In vitro evidence for the potential of EGFR inhibitors to decrease the TGF-β1-induced dispersal of circulating tumour cell clusters mediated by EGFR overexpression
Bibliographic record
Abstract
Most cancer-related deaths are due to the spread of tumour cells throughout the body-a process known as metastasis. While in the vasculature, these cells are referred to as circulating tumour cells (CTCs) and can be found as either single cells or clusters of cells (often including platelets), with the latter having the highest metastatic potential. However, the biology of CTC clusters is poorly understood, and there are no therapies that specifically target them. We previously developed an in vitro model system for CTC clusters and proposed a new extravasation model that involves cluster dissociation, adherence, and single-cell invasion in response to TGF-β1 released by platelets. Here, we investigated TGF-β1-induced gene expression changes in this model, focusing on genes for which targeted drugs are available. In addition to the upregulation of the TGF-β1 signalling pathway, we found that (i) genes in the EGF/EGFR pathway, including those coding for EGFR and several EGFR ligands, were also induced, and (ii) Erlotinib and Osimertinib, two therapeutic EGFR/tyrosine kinase inhibitors, decreased the TGF-β1-induced adherence and invasion of the CTC cluster-like line despite the line expressing wild-type EGFR. Overall, we suggest that EGFR inhibitors have the potential to decrease the dispersal of CTC clusters that respond to TGF-β1 and overexpress EGFR (irrespective of its status) and thus could improve patient survival.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".