68
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The Stanford Tissue Microarray Database

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The Stanford Tissue Microarray Database (TMAD; http://tma.stanford.edu) is a public resource for disseminating annotated tissue images and associated expression data. Stanford University pathologists, researchers and their collaborators worldwide use TMAD for designing, viewing, scoring and analyzing their tissue microarrays. The use of tissue microarrays allows hundreds of human tissue cores to be simultaneously probed by antibodies to detect protein abundance (Immunohistochemistry; IHC), or by labeled nucleic acids ( in situ hybridization; ISH) to detect transcript abundance. TMAD archives multi-wavelength fluorescence and bright-field images of tissue microarrays for scoring and analysis. As of July 2007, TMAD contained 205 161 images archiving 349 distinct probes on 1488 tissue microarray slides. Of these, 31 306 images for 68 probes on 125 slides have been released to the public. To date, 12 publications have been based on these raw public data. TMAD incorporates the NCI Thesaurus ontology for searching tissues in the cancer domain. Image processing researchers can extract images and scores for training and testing classification algorithms. The production server uses the Apache HTTP Server, Oracle Database and Perl application code. Source code is available to interested researchers under a no-cost license.

          Related collections

          Most cited references31

          • Record: found
          • Abstract: found
          • Article: not found

          Cluster analysis and display of genome-wide expression patterns.

          A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen in genome-wide expression experiments can be interpreted as indications of the status of cellular processes. Also, coexpression of genes of known function with poorly characterized or novel genes may provide a simple means of gaining leads to the functions of many genes for which information is not available currently.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Tissue microarrays for high-throughput molecular profiling of tumor specimens.

            Many genes and signalling pathways controlling cell proliferation, death and differentiation, as well as genomic integrity, are involved in cancer development. New techniques, such as serial analysis of gene expression and cDNA microarrays, have enabled measurement of the expression of thousands of genes in a single experiment, revealing many new, potentially important cancer genes. These genome screening tools can comprehensively survey one tumor at a time; however, analysis of hundreds of specimens from patients in different stages of disease is needed to establish the diagnostic, prognostic and therapeutic importance of each of the emerging cancer gene candidates. Here we have developed an array-based high-throughput technique that facilitates gene expression and copy number surveys of very large numbers of tumors. As many as 1000 cylindrical tissue biopsies from individual tumors can be distributed in a single tumor tissue microarray. Sections of the microarray provide targets for parallel in situ detection of DNA, RNA and protein targets in each specimen on the array, and consecutive sections allow the rapid analysis of hundreds of molecular markers in the same set of specimens. Our detection of six gene amplifications as well as p53 and estrogen receptor expression in breast cancer demonstrates the power of this technique for defining new subgroups of tumors.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              A landscape effect in tenosynovial giant-cell tumor from activation of CSF1 expression by a translocation in a minority of tumor cells.

              Tenosynovial giant-cell tumor (TGCT) and pigmented villonodular synovitis (PVNS) are related conditions with features of both reactive inflammatory disorders and clonal neoplastic proliferations. Chromosomal translocations involving chromosome 1p13 have been reported in both TGCT and PVNS. We confirm that translocations involving 1p13 are present in a majority of cases of TGCT and PVNS and show that CSF1 is the gene at the chromosome 1p13 breakpoint. In some cases of both TGCT and PVNS, CSF1 is fused to COL6A3 (2q35). The CSF1 translocations result in overexpression of CSF1. In cases of TGCT and PVNS carrying this translocation, it is present in a minority of the intratumoral cells, leading to CSF1 expression only in these cells, whereas the majority of cells express CSF1R but not CSF1, suggesting a tumor-landscaping effect with aberrant CSF1 expression in the neoplastic cells, leading to the abnormal accumulation of nonneoplastic cells that form a tumorous mass.
                Bookmark

                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                January 2008
                7 November 2007
                7 November 2007
                : 36
                : Database issue , Database issue
                : D871-D877
                Affiliations
                1Department of Biochemistry, Stanford University School of Medicine, 2Howard Hughes Medical Institute, 3Department of Pathology, Stanford University School of Medicine, 4Department of Medicine, Stanford University and 5Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
                Author notes
                *To whom correspondence should be addressed.+1 650 723 6719+1 650 725 7811 bobm@ 123456stanford.edu
                Article
                10.1093/nar/gkm861
                2238948
                17989087
                c898e139-e822-47ec-b33b-8f3969231f00
                © 2007 The Author(s)

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

                History
                : 9 August 2007
                : 27 September 2007
                : 27 September 2007
                Categories
                Articles

                Genetics
                Genetics

                Comments

                Comment on this article