Rapid expansion of podoplanin-positive fibroblasts following radiation limits the anti-tumour CD8+ T-cell response to radiotherapy
Bibliographic record
Abstract
Abstract Radiotherapy is known to cause changes in the tumour stroma which can undermine treatment efficacy. Our understanding of this process has historically centred around effects driven by Transforming Growth Factor-beta (TGF-β) and alpha-smooth muscle actin (α-SMA)+ fibroblasts. Here, we identified a rapid expansion of podoplanin (PDPN)+ fibroblasts following radiotherapy in breast, head and neck and melanoma tumours. This fibrosis was not dependent on TGF-β, but was downstream of a radiotherapy-induced adaptive immune response. CD8+ T-cells entering the tumour after radiation were sequestered at the interface between residual tumour cells and PDPN+ fibroblasts and failed to enter the tumour core. Genetic deletion of PDPN in fibroblasts impacted their cytoskeleton and ability to organise extracellular matrix. This was associated with increased CD8+ T-cell entry and spontaneous tumour regression. Overall, we identify a mechanism whereby PDPN+ fibrosis limits immune-mediated radiation cell kill and demonstrate that disruption of PDPN signalling favours tumour control. Significance In this study we show that rapid podoplanin (PDPN)+ fibroblast expansion following radiotherapy limits immune-mediated radiation cell kill. Targeting PDPN and associated downstream signalling improves tumour control and is a promising strategy in combination with radiotherapy. Graphical abstract
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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) | 0.020 | 0.003 |
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".