Regulation of site-specific liver metastasis by extracellular matrix proteins
Bibliographic record
Abstract
Metastatic disease remains the main cause of death from cancer. Few therapeutic options for patients have demonstrated potential in curing metastatic disease. The molecular mechanisms underlying site-specific metastasis and the factors mediating tumor cell homing remain largely unknown. Based on a murine Lewis lung carcinoma tumour model of site-specific metastasis mediated by the expression of the insulin-like growth factor - I receptor (IGF-IR), we identified ECM components that show particular promise in regulating metastasis to a specific site. Specifically, we identified collagen IVα1 and α2 as differentially expressed in liver- and lung-colonizing cells. The overexpression of these genes caused major changes to cell structure and function including differences in cellular morphology, anchorage-independent growth, and resulted in a switch from a lung- to a liver-metastasizing phenotype. These changes were at least in part due to α2-integrin-mediated activation of focal adhesion kinase (FAK) and protection from anoikis. Collagen IV α1 suppression resulted in increased anoikis and decreased tumour cell colonization of the liver, making it an essential and sufficient gene in liver metastasis in our model. Moreover, type IV collagen overexpression resulted in major changes to ECM and ECM-degradation genes decreasing MMP-3, MMP-9, MMP-13, and collagen type III. Uveal melanoma cells with distinct metastatic phenotypes also showed major changes to these genes. Finally, by analyzing human specimens of metastatic disease, collagen IV was shown to be expressed only in metastatic and specifically hepatic metastases when compared to primary tumours and metastases to other organs. Collectively, these findings implicate collagen IV as a clinically relevant marker and potential target against site-specific metastasis to the liver.
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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".