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      Establishment of the epithelial-specific transcriptome of normal and malignant human breast cells based on MPSS and array expression data

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          Abstract

          Introduction

          Diverse microarray and sequencing technologies have been widely used to characterise the molecular changes in malignant epithelial cells in breast cancers. Such gene expression studies to identify markers and targets in tumour cells are, however, compromised by the cellular heterogeneity of solid breast tumours and by the lack of appropriate counterparts representing normal breast epithelial cells.

          Methods

          Malignant neoplastic epithelial cells from primary breast cancers and luminal and myoepithelial cells isolated from normal human breast tissue were isolated by immunomagnetic separation methods. Pools of RNA from highly enriched preparations of these cell types were subjected to expression profiling using massively parallel signature sequencing (MPSS) and four different genome wide microarray platforms. Functional related transcripts of the differential tumour epithelial transcriptome were used for gene set enrichment analysis to identify enrichment of luminal and myoepithelial type genes. Clinical pathological validation of a small number of genes was performed on tissue microarrays.

          Results

          MPSS identified 6,553 differentially expressed genes between the pool of normal luminal cells and that of primary tumours substantially enriched for epithelial cells, of which 98% were represented and 60% were confirmed by microarray profiling. Significant expression level changes between these two samples detected only by microarray technology were shown by 4,149 transcripts, resulting in a combined differential tumour epithelial transcriptome of 8,051 genes. Microarray gene signatures identified a comprehensive list of 907 and 955 transcripts whose expression differed between luminal epithelial cells and myoepithelial cells, respectively. Functional annotation and gene set enrichment analysis highlighted a group of genes related to skeletal development that were associated with the myoepithelial/basal cells and upregulated in the tumour sample. One of the most highly overexpressed genes in this category, that encoding periostin, was analysed immunohistochemically on breast cancer tissue microarrays and its expression in neoplastic cells correlated with poor outcome in a cohort of poor prognosis estrogen receptor-positive tumours.

          Conclusion

          Using highly enriched cell populations in combination with multiplatform gene expression profiling studies, a comprehensive analysis of molecular changes between the normal and malignant breast tissue was established. This study provides a basis for the identification of novel and potentially important targets for diagnosis, prognosis and therapy in breast cancer.

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          Most cited references43

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Breast cancer molecular subtypes respond differently to preoperative chemotherapy.

            Molecular classification of breast cancer has been proposed based on gene expression profiles of human tumors. Luminal, basal-like, normal-like, and erbB2+ subgroups were identified and were shown to have different prognoses. The goal of this research was to determine if these different molecular subtypes of breast cancer also respond differently to preoperative chemotherapy. Fine needle aspirations of 82 breast cancers were obtained before starting preoperative paclitaxel followed by 5-fluorouracil, doxorubicin, and cyclophosphamide chemotherapy. Gene expression profiling was done with Affymetrix U133A microarrays and the previously reported "breast intrinsic" gene set was used for hierarchical clustering and multidimensional scaling to assign molecular class. The basal-like and erbB2+ subgroups were associated with the highest rates of pathologic complete response (CR), 45% [95% confidence interval (95% CI), 24-68] and 45% (95% CI, 23-68), respectively, whereas the luminal tumors had a pathologic CR rate of 6% (95% CI, 1-21). No pathologic CR was observed among the normal-like cancers (95% CI, 0-31). Molecular class was not independent of conventional cliniocopathologic predictors of response such as estrogen receptor status and nuclear grade. None of the 61 genes associated with pathologic CR in the basal-like group were associated with pathologic CR in the erbB2+ group, suggesting that the molecular mechanisms of chemotherapy sensitivity may vary between these two estrogen receptor-negative subtypes. The basal-like and erbB2+ subtypes of breast cancer are more sensitive to paclitaxel- and doxorubicin-containing preoperative chemotherapy than the luminal and normal-like cancers.
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              Gene expression analysis by massively parallel signature sequencing (MPSS) on microbead arrays.

              We describe a novel sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 microm diameter microbeads. After constructing a microbead library of DNA templates by in vitro cloning, we assembled a planar array of a million template-containing microbeads in a flow cell at a density greater than 3x10(6) microbeads/cm2. Sequences of the free ends of the cloned templates on each microbead were then simultaneously analyzed using a fluorescence-based signature sequencing method that does not require DNA fragment separation. Signature sequences of 16-20 bases were obtained by repeated cycles of enzymatic cleavage with a type IIs restriction endonuclease, adaptor ligation, and sequence interrogation by encoded hybridization probes. The approach was validated by sequencing over 269,000 signatures from two cDNA libraries constructed from a fully sequenced strain of Saccharomyces cerevisiae, and by measuring gene expression levels in the human cell line THP-1. The approach provides an unprecedented depth of analysis permitting application of powerful statistical techniques for discovery of functional relationships among genes, whether known or unknown beforehand, or whether expressed at high or very low levels.
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                Author and article information

                Journal
                Breast Cancer Res
                Breast Cancer Research
                BioMed Central (London )
                1465-5411
                1465-542X
                2006
                2 October 2006
                : 8
                : 5
                : R56
                Affiliations
                [1 ]Ludwig Institute for Cancer Research/University College London Breast Cancer Laboratory, 91 Riding House Street, London, W1W 7BS, UK
                [2 ]The Breakthrough Breast Cancer Research Centre, Institute of Cancer Research, 237 Fulham Road, London, SW3 6JB, UK
                [3 ]Office of Information Technology, Ludwig Institute for Cancer Research and Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
                [4 ]Ludwig Institute for Cancer Research, New York Branch at Memorial Sloan-Kettering Cancer Centre, New York, NY 10021, USA
                [5 ]The J. Craig Venter Institute, 9704 Medical Center Drive, Rockville, MD 20850, USA
                [6 ]Department of Neurodegenerative Disease, Institute of Neurology, London, WC1N 3BG, UK
                Article
                bcr1604
                10.1186/bcr1604
                1779497
                17014703
                793893f9-4a36-4b95-b397-97377935936b
                Copyright © 2006 Grigoriadis et al.; licensee BioMed Central Ltd.

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 July 2006
                : 7 August 2006
                : 7 September 2006
                : 2 October 2006
                Categories
                Research Article

                Oncology & Radiotherapy
                Oncology & Radiotherapy

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