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      Expression profiling of formalin-fixed paraffin-embedded primary breast tumors using cancer-specific and whole genome gene panels on the DASL ® platform

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          Abstract

          Background

          The c DNA-mediated Annealing, extension, Selection and Ligation (DASL) assay has become a suitable gene expression profiling system for degraded RNA from paraffin-embedded tissue. We examined assay characteristics and the performance of the DASL 502-gene Cancer Panel v1 (1.5K) and 24,526-gene panel (24K) platforms at differentiating nine human epidermal growth factor receptor 2- positive (HER2+) and 11 HER2-negative (HER2-) paraffin-embedded breast tumors.

          Methods

          Bland-Altman plots and Spearman correlations evaluated intra/inter-panel agreement of normalized expression values. Unequal-variance t-statistics tested for differences in expression levels between HER2 + and HER2 - tumors. Regulatory network analysis was performed using Metacore (GeneGo Inc., St. Joseph, MI).

          Results

          Technical replicate correlations ranged between 0.815-0.956 and 0.986-0.997 for the 1.5K and 24K panels, respectively. Inter-panel correlations of expression values for the common 498 genes across the two panels ranged between 0.485-0.573. Inter-panel correlations of expression values of 17 probes with base-pair sequence matches between the 1.5K and 24K panels ranged between 0.652-0.899. In both panels, erythroblastic leukemia viral oncogene homolog 2 ( ERBB2) was the most differentially expressed gene between the HER2 + and HER2 - tumors and seven additional genes had p-values < 0.05 and log2 -fold changes > |0.5| in expression between HER2 + and HER2 - tumors: topoisomerase II alpha ( TOP2A), cyclin a2 ( CCNA2), v-fos fbj murine osteosarcoma viral oncogene homolog ( FOS), wingless-type mmtv integration site family, member 5a ( WNT5A), growth factor receptor-bound protein 7 ( GRB7), cell division cycle 2 ( CDC2), and baculoviral iap repeat-containing protein 5 ( BIRC5). The top 52 discriminating probes from the 24K panel are enriched with genes belonging to the regulatory networks centered around v-myc avian myelocytomatosis viral oncogene homolog ( MYC), tumor protein p53 ( TP53), and estrogen receptor α ( ESR1). Network analysis with a two-step extension also showed that the eight discriminating genes common to the 1.5K and 24K panels are functionally linked together through MYC, TP53, and ESR1.

          Conclusions

          The relative RNA abundance obtained from two highly differing density gene panels are correlated with eight common genes differentiating HER2 + and HER2 - breast tumors. Network analyses demonstrated biological consistency between the 1.5K and 24K gene panels.

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          Most cited references29

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          Oncogenic pathway signatures in human cancers as a guide to targeted therapies.

          The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.
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            Gene-expression signatures in breast cancer.

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              A two-gene expression ratio predicts clinical outcome in breast cancer patients treated with tamoxifen.

              Tamoxifen significantly reduces tumor recurrence in certain patients with early-stage estrogen receptor-positive breast cancer, but markers predictive of treatment failure have not been identified. Here, we generated gene expression profiles of hormone receptor-positive primary breast cancers in a set of 60 patients treated with adjuvant tamoxifen monotherapy. An expression signature predictive of disease-free survival was reduced to a two-gene ratio, HOXB13 versus IL17BR, which outperformed existing biomarkers. Ectopic expression of HOXB13 in MCF10A breast epithelial cells enhances motility and invasion in vitro, and its expression is increased in both preinvasive and invasive primary breast cancer. The HOXB13:IL17BR expression ratio may be useful for identifying patients appropriate for alternative therapeutic regimens in early-stage breast cancer.
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                Author and article information

                Journal
                BMC Med Genomics
                BMC Medical Genomics
                BioMed Central
                1755-8794
                2010
                20 December 2010
                : 3
                : 60
                Affiliations
                [1 ]Department of Laboratory Medicine and Pathology, Mayo Clinic, 200 First St SW, Rochester, Minnesota, 55905, USA
                [2 ]Division of Biomedical Statistics and Informatics Mayo Clinic, 200 First St SW, Rochester, Minnesota, 55905, USA
                [3 ]Department of Pathology, Mayo Clinic, 13400 E. Shea Blvd, Scottsdale, Arizona, 85259, USA
                [4 ]Section of Biostatistics, 13400 E. Shea Blvd, Scottsdale, Arizona, 85259, USA
                [5 ]Department of Scientific Research, Illumina Inc., 9885 Towne Centre Drive, San Diego, California, 92121, USA
                [6 ]Division of Pulmonary and Critical Care Medicine, Mayo Clinic, 200 First St SW, Rochester, Minnesota, 55905, USA
                [7 ]Division of Hematology and Oncology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, Florida, 32224, USA
                Article
                1755-8794-3-60
                10.1186/1755-8794-3-60
                3022545
                21172013
                c6d105c9-64bd-4b05-b6a4-7d0d724e8a45
                Copyright ©2010 Reinholz et al; licensee BioMed Central Ltd.

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

                History
                : 25 February 2010
                : 20 December 2010
                Categories
                Research Article

                Genetics
                Genetics

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