PYCR1-dependent proline synthesis in cancer-associated fibroblasts is required for the deposition of pro-tumorigenic extracellular matrix
Bibliographic record
Abstract
TElevated production of collagen-rich extracellular matrix (ECM) is a hallmark of cancer associated fibroblasts (CAFs) and a central driver of cancer aggressiveness. How to target ECM production to oppose cancer is yet unclear, since targeting CAFs has been shown to restrain but also promote cancer progression. Metabolic rewiring is a hallmark of CAFs. Here we find that proline, which is a highly abundant amino acid in collagen proteins, is newly synthesised from glutamine to make tumour collagen in breast cancer xenografts, and that its production is elevated in breast cancer CAFs. PYCR1 is the rate-limiting enzyme for proline synthesis and is highly expressed in the tumour stroma of breast cancer patients and in CAFs. Reducing PYCR1 levels in CAFs is sufficient to reduce tumour collagen production, tumour growth and metastatic spread in vivo and cancer cell proliferation in vitro. PYCR1 and COL1A1 are overexpressed in patients with invasive ductal carcinoma with poor prognosis. Both collagen and glutamine-derived proline synthesis in CAFs are enhanced by increased pyruvate dehydrogenase-derived acetyl-CoA levels, via gene expression regulation through the epigenetic regulator histone acetyl transferase EP300. Altogether, our work unveils unprecedented roles of CAF metabolism to support pro-tumorigenic collagen production. PYCR1 is a recognised cancer cell vulnerability and potential target for therapy, hence, our work provides evidence that targeting PYCR1 in tumours may have the additional benefit of halting the production of pro-tumorigenic ECM.
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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 | 0.001 |
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".