A 9-kDa matricellular SPARC fragment released by cathepsin D exhibits pro-tumor activity in the triple-negative breast cancer microenvironment
Bibliographic record
Abstract
ABSTRACT Extracellular matrix (ECM) remodeling by proteases results in the release of protein fragments that promote tumor progression and metastasis. The protease cathepsin D (cath-D), a marker of poor prognosis in triple-negative breast cancer (TNBC), is aberrantly secreted in the tumor microenvironment. Using degradomic analyses by TAILS, we discovered that the matricellular protein SPARC is a substrate of extracellular cath-D. In vitro , cath-D induced limited proteolysis of SPARC C-terminal extracellular Ca 2+ binding domain at acidic pH, leading to the production of SPARC fragments (34-, 27-, 16-, 9-, and 6-kDa). Similarly, cath-D secreted by TNBC cells cleaved fibroblast- and cancer cell-derived SPARC at the tumor pericellular acidic pH. SPARC cleavage also occurred in TNBC tumors. Among these fragments, only the 9-kDa SPARC fragment inhibited TNBC cell adhesion and spreading on fibronectin, and stimulated their migration, endothelial transmigration, and invasion. Our study establishes a novel crosstalk between proteases and matricellular proteins in the tumor microenvironment through limited proteolysis of SPARC, revealing a novel targetable 9-kDa bioactive SPARC fragment for new TNBC treatments.
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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".