Gene Expression In Breast Tissue Research Articles

Abstract 2206: Pilot phase I clinical trial of RANKL inhibition and breast tissue gene expression in high-risk premenopausal women with dense breasts

Abstract Background: Identifying pathways that can be targeted to reduce breast density and breast cancer incidence is an unmet need, especially in premenopausal women. Receptor activator of nuclear factor-κB ligand (RANKL) signaling mediates the major proliferative response of mammary epithelium to progesterone and is positively associated with breast density but there are no clinical trial data on the impact of RANKL inhibition on breast tissue markers in women with dense breasts. We, therefore, determined the impact of RANKL inhibition on breast tissue gene expression in high-risk premenopausal women with dense breasts. Study design: Pilot phase I clinical trial of 9 healthy high-risk pre-menopausal women (≥35 years of age) with dense breasts performed at Washington University School of Medicine, St. Louis, MO from July 2018-December 2018. Fifty-five percent of participants had a positive family history of breast cancer in a first degree relative. Intervention: Participants were given a single dose of subcutaneous RANKL antibody, denosumab (60mg), at baseline (Day 1). On the same day, prior to the denosumab injection, participants had an ultrasound guided core needle biopsy and a blood draw. All study participants returned for repeat core needle biopsy and blood draw after 60 days (Day 60). Outcome Measures: Changes in breast tissue gene expression between Day 1 and Day 60 with a focus on immune/inflammatory and hormone markers. Gene expression was profiled using NanoString nCounter platform. We performed pathway enrichment analysis and used Cell Maker Enrichment library from EnrichR to identify differentially expressed genes. Genes with a p-value<0.05 on differential expression analysis were considered statistically significant for utilization in pathway enrichment analysis. Libraries utilized for pathway enrichment using the EnrichR platform included the ChEA transcription factor library, GO Biological Process library, and Cellular Components library. Results: Macrophage markers, including CCR5, CD86, CD300A, and CD84 and targets downstream of IRF8: (p-value =4.73e-7), neutrophil degranulation/neutrophil activation in the immune response (p-value=6.0e-21), and the secretory granule membrane pathways (p-value=2.4e-14) were downregulated in breast tissues on Day 60. Pathways involved in progesterone metabolism (p-value=0.0003), estrogen response (p-value=0.0014) and fatty acid metabolism, (p-value=0.0001) were significantly upregulated on Day 60. Specifically, genes involved in steroid hormone metabolism such as DHRS2 (p-value=3.13e-05), and AKR1B15: p-value=4.12e-05) were upregulated on Day 60. Conclusions: A single 60 mg dose of subcutaneous denosumab injection was associated with alterations in pathways involved in hormone, immune and inflammatory signaling in the breast tissues of healthy premenopausal women with dense breasts. Citation Format: Michael Waters, Kay Jayachandran, Debbie Bennet, Jin Zhang, Katherine Weilbaecher, Ian S. Hagemann, Graham A. Colditz, Catherine M. Appleton, Adetunji T. Toriola. Pilot phase I clinical trial of RANKL inhibition and breast tissue gene expression in high-risk premenopausal women with dense breasts [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2206.

Increased trunk fat is associated with altered gene expression in breast tissue of normal weight women

Increased trunk fat is associated with an elevated risk of breast cancer in normal-weight postmenopausal women. The main objective of this study was to determine whether levels of trunk fat are associated with changes in breast gene expression in normal-weight women. Non-tumorous breast tissue was collected from 32 normal BMI women who underwent mastectomy for breast cancer risk reduction or treatment. Body composition was measured by dual-energy x-ray absorptiometry. High levels of trunk fat were associated with a large number of differentially expressed genes and changes in multiple pathways and processes potentially linked to breast cancer pathogenesis. High levels of trunk fat were also associated with an elevated immune score and increased levels of leptin, CCL2, VEGF-C, IL6, and aromatase. Collectively, these results help to explain why high levels of trunk fat are associated with an increased risk of breast cancer in normal BMI women.

Abstract 106: Assessing the impact of staggered light exposure on breast cancer risk and metabolism

Abstract Background: Numerous epidemiological studies, including the Nurses' Health Study II, found that shift workers have a higher risk for breast cancer. These studies hypothesize that suppressed melatonin and disrupted clock gene expression likely lead to an immune compromised, proliferative phenotype. However, the majority of studies to date rely on correlative evidence. With the onset of the COVID-19 pandemic, shift work has become increasingly prevalent, making the need to understand the mechanisms promoting disease, including breast cancer, even more important. Objective: The objective of this study is to analyze the impact of staggered light exposure on the immune function and breast tissue gene expression in order to identify how breast cancer risk is mediated. Methods: An 18-week study with 10 C57BL/6 female mice was conducted whereby half of the mice were exposed to a staggered light/dark (LD) cycle, as would be experienced by shift workers, and half were exposed to a regular LD cycle. Body weight was monitored weekly and sera and mammary tissues were collected at termination. The sera from each group was pooled and cytokine levels assessed using an antibody-pair-based assay. Results: Intriguingly, although the mice from the two study groups did not significantly differ in average body weight, the staggered LD exposure group had higher levels of inflammatory cytokines indicative of chronic, low-grade inflammation. Notably, the staggered LD group had 75% higher IL-6 levels, 157% higher IL-10 levels, and 49% higher TNF-alpha levels compared to the control group. To determine the impact of staggered light exposure on localized breast tissue gene expression, qPCR arrays of breast cancer risk and inflammatory genes will be done comparing expression levels between the control and staggered LD groups. IHC techniques will be used to determine Ki67 and macrophage subtype infiltration in the breast tissue of the two groups. Conclusion: Our data show that staggered LD exposure induces chronic, low-grade inflammation independent from body mass, which can be indicative of increased breast cancer risk. These data help elucidate the metabolic and immune responses induced by staggered light exposure, helping to define the mechanisms promoting health risks imposed by shift work, with the ultimate goal to develop preventive measures to limit breast cancer development. Citation Format: Lauren Palluth, Brittany Harlow, Bryan McCellan, Christopher Jolly, Molly Bray, Linda deGraffenried. Assessing the impact of staggered light exposure on breast cancer risk and metabolism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 106.

Involvement of fine particulate matter exposure with gene expression pathways in breast tumor and adjacent-normal breast tissue

BackgroundFine particulate matter (PM2.5) has been associated with breast cancer specific mortality, particularly for women with Stage I cancer. We examined the biological pathways that are perturbed by PM2.5 exposures by analyzing gene expression measurements from breast tissue specimens. MethodsThe Nurses' Health Studies (NHS and NHSII) are prospective cohorts with archival breast tissue specimens from breast cancer cases. Global gene expression data were ascertained with the Affymetrix Glue Human Transcriptome Array 3.0. PM2.5 was estimated using spatio-temporal models linked to participants’ home addresses. All analyses were performed separately in tumor (n = 591) and adjacent-normal (n = 497) samples, and stratified by estrogen receptor (ER) status and stage. We used multivariable linear regression, gene-set enrichment analyses (GSEA), and the least squares kernel machine (LSKM) to assess whether 3-year cumulative average pre-diagnosis PM2.5 exposure was associated with breast-tissue gene expression pathways among predominately Stage I and II women (90.7%) and postmenopausal (81.2%) women. Replication samples (tumor, n = 245; adjacent-normal, n = 165) were measured on Affymetrix Human Transcriptome Array (HTA 2.0). ResultsOverall, no pathways in the tumor area were significantly associated with PM2.5 exposure. Among 272 adjacent-normal samples from Stage I ER-positive women, PM2.5 was associated with perturbations in the oxidative phosphorylation, protein secretion, and mTORC1 signaling pathways (GSEA and LSKM p-values <0.05); however, results were not replicated in a small set of replication samples (n = 80). ConclusionsPM2.5 was generally not associated with breast tissue gene expression though was suggested to perturb oxidative phosphorylation and regulation of proteins and cellular signaling in adjacent-normal breast tissue. More research is needed on the biological role of PM2.5 that influences breast tumor progression.

CC chemokine ligand 2 (CCL2) stimulates local aromatase expression in mammary adipose tissue via MAPK/ERK signaling pathway

Obesity is an important risk factor for the most common type of breast cancer, i.e., estrogen‐dependent postmenopausal breast cancer. Obesity‐related inflammation, defined by enhanced tissue macrophage infiltration and increased levels of proinflammatory factors, stimulates estrogen production and, in turn, promotes tumor formation and progression. The augmented inflammatory milieu activating aromatase expression, the rate‐limiting enzyme for estrogen synthesis, is suggested to be the major mechanism behind the increased risk for breast cancer. The regulation of aromatase (CYP19A1) gene expression in breast tissue is complex and not fully understood. This study focuses on the role of CCL2, a macrophage recruiter often overexpressed in the obese and cancerous breast tissue, on the aromatase expression in mammary adipose tissue. We utilized mammary adipose tissue explants obtained from female hARO‐Luc aromatase reporter mice and cultured primary human breast adipose stromal cells (hASCs). In tissue explants, aromatase expression was measured by quantification of luciferase reporter activity, while in cultured hASCs, qPCR was used to examine expression of CYP19A1 and its specific promoter I.4 and II transcripts. Both, in mammary adipose tissue and in hASCs, CCL2 induced aromatase expression via the distal promoter I.4. Moreover, inhibitors of CCL2 receptor and MAPKs pathway, ERK1/2, blocked CCL2–induced CYP19A1 and PI.4 mRNA expression. In conclusion, these findings indicate that CCL2, an intrinsic pro‐inflammatory chemokine in adipose tissue, may stimulate aromatase expression and play an important role in controlling estrogen biosynthesis in the breast adipose stroma.Support or Funding InformationThis research is supported by the University of Turku Graduate School, the Finnish Cultural Foundation, the Turun yliopistosäätiö and the Institute of Biomedicine, University of TurkuThis abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Abstract 3262: Biomarkers of response to deslorelin in women at high risk of breast cancer

Abstract Women with BRCA1 or BRCA2 mutations have a high breast cancer (BC) risk. Breast density is an independent biomarker of BC risk. We demonstrated that deslorelin (D), a gonadotropin-releasing hormone agonist (GnRHA), reduced mammographic breast density among BRCA1 carriers (Weitzel, CCR 2007). We report here on correlative studies in a Phase II chemoprevention study of intranasal D plus low-dose add-back estradiol and testosterone for 10 months in 10 premenopausal high-risk women. Pre- and post-study measures included mammographic and MRI breast density; serum hormone levels; breast tissue gene expression by Affymetrix GeneChip Human Genome U133 Plus 2.0 Array; Q-PCR to validate the top genes; analysis of sera by 2D-PAGE and LC-MS/MS Mass Spectrometry, validated by a marker specific ELISA. Nine of 10 participants showed a mean reduction in MRI volumetric breast density of 17.3% (p=0.045), a mean reduction in MRI fibroglandular volume of 15.1% (p=0.041) and a mean reduction in mammographic density of 31.9% (p=0.0033). The top five differentially expressed genes in breast tissue were: FOS, EGR1, FABP3, COL3A1 and COL1A1. From seven protein spots up-regulated following treatment, 65 sera proteins were initially identified as response biomarkers, with two of these, adiponectin and Vitamin D-binding protein, predicted by Ingenuity Pathway Analyses (IPA) to have a direct role in reducing breast density by possibly altering stromal and epithelial tissue viability and fat metabolism. FOS was identified through IPA in the Vitamin D-binding protein network and both COL3A1 and COL1A1 were identified in the adiponectin network. Of the nine subjects two had an increase of ≥ 5% BMI and two had a decrease of ≥ 5% BMI. There was no significant correlation between adiponectin level changes and BMI changes.Tissue micro-array gene expression and proteomic analyses of sera identified changes in biomarkers that correlated with reduced breast density among young high-risk women receiving a GnRHA-based chemoprevention regimen. Pre/Post Human Serum Adiponectin LevelsA_Pre: 0 MB_ Post: 10 MFold (Post/Pre)Log A (0M)Log B (10M)T-Test (p) pairedS10.4630.3860.835-0.335-0.4130.014S20.3760.4131.097-0.425-0.385S30.2620.3671.403-0.583-0.435S40.6640.8021.209-0.178-0.096S50.2450.6082.487-0.612-0.216S60.3150.7552.399-0.502-0.122S70.4130.7991.936-0.385-0.098S80.5750.6461.123-0.240-0.190S90.4360.7331.680-0.361-0.135Average1.574224 Citation Format: Jeffrey N. Weitzel, Michael F. Press, Sharon R. Sand, Lei Zhang, Catherine Klifa, Malcolm Pike, Giske Ursin, Darcy V. Spicer, AnnaMarie Daniels, Josef Herzog, Yunan Miao, Gerald Wuenschell, Chun Li, Robert Hickey. Biomarkers of response to deslorelin in women at high risk of breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3262.

A transcriptome-wide association study of 229,000 women identifies new candidate susceptibility genes for breast cancer.

The breast cancer risk variants identified in genome-wide association studies explain only a small fraction of the familial relative risk, and the genes responsible for these associations remain largely unknown. To identify novel risk loci and likely causal genes, we performed a transcriptome-wide association study evaluating associations of genetically predicted gene expression with breast cancer risk in 122,977 cases and 105,974 controls of European ancestry. We used data from the Genotype-Tissue Expression Project to establish genetic models to predict gene expression in breast tissue and evaluated model performance using data from The Cancer Genome Atlas. Of the 8,597 genes evaluated, significant associations were identified for 48 at a Bonferroni-corrected threshold of P < 5.82 × 10-6, including 14 genes at loci not yet reported for breast cancer. We silenced 13 genes and showed an effect for 11 on cell proliferation and/or colony-forming efficiency. Our study provides new insights into breast cancer genetics and biology.

Affinity proteomic profiling of plasma for proteins associated to area-based mammographic breast density

BackgroundMammographic breast density is one of the strongest risk factors for breast cancer, but molecular understanding of how breast density relates to cancer risk is less complete. Studies of proteins in blood plasma, possibly associated with mammographic density, are well-suited as these allow large-scale analyses and might shed light on the association between breast cancer and breast density.MethodsPlasma samples from 1329 women in the Swedish KARMA project, without prior history of breast cancer, were profiled with antibody suspension bead array (SBA) assays. Two sample sets comprising 729 and 600 women were screened by two different SBAs targeting a total number of 357 proteins. Protein targets were selected through searching the literature, for either being related to breast cancer or for being linked to the extracellular matrix. Association between proteins and absolute area-based breast density (AD) was assessed by quantile regression, adjusting for age and body mass index (BMI).ResultsPlasma profiling revealed linear association between 20 proteins and AD, concordant in the two sets of samples (p < 0.05). Plasma levels of seven proteins were positively associated and 13 proteins negatively associated with AD. For eleven of these proteins evidence for gene expression in breast tissue existed. Among these, ABCC11, TNFRSF10D, F11R and ERRF were positively associated with AD, and SHC1, CFLAR, ACOX2, ITGB6, RASSF1, FANCD2 and IRX5 were negatively associated with AD.ConclusionsScreening proteins in plasma indicates associations between breast density and processes of tissue homeostasis, DNA repair, cancer development and/or progression in breast cancer. Further validation and follow-up studies of the shortlisted protein candidates in independent cohorts will be needed to infer their role in breast density and its progression in premenopausal and postmenopausal women.

Germline copy number variations are associated with breast cancer risk and prognosis

Breast cancer is one of the most common cancers among women, and susceptibility is explained by genetic, lifestyle and environmental components. Copy Number Variants (CNVs) are structural DNA variations that contribute to diverse phenotypes via gene-dosage effects or cis-regulation. In this study, we aimed to identify germline CNVs associated with breast cancer susceptibility and their relevance to prognosis. We performed whole genome CNV genotyping in 422 cases and 348 controls using Human Affymetrix SNP 6 array. Principal component analysis for population stratification revealed 84 outliers leaving 366 cases and 320 controls of Caucasian ancestry for association analysis; CNVs with frequency > 10% and overlapping with protein coding genes were considered for breast cancer risk and prognostic relevance. Coding genes within the CNVs identified were interrogated for gene- dosage effects by correlating copy number status with gene expression profiles in breast tumor tissue. We identified 200 CNVs associated with breast cancer (q-value < 0.05). Of these, 21 CNV regions (overlapping with 22 genes) also showed association with prognosis. We validated representative CNVs overlapping with APOBEC3B and GSTM1 genes using the TaqMan assay. Germline CNVs conferred dosage effects on gene expression in breast tissue. The candidate CNVs identified in this study warrant independent replication.

Intermittent energy restriction induces changes in breast gene expression and systemic metabolism.

BackgroundObservational studies suggest weight loss and energy restriction reduce breast cancer risk. Intermittent energy restriction (IER) reduces weight to the same extent as, or more than equivalent continuous energy restriction (CER) but the effects of IER on normal breast tissue and systemic metabolism as indicators of breast cancer risk are unknown.MethodsWe assessed the effect of IER (two days of 65 % energy restriction per week) for one menstrual cycle on breast tissue gene expression using Affymetrix GeneChips, adipocyte size by morphometry, and systemic metabolism (insulin resistance, lipids, serum and urine metabolites, lymphocyte gene expression) in 23 overweight premenopausal women at high risk of breast cancer. Unsupervised and supervised analyses of matched pre and post IER biopsies in 20 subjects were performed, whilst liquid and gas chromatography mass spectrometry assessed corresponding changes in serum and urine metabolites in all subjects after the two restricted and five unrestricted days of the IER.ResultsWomen lost 4.8 % (±2.0 %) of body weight and 8.0 % (±5.0 %) of total body fat. Insulin resistance (homeostatic model assessment (HOMA)) reduced by 29.8 % (±17.8 %) on the restricted days and by 11 % (±34 %) on the unrestricted days of the IER. Five hundred and twenty-seven metabolites significantly increased or decreased during the two restricted days of IER. Ninety-one percent of these returned to baseline after 5 days of normal eating. Eleven subjects (55 %) displayed reductions in energy restriction-associated metabolic gene pathways including lipid synthesis, gluconeogenesis and glycogen synthesis. Some of these women also had increases in genes associated with breast epithelial cell differentiation (secretoglobulins, milk proteins and mucins) and decreased collagen synthesis (TNMD, PCOLCE2, TIMP4). There was no appreciable effect of IER on breast gene expression in the other nine subjects. These groups did not differ in the degree of changes in weight, total body fat, fat cell size or serum or urine metabolomic markers. Corresponding gene changes were not seen in peripheral blood lymphocytes.ConclusionThe transcriptional response to IER is variable in breast tissue, which was not reflected in the systemic response, which occurred in all subjects. The mechanisms of breast responsiveness/non-responsiveness require further investigation.Trial registrationISRCTN77916487 31/07/2012.Electronic supplementary materialThe online version of this article (doi:10.1186/s13058-016-0714-4) contains supplementary material, which is available to authorized users.

Relationship of Mammographic Density and Gene Expression: Analysis of Normal Breast Tissue Surrounding Breast Cancer

Previous 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. Breast 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. 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 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. By 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.

Abstract PL04-02: Insights on cancer risk from normal breast tissue gene expression

Abstract Breast cancers evolve through interactions with the surrounding non-malignant tissue, with the non-malignant tissue providing a selective pressure during carcinogenesis. By conducting observational studies of non-malignant tissue, it is possible to identify pathways and phenotypes that contribute to cancer etiology and progression. Our gene expression profiling studies have demonstrated that cancer adjacent normal breast tissue is highly variable, with at least two distinct molecular subtypes. These subtypes are associated with histologic differences and distinct risk factor profiles, notably mammographic density. Given that mammographic density is the strongest, yet most poorly understood breast cancer risk factor, molecular studies to better understand this phenotype are important. We have also conducted studies of risk factor-associated gene expression in reduction mammoplasty tissue. Supervised analysis of gene expression in normal tissue is identifying novel biological phenotypes associated with established risk factors. For example, we have shown that aging is associated with specific gene expression changes in normal breast. Interestingly, the age-related pathways in non-diseased tissue are also dysregulated in the breast cancers that are more common in young women, providing a biological link between the aging process and tumor aggressiveness. Likewise, obesity causes alterations to the stromal milieu in normal breast tissue, with implications for the progression of basal-like breast cancers. Our results demonstrate that to study the evolution and outcome of breast cancers, it is important to consider the normal tissue microenvironment from which these cancers evolve. Citation Format: Melissa A. Troester. Insights on cancer risk from normal breast tissue gene expression. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr PL04-02.

Abstract 1680: Age-associated gene expression in normal breast tissue provide insights regarding associations between aging and aggressive tumor characteristics

Abstract Age is the strongest breast cancer risk factor, with overall breast cancer risk increasing steadily beginning at approximately 30 years of age. Though several genomic studies have characterized differences in the biology of tumors arising in younger vs. older women, understanding of age-related gene expression in the non-malignant tissue is lacking. Because non-malignant tissue represents the microenvironment and/or field from which the tumor arises, an improved characterization of the age-related change in normal breast may provide suggestions of important pathways in the etiology of distinct breast cancer subtypes. To identify age-related changes in the breast microenvironment, 96 normal tissue specimens from reduction mammoplasty patients ranging in age from 14 to 70 were assayed by gene expression microarray. Significant associations between gene expression levels and age were identified for 802 genes (481 increased and 321 decreased with increasing age). Enriched functions included ‘aging of cells’, ‘shape change’, and ‘chemotaxis’, and enriched pathways included Wnt/beta-catenin signaling, Ephrin Receptor Signaling, Oncostatin M signaling, and JAK/Stat Signaling. Applying the age-associated genes to publicly available tumor datasets, the age-associated pathways defined two groups of tumors with distinct survival. The poor prognosis tumor group shared features of ‘younger’ breast tissue gene expression, providing a biological link to qualitative patterns in age at incidence. Aggressive tumors common in younger women (such as ER negative and basal-like breast cancers) harbor gene expression patterns that reflect the normal tissue from which they arise. For the young, these signatures portend more aggressive disease. These data show that studies of normal tissue gene expression can yield important insights about the pathways and biological selection factors that are relevant during tumor progression. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 1680. doi:1538-7445.AM2012-1680

Prediction of menopausal status from estrogen-related gene expression in benign breast tissue

The utility of archived paraffin-embedded breast tissue for risk-related research is often limited by missing menopausal status data. We tested the hypothesis that breast tissue gene expression patterns can improve menopausal stratification. Healthy high-risk participants in a clinical trial underwent breast random fine-needle aspiration (rFNA); 100ng of RNA extracted from rFNA samples was reverse-transcribed; the expression of 28 estrogen-responsive genes was evaluated by real-time PCR. True menopausal status (TMS) was determined by measurement of plasma hormones and age. Differentially expressed genes and age were analyzed by logistic regression. The accuracy of the menopause prediction was assessed using receiver-operator characteristic (ROC) analysis, and validated in a second independent set of 44 women. In the test set, postmenopausal women demonstrated significantly lower expression of five estrogen-responsive genes: GREB1, PGR, TFF1, PRLR, and CCND1 (adjusted P<0.03 for all). In the validation set, three of these genes were expressed at lower levels in postmenopausal women (GREB1, PGR, TFF1) (adjusted P<0.06 for all). In the test set, the modeled area under the curve (AUC) for age and three genes was higher than for age >50 alone (AUC 96.1% vs. 87.2%, P=0.002), and remained better than for age alone in the validation set (99.0% vs. 95.5%, P=0.16). Estrogen-related gene expression in breast specimens can be used to improve menopausal classification, reducing the biological noise related to menopause in studies that seek to identify RNA or protein risk biomarkers in archived breast samples.

Hormone replacement therapy dependent changes in breast cancer-related gene expression in breast tissue of healthy postmenopausal women

Risk assessment of future breast cancer risk through exposure to sex steroids currently relies on clinical scorings such as mammographic density. Knowledge about the gene expression patterns in existing breast cancer tumors may be used to identify risk factors in the breast tissue of women still free of cancer. The differential effects of estradiol, estradiol together with gestagens, or tibolone on breast cancer-related gene expression in normal breast tissue samples taken from postmenopausal women may be used to identify gene expression profiles associated with a higher breast cancer risk. Breast tissue samples were taken from 33 healthy postmenopausal women both before and after a six month treatment with either 2 mg micronized estradiol [E2], 2 mg micronized estradiol and 1 mg norethisterone acetate [E2 + NETA], 2.5 mg tibolone [T] or [no HRT]. Except for [E2], which was only given to women after hysterectomy, the allocation to each of the three groups was randomized. The expression of 102 mRNAs and 46 microRNAs putatively involved in breast cancer was prospectively determined in the biopsies of 6 women receiving [no HRT], 5 women receiving [E2], 5 women receiving [E2 + NETA], and 6 receiving [T]. Using epithelial and endothelial markers genes, non-representative biopsies from 11 women were eliminated. Treatment of postmenopausal women with [E2 + NETA] resulted in the highest number of differentially ( p < 0.05) regulated genes (16.2%) compared to baseline, followed by [E2] (10.1%) and [T] (4.7%). Among genes that were significantly down-regulated by [E2 + NETA] ranked estrogen-receptor-1 ( ESR1, p = 0.019) and androgen receptor ( AR, p = 0.019), whereas CYP1B1, a gene encoding an estrogen-metabolizing enzyme, was significantly up-regulated ( p = 0.016). Mammary cells triggered by [E2 + NETA] and [E2] adjust for steroidogenic up-regulation through down-regulation of the estrogen-receptor pathway. In this prospective study, prolonged administration of [E2 + NETA] and to a lesser extent of [E2] but not [T] were associated in otherwise healthy breast tissue with a change in the expression of genes putatively involved in breast cancer. Our data suggest that normal mammary cells triggered by [E2 + NETA] adjust for steroidogenic up-regulation through down-regulation of the estrogen-receptor pathway. This feasibility study provides the basis for whole genome analyses to identify novel markers involved in increased breast cancer risk.

Abstract B43: Gene expression in breast tissue associated with mammographic density in healthy women

Abstract Introduction: Mammographic 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, high 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 MD are virtually unknown. Methods: Gene expression analysis using whole genome arrays was performed on breast biopsies from 143 women; 79 women with no malignancy (healthy women) recruited through mammographic centres and 64 newly diagnosed breast cancer patients. MD percentage was determined using a previously validated, computerized method on scanned mammograms. Unsupervised hierarchical clustering was used to investigate the presence of subgroups of samples within the normal biopsies. Significance analysis of microarrays (SAM) was performed to identify genes associated with MD. Results: Hierarchical clustering of normal biopsies on the basis of the gene expression data split the individuals into two main groups. These were not significantly associated with MD or BMI. SAM identified 24 genes differentially expressed between high and low MD in the healthy women. Several single nucleotide polymorphisms (SNPs) associated with the expression of these 24 genes were identified. Conclusions: Breast biopsies from healthy women show substantial variation in their gene-expression profiles. Twenty-four genes associated with MD and SNPs associated with their expression were identified. Several of these genes have previously been found to be linked to breast biology and oestrogen metabolism. Validation and further analysis of these genes is ongoing. Citation Information: Clin Cancer Res 2010;16(7 Suppl):B43

BioCarta

BioCarta