Metastatic Breast Carcinoma–Associated Fibroblasts Have Enhanced Protumorigenic Properties Related to Increased IGF2 Expression
Bibliographic record
Abstract
Abstract Purpose: The microenvironment of metastatic breast cancer is incompletely characterized, despite prior evidence that it plays a key role in the biology of metastasis. A major component of the tumor stroma is the carcinoma-associated fibroblast (CAF), which has been shown to communicate with other stromal and cancer cells to create a protumorigenic milieu. Our study was designed to characterize human CAFs from different metastatic sites. Experimental Design: We collected eight carcinoma-associated fibroblasts (mCAFs) from different metastatic sites and compared them with CAFs from primary tumors (pCAFs) and with normal breast fibroblasts (NFs). Molecular profiles and effects on breast cancer cell growth, on response to doxorubicin and on T-cell proliferation were compared. Results: We observed marked differences in mCAFs compared with pCAFs and NFs with respect to in vitro proliferation and effects on breast cancer cell migration, spheroid growth, invasion, response to doxorubicin, and in vivo tumor growth. We found marked transcriptomic differences between mCAFs and pCAFs, including increased expression of IFN-related genes and IGF2 in the former. Cluster analysis revealed two groups of mCAFs, with the liver mCAFs clustering together, with increased PDGFA expression. Treatment with an antibody against insulin-like growth factors (BI836845) inhibited growth of mixed mCAF-tumor cell xenografts in vivo. Also, mCAFs had a suppressive effect on T-cell proliferation. Conclusions: This is the first comparative analysis of a set of CAFs from metastatic sites in breast cancer. It revealed a marked protumorigenic effect in these mCAFs, which occurs in part through increased expression of IGF2.
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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".