Collagen I but not Matrigel matrices provide an MMP-dependent barrier to ovarian cancer cell penetration
Bibliographic record
Abstract
BACKGROUND: The invasive potential of cancer cells is usually assessed in vitro using Matrigel as a surrogate basement membrane. Yet cancer cell interaction with collagen I matrices is critical, particularly for the peritoneal metastatic route undertaken by several cancer types including ovarian. Matrix metalloprotease (MMP) activity is important to enable cells to overcome the barrier constraints imposed by basement membranes and stromal matrices in vivo. Our objective was to compare matrices reconstituted from collagen I and Matrigel as representative barriers for ovarian cancer cell invasion. METHODS: The requirement of MMP activity for ovarian cancer cell penetration of Matrigel and collagen matrices was assessed in 2D transwell and 3D spheroid culture systems. RESULTS: The broad range MMP inhibitor GM6001 completely prevented cell perforation of polymerised collagen I-coated transwell membranes. In contrast, GM6001 decreased ES-2 cell penetration of Matrigel by only approximately 30% and had no effect on HEY cell Matrigel penetration. In 3D culture, ovarian cancer cells grown as spheroids also migrated into surrounding Matrigel matrices despite MMP blockade. In contrast, MMP activity was required for invasion into 3D matrices of collagen I reconstituted from acid-soluble rat-tail collagen I, but not from pepsin-extracted collagen I (Vitrogen/Purecol), which lacks telopeptide regions. CONCLUSION: Matrigel does not form representative barriers to ovarian cancer cells in either 2D or 3D culture systems. Our findings support the use of collagen I rather than Matrigel as a matrix barrier for invasion studies to better approximate critical interactions and events associated with peritoneal metastasis.
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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".