Mammographic density and breast tissue expression of inflammatory markers, growth factors, and vimentin.
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ABSTRACT: Background
Mammographic density is a known risk factor for breast cancer, but the underlying pathologic characteristics are not well understood. The current analysis investigated the expression of several markers of interest, e.g., inflammation and growth, with mammographic density (MD) in normal and malignant breast tissue specimens from 279 women of the Multiethnic Cohort (MEC).Methods
Breast cancer cases, recruited from a nested case-control study within the MEC, provided mammograms for density evaluation. Protein expression (COX-2, TNF-α, TGF-β, IGF-1R, IGFBP-2, and vimentin) was assessed by immunohistochemical detection. Linear regression was applied to evaluate the relation between marker expression and percent density and to compute adjusted means with 95% confidence intervals (CI) by marker status while adjusting for confounders.Results
Due to missing cores and tissue, normal tissue could only be evaluated for COX-2 and vimentin. No significant associations with mammographic density were detected for all markers analyzed. For inflammatory markers (TNF-α, COX-2, and TGF-β) in tumor tissue, MD were non-significantly higher with stronger expression but the differences were very small. For example, the mean MD values for no, weak, and strong TNF-α expression were 35% (95% CI 24-47%), 39% (95% CI 29-48%), and 38% (95% CI 27-50%). In a posthoc analysis among postmenopausal women only, the difference across categories of TNF-α expression increased to 25% (95% CI 12-39%), 35% (95% CI 23-48%), and 35% (95% CI 20-49%).Conclusions
The current analysis offers little support for an involvement of immunohistochemical markers representing inflammatory and growth factor pathways as predictors of breast density.
SUBMITTER: Maskarinec G
PROVIDER: S-EPMC6267911 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThis study aimed to determine if associations of pre-diagnostic percent breast density, absolute dense area, and non-dense area with subsequent breast cancer risk differ by the tumour's molecular marker status.MethodsWe included 1010 postmenopausal women with breast cancer and 2077 matched controls from the Nurses' Health Study (NHS) and the Nurses' Health Study II (NHS II) cohorts. Breast density was estimated from digitised film mammograms using computer-assisted thresholding techniques. Information on breast cancer risk factors was obtained prospectively from biennial questionnaires. Polychotomous logistic regression was used to assess associations of breast density measures with tumour subtypes by the status of selected tissue markers. All tests of statistical significance were two sided.ResultsThe association of percent density with breast cancer risk appeared to be stronger in ER- as compared with ER+ tumours, but the difference did not reach statistical significance (density ⩾50% vs <10% odds ratio (OR)=3.06, 95% confidence interval (CI) 2.17-4.32 for ER+; OR=4.61, 95% CI 2.36-9.03 for ER-, Pheterogeneity=0.08). Stronger positive associations were found for absolute dense area and CK5/6- and EGFR- as compared with respective marker-positive tumours (Pheterogeneity=0.002 and 0.001, respectively). Stronger inverse associations of non-dense area with breast cancer risk were found for ER- as compared with ER+ tumours (Pheterogeneity=0.0001) and for AR+, CK5/6+, and EGFR+ as compared with respective marker-negative tumours (Pheterogeneity=0.03, 0.005, and 0.009, respectively). The associations of density measures with breast cancer did not differ by progesterone receptor and human epidermal growth factor receptor 2 status.ConclusionsBreast density influences the risk of breast cancer subtypes by potentially different mechanisms.
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Project description:Mammographic breast density (MBD) is a risk factor for breast cancer, but its molecular basis is poorly understood. Growth factors stimulate cellular and epithelial proliferation and could influence MBD via these mechanisms. Studies investigating the associations of circulating growth factors with MBD have, however, yielded conflicting results especially in postmenopausal women. We, therefore, investigated the associations of plasma growth factor gene expression [insulin-like growth factor (IGF)-1, IGF-binding protein 3, FGF-1, FGF-12, TGFβ1 and bone morphogenetic protein (BMP)-2] with MBD in postmenopausal women. We used NanoString nCounter platform to quantify plasma growth factor gene expression and Volpara to evaluate volumetric MBD measures. We investigated the associations of growth factor gene expression with MBD using both multiple linear regression (fold change) and multinomial logistic regression models, adjusted for potential confounders. The mean age of the 368 women enrolled was 58 years (range, 50-64). In analyses using linear regression models, one unit increase in IGF-1 gene expression was associated with a 35% higher volumetric percent density (VPD, 1.35; 95% confidence interval (CI), 1.13-1.60; P = 0.001). There were suggestions that TGFβ1 gene expression was positively associated with VPD while BMP-2 gene expression was inversely associated with VPD, but these were not statistically significant. In analyses using multinomial logistic regression, TGFβ1 gene expression was 33% higher (OR = 1.33; 95% CI, 1.13-1.56; P = 0.0008) in women with extremely dense breasts than those with almost entirely fatty breasts. There were no associations between growth factor gene expression and dense volume or nondense volume. Our study provides insights into the associations of growth factors with MBD in postmenopausal women and requires confirmation in other study populations.Prevention relevanceMammographic breast density is a strong risk factor for breast cancer. Understanding its underlying biological mechanisms could have utility in breast cancer prevention.
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Project description:PurposePrevious studies of breast tissue gene expression have shown that the extratumoral microenvironment has substantial variability across individuals, some of which can be attributed to epidemiologic factors. To evaluate how mammographic density and breast tissue composition relate to extratumoral microenvironment gene expression, we used data on 121 patients with breast cancer from the population-based Polish Women's Breast Cancer Study.Experimental designBreast cancer cases were classified on the basis of a previously reported, biologically defined extratumoral gene expression signature with two subtypes: an Active subtype, which is associated with high expression of genes related to fibrosis and wound response, and an Inactive subtype, which has high expression of cellular adhesion genes. Mammographic density of the contralateral breast was assessed using pretreatment mammograms and a quantitative, reliable computer-assisted thresholding method. Breast tissue composition was evaluated on the basis of digital image analysis of tissue sections.ResultsThe Inactive extratumoral subtype was associated with significantly higher percentage mammographic density (PD) and dense area (DA) in univariate analysis (PD: P = 0.001; DA: P = 0.049) and in multivariable analyses adjusted for age and body mass index (PD: P = 0.004; DA: P = 0.049). Inactive/higher mammographic density tissue was characterized by a significantly higher percentage of stroma and a significantly lower percentage of adipose tissue, with no significant change in epithelial content. Analysis of published gene expression signatures suggested that Inactive/higher mammographic density tissue expressed increased estrogen response and decreased TGF-β signaling.ConclusionsBy linking novel molecular phenotypes with mammographic density, our results indicate that mammographic density reflects broad transcriptional changes, including changes in both epithelia- and stroma-derived signaling.
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Project description:Purpose: Previous studies of breast tissue gene expression have demonstrated that the extratumoral microenvironment has substantial variability across individuals, some of which can be attributed to epidemiologic factors. To evaluate how mammographic density (MD) and breast tissue composition relate to extratumoral microenvironment gene expression, we used data on 121 breast cancer patients from the population-based Polish Women’s Breast Cancer Study.Design: Breast cancer cases were classified based on a previously reported, biologically-defined extratumoral gene expression signature with two subtypes: an Active subtype, which is associated with high expression of genes related to fibrosis and wound response, and an Inactive subtype, which has high expression of cellular adhesion genes. MD of the contralateral breast was assessed using pre-treatment mammograms and a quantitative, reliable computer-assisted thresholding method. Breast tissue composition was evaluated based on digital image analysis of tissue sections. Results:The Inactive extratumoral subtype was associated with significantly higher percentage mammographic density (PD) and dense area (DA) in univariate analysis (PD: p=0.001; DA: p=0.049) and in multivariable analyses adjusted for age and body mass index (PD: p=0.004; DA: p=0.049). Inactive/higher MD tissue was characterized by a significantly higher percentage of stroma and a significantly lower percentage of adipose tissue, with no significant change in epithelial content. Analysis of published gene expression signatures suggested that Inactive/higher MD tissue expressed increased estrogen response and decreased TGF-ß signaling. Conclusions:By linking novel molecular phenotypes with MD, our results indicate that MD reflects broad transcriptional changes, including changes in both epithelia- and stroma-derived signaling.
2013-10-21 | GSE49175 | GEO
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2013-10-21 | E-GEOD-49175 | biostudies-arrayexpress
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Project description:Breast density, a breast cancer risk factor, is a radiologic feature that reflects fibroglandular tissue content relative to breast area or volume. Its histology is incompletely characterized. Here we use deep learning approaches to identify histologic correlates in radiologically-guided biopsies that may underlie breast density and distinguish cancer among women with elevated and low density. We evaluated hematoxylin and eosin (H&E)-stained digitized images from image-guided breast biopsies (n = 852 patients). Breast density was assessed as global and localized fibroglandular volume (%). A convolutional neural network characterized H&E composition. In total 37 features were extracted from the network output, describing tissue quantities and morphological structure. A random forest regression model was trained to identify correlates most predictive of fibroglandular volume (n = 588). Correlations between predicted and radiologically quantified fibroglandular volume were assessed in 264 independent patients. A second random forest classifier was trained to predict diagnosis (invasive vs. benign); performance was assessed using area under receiver-operating characteristics curves (AUC). Using extracted features, regression models predicted global (r = 0.94) and localized (r = 0.93) fibroglandular volume, with fat and non-fatty stromal content representing the strongest correlates, followed by epithelial organization rather than quantity. For predicting cancer among high and low fibroglandular volume, the classifier achieved AUCs of 0.92 and 0.84, respectively, with epithelial organizational features ranking most important. These results suggest non-fatty stroma, fat tissue quantities and epithelial region organization predict fibroglandular volume. The model holds promise for identifying histological correlates of cancer risk in patients with high and low density and warrants further evaluation.
| S-EPMC6864056 | biostudies-literature
Project description:IntroductionMammographic density (MD), as assessed from film screen mammograms, is determined by the relative content of adipose, connective and epithelial tissue in the female breast. In epidemiological studies, a high percentage of MD confers a four to six fold risk elevation of developing breast cancer, even after adjustment for other known breast cancer risk factors. However, the biologic correlates of density are little known.MethodsGene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) and 64 newly diagnosed breast cancer patients, both included from mammographic centres. Percent MD was determined using a previously validated, computerized method on scanned mammograms. Significance analysis of microarrays (SAM) was performed to identify genes influencing MD and a linear regression model was used to assess the independent contribution from different variables to MD.ResultsSAM-analysis identified 24 genes differentially expressed between samples from breasts with high and low MD. These genes included three uridine 5'-diphospho-glucuronosyltransferase (UGT) genes and the oestrogen receptor gene (ESR1). These genes were down-regulated in samples with high MD compared to those with low MD. The UGT gene products, which are known to inactivate oestrogen metabolites, were also down-regulated in tumour samples compared to samples from healthy individuals. Several single nucleotide polymorphisms (SNPs) in the UGT genes associated with the expression of UGT and other genes in their vicinity were identified.ConclusionsThree UGT enzymes were lower expressed both in breast tissue biopsies from healthy women with high MD and in biopsies from newly diagnosed breast cancers. The association was strongest amongst young women and women using hormonal therapy. UGT2B10 predicts MD independently of age, hormone therapy and parity. Our results indicate that down-regulation of UGT genes in women exposed to female sex hormones is associated with high MD and might increase the risk of breast cancer.
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