Chromatin insulation orchestrates matrix metalloproteinase gene cluster expression reprogramming in aggressive breast cancer tumors
Bibliographic record
Abstract
Abstract Background Triple-negative breast cancer (TNBC) is an aggressive subtype that exhibits a high incidence of distant metastases and lacks targeted therapeutic options. Here we explored how the epigenome may contribute to matrix metalloprotease (MMP) dysregulation given their key role in invasion, which is the first step of the metastatic process.Methods We combined RNA expression and chromatin interaction data to identify insulator elements potentially associated with invasion. We stably disrupted the CCCTC-Binding Factor (CTCF) binding site of a single insulator element in two TNBC cellular models. We characterized these models by combining Hi-C, ATAC-seq, and RNA-seq with functional experiments to determine invasive ability. Our findings were then also tested in a ductal carcinoma in situ (DCIS) cohort.Results We explored the clinical relevance of an insulator element located within the Chr11q22.2 locus, downstream of the MMP8 gene (IE8). This regulatory element resulted in a topologically associating domain (TAD) boundary that isolated nine MMP genes into two anti-correlated expression clusters. This expression pattern was strongly associated with worse relapse-free (HR = 1.57 [1.06 − 2.33]; p = 0.023) and overall (HR = 2.65 [1.31 − 5.37], p = 0.005) survival of TNBC patients. After CRISPR/Cas9-mediated disruption of IE8, cancer cells showed a switch in the MMP expression signature, specifically downregulating the pro-invasive MMP1 gene and upregulating the antitumorigenic MMP8 gene, resulting in reduced invasive ability. Finally, we observed that the imbalance in the MMP expression predicts DCIS that eventually progresses into invasive ductal carcinomas (AUC = 0.77, p < 0.01).Conclusion Our study demonstrates how the activation of an IE near the MMP8 gene determines the regional transcriptional regulation of MMP genes with opposing functional activity, ultimately influencing the invasive properties of aggressive forms of breast cancer.
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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".