Epigenetic memory of radiotherapy in dermal fibroblasts impairs wound repair capacity in cancer survivors
Bibliographic record
Abstract
Abstract Long-term toxicities caused by cancer treatments have recently gained increasing recognition due to a steadily growing population of cancer survivors. Radiotherapy (RT) is a common treatment known to unintentionally harm surrounding normal tissuesincluding the skin, hindering wound healing even years after treatment. Our study aimed to elucidate the underlying mechanisms of these late-onset adverse effects caused by RT. By comparing paired skin biopsies from previously irradiated (RT+) and non-irradiated (RT−) sites in breast cancer survivors who underwent RT years ago, we discovered compromised wound healing capacity and impaired fibroblast functions in the RT+ skin. By employing ATAC-seq, we identified altered chromatin landscapes in RT+ fibroblasts, pinpointing THBS1 as a crucial epigenetically primed wound repair-related gene. Further confirmation of THBS1's significance during wound repair came from single-cell RNA-sequencing and spatial transcriptomic analysis of human wounds. Remarkably, heightened and sustained THBS1 expression was observed in RT+ fibroblasts in both mouse and human radiation wound models, leading to impaired fibroblast motility and contractility. Encouragingly, our study found that treatment with anti-THBS1 antibodies promoted ex vivo wound closure in RT+ skin from breast cancer survivors. These findings indicate that dermal fibroblasts retain a long-term radiation memory recorded in the form of epigenetic changes. Targeting this maladaptive epigenetic memory shows promise for mitigating the late-onset adverse effects caused by RT, offering potential solutions to improve the quality of life for cancer survivors.
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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) | 0.002 | 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".