Pro-cathepsin D interacts with the extracellular domain of the β chain of LRP1 and promotes LRP1-dependent fibroblast outgrowth
Bibliographic record
Abstract
Interactions between cancer cells and fibroblasts are crucial in cancer progression. We have previously shown that the aspartic protease cathepsin D (cath-D), a marker of poor prognosis in breast cancer that is overexpressed and highly secreted by breast cancer cells, triggers mouse embryonic fibroblast outgrowth via a paracrine loop. Here, we show the requirement of secreted cath-D for human mammary fibroblast outgrowth using a three-dimensional co-culture assay with breast cancer cells that do or do not secrete pro-cath-D. Interestingly, proteolytically-inactive pro-cath-D remains mitogenic, indicating a mechanism involving protein-protein interaction. We identify the low-density lipoprotein (LDL) receptor-related protein-1, LRP1, as a novel binding partner for pro-cath-D in fibroblasts. Pro-cath-D binds to residues 349-394 of the β chain of LRP1, and is the first ligand of the extracellular domain of LRP1β to be identified. We show that pro-cath-D interacts with LRP1β in cellulo. Interaction occurs at the cell surface, and overexpressed LRP1β directs pro-cath-D to the lipid rafts. Our results reveal that the ability of secreted pro-cath-D to promote human mammary fibroblast outgrowth depends on LRP1 expression, suggesting that pro-cath-D-LRP1β interaction plays a functional role in the outgrowth of fibroblasts. Overall, our findings strongly suggest that pro-cath-D secreted by epithelial cancer cells promotes fibroblast outgrowth in a paracrine LRP1-dependent manner in the breast tumor microenvironment.
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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".