A complex containing LPP and α-Actinin mediates TGFβ-induced migration and invasion of ErbB2-expressing breast cancer cells
Bibliographic record
Abstract
Transforming growth factor β (TGFβ) is a potent modifier of the malignant phenotype in ErbB2-expressing breast cancers. We demonstrate that epithelial-derived breast cancer cells, which undergo a TGFβ-induced epithelial-to-mesenchymal transition (EMT), engage signaling molecules that normally facilitate cellular migration and invasion of mesenchymal cells. We identify lipoma preferred partner (LPP) as an indispensable regulator of TGFβ-induced migration and invasion of ErbB2-expressing breast cancer cells. We show that LPP re-localizes to focal adhesion complexes upon TGFβ stimulation and is a critical determinant in TGFβ-mediated focal adhesion turnover. Finally, we have determined that the interaction between LPP and α-actinin, an actin cross-linking protein, is necessary for TGFβ-induced migration and invasion of ErbB2-expressing breast cancer cells. Thus, our data reveal that LPP, which is normally operative in cells of mesenchymal origin, can be co-opted by breast cancer cells during an EMT to promote their migration and invasion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".