<strong></strong>Interclonal Mutually Beneficial Cooperation Mediated by TGF-β1 Enhances Invasion of Breast Cancer Cells
Bibliographic record
Abstract
Intratumour heterogeneity is often associated with poor response to treatment and bad prognosis. In addition to genetic and epigenetic sources, phenotypic heterogeneity can also reflect plastic responses to signals from other cells. The latter can be mediated by various cell-cell interactions, from antagonistic (i.e., competition) to commensalistic or cooperative (mutually beneficial or altruistic). Positive exchanges can increase the fitness of clones and contribute to tumour growth, resistance to drugs and metastasis. Consequently, understanding the pathways involved in such interactions is of great significance for cancer treatment. This study used two breast cancer cell lines with different aggressiveness levels and very different secretome profiles (i.e., MDA-MB-231 and MCF7) to address the nature and mechanistic basis of interclonal crosstalk through paracrine signalling involving soluble factors during the early stages of metastasis. Our data show that MDA-MB-231 is able to recruit MCF7, through TGFβ1-mediated paracrine signalling, into expressing mesenchymal features and increased migration. On the other hand, MCF7 has no effect on the migration of MDA, suggesting a passive/commensalistic interaction. However, we found that the invasive potentials of both lines are enhanced when they are co-cultured, indicating that the two lines act synergistically and that such interclonal interactions can be mutually beneficial in vivo. Taking into account the negative impact that metastasis has on cancer prognosis and the lack of therapies to directly affect this process, interfering with the specific cooperative behaviours that tumour cells engage in during tumour progression should provide an additional strategy to increase 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) | 0.003 | 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".