we observed sustained p53 signaling in these cell types (ie. fibroblasts and endothelial cells), marked by prolonged Cdkn1a upregulation. These findings indicate that LRT-induced DNA damage may trigger a senescence-associated secretory phenotype, which initially amplifies anti-tumor immune responses but later promotes pro-tumorigenic remodeling.
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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) | 0.001 | 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".