Paracrine Crosstalk between Fibroblasts and ER+ Breast Cancer Cells Creates an IL1β-Enriched Niche that Promotes Tumor Growth
Bibliographic record
Abstract
Breast cancer-induced activated fibroblasts support tumor progression. However, the role of normal fibroblasts in tumor progression remains controversial. In this study, we used modified patient-derived organoid cultures and demonstrate that constitutively secreted cytokines from normal breast fibroblasts initiate a paracrine signaling mechanism with estrogen receptor-positive (ER + ) breast cancer cells, which results in the creation of an interleukin (IL)-1β-enriched microenvironment. We found that this paracrine signaling mechanism is shared between normal and activated fibroblasts. Interestingly, we observed that in reconstructed tumor microenvironment containing autologous ER + breast cancer cells, activated fibroblasts, and immune cells, tamoxifen is more effective in reducing tumor cell proliferation when this paracrine signaling is blocked. Our findings then suggest that ER + tumor cells could create a growth-promoting environment without activating stromal fibroblasts and that in breast-conserving surgeries, normal fibroblasts could be a significant modulator of tumor recurrence by enhancing the proliferation of residual breast cancer cells in the tumor-adjacent breast tissue.
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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 | 0.001 | 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".