Protein-tyrosine Pseudokinase 7 (PTK7) Directs Cancer Cell Motility and Metastasis
Bibliographic record
Abstract
It is well established that widely expressed PTK7 is essential for vertebrate tissue morphogenesis. In cancer, the functionality of PTK7 is selectively regulated by membrane type-1 matrix metalloproteinase (MT1-MMP), ADAMs (a disintegrin domain and metalloproteinases), and γ-secretase proteolysis. Here, we established that the full-length membrane PTK7, its Chuzhoi mutant with the two functional MT1-MMP cleavage sites, and its L622D mutant with the single inactivated MT1-MMP cleavage site differentially regulate cell motility in a two-dimensional versus three-dimensional environment. We also demonstrated that in polarized cancer cells, the levels of PTK7 expression and proteolysis were directly linked to the structure and kinetics of cell protrusions, including lamellipodia and invadopodia. In the functionally relevant and widely accepted animal models of metastasis, mouse and chick embryo models, both the overexpression and knock-out of PTK7 in HT1080 cells abrogated metastatic dissemination. Our analysis of human tissue specimens confirmed intensive proteolysis of PTK7 in colorectal cancer tumors, but not in matching normal tissue. Our results provide convincing evidence that both PTK7 expression and proteolysis, rather than the level of the cellular full-length PTK7 alone, contribute to efficient directional cell motility and metastasis in cancer.
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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".