Epithelial state-transitions permit inflammation-induced tumorigenesis
Bibliographic record
Abstract
Abstract Chronic inflammation across tissues is associated with an increased risk of developing cancer 1–3 . While potentially oncogenic somatic mutations have been demonstrated to persist and expand in healthy organs 4–6 , what triggers a subset of cells harbouring deleterious mutations to transition into a neoplasm or an aggressive adenoma with poor prognosis 7,8 is not well-understood. Unlike normal, healthy cells, benign cells harbouring mutations perceive inflammation in chronic disease differently, potentiating the progression from physiological inflammation to tumorigenesis 9 . Here, we reveal that a subset of epithelial cells with mutations are poised to transition from pre-neoplastic state to early neoplasm, through rewiring of epithelial IL-1β responses and inflammatory macrophage recruitment. We characterise this process by leveraging a mouse model of biliary tract cancer (cholangiocarcinoma), in which deleterious mutations are introduced to tumour suppressor genes in common cancer pathways ( Trp53 and Pten ), and by quantifying differences in cell states and corresponding gene expression dependencies in the absence or presence of liver inflammation. Critically, we find that targeting the epithelial-derived signals of tissue-wide inflammation (namely COX2) is insufficient to limit tumorigenesis; rather, targeting the reactivation of oncogene-induced developmental signals, such as NOTCH, prevents this pre-neoplastic to neoplastic transition, demonstrating that oncofoetal switching is a pharmacologically-tractable target in patients with a high risk of developing cancers on the background of inflammation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".