Receptor-type protein tyrosine phosphatase alpha (PTPα) mediates MMP14 localization and facilitates triple-negative breast cancer cell invasion
Bibliographic record
Abstract
The ability of cancer cells to invade surrounding tissues requires degradation of the extracellular matrix (ECM). Invasive structures, such as invadopodia, form on the plasma membranes of cancer cells and secrete ECM-degrading proteases that play crucial roles in cancer cell invasion. We have previously shown that the protein tyrosine phosphatase alpha (PTPα) regulates focal adhesion formation and migration of normal cells. Here we report a novel role for PTPα in promoting triple-negative breast cancer cell invasion in vitro and in vivo. We show that PTPα knockdown reduces ECM degradation and cellular invasion of MDA-MB-231 cells through Matrigel. PTPα is not a component of TKS5-positive structures resembling invadopodia; rather, PTPα localizes with endosomal structures positive for MMP14, caveolin-1, and early endosome antigen 1. Furthermore, PTPα regulates MMP14 localization to plasma membrane protrusions, suggesting a role for PTPα in intracellular trafficking of MMP14. Importantly, we show that orthotopic MDA-MB-231 tumors depleted in PTPα exhibit reduced invasion into the surrounding mammary fat pad. These findings suggest a novel role for PTPα in regulating the invasion of triple-negative breast cancer cells.
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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".