Abstract 71: Relationship of mammographic density and gene expression analysis of normal breast tissue surrounding breast cancer.
Bibliographic record
Abstract
Abstract Gene expression signatures of benign breast tissues surrounding breast cancers demonstrate marked heterogeneity, comparable to the diversity found in tumor tissues. Relationships between epidemiologic factors and gene expression in the breast cancer microenvironment have not been fully explored. In particular, the correspondence between high mammographic density (MD), a strong breast cancer risk factor reflecting an increased percentage of fibroglandular tissue, and gene expression is ill-defined. To evaluate how MD and breast tissue composition relate to gene expression of extratumoral microenvironment, we used data on 121 breast cancer patients aged 20-74 years from the population-based Polish Women's Breast Cancer Study. Subjects were classified into two groups based on the gene expression of normal breast tissue by the previously reported, biologically-defined extratumoral gene expression signature: Active extratumoral subtype is associated with high expression of genes related to fibrosis and wound response, while Inactive subtype has high expression of cellular adhesion genes. MD was assessed using pre-treatment digitized mammograms of unaffected breasts. Breast tissue composition was evaluated based on digital image analysis of tissue sections. Information on related breast cancer risk factors was collected using medical records and interviews. Roughly half of the patients had Active extratumoral subtype (n=59) and half had Inactive extratumoral subtype (n=62). Inactive subtype was significantly associated with higher percentage density (PD) and dense area (DA) in univariate analysis (PD: p=0.001; DA: p=0.049) and after adjusting for age and body mass index (PD: p=0.004; DA: p=0.049). Inactive/high MD group (defined as PD ≥ 25%) had significantly lower percentage of adipose tissue (mean percentage: 47% in Inactive vs. 78% in Active; 56% in high MD vs. 69% in low MD) and higher percentage of stroma (mean percentage: 42% in Inactive vs. 13% in Active; 34% in high MD vs. 21% in low MD), but no significant difference was detected in the percentage of epithelial tissue. Further analysis of published gene expression signatures suggested high MD and Inactive subtype were strongly associated with positive expression of age-related (younger) and estrogen response signatures, but decreased expression of obesity and transforming growth factor beta signatures. These results indicate that MD reflects broad transcriptional changes. Moreover, based on strong associations between the genomic phenotypes and MD, Active/Inactive subtype may be associated with breast cancer risk. Further research to better understand the molecular characteristics of normal breast genomic subtypes could identify pathways that are targetable in preventing mammographic-density associated risk. Citation Format: Xuezheng Sun, Gretchen L. Gierach, Rupninder Sandhu, Tyisha Williams, Norman Boyd, Nicole B. Johnson, Jonine D. Figueroa, Mark Sherman, Melissa A. Troester. Relationship of mammographic density and gene expression analysis of normal breast tissue surrounding breast cancer. [abstract]. In: Proceedings of the AACR Special Conference on Post-GWAS Horizons in Molecular Epidemiology: Digging Deeper into the Environment; 2012 Nov 11-14; Hollywood, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2012;21(11 Suppl):Abstract nr 71.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".