Immune evasion and immunotherapy resistance via TGF-beta activation of extracellular matrix genes in cancer associated fibroblasts
Bibliographic record
Abstract
The ability to disseminate, invade and successfully colonise other tissues is a critical hallmark of cancer that involves remodelling of the extracellular matrix (ECM) laid down by fibroblasts 1 . Moreover, Cancer-Associated-Fibroblasts (CAFs) produce key growth factors and cytokines as components of the ECM that fuel tumour growth, metastasis and chemoresistance, and immune response 2-4 . ECM changes also predict prognosis in pancreatic 5 and colorectal cancers 6,7 . Here, we examine the landscape of ECM-gene dysregulation pan-cancer and find that a subset of ECM genes is ( i ) dysregulated specifically in cancer, ( ii ) adversely prognostic, ( iii ) linked to TGF-beta signalling and transcription in Cancer-Associated-Fibroblasts, ( iv ) enriched in immunologically active cancers, and ( v ) predicts responses to Immune checkpoint blockade better than mutation burden, cytolytic activity, or an interferon signature, thus identifying a novel mechanism of immune evasion for patient stratification in precision immunotherapy and pharmacological modulation.
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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".