Inflammation promotes tumor aggression by stimulating stromal cell-dependent collagen crosslinking and stromal stiffening
Bibliographic record
Abstract
Abstract Collagen deposition and stromal stiffening accompany malignancy, compromise treatment, and promote tumor aggression. Clarifying the molecular nature of and the factors that regulate extracellular matrix stiffening in tumors should identify biomarkers to stratify patients for therapy and therapeutic interventions to improve outcome. We profiled lysyl hydroxylase- and lysyl oxidase-mediated collagen crosslinks and quantified the greatest abundance of total and complex collagen crosslinks in more aggressive human breast cancer subtypes with the stiffest stroma. These tissues also harbored the highest number of tumor-associated macrophages (TAM), whose therapeutic ablation not only reduced metastasis, but also concomitantly decreased accumulation of collagen crosslinks and stromal stiffening. Epithelial-targeted expression of the crosslinking enzyme lysyl oxidase had no impact on collagen crosslinking in PyMT mammary tumors, whereas stromal cell targeting did. Consistently, stromal cells in microdissected human tumors expressed the highest level of collagen crosslinking enzymes. Immunohistochemical analysis of a cohort of breast cancer patient biopsies revealed that stromal expression of lysyl hydroxylase two, an enzyme that induces hydroxylysine aldehyde-derived collagen crosslinks and stromal stiffening correlated significantly disease specific mortality. The findings link tissue inflammation, stromal cell-mediated collagen crosslinking and stiffening to tumor aggression and identify lysyl hydroxylase two as a novel stromal biomarker. Significance We show infiltrating macrophages induce stromal fibroblast, and not epithelial, expression of collagen crosslinking enzymes that drive tumor stiffening. Stromal enzyme LH2 is significantly upregulated in breast cancer patients with the stiffest stroma, the most trivalent HLCCs and the worst prognosis, underscoring its potential as a biomarker and therapeutic target.
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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) | 0.002 | 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".