Pre-neoplastic stromal cells drive BRCA1-mediated breast tumorigenesis
Bibliographic record
Abstract
Summary Women with germline mutations in BRCA1 (BRCA1 +/mut ) have increased risk for developing hereditary breast cancer 1, 2 . Cancer initiation in BRCA1 +/mut is associated with pre-malignant changes in the breast epithelium including altered differentiation 3–5 , proliferative stress 6 and genomic instability 7 . However, the role of the epithelium- associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here, we show that the pre-malignant stromal niche promotes epithelial proliferation and BRCA1- driven cancer initiation in trans . Using single-cell RNAseq (scRNAseq) analysis of human pre-neoplastic BRCA1 +/mut and control breast tissues, we show that stromal cells provide numerous pro-proliferative paracrine signals inducing epithelial proliferation. We identify a subpopulation of pre-cancer associated fibroblasts (pre-CAFs) that produces copious amounts of pro-tumorigenic factors including matrix metalloproteinase 3 (MMP3) 8, 9 , and promotes BRCA1-driven tumorigenesis in vivo . Our gene-signature analysis and mathematical modeling of epithelial differentiation reveals that stromal-induced proliferation leads to the accumulation of luminal progenitor cells with altered differentiation, and thus contributes to increased breast cancer risk in BRCA1 +/mut . Our results demonstrate how alterations in cell-cell communication can induce imbalances in epithelial homeostasis ultimately leading to cancer initiation. We anticipate our results to form the foundation for novel disease monitoring and therapeutic strategies to improve patient management in hereditary breast cancer. For example, pre-CAF specific proteins may serve as biomarkers for pre-cancerous disease initiation to inform whether radical bilateral mastectomy is needed. In addition, MMP inhibitors could be re-indicated for primary cancer prevention treatment in women with high-risk BRCA1 mutations.
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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".