FAK signaling is critical for ErbB-2/ErbB-3 receptor cooperation for oncogenic transformation and invasion
Bibliographic record
Abstract
The overexpression of members of the ErbB tyrosine kinase receptor family has been associated with cancer progression. We demonstrate that focal adhesion kinase (FAK) is essential for oncogenic transformation and cell invasion that is induced by ErbB-2 and -3 receptor signaling. ErbB-2/3 overexpression in FAK-deficient cells fails to promote cell transformation and rescue chemotaxis deficiency. Restoration of FAK rescues both oncogenic transformation and invasion that is induced by ErbB-2/3 in vitro and in vivo. In contrast, the inhibition of FAK in FAK-proficient invasive cancer cells prevented cell invasion and metastasis formation. The activation of ErbB-2/3 regulates FAK phosphorylation at Tyr-397, -861, and -925. ErbB-induced oncogenic transformation correlates with the ability of FAK to restore ErbB-2/3-induced mitogen-activated protein kinase (MAPK) activation; the inhibition of MAPK prevented oncogenic transformation. In contrast, the inhibition of Src but not MAPK prevented ErbB-FAK-induced chemotaxis. In migratory cells, activated ErbB-2/3 receptors colocalize with activated FAK at cell protrusions. This colocalization requires intact FAK. In summary, distinct FAK signaling has an essential function in ErbB-induced oncogenesis and invasiveness.
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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) | 0.002 | 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".