The EGF/CSF-1 Paracrine Invasion Loop Can Be Triggered by Heregulin β1 and CXCL12
Bibliographic record
Abstract
An important step in the process of metastasis from the primary tumor is invasive spread into the surrounding stroma. Using an in vivo invasion assay, we have previously shown that imposed gradients of epidermal growth factor (EGF) or colony-stimulating factor-1 (CSF-1) can induce invasion through an EGF/CSF-1 paracrine loop between cancer cells and macrophages. We now report that invasion induced by other ligands also relies on this EGF/CSF-1 paracrine invasive loop. Using an in vivo invasion assay, we show that MTLn3 breast cancer cells overexpressing ErbB3 exhibit enhanced invasion compared with control MTLn3 cells in response to the ErbB3 ligand HRG-beta1. The invasive response of both MTLn3-ErbB3 and transgenic MMTV-Neu tumors to HRG-beta1 is inhibited by blocking EGF receptor, CSF-1 receptor, or macrophage function, indicating that invasiveness to HRG-beta1 is dependent on the EGF/CSF-1 paracrine loop. Furthermore, we show that CXCL12 also triggers in vivo invasion of transgenic MMTV-PyMT tumors in an EGF/CSF-1-dependent manner. Although the invasion induced by HRG-beta1 or CXCL12 is dependent on the EGF/CSF-1 paracrine loop, invasion induced by EGF is not dependent on HRG-beta1 or CXCL12 signaling, showing an asymmetrical relationship between different ligand/receptor systems in driving invasion. Our results identify a stromal/tumor interaction that acts as an engine underlying invasion induced by multiple ligands.
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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".