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A tool to facilitate clinical biomarker studies - a tissue dictionary based on the Human Protein Atlas
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology. (pontén)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology. (pontén)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular and Morphological Pathology. (pontén)
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2012 (English)In: BMC Medicine, ISSN 1741-7015, E-ISSN 1741-7015, Vol. 10, p. 103-Article in journal (Refereed) Published
Abstract [en]

The complexity of tissue and the alterations that distinguish normal from cancer remain a challenge for translating results from tumor biological studies into clinical medicine. This has generated an unmet need to exploit the findings from studies based on cell lines and model organisms to develop, validate and clinically apply novel diagnostic, prognostic and treatment predictive markers. As one step to meet this challenge, the Human Protein Atlas project has been set up to produce antibodies towards human protein targets corresponding to all human protein coding genes and to map protein expression in normal human tissues, cancer and cells. Here, we present a dictionary based on microscopy images created as an amendment to the Human Protein Atlas. The aim of the dictionary is to facilitate the interpretation and use of the image-based data available in the Human Protein Atlas, but also to serve as a tool for training and understanding tissue histology, pathology and cell biology. The dictionary contains three main parts, normal tissues, cancer tissues and cells, and is based on high-resolution images at different magnifications of full tissue sections stained with H & E. The cell atlas is centered on immunofluorescence and confocal microscopy images, using different color channels to highlight the organelle structure of a cell. Here, we explain how this dictionary can be used as a tool to aid clinicians and scientists in understanding the use of tissue histology and cancer pathology in diagnostics and biomarker studies.

Place, publisher, year, edition, pages
2012. Vol. 10, p. 103-
Keywords [en]
Antibody-based proteomics, cancer biomarkers, tissue and cell dictionary, immunohistochemistry, protein expression, histology, pathology
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:uu:diva-192083DOI: 10.1186/1741-7015-10-103ISI: 000312389800001OAI: oai:DiVA.org:uu-192083DiVA, id: diva2:588951
Available from: 2013-01-16 Created: 2013-01-16 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Aspects of Gene Expression Profiling in Disease and Health
Open this publication in new window or tab >>Aspects of Gene Expression Profiling in Disease and Health
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The aim of this thesis is to in various ways explore protein expression in human normal tissue and in cancer and to apply that knowledge in biomarker discovery.

In Paper I the prognostic significance of RNA-binding motif protein 3 (RBM3) is explored in malignant melanoma. To further evaluate the prognostic significance of RBM3 expression was assessed in 226 incident cases of malignant melanoma from the prospective populationbased cohort study Malmö Diet and Cancer Study using tissue microarray technique (TMA). RBM3 was shown to be down regulated in metastatic melanoma and high nuclear expression in the primary tumor was an independent marker of prolonged over all survival. As a tool to facilitate clinical biomarker studies the Human Protein Atlas has created a tissue dictionary as an introduction to human histology and histopathology. In Paper II this work is introduced.

A cancer diagnosis can be a complex process with difficulties of establishing tumor type in localized disease or organ of origin in generalized disease. Immunohistochemically assisted diagnosis of cancer is common practice among pathologists where its application combined with known protein expression profiles of different cancer types, can strengthen or help dismiss a suspected diagnosis. In Paper III the diagnostic performance of 27 commonly used antibodies are tested in a predominantly metastatic, multicancer cohort using TMA technique. Overall these 27 diagnostic markers showed a low sensitivity and specificity for its intended use, highlighting the need for novel, more specific markers.

Breast, ovarian, endometrial and ovarian cancers affect predominantly women. Differential diagnostics between these cancer types can be challenging. In Paper IV an algorithm, based on six different IHC markers, to differentiate between these cancer types is presented. A new diagnostic marker for breast cancer, namely ZAG is also introduced.

In Paper V the transcriptomic landscape of the adrenal gland is explored by combining a transcriptomic approach with a immunohistochemistry based proteomic approach. In the adrenal gland we were able to detect 253 genes with an elevated pattern of expression in the adrenal gland, as compared to 31 other normal human tissue types analyzed. This combination of a transcriptomic and immunohistochemical approach provides a foundation for a deeper understanding of the adrenal glands function and physiology.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. p. 43
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Medicine, ISSN 1651-6206 ; 1294
Keywords
Cancer, biomarkers, differential diagnostics, immunohistochemistry, transcriptomics, protein profiling, adrenal gland.
National Category
Basic Medicine
Research subject
Pathology
Identifiers
urn:nbn:se:uu:diva-312939 (URN)978-91-554-9802-3 (ISBN)
Public defence
2017-03-10, Fåhraeussalen, Rudbecklaboratoriet, Dag Hammarskjölds v 20, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2017-02-17 Created: 2017-01-16 Last updated: 2018-01-13

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