Abstract 71: Relationship of mammographic density and gene expression analysis of normal breast tissue surrounding breast cancer.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».