Gene expression in histologically normal epithelium from breast cancer patients and from cancer-free prophylactic mastectomy patients shares a similar profile

Boston University School of Medicine and Boston Medical Center, MA, USA.
British Journal of Cancer (Impact Factor: 4.84). 03/2010; 102(8):1284-93. DOI: 10.1038/sj.bjc.6605576
Source: PubMed


We hypothesised that gene expression in histologically normal (HN) epithelium (NlEpi) would differ between breast cancer patients and usual-risk controls undergoing reduction mammoplasty (RM), and that gene expression in NlEpi from cancer-free prophylactic mastectomy (PM) samples from high-risk women would resemble HN gene expression.
We analysed gene expression in 73 NlEpi samples microdissected from frozen tissue. In 42 samples, we used microarrays to compare gene expression between 18 RM patients and 18 age-matched HN (9 oestrogen receptor (ER)+, 9 ER-) and 6 PM patients. Data were analysed using a Bayesian approach (BADGE), and validated with quantitative real-time PCR (qPCR) in 31 independent NlEpi samples from 8 RM, 17 HN, and 6 PM patients.
A total of 98 probe sets (86 genes) were differentially expressed between RM and HN samples. Performing hierarchical analysis with these 98 probe sets, PM and HN samples clustered together, away from RM samples. qPCR validation of independent samples was high (84%) and uniform in RM compared with HN patients, and lower (58%), but more heterogeneous, in RM compared with PM patients. The 86 genes were implicated in many processes including transcription and the MAPK pathway.
Gene expression differs between the NlEpi of breast cancer cases and controls. The profile of cancer cases can be discerned in high-risk NlEpi from cancer-free breasts. This suggests that the profile is not an effect of the tumour, but may mark increased risk and reveal the earliest genomic changes of breast cancer.

Download full-text


Available from: Antonio de Las Morenas, Oct 06, 2015
21 Reads
  • Source
    • "For large-scale GRN inference, I used a set of mRNA expression measurements obtained from human epithelium at different stages of cancer development (Graham et al., 2010). The dataset was produced by Graham et al. (2010) who performed gene expression analysis of breast epithelium tissue samples obtained from 42 patients (18 cancer free, 18 had prophylactic mammoplasty, and 6 had reduction mammoplasty) in order to understand the differences in expression profiles of histologically normal breast epithelium and usual-risk controls undergoing reduction mammoplasty. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Reconstruction of gene regulatory networks (GRNs) from experimental data is a fundamental challenge in systems biology. A number of computational approaches have been developed to infer GRNs from mRNA expression profiles. However, expression profiles alone are proving to be insufficient for inferring GRN topologies with reasonable accuracy. Recently, it has been shown that integration of external data sources (such as gene and protein sequence information, gene ontology data, protein–protein interactions) with mRNA expression profiles may increase the reliability of the inference process. Here, I propose a new approach that incorporates transcription factor binding sites (TFBS) and physical protein interactions (PPI) among transcription factors (TFs) in a Bayesian variable selection (BVS) algorithm which can infer GRNs from mRNA expression profiles subjected to genetic perturbations. Using real experimental data, I show that the integration of TFBS and PPI data with mRNA expression profiles leads to significantly more accurate networks than those inferred from expression profiles alone. Additionally, the performance of the proposed algorithm is compared with a series of least absolute shrinkage and selection operator (LASSO) regression-based network inference methods that can also incorporate prior knowledge in the inference framework. The results of this comparison suggest that BVS can outperform LASSO regression-based method in some circumstances.
    Frontiers in Bioengineering and Biotechnology 05/2014; 2. DOI:10.3389/fbioe.2014.00013
  • Source
    • "Over 5700 RNAs are spiked in at relative concentrations ranging from 1- to 4-fold, and the arrays from each condition are balanced with respect to both total RNA amount and degree of positive versus negative fold change. The second data set is recently represented in [21]. These cancer data sets consist of 18 breast cancer patients' usual-risk controls undergoing reduction mammoplasty (RM), and histologically normal (HN) patients. "
    [Show abstract] [Hide abstract]
    ABSTRACT: In response to the rapid development of DNA Microarray Technologies, many differentially expressed genes selection algorithms have been developed, and different comparison studies of these algorithms have been done. However, it is not clear how these methods compare with each other, especially when we used different developments tools. Here, we considered three commonly used differentially expressed genes selection approaches, namely: Fold Change, T-test and SAM, using Bioinformatics Matlab Toolbox and R/BioConductor. We used two datasets, issued from the affymetrix technology, to present results of used methods and software's in gene selection process. The results, in terms of sensitivity and specificity, indicate that the behavior of SAM is better compared to Fold Change and T-test using R/BioConductor. While, no practical differences were observed between the three gene selection methods when using Bioinformatics Matlab Toolbox. In face of our result, the ROC curve shows that: on the one hand R/BioConductor using SAM is favored for microarray selection compared to the other methods. And, on the other hand, results of the three studied gene selection methods using Bioinformatics Matlab Toolbox are still comparable for the two datasets used.
    Bioinformation 12/2013; 9(20):1019-22. DOI:10.6026/97320630091019 · 0.50 Impact Factor
  • Source
    • "Four GEO Series [33,34] of Affymetrix Human Genome U133A array data were taken: GSE17705 (title: “Endocrine Sensitivity Index Validation Dataset”, 298 samples, [35]), GSE10780 (title: “Proliferative genes dominate malignancy-risk gene signature in histologically-normal breast tissue”, 185 samples, [36]), GSE20711 (title: “Epigenetic portraits of human breast cancers (expression data)”, 90 samples, [37]), GSE20437 (title: “Histologically normal epithelium from breast cancer patients and cancer-free prophylactic mastectomy patients”, 42 samples, [38]). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Quantification and normalization of RT-qPCR data critically depends on the expression of so called reference genes. Our goal was to develop a strategy for the selection of reference genes that utilizes microarray data analysis and combines known approaches for gene stability evaluation and to select a set of appropriate reference genes for research and clinical analysis of breast samples with different receptor and cancer status using this strategy. A preliminary search of reference genes was based on high-throughput analysis of microarray datasets. The final selection and validation of the candidate genes were based on the RT-qPCR data analysis using several known methods for expression stability evaluation: comparative [increment]Ct method, geNorm, NormFinder and Haller equivalence test. A set of five reference genes was identified: ACTB, RPS23, HUWE1, EEF1A1 and SF3A1. The initial selection was based on the analysis of publically available well-annotated microarray datasets containing different breast cancers and normal breast epithelium from breast cancer patients and epithelium from cancer-free patients. The final selection and validation were performed using RT-qPCR data from 39 breast cancer biopsy samples. Three genes from the final set were identified by the means of microarray analysis and were novel in the context of breast cancer assay. We showed that the selected set of reference genes is more stable in comparison not only with individual genes, but also with a system of reference genes used in commercial OncotypeDX test. A selection of reference genes for RT-qPCR can be efficiently performed by combining a preliminary search based on the high-throughput analysis of microarray datasets and final selection and validation based on the analysis of RT-qPCR data with a simultaneous examination of different expression stability measures. The identified set of reference genes proved to be less variable and thus potentially more efficient for research and clinical analysis of breast samples comparing to individual genes and the set of reference genes used in OncotypeDX assay.
    Journal of Clinical Bioinformatics 07/2013; 3(1):13. DOI:10.1186/2043-9113-3-13
Show more

Similar Publications