SNARE-dependent interaction of Src, EGFR and β1 integrin regulates invadopodia formation and tumor cell invasion
Bibliographic record
Abstract
Acquisition of an invasive phenotype is prerequisite for tumor metastasis. Degradation of the extracellular matrix (ECM), and subsequent invasion by tumor cells, is mediated, in part, through subcellular structures called invadopodia. Src-dependent cytoskeletal rearrangements are required to form invadopodia, and here we identify an association between Src, epidermal growth factor receptor (EGFR), and β1 integrin that facilitates invadopodia formation. The association of Src, EGFR and β1 integrin is dependent upon membrane traffic that is mediated by syntaxin13 (officially known as STX12) and SNAP23; a similar dependence on these two SNARE proteins was observed for invadopodium-based matrix degradation and cell invasion. Inhibition of SNARE function impaired the delivery of Src and EGFR to developing invadopodia, as well as the β1-integrin-dependent activation of Src and phosphorylation of EGFR on Tyr residue 845. We also identified an association between SNAP23 and β1 integrin, and inhibition of β1 integrin increased this association, whereas the interaction between syntaxin13 and SNAP23 was reduced. The results suggest that SNARE-dependent trafficking is regulated, in part, by β1 integrin and is required for the delivery of Src and EGFR to sites of invadopodia formation in order to support tumor cell invasion.
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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".