Phenotypic Plasticity during the Dissemination of Circulating Tumour Cell Clusters: A Model Involving TGFβ1-Mediated Cluster Dissociation, Adherence and Single-Cell Extravasation
Bibliographic record
Abstract
Metastasis – the ability of cancer cells to disperse and colonize distant locations in the body, is responsible for the majority of cancer-related deaths. While in the vasculature, tumour cells are referred to as circulating tumour cells (CTCs) and can manifest either as single cells or clusters of cells, with the latter being the most aggressive. Despite their significant role in the metastatic process, the mechanisms through which CTC clusters extravasate and disseminate remain largely unknown. Notably, CTC clusters have been found to contain platelets, which are known to secrete many factors, including Transforming Growth Factor Beta 1 (TGF-β1) – a signaling molecule that has been widely implicated in many aspects of cancer, including the extravasation of single CTCs. To address whether the interaction between platelets and CTC clusters might also facilitate the extravasation of CTC clusters, we evaluated the effect of exogenous TGF-β1 on an experimentally evolved lung cancer cell line that grows as cell clusters that we previously developed and used to investigate the biology of CTC clusters. We found that exogenous TGFβ1 induces the dissociation of clusters and cell adherence. Furthermore, once adhered, cells release their own TGF-β1 and are able to migrate and invade in the absence of exogenous TGFβ1. Based on these findings we propose a model that involves both paracrine and autocrine TGFβ1-mediated phenotypic plasticity resulting in the acquisition of traits that enable the extravasation of CTC clusters as single 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".