Fibroblast activation protein‐α, a stromal cell surface protease, shapes key features of cancer associated fibroblasts through proteome and degradome alterations
Bibliographic record
Abstract
Cancer associated fibroblasts (CAFs) constitute an abundant stromal component of most solid tumors. Fibroblast activation protein (FAP) α is a cell surface protease that is expressed by CAFs. We corroborate this expression profile by immunohistochemical analysis of colorectal cancer specimens. To better understand the tumor-contextual role of FAPα, we investigate how FAPα shapes functional and proteomic features of CAFs using loss- and gain-of function cellular model systems. FAPα activity has a strong impact on the secreted CAF proteome ("secretome"), including reduced levels of anti-angiogenic factors, elevated levels of transforming growth factor (TGF) β, and an impact on matrix processing enzymes. Functionally, FAPα mildly induces sprout formation by human umbilical vein endothelial cells. Moreover, loss of FAPα leads to a more epithelial cellular phenotype and this effect was rescued by exogenous application of TGFβ. In collagen contraction assays, FAPα induced a more contractile cellular phenotype. To characterize the proteolytic profile of FAPα, we investigated its specificity with proteome-derived peptide libraries and corroborated its preference for cleavage carboxy-terminal to proline residues. By "terminal amine labeling of substrates" (TAILS) we explored FAPα-dependent cleavage events. Although FAPα acts predominantly as an amino-dipeptidase, putative FAPα cleavage sites in collagens are present throughout the entire protein length. In contrast, putative FAPα cleavage sites in non-collagenous proteins cluster at the amino-terminus. The degradomic study highlights cell-contextual proteolysis by FAPα with distinct positional profiles. Generally, our findings link FAPα to key aspects of CAF biology and attribute an important role in tumor-stroma interaction to FAPα.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".