2.1 Study population

A detailed description of the recruitment proses and study population as well as ethical aspects of genetic research in healthy populations are presented and discussed in our previously published article.¹³ Briefly, study participants were recruited through the national mammography screening program at the Breast Diagnostic Center at the University Hospital of North Norway (UNN), Tromsø, Norway, during the years 2010–2011. They were not referred due to pathological clinical findings or irregularities on previous mammograms but attended a scheduled routine mammogram. Eligible women were age between 53 and 67 years, were post-menopausal, and were already participating in the nationally representative Norwegian Women and Cancer study (NOWAC).¹⁴ Exclusion criteria for the present study included self-reported previous history of breast cancer, positive mammogram, other current malignant diseases and use of anticoagulation therapy with warfarin, heparin, dipyridamole, or clopidogrel. Eligible women who agreed to participate received written and oral information, signed an informed consent form, and answered a two-page questionnaire regarding menopausal status, weight and height, smoking and alcohol consumption, use of HT and other medication. The number of included participants was 317. Three years after inclusion, data was linked to the Cancer Registry of Norway, using the unique personal identification number. This resulted in the exclusion of five participants who developed breast cancer within 3 years after the biopsy was taken, and one participant due to prior lymphoma diagnosis with unknown treatment. Thus, the final number of women included for statistical analysis was 311. The North Norway Regional Committee for Medical and Health Research Ethics (REK-Nord case no # 200603551) approved the study.

2.2 Definition of exposures

Information on year of birth, menopausal status, current height and weight and exposures (HT use, smoking and alcohol consumption) was extracted from the two-page questionnaires answered at the time of inclusion.

Body mass index (BMI) was calculated, and obesity was defined according to the definition of the World Health Organization (WHO, BMI > 30). Women were considered post-menopausal if they reported that menstruation had ceased. In case of incomplete information, women were defined as post-menopausal if they were older than 53 years. Women who had consumed alcohol during the week prior to the biopsy, regardless of the type or amount, were defined as alcohol consumers. Similarly, women who had smoked during the week prior to biopsy were defined as smokers. Only women who were current users of systemic HT (tablets or patch) were defined as HT users. Data on parity was retrieved from the NOWAC database, and the variable was dichotomized into parous versus non-parous for analyses of gene expression.

We also collected data on smoking status and alcohol consumption (g/day) from the more comprehensive eight-page questionnaire answered by the participants as part of the prospective data collection in NOWAC. Participants of the biopsy study answered the eight-page questionnaire 0–20 years prior to donating a biopsy (1991–2011). These data were used for a sensitivity analysis.

2.3 Tissue samples

An experienced radiologist obtained tissue samples after mammography, by ultrasound-guided needle-biopsy (14 gauges) from the gland tissue of the upper lateral quadrant of the left breast. The procedure was standardized. We collected one biopsy from every participant. In case of macroscopically sparse material, a second biopsy was obtained. The biopsies were kept at room temperature in RNA Later (Qiagen) for <24 h until storage at −70°C.

2.4 RNA preparation

RNA preparation was conducted at The Department of Cancer Genetics, Oslo University Hospital, Oslo, Norway. Tissue biopsies were homogenized using TissueLyser LT (Qiagen, Hilden, Germany) and 5 mm steel beads. Total RNA and genomic DNA were isolated using the AllPrep DNA/RNA Mini Kit from Qiagen (Cat. No. 80204) and the QIAcube instrument (Qiagen, Hilden, Germany). A custom protocol was followed for the extraction (RNA_AllPrepDNARNA_AnimalCells_AllPrep350_ID2481). RNA was stored at −80°C. RNA quantity was measured by NanoDrop (Thermo Fisher Scientific, Wilmington, Delaware, USA). The BioAnalyzer 2100 and Agilent RNA 6000 Nano kit (cat. No. 5067–1511) were used to evaluate RNA integrity (Agilent, Santa Clara, US).

2.5 Gene expression analysis

mRNA gene expression was analyzed at a certified Illumina service provider (NTNU Genomics Core Facility, Trondheim, Norway). Briefly, RNA was amplified with Ambion's Illumina® TotalPrep RNA amplification kit (Cat #AMIL 1791) using 400 ng of total RNA as input material. Incorporation of biotin-labeled nucleotides was performed overnight (14 h) at 37°C in vitro transcription (IVT). cRNA was quantified using the NanoDrop ND-1000 (NanoDrop, Wilmington, USA), and cRNA integrity was determined by electrophoresis using the Experion Bioanalyzer (BioRad). A total of 750 ng of biotin-labeled cRNA was hybridized to IlluminaHumanHT-12 v.4 expression bead chip (Illumina®). Beadchips were scanned with Illumina BeadArray Reader. Numerical results were extracted with Bead Studio v3.0.19.0 without any normalization or background subtraction.

2.6 Statistical analysis

Data analysis was done using R (r-project.org). Raw files were quantile normalized using the Bioconductor lumi package.¹⁵ Principal component analysis (PCA) and clustering was used for initial analysis of the dataset. The PCA was computed with all genes included. In order to obtain distinct clusters that correlate with the PCA scores to simplify interpretation we clustered the genes with the most variability (inter quantile range [IQR] > 1 log2 unit).

The three gene clusters identified were analyzed for overrepresented gene ontology (GO) terms using the clusterProfiler package.¹⁶ This analysis highlighted cell composition as a potentially important co variate and non-negative matrix factorization (NMF) was used to obtain improved cellular composition estimates.¹⁷ NMF was run with the “nsNMF” method and initialized with non-negative singular value decomposition to get a sparser estimate for the gene profiles of the cell types. The association of exposures and covariates on gene expression was determined using linear models for microarrays (LIMMA), with the scores on principal component one and two included as covariates to correct for bias due to the cellular composition of the biopsies. p Values from the linear models were corrected for multiple testing using the method of Benjamin and Hochberg.¹⁸ Finally Camera¹⁹ was used to identify pathways and GO terms that were related to the exposure variables. The Camera analysis was carried out with all genes using the same model as for the limma analysis, that is, PC1 and PC2 were included to correct for cell type composition.

STATA (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC.) was used for descriptive statistics. T tests were used for BMI and age as continuous variables, and chi-square tests were used for the categorical variables.

3.1 Characteristics of study participants

For this study, 311 cancer-free, post-menopausal women were included. Average age of the study population was 60 years. More than half of the participants (54.7%) were classified as overweight according to WHO (BMI > 25), with an average BMI for the whole study populations of 26.2. More than 79% of the women had consumed alcohol, and 21% had been smoking prior to biopsy sampling. Very few participants used HT (8.7%), and most of the women had completed at least one full term pregnancy (Table 1).

3.2 Gene expression in normal breast tissue

3.2.1 Unsupervised clustering

After normalization of data, the initial analyses identified 607 genes with high level of variance with IQR larger than one log2 unit. These 607 genes were analyzed by K-means clustering and three dominating cluster were identified (Figure 1). These clusters appear unrelated to either of the exposures. PCA analysis with the exposure variables illustrated are shown in Figure S1.

Genes from the three clusters were analyzed using cluster profiler to identify GO categories that describe the functionality of the clusters (Figure 2).

Cluster 1 is dominated by processes related to epithelial cells, both differentiation processes, cell functions, and proliferation. Cluster 2 is dominated by genes involved in immune system processes and immune cell specific processes such as leucocyte chemotaxis, regulation of leucocytes proliferation, and interleukin production. Metabolic genes specifically related to lipid metabolism and fat cell differentiation dominate the third cluster.

PCA of the full dataset showed that 47% of the total variability of the gene expression data was captured by the two first principal components. Principal component one therefore reflects the balance between fatty tissue and epithelial tissue in the biopsy and principal component two reflects the fraction of immune cells included (Figure 3). NMF identified three factors that correlate with well-known cell type markers (Supporting Information S1). However, as the dominating cell types are epithelial and adipose cells with only a small fraction of immune cells, epithelial and adipose markers correlate strongly (negatively and positively respectively) with the first principal component. This enables the use of a single factor (PC1) to represent epithelial versus adipose tissue which reduces collinearity problems in the linear modeling. Hence, NMF was not included in further analyses.

4.1 Obesity

BMI was the most influential risk factor in this gene expression study, with 1577 differentially expressed genes, and more than 600 differentially expressed gene sets. Overall, processes like immunology, estrogen metabolism and energy metabolism dominated the BMI-related results. In a wide perspective, our results reflect current hypotheses related to the causal association between obesity and breast cancer risk.

Our results indicate that immune-related processes are activated in the breast tissue of women with obesity. We identified five toll-like receptor (TLR)-related gene sets, 34 interleukin-related gene sets (including IL-1, -1β, -2, -6, -8, and -17), 12 interferon-related gene sets (IFN-α, -β, -γ), and three NF-κB gene sets. All of the mentioned gene sets were up-regulated. This finding is in line with established hypotheses on adipose tissue as a mediator for establishment of chronic inflammation, which is ultimately linked to increased risk of cancer.²⁰ During obesity, macrophages, putatively of the M1 type, accumulate in the adipose tissue, serving as a rich source of cytokines.^{20, 21} In obese breast tissue, inflammatory foci with dead adipocytes circled by macrophages, have been observed.^{22, 23} In the breast tissue, macrophages are exposed to saturated fatty acids from lipolysis leading to TLR 4 signaling via NFκ-b, culminating in increased expression of pro-inflammatory genes like COX-2, IL-1β, IL-6, and TNF-α.²⁰ These obesity-linked pro-inflammatory mediators may have local, pro-neoplastic effects, but also contribute to diminished overall health in obesity. However, our study cannot distinguish between breast tissue transcriptomic patterns associated with local inflammation, and transcriptomic patterns associated with systemic, circulating inflammatory factors. Biologically, however, this distinction is somewhat artificial, as the local and systemic effects of obesity are closely interrelated.²⁰

Estrogen receptor signaling, increased by obesity and inflammation, is a key contributor to increased risk of hormone-positive breast cancer. In our data, five prostaglandin-related gene sets were identified. Prostaglandin contributes to increased aromatase expression in breast tissue, which is the rate-limiting enzyme in estrogen biosynthesis.^{24, 25} Hence, our results support the “obesity-inflammation-aromatase” axis²³ that leads to elevated estrogen levels in obese, postmenopausal women, resulting in increased breast cancer risk. The down-regulated gene sets in our results were dominated by processes such as aerobic oxidation and fatty acid metabolism. Imbalances in these energy metabolism pathways are closely linked to accumulation of body fat leading to obesity.²⁶ In the breast, adipocytes are involved in normal tissue development, but there is also close interaction between stromal adipocytes and tumor cells.²⁷ Our results are in line with the finding of gene expression related to lipogenesis and fatty acid oxidation being downregulated in subcutaneous fat of both moderately and morbidly obese women, potentially as a mechanism for limiting further development of fat mass.^{28, 29} Of note, it has been suggested that this profile may be reversed by tumor cells, allowing adipocytes to provide lipids for the growing tumor.²⁷

We also identified several down-regulated gene sets related to protein translation in the mitochondria. Metabolic imbalance is closely related to mitochondrial function, as they in addition to ATP production are involved in production and elimination of reactive oxygen species (ROS).³⁰ Obesity causes increased inflammation and oxidative stress through ROS production, which in turn may lead to mitochondrial dysfunction.³⁰ In adipose tissue and skeletal muscle, numbers of mitochondria and rate of mitochondria biogenesis may decrease during obesity.³⁰ Although these processes have not been described in breast tissue, our gene expression data from normal breast tissue supports this overall concept.

4.2 Smoking

Our results revealed several genes and pathways significantly associated with smoking (Tables 5 and 6). Eight genes were up-regulated, one gene was down-regulated, and 19 gene sets were associated with smoking. Cytochrome P-450 1A1 and -1B1 (CYP1A1 and CYP1B1) were among the top up-regulated genes. These genes are involved in metabolism of carcinogens including combustion products like polycyclic aromatic hydrocarbons found in cigarette smoke, but they are also involved in estrogen metabolism,³¹ as well as breast cancer proliferation and survival.³² Several aspects of CYP gene biology (expression levels, methylation levels, gene function) are related to smoking. Tsai et al.³³ investigated smoking-associated DNA methylation and gene expression variation in adipose tissue biopsies. Five of the identified genes in that study (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and upregulated in current smokers. Four of those five genes were identified in our study (CYP1A1, CYP1B1, CYTL1, and F2RL3). Furthermore, CYP gene biology was associated with smoking in studies of breast cancer patient survival,³⁴ lung tissue,^{35, 36} prostate cancer cells,³⁷ and fetal placenta and livers.³⁸ Complementing these previous findings with our smoking-related gene expression data in normal breast tissue, gives important clues to the biological effects of smoking, that may contribute to increased breast cancer risk.

Similar to our findings on CYP1A1 and –B1, coagulation factor II receptor-like 3 (F2RL3, coding for the proteinase-activated receptor 4 protein) was up-regulated in our data, in accordance with previous reports. In an epigenome-wide study of DNA from pre-diagnostic blood samples, F2RL3 hypo-methylation strongly correlated with smoking.³⁹ Further, F2RL3 methylation was suggested as a biomarker of smoking,⁴⁰ and over-expression and hypo-methylation were associated with higher risk of lung cancer,⁴¹ and with tumor aggressiveness and poor survival in renal cancer.⁴²

In the smoking group, stabilin-1 (STAB1) was up-regulated, and secreted protein acidic and rich in cysteine (SPARC) was down-regulated. STAB1 is a scavenger receptor mediating both phagocytosis of unwanted self-components, intracellular sorting, and endocytosis of extracellular ligands such as the extracellular matrix component SPARC.⁴³ STAB1 was up-regulated in smokers and COPD patients compared to non-smokers, although non-significantly.⁴⁴ STAB1 is expressed on tumor-associated macrophages in several cancers, and in human breast cancer STAB1 was found in stage I and IV disease, suggesting a role in early primary tumor growth and progression.⁴³ Studies have reported that SPARC induction inhibits breast cancer cell proliferation,⁴⁵ and down-regulated expression of SPARC correlated with poor breast cancer prognosis.⁴⁶ However, the role of SPARC may be highly dependent on context.⁴⁷

Among the 19 pathways associated with smoking, the most prominent feature were energy metabolism and fatty acid metabolism pathways, including fatty acid degradation processes, acetyl-CoA metabolism, and the citrate cycle. Smoking has well-established effects on adipose tissue, termed smoking-induced dyslipidemia. In this state, lipolysis and free fatty acids are increased, involving hormone sensitive lipase and adipocyte differentiation.⁴⁸ Systemically administered nicotine induces lipolysis, in part by activating the classical adrenergic mechanism, and in part by directly activating a nicotinic cholinergic lipolytic receptor located in adipose tissue.⁴⁹

4.3 Alcohol

Exposure to alcohol was associated with 9 differentially expressed genes, and 80 gene sets (Tables 7 and 8). Overall, the magnitude of the differential gene expression is comparable to previous analyses of alcohol and gene expression in breast tumors.⁵⁰ Interestingly, the up-regulated BCDIN3 domain containing RNA methyltransferase gene (BCDIN3D) has been clearly linked to breast cancer progression, via down-regulation of tumor suppressor miRNAs.⁵¹ In a cohort of 227 breast cancer patients, tumor levels of BCDIN3D was associated with lower disease-free survival.⁵² Hence, BCDIN3D could serve as a link between alcohol consumption and breast cancer tumorigenesis and survival.

Among the 80 pathways associated with alcohol consumption, 30 were down-regulated. All of these were related to immunological processes, and the majority describe aspects of the innate immune system. Particularly, mast cell mediated immunity was present, including mast cell activation and degranulation. Mast cells have been linked to alcohol consumption, as they may mediate the damaging effects of alcohol by contributing to chronic inflammation, tissue damage, and remodeling, especially in the gastrointestinal tract.⁵³ Their role in cancer,⁵⁴ including breast cancer, is controversial, with conflicting results on the association with disease subtypes and prognosis.^{55, 56} In sum, these findings warrant further investigation of the effects of alcohol in the pre-cancerous breast tissue environment.

Fifty pathways were up-regulated in alcohol consumers (Table 8). Among those, two related processes were represented: aerobic oxidation, including translation of mitochondrial proteins for oxidative phosphorylation, and fatty acid metabolism. As in the liver, alcohol is metabolized in breast tissue into acetaldehyde, a class 1 carcinogen forming DNA and protein adducts, and further into acetic acid and acetyl-CoA, the latter which enters the citric acid cycle.^{57, 58} This increased acetyl-CoA input may drive energy metabolism and increase the cellular energy state.⁵⁹ Furthermore, it has been suggested that acetyl-CoA is not merely a passive metabolite, but rather an important signaling molecule dictating cell function in a variety of settings.⁵⁸ Similarly, the various metabolites of the tricarboxylic acid cycle (TCA), increasing in concentrations upon an up-regulation of the cycle, affect intracellular and organismal processes, such as innate immunity, inflammation, and immune effector cells (succinate), and tumor cell growth (fumarate). TCA metabolite release from the mitochondria are one of the main processes by which the mitochondria influence cell function.⁵⁸ With the upregulation of aerobic oxidation pathways in our data, perhaps as a response to increased levels of acetyl-CoA from ethanol, it is also evident that the genotoxic acetaldehyde may be present in the breast tissue. Taken together, our data suggests that alcohol consumption may influence gene expression related to breast tissue physiology and metabolism.

4.4 Hormone therapy

A current exposure to HT in our study was associated with two upregulated single genes, but no significant pathways (Tables 9 and 10). Prolonged, systemic use of all, yet especially combined HT is associated with increased risk for breast cancer.⁶⁰ Hall et al. found a distinct gene expression profile in breast cancer tissue associated with HT use, and linked HT use to better recurrence-free survival.⁶¹ Changes in gene expression patterns in normal breast tissue after treatment with HT was observed in one experimental study,⁶² and a recent study on DNA methylation showed association between HT use and epigenetic changes in normal breast tissue.⁶³ We did not observe similar changes in our data. Few participants exposed and lack of information on prior HT use, as well as duration of use, could partially explain these results.

4.5 Parity

In our study, parity was not associated with any significant single genes or pathways (Tables 11 and 12). Epidemiological studies have shown that a first full time pregnancy at an early age, as well as multiple pregnancies, are associated with long-term risk reduction for breast cancer.⁶⁴ Several studies found genomic signature of pregnancy in the breast tissue by comparing gene expression profiles of parous and non-parous postmenopausal women.^{65, 66} We did not reproduce these findings, perhaps related to a low number of non-parous women in our cohort (55 women, 17% of the study sample).

4.6 Sensitivity analysis

As a sensitivity analysis, we combined two sources of exposure data for smoking and alcohol: the detailed, eight-page questionnaire answered 0–20 years prior to the biopsy, and the two-page questionnaire answered at the time of the biopsy. The combined information on smoking exposure provided no further insight. This was also true for participants being classified as former smokers. Similarly, combining information on alcohol intake during the week before the biopsy with the data on alcohol intake 0–20 years prior to the biopsy provided no further insight. The average alcohol consumption of our participants was low (median: 3.08 g/day), which limits our ability to discern effects of higher alcohol consumption.

For these sensitivity analyses of smoking and alcohol, the detailed exposure information was separated with up to 20 years in time from the more limited two-page questionnaire answered at the time of the biopsy. As the additional data did to add much, we chose to present the most recent exposure information as our main result, even though it was less detailed compared to the eight-page questionnaire. The lack of additional findings ties in with the understanding of gene expression being a highly dynamic and responsive biological process, which is likely to reflect recent exposures rather than exposure history. The results are in line with findings on gene expression in blood related to smoking history and current smoking.⁶⁷ In comparison, DNA methylation patterns may to a larger extent reflect previous exposure.⁶⁸