Breast cancer cells alter the dynamics of stromal fibronectin-collagen interactions
Bibliographic record
Abstract
Breast cancer cells recruit surrounding stromal cells, such as cancer-associated fibroblasts (CAFs), to remodel their extracellular matrix (ECM) and promote invasive tumor growth. Two major ECM components, fibronectin (Fn) and collagen I (Col I), are known to interact with each other to regulate cellular behavior. In this study, we seek to understand how Fn and Col I interplay and promote a dysregulated signaling pathway to facilitate tumor progression. Specifically, we investigated the evolution of tumor-conditioned stromal ECM composition, structure, and relaxation. Furthermore, we assessed how evolving Fn-Col I interactions gradually affected pro-angiogenic signaling. Our data first indicate that CAFs initially assembled a strained, viscous, and unfolded Fn matrix. This early altered Fn matrix was later remodeled into a thick Col I-rich matrix that was characteristic of a dense tumor mass. Next, our results suggest that this ECM remodeling was primarily mediated by matrix metalloproteinases (MMPs). This MMP activity caused profound structural and mechanical changes in the developing ECM, which then modified vascular endothelial growth factor (VEGF) secretion by CAFs and matrix sequestration. Collectively, these findings enhance our understanding of the mechanisms by which Fn and Col I synergistically interplay in promoting a sustained altered signaling cascade to remodel the breast tumor stroma for invasive breast tumor growth.
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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".