Impact of Estrogen Deprivation on Gene Expression Profiles of Normal Postmenopausal Breast Tissue In vivo

Direct integration of intensity-level data from Affymetrix and Illumina microarrays improves statistical power for robust reanalysis

Background

Affymetrix GeneChips and Illumina BeadArrays are the most widely used commercial single channel gene expression microarrays. Public data repositories are an extremely valuable resource, providing array-derived gene expression measurements from many thousands of experiments. Unfortunately many of these studies are underpowered and it is desirable to improve power by combining data from more than one study; we sought to determine whether platform-specific bias precludes direct integration of probe intensity signals for combined reanalysis.

Results

Using Affymetrix and Illumina data from the microarray quality control project, from our own clinical samples, and from additional publicly available datasets we evaluated several approaches to directly integrate intensity level expression data from the two platforms. After mapping probe sequences to Ensembl genes we demonstrate that, ComBat and cross platform normalisation (XPN), significantly outperform mean-centering and distance-weighted discrimination (DWD) in terms of minimising inter-platform variance. In particular we observed that DWD, a popular method used in a number of previous studies, removed systematic bias at the expense of genuine biological variability, potentially reducing legitimate biological differences from integrated datasets.

Conclusion

Normalised and batch-corrected intensity-level data from Affymetrix and Illumina microarrays can be directly combined to generate biologically meaningful results with improved statistical power for robust, integrated reanalysis.

Maintaining Breast Cancer Specimen Integrity and Individual or Simultaneous Extraction of Quality DNA, RNA, and Proteins from Allprotect-Stabilized and Nonstabilized Tissue Samples

The Saint James's Hospital Biobank was established in 2008, to develop a high-quality breast tissue BioResource, as a part of the breast cancer clinical care pathway. The aims of this work were: (1) to ascertain the quality of RNA, DNA, and protein in biobanked carcinomas and normal breast tissues, (2) to assess the efficacy of AllPrep(®) (Qiagen) in isolating RNA, DNA, and protein simultaneously, (3) to compare AllPrep with RNEasy(®) and QIAamp(®) (both Qiagen), and (4) to examine the effectiveness of Allprotect(®) (Qiagen), a new tissue stabilization medium in preserving DNA, RNA, and proteins. One hundred eleven frozen samples of carcinoma and normal breast tissue were analyzed. Tumor and normal tissue morphology were confirmed by frozen sections. Tissue type, tissue treatment (Allprotect vs. no Allprotect), extraction kit, and nucleic acid quantification were analyzed by utilizing a 4 factorial design (SPSS PASW 18 Statistics Software(®)). QIAamp (DNA isolation), AllPrep (DNA, RNA, and Protein isolation), and RNeasy (RNA isolation) kits were assessed and compared. Mean DNA yield and A(260/280) values using QIAamp were 33.2 ng/μL and 1.86, respectively, and using AllPrep were 23.2 ng/μL and 1.94. Mean RNA yield and RNA Integrity Number (RIN) values with RNeasy were 73.4 ng/μL and 8.16, respectively, and with AllPrep were 74.8 ng/μL and 7.92. Allprotect-treated tissues produced higher RIN values of borderline significance (P=0.055). No discernible loss of RNA stability was detected after 6 h incubation of stabilized or nonstabilized tissues at room temperature or 4°C or in 9 freeze-thaw cycles. Allprotect requires further detailed evaluation, but we consider AllPrep to be an excellent option for the simultaneous extraction of RNA, DNA, and protein from tumor and normal breast tissues. The essential presampling procedures that maintain the diagnostic integrity of pathology specimens do not appear to compromise the quality of molecular isolates.

Sex hormones and gene expression signatures in peripheral blood from postmenopausal women - the NOWAC postgenome study

Background

Postmenopausal hormone therapy (HT) influences endogenous hormone concentrations and increases the risk of breast cancer. Gene expression profiling may reveal the mechanisms behind this relationship.

Our objective was to explore potential associations between sex hormones and gene expression in whole blood from a population-based, random sample of postmenopausal women

Methods

Gene expression, as measured by the Applied Biosystems microarray platform, was compared between hormone therapy (HT) users and non-users and between high and low hormone plasma concentrations using both gene-wise analysis and gene set analysis. Gene sets found to be associated with HT use were further analysed for enrichment in functional clusters and network predictions. The gene expression matrix included 285 samples and 16185 probes and was adjusted for significant technical variables.

Results

Gene-wise analysis revealed several genes significantly associated with different types of HT use. The functional cluster analyses provided limited information on these genes. Gene set analysis revealed 22 gene sets that were enriched between high and low estradiol concentration (HT-users excluded). Among these were seven oestrogen related gene sets, including our gene list associated with systemic estradiol use, which thereby represents a novel oestrogen signature. Seven gene sets were related to immune response. Among the 15 gene sets enriched for progesterone, 11 overlapped with estradiol. No significant gene expression patterns were found for testosterone, follicle stimulating hormone (FSH) or sex hormone binding globulin (SHBG).

Conclusions

Distinct gene expression patterns associated with sex hormones are detectable in a random group of postmenopausal women, as demonstrated by the finding of a novel oestrogen signature.

An "omics" approach to determine the mechanisms of acquired aromatase inhibitor resistance

Aromatase inhibitors (AIs) are the major types of drugs to treat hormone-dependent breast cancer. Although these drugs work effectively, cancer still recurs in many patients after treatment as a result of acquired resistance to the AIs. To characterize the resistant mechanisms, a set of MCF-7aro cell lines that acquired resistance to the AIs was generated. Through an "Omics" approach, we found that the resistance mechanisms of the three AIs (anastrozole, letrozole, and exemestane) differ and activation of estrogen receptor alpha (ERα) is critical for acquired AI resistance. Our results reveal that growth factor/signal transduction pathways are upregulated after ERα-dependent pathways are suppressed by AIs, and ERα can then be activated through different crosstalk mechanisms.

SGK3 is an estrogen-inducible kinase promoting estrogen-mediated survival of breast cancer cells

Serum- and glucocorticoid-inducible kinase 3 (SGK3) is a protein kinase of the AGC family of protein kinase A, protein kinase G, and protein kinase C and functions downstream of phosphatidylinositol 3-kinase (PI3K). Recent study revealed that SGK3 plays a pivotal role in Akt/protein kinase B independent signaling downstream of oncogenic PI3KCA mutations in breast cancer. Here we report that SGK3 is an estrogen receptor (ER) transcriptional target and promotes estrogen-mediated cell survival of ER-positive breast cancer cells. Through a meta-analysis on 22 microarray studies of breast cancer in the Oncomine database, we found that the expression of SGK3 is significantly higher (5.7-fold, P < 0.001) in ER-positive tumors than in ER-negative tumors. In ER-positive breast cancer cells, SGK3 expression was found to be induced by 17β-estradiol (E(2)) in a dose- and time-dependent manner, and the induction of SGK3 mRNA by E(2) is independent of newly synthesized proteins. We identified two ERα-binding regions at the sgk3 locus through chromatin immunoprecipitation with massively parallel DNA sequencing. Promoter analysis revealed that ERα stimulates the activity of sgk3 promoters by interaction with these two ERα-binding regions on E(2) treatment. Loss-of-function analysis indicated that SGK3 is required for E(2)-mediated cell survival of MCF-7 breast carcinoma cells. Moreover, overexpression of SGK3 could partially protect MCF-7 cells against apoptosis caused by antiestrogen ICI 182,780. Together, our study defines the molecular mechanism of regulation of SGK3 by estrogen/ER and provides a new link between the PI3K pathway and ER signaling as well as a new estrogen-mediated cell survival mechanism mediated by SGK3 in breast cancer cells.

Mammographic density change with 1 year of aerobic exercise among postmenopausal women: a randomized controlled trial

BACKGROUND

The Alberta Physical Activity and Breast Cancer Prevention (ALPHA) Trial examined the influence of aerobic exercise on biological factors that are associated with breast cancer risk. Mammographic density, a secondary outcome, is reported here.

METHODS

The ALPHA Trial was a parallel group randomized controlled trial conducted between May 2003 and July 2007. Postmenopausal, sedentary women ages 50 to 74 years (n = 320) were evenly randomized to aerobic exercise (45 minutes, 5 days per week) or control (usual life-style) for 1 year. Dense fibroglandular tissue and nondense fatty tissue were measured from mammograms at baseline and 1 year using computer-assisted thresholding software for area measurements and a new technique that relies on the calibration of mammography units with a tissue-equivalent phantom for volumetric measurements.

RESULTS

Nondense volume decreased in the exercise group relative to the control group (difference between groups = -38.5 cm(3); 95% confidence interval, -61.6 to 15.4; P = 0.001). Changes in total body fat accounted for this decrease. Changes in dense area and dense volume, measures that have previously been associated with breast cancer risk, were not significantly different between the groups (P > or = 0.26).

CONCLUSIONS

Achieving changes in mammographic measures may require more exercise or a study population with higher baseline levels of sex hormones or a wider range of mammographic density. The data from this study, however, suggest that the protective effect of exercise on breast cancer risk may operate through a mechanism other than mammographic density.

Biomarkers of dietary energy restriction in women at increased risk of breast cancer

Dietary energy restriction (DER) reduces risk of spontaneous mammary cancer in rodents. In humans, DER in premenopausal years seems to reduce risk of postmenopausal breast cancer. Markers of DER are required to develop acceptable DER regimens for breast cancer prevention. We therefore examined markers of DER in the breast, adipose tissue, and serum. Nineteen overweight or obese women at moderately increased risk of breast cancer (lifetime risk, 1 in 6 to 1 in 3) ages between 35 and 45 were randomly allocated to DER [liquid diet, 3,656 kJ/d (864 kcal/d); n = 10] or asked to continue their normal eating patterns (n = 9) for one menstrual cycle. Biopsies of the breast and abdominal fat were taken before and after the intervention. RNA was extracted from whole tissues and breast epithelium (by laser capture microdissection) and hybridized to Affymetrix GeneChips. Longitudinal plasma and urine samples were collected before and after intervention, and metabolic profiles were generated using gas chromatography-mass spectrometry. DER was associated with significant reductions in weight [-7.0 (+/-2.3) kg] and in alterations of serum biomarkers of breast cancer risk (insulin, leptin, total and low-density lipoprotein cholesterol, and triglycerides). In both abdominal and breast tissues, as well as isolated breast epithelial cells, genes involved in glycolytic and lipid synthesis pathways (including stearoyl-CoA desaturase, fatty acid desaturase, and aldolase C) were significantly down-regulated. We conclude that reduced expressions of genes in the lipid metabolism and glycolytic pathways are detectable in breast tissue following DER, and these may represent targets for DER mimetics as effective chemoprophylactic agents.

Genome-wide transcriptomic profiling of microdissected human breast tissue reveals differential expression of KIT (c-Kit, CD117) and oestrogen receptor-alpha (ERalpha) in response to therapeutic radiation

The pathogenesis of late normal tissue fibrosis after high-dose ionizing radiation involves multiple cell types and signalling pathways but is not well understood. To identify the molecular changes occurring after radiotherapy, paired normal tissue samples were collected from the non-irradiated breast and from the treated breast of women who had undergone curative radiotherapy for early breast cancer months or years previously. As radiation may induce distinct transcriptional changes in the different components of the breast, laser capture microdissection and gene expression microarray profiling were performed separately for epithelial and stromal components and selected genes were validated using immunohistochemistry. In the epithelial compartment, a reduction of KIT (c-Kit; CD117) and a reciprocal increase in ESR1 (oestrogen receptor-alpha, ERalpha) mRNA and protein levels were seen in irradiated compared to non-irradiated samples. In the stromal compartment, extracellular matrix genes including FN1 (fibronectin 1) and CTGF (connective tissue growth factor; CCN2) were increased. Further investigation revealed that c-Kit and ERalpha were expressed in distinct subpopulations of luminal epithelial cells. Interlobular c-Kit-positive mast cells were also increased in irradiated cases not showing features of post-radiation atrophy. Pathway analysis revealed 'cancer, reproductive system disease and tumour morphology' as the most significantly enriched network in the epithelial compartment, whereas in the stromal component, a significant enrichment for 'connective tissue disorders, dermatological diseases and conditions, genetic disorder' and 'cancer, tumour morphology, infection mechanism' networks was observed. These data identify previously unreported changes in the epithelial compartment and show altered expression of genes implicated in late normal tissue injury in the stromal compartment of normal breast tissue. The findings are relevant to both fibrosis and atrophy occurring after radiotherapy for early breast cancer.

Bioinformatics and breast cancer: what can high-throughput genomic approaches actually tell us?

High-throughput genomic technology has rapidly become a major tool for the study of breast cancer. Gene expression profiling has been applied to many areas of research from basic science to translational studies, with the potential to identify new targets for treatment, mechanisms of resistance and to improve on current tools for the analysis of prognosis. However, the sheer scale of the data generated along with the number of different protocols, platforms and analysis methods can make these studies difficult for clinicians to comprehend. Similarly, computational scientists and statisticians that may be called upon to analyse the data generated are often unaware of the processes involved in sample collection or the relevance and impact of genetics and pathological characteristics. There is a pressing need for better understanding of the challenges and limitations of microarray approaches, both in experimental design and data analysis. Holistic, whole-genome approaches are still relatively new and critics have been quick to highlight non-overlapping results from groups testing similar hypotheses. However, it is often subtle differences in the experimental design and technology that underpin the variation between these studies. Rather than indicating that the data are meaningless, this suggests that many findings are real, but highly context dependent. This review explores both the current state and potential of bioinformatics to bring meaning to high-throughput genomic approaches in the understanding of breast cancer.