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  • 1.
    Bergman, Julia
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi.
    Aspects of Gene Expression Profiling in Disease and Health2017Doktoravhandling, med artikler (Annet vitenskapelig)
    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.

    Delarbeid
    1. Low RBM3 protein expression correlates with tumour progression and poor prognosis in malignant melanoma: An analysis of 215 cases from the Malmo Diet and Cancer Study
    Åpne denne publikasjonen i ny fane eller vindu >>Low RBM3 protein expression correlates with tumour progression and poor prognosis in malignant melanoma: An analysis of 215 cases from the Malmo Diet and Cancer Study
    Vise andre…
    2011 (engelsk)Inngår i: Journal of Translational Medicine, ISSN 1479-5876, E-ISSN 1479-5876, Vol. 9, s. 114-Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    Background: We have previously reported that expression of the RNA-and DNA-binding protein RBM3 is associated with a good prognosis in breast cancer and ovarian cancer. In this study, the prognostic value of immunohistochemical RBM3 expression was assessed in incident cases of malignant melanoma from a prospective population-based cohort study. Methods: Until Dec 31(st) 2008, 264 incident cases of primary invasive melanoma had been registered in the Malmo Diet and Cancer Study. Histopathological and clinical information was obtained for available cases and tissue microarrays (TMAs) constructed from 226 (85.6%) suitable paraffin-embedded tumours and 31 metastases. RBM3 expression was analysed by immunohistochemistry on the TMAs and a subset of full-face sections. Chi-square and Mann-Whitney U tests were used for comparison of RBM3 expression and relevant clinicopathological characteristics. Kaplan Meier analysis and Cox proportional hazards modelling were used to assess the relationship between RBM3 and recurrence free survival (RFS) and overall survival (OS). Results: RBM3 could be assessed in 215/226 (95.1%) of primary tumours and all metastases. Longitudinal analysis revealed that 16/31 (51.6%) of metastases lacked RBM3 expression, in contrast to the primary tumours in which RBM3 was absent in 3/215 (1.4%) cases and strongly expressed in 120/215 (55.8%) cases. Strong nuclear RBM3 expression in the primary tumour was significantly associated with favourable clinicopathological parameters; i. e. non-ulcerated tumours, lower depth of invasion, lower Clark level, less advanced clinical stage, low mitotic activity and non-nodular histological type, and a prolonged RFS (RR = 0.50; 95% CI = 0.27-0.91) and OS (RR = 0.36, 95% CI = 0.20-0.64). Multivariate analysis demonstrated that the beneficial prognostic value of RBM3 remained significant for OS (RR = 0.33; 95% CI = 0.18-0.61). Conclusions: In line with previous in vitro data, we here show that RBM3 is down-regulated in metastatic melanoma and high nuclear RBM3 expression in the primary tumour is an independent marker of a prolonged OS. The potential utility of RBM3 in treatment stratification of patients with melanoma should be pursued in future studies.

    Identifikatorer
    urn:nbn:se:uu:diva-158331 (URN)10.1186/1479-5876-9-114 (DOI)000293917900001 ()
    Tilgjengelig fra: 2011-09-06 Laget: 2011-09-06 Sist oppdatert: 2017-12-08bibliografisk kontrollert
    2. A tool to facilitate clinical biomarker studies - a tissue dictionary based on the Human Protein Atlas
    Åpne denne publikasjonen i ny fane eller vindu >>A tool to facilitate clinical biomarker studies - a tissue dictionary based on the Human Protein Atlas
    Vise andre…
    2012 (engelsk)Inngår i: BMC Medicine, ISSN 1741-7015, E-ISSN 1741-7015, Vol. 10, s. 103-Artikkel i tidsskrift (Fagfellevurdert) 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.

    Emneord
    Antibody-based proteomics, cancer biomarkers, tissue and cell dictionary, immunohistochemistry, protein expression, histology, pathology
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-192083 (URN)10.1186/1741-7015-10-103 (DOI)000312389800001 ()
    Tilgjengelig fra: 2013-01-16 Laget: 2013-01-16 Sist oppdatert: 2017-12-06bibliografisk kontrollert
    3. A systematic analysis of commonly used antibodies in cancer diagnostics
    Åpne denne publikasjonen i ny fane eller vindu >>A systematic analysis of commonly used antibodies in cancer diagnostics
    Vise andre…
    2014 (engelsk)Inngår i: Histopathology, ISSN 0309-0167, E-ISSN 1365-2559, Vol. 64, nr 2, s. 293-305Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    AimsImmunohistochemistry plays a pivotal role in cancer differential diagnostics. To identify the primary tumour from a metastasis specimen remains a significant challenge, despite the availability of an increasing number of antibodies. The aim of the present study was to provide evidence-based data on the diagnostic power of antibodies used frequently for clinical differential diagnostics. Methods and resultsA tissue microarray cohort comprising 940 tumour samples, of which 502 were metastatic lesions, representing tumours from 18 different organs and four non-localized cancer types, was analysed using immunohistochemistry with 27 well-established antibodies used in clinical differential diagnostics. Few antibodies, e.g. prostate-specific antigen and thyroglobulin, showed a cancer type-related sensitivity and specificity of more than 95%. A majority of the antibodies showed a low degree of sensitivity and specificity for defined cancer types. Combinations of antibodies provided limited added value for differential diagnostics of cancer types. ConclusionsThe results from analysing 27 diagnostic antibodies on consecutive sections of 940 defined tumours provide a unique repository of data that can empower a more optimal use of clinical immunohistochemistry. Our results highlight the benefit of immunohistochemistry and the unmet need for novel markers to improve differential diagnostics of cancer.

    Emneord
    biological tumour markers, differential diagnosis, immunohistochemistry, surgical pathology, tissue microarray analysis
    HSV kategori
    Identifikatorer
    urn:nbn:se:uu:diva-216054 (URN)10.1111/his.12255 (DOI)000328347800012 ()
    Merknad

    De två (2) första författarna delar förstaförfattarskapet.

    Tilgjengelig fra: 2014-01-20 Laget: 2014-01-17 Sist oppdatert: 2018-02-01bibliografisk kontrollert
    4. A six marker panel for differential diagnostics of female cancers
    Åpne denne publikasjonen i ny fane eller vindu >>A six marker panel for differential diagnostics of female cancers
    (engelsk)Manuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    Aim: To present a new immunohistochemistry-based panel for clinical differential diagnostics of breast, ovarian, endometrial and cervical cancer.

     

    Background: Diagnostics of metastatic ER-positive tumors can present a clinical challenge. Breast and gynecological cancers are to a varying degree ER+, and can have similar growth patterns. The close proximity of the ovaries, endometrium and cervix also renders difficulties to distinguish between primary gynecological cancer in advanced stages.

     

    Material and Methods: As a discovery set for the selection of antibodies, tissue microarray (TMA) blocks containing 60 breast, 60 ovarian, 60 endometrial and 60 cervical tumor samples of predominantly metastatic sources were sectioned and immunohistochemically stained using 43 different primary antibodies, including both well accepted diagnostic markers and novel candidate markers. The results were analyzed for best possible differential diagnostic power to discriminate between these forms of female cancer.

     

    Results: By the implementation of a decision tree we were able to define a six-marker panel including antibodies detecting the WT1, ZAG, VIM, CK5, GATA3 and PAX8 proteins. This antibody panel enabled differentiation between breast, ovarian, endometrial and cervical cancer with an accuracy of 80%. The selected markers were then examined in a second cohort of 452 cancer samples comprising 60 breast, 48 ovary, 233 endometrium and 111 cervix patients. A decision tree classifier was used to evaluate the performance of various combinations of these markers in differential diagnosis of the female cancers.  The results suggest that this panel could be used in a clinical setting to achieve a more accurate diagnosis and thus provide a basis for further prospective clinical studies.

    Emneord
    Differential diagnostics, immunohistochemistry
    HSV kategori
    Forskningsprogram
    Patologi
    Identifikatorer
    urn:nbn:se:uu:diva-312936 (URN)
    Prosjekter
    Human Protein Atlas
    Forskningsfinansiär
    Knut and Alice Wallenberg Foundation
    Tilgjengelig fra: 2017-01-16 Laget: 2017-01-16 Sist oppdatert: 2017-01-16
    5. The human adrenal gland proteome defined by transcriptomics and antibody-based profiling
    Åpne denne publikasjonen i ny fane eller vindu >>The human adrenal gland proteome defined by transcriptomics and antibody-based profiling
    Vise andre…
    2017 (engelsk)Inngår i: Endocrinology, ISSN 0013-7227, E-ISSN 1945-7170, Vol. 158, nr 2, s. 239-251Artikkel i tidsskrift (Fagfellevurdert) Published
    Abstract [en]

    The adrenal gland is a composite endocrine organ with vital functions that include the synthesis and release of glucocorticoids and catecholamines. To define the molecular landscape that underlies the specific functions of the adrenal gland, we combined a genome-wide transcriptomics approach based on mRNA sequencing of human tissues with immunohistochemistry-based protein profiling on tissue microarrays. Approximately two-thirds of all putative protein coding genes were expressed in the adrenal gland and the analysis identified 253 genes with an elevated pattern of expression in the adrenal gland, with only 37 genes showing a markedly higher expression level (>5-fold) in the adrenal gland compared to 31 other normal human tissue types analyzed. The analyses allowed for an assessment of the relative expression levels for well-known proteins involved in adrenal gland function, but also identified previously poorly characterized proteins in the adrenal cortex, such as FERM domain containing 5 (FRMD5) and protein NOV homolog (NOV). In summary, we provide a global analysis of the adrenal gland transcriptome and proteome, with a comprehensive list of genes with elevated expression in the adrenal gland and spatial information with examples of protein expression patterns for corresponding proteins. These genes and proteins constitute important starting points for an improved understanding of the normal function and pathophysiology of the adrenal glands.

    HSV kategori
    Forskningsprogram
    Patologi
    Identifikatorer
    urn:nbn:se:uu:diva-312934 (URN)10.1210/en.2016-1758 (DOI)000397101700008 ()27901589 (PubMedID)
    Forskningsfinansiär
    Knut and Alice Wallenberg Foundation
    Tilgjengelig fra: 2017-01-16 Laget: 2017-01-16 Sist oppdatert: 2019-03-29bibliografisk kontrollert
  • 2.
    Bergman, Julia
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Botling, Johan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Klinisk och experimentell patologi.
    Fagerberg, Linn
    KTH Royal Inst Technol, Sci Life Lab, SE-17121 Stockholm, Sweden.
    Hallström, Björn M.
    KTH Royal Inst Technol, Sci Life Lab, SE-17121 Stockholm, Sweden.
    Djureinovic, Dijana
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Klinisk och experimentell patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Uhlén, Mathias
    KTH Royal Inst Technol, Sci Life Lab, SE-17121 Stockholm, Sweden.
    Ponten, Fredrik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Klinisk och experimentell patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    The human adrenal gland proteome defined by transcriptomics and antibody-based profiling2017Inngår i: Endocrinology, ISSN 0013-7227, E-ISSN 1945-7170, Vol. 158, nr 2, s. 239-251Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The adrenal gland is a composite endocrine organ with vital functions that include the synthesis and release of glucocorticoids and catecholamines. To define the molecular landscape that underlies the specific functions of the adrenal gland, we combined a genome-wide transcriptomics approach based on mRNA sequencing of human tissues with immunohistochemistry-based protein profiling on tissue microarrays. Approximately two-thirds of all putative protein coding genes were expressed in the adrenal gland and the analysis identified 253 genes with an elevated pattern of expression in the adrenal gland, with only 37 genes showing a markedly higher expression level (>5-fold) in the adrenal gland compared to 31 other normal human tissue types analyzed. The analyses allowed for an assessment of the relative expression levels for well-known proteins involved in adrenal gland function, but also identified previously poorly characterized proteins in the adrenal cortex, such as FERM domain containing 5 (FRMD5) and protein NOV homolog (NOV). In summary, we provide a global analysis of the adrenal gland transcriptome and proteome, with a comprehensive list of genes with elevated expression in the adrenal gland and spatial information with examples of protein expression patterns for corresponding proteins. These genes and proteins constitute important starting points for an improved understanding of the normal function and pathophysiology of the adrenal glands.

  • 3.
    Gremel, Gabriela
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Bergman, Julia
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Djureinovic, Dijana
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Edqvist, Per-Henrik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Maindad, Vikas
    Bharambe, Bhavana M.
    Khan, Wasif Ali Z. A.
    Navani, Sanjay
    Elebro, Jacob
    Jirstrom, Karin
    Hellberg, Dan
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centrum för klinisk forskning Dalarna. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för kvinnors och barns hälsa.
    Uhlen, Mathias
    Micke, Patrick
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Pontén, Fredrik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    A systematic analysis of commonly used antibodies in cancer diagnostics2014Inngår i: Histopathology, ISSN 0309-0167, E-ISSN 1365-2559, Vol. 64, nr 2, s. 293-305Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    AimsImmunohistochemistry plays a pivotal role in cancer differential diagnostics. To identify the primary tumour from a metastasis specimen remains a significant challenge, despite the availability of an increasing number of antibodies. The aim of the present study was to provide evidence-based data on the diagnostic power of antibodies used frequently for clinical differential diagnostics. Methods and resultsA tissue microarray cohort comprising 940 tumour samples, of which 502 were metastatic lesions, representing tumours from 18 different organs and four non-localized cancer types, was analysed using immunohistochemistry with 27 well-established antibodies used in clinical differential diagnostics. Few antibodies, e.g. prostate-specific antigen and thyroglobulin, showed a cancer type-related sensitivity and specificity of more than 95%. A majority of the antibodies showed a low degree of sensitivity and specificity for defined cancer types. Combinations of antibodies provided limited added value for differential diagnostics of cancer types. ConclusionsThe results from analysing 27 diagnostic antibodies on consecutive sections of 940 defined tumours provide a unique repository of data that can empower a more optimal use of clinical immunohistochemistry. Our results highlight the benefit of immunohistochemistry and the unmet need for novel markers to improve differential diagnostics of cancer.

  • 4.
    Gremel, Gabriela
    et al.
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi.
    Djureinovic, Dijana
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Klinisk och experimentell patologi.
    Niinivirta, Marjut
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Experimentell och klinisk onkologi.
    Laird, Alexander
    Univ Edinburgh, MRC Human Genet Unit, Edinburgh, Midlothian, Scotland.;Univ Edinburgh, Inst Genet & Mol Med, Edinburgh Urol Canc Grp, Edinburgh, Midlothian, Scotland..
    Ljungqvist, Oscar
    Atlas Antibodies AB, Stockholm, Sweden..
    Johannesson, Henrik
    Atlas Antibodies AB, Stockholm, Sweden..
    Bergman, Julia
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Klinisk och experimentell patologi.
    Edqvist, Per-Henrik D
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Experimentell och klinisk onkologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Navani, Sanjay
    Lab Surgpath, Bombay, Maharashtra, India..
    Khan, Naila
    Lab Surgpath, Bombay, Maharashtra, India..
    Patil, Tushar
    Lab Surgpath, Bombay, Maharashtra, India..
    Sivertsson, Asa
    Royal Inst Technol, Sci Life Lab, Stockholm, Sweden..
    Uhlen, Mathias
    Royal Inst Technol, Sci Life Lab, Stockholm, Sweden..
    Harrison, David J.
    Univ St Andrews, Sch Med, St Andrews, Fife, Scotland..
    Ullenhag, Gustav
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Experimentell och klinisk onkologi.
    Stewart, Grant D.
    Univ Edinburgh, Inst Genet & Mol Med, Edinburgh Urol Canc Grp, Edinburgh, Midlothian, Scotland.;Univ Cambridge, Addenbrookes Hosp, Acad Urol Grp, Box 43,Cambridge Biomed Campus,Hills Rd, Cambridge CB2 0QQ, England..
    Pontén, Fredrik
    Uppsala universitet, Science for Life Laboratory, SciLifeLab. Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Klinisk och experimentell patologi.
    A systematic search strategy identifies cubilin as independent prognostic marker for renal cell carcinoma2017Inngår i: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 17, artikkel-id 9Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: There is an unmet clinical need for better prognostic and diagnostic tools for renal cell carcinoma (RCC). Methods: Human Protein Atlas data resources, including the transcriptomes and proteomes of normal and malignant human tissues, were searched for RCC-specific proteins and cubilin (CUBN) identified as a candidate. Patient tissue representing various cancer types was constructed into a tissue microarray (n = 940) and immunohistochemistry used to investigate the specificity of CUBN expression in RCC as compared to other cancers. Two independent RCC cohorts (n = 181; n = 114) were analyzed to further establish the sensitivity of CUBN as RCC-specific marker and to explore if the fraction of RCCs lacking CUBN expression could predict differences in patient survival. Results: CUBN was identified as highly RCC-specific protein with 58% of all primary RCCs staining positive for CUBN using immunohistochemistry. In venous tumor thrombi and metastatic lesions, the frequency of CUBN expression was increasingly lost. Clear cell RCC (ccRCC) patients with CUBN positive tumors had a significantly better prognosis compared to patients with CUBN negative tumors, independent of T-stage, Fuhrman grade and nodal status (HR 0.382, CI 0.203-0.719, P = 0.003). Conclusions: CUBN expression is highly specific to RCC and loss of the protein is significantly and independently associated with poor prognosis. CUBN expression in ccRCC provides a promising positive prognostic indicator for patients with ccRCC. The high specificity of CUBN expression in RCC also suggests a role as a new diagnostic marker in clinical cancer differential diagnostics to confirm or rule out RCC.

  • 5.
    Kampf, Caroline
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi.
    Bergman, Julia
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi.
    Oksvold, Per
    Asplund, Anna
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi.
    Navani, Sanjay
    Wiking, Mikaela
    Lundberg, Emma
    Uhlen, Mathias
    Ponten, Fredrik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi.
    A tool to facilitate clinical biomarker studies - a tissue dictionary based on the Human Protein Atlas2012Inngår i: BMC Medicine, ISSN 1741-7015, E-ISSN 1741-7015, Vol. 10, s. 103-Artikkel i tidsskrift (Fagfellevurdert)
    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.

  • 6. Nodin, Bjorn
    et al.
    Fridberg, Marie
    Jonsson, Liv
    Bergman, Julia
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi.
    Uhlen, Mathias
    Jirstrom, Karin
    High MCM3 expression is an independent biomarker of poor prognosis and correlates with reduced RBM3 expression in a prospective cohort of malignant melanoma2012Inngår i: Diagnostic Pathology, ISSN 1746-1596, E-ISSN 1746-1596, Vol. 7, s. 82-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Malignant melanoma is the most lethal form of skin cancer with a variable clinical course even in patients with thin melanomas and localized disease. Despite increasing insights into melanoma biology, no prognostic biomarkers have yet been incorporated into clinical protocols. Reduced expression of the RNA binding motif protein 3 (RBM3) has been shown to correlate with tumour progression and poor prognosis in melanoma and several other cancer forms. In ovarian cancer, an inverse association was found between expression of RBM3 and the minichromosome maintenance 3 (MCM3) gene and protein. In melanoma, gene expression analysis and immunohistochemical validation has uncovered MCM3 as a putative prognostic biomarker. The aim of the present study was to examine the associations of MCM3 expression with clinical outcome and RBM3 expression in a prospective, population-based cohort of melanoma.

    Methods: Immunohistochemical MCM3 expression was examined in 224 incident cases of primary melanoma from the Malmo Diet and Cancer Study, previously analysed for RBM3 expression. Spearman's Rho and Chi-Square tests were used to explore correlations between MCM3 expression, clinicopathological factors, and expression of RBM3 and Ki67. Kaplan Meier analysis, the log rank test, and univariable and multivariable Cox proportional hazards modelling were used to assess the impact of MCM3 expression on disease-free survival (DFS) and melanoma-specific survival (MSS).

    Results: High MCM3 expression was significantly associated with unfavourable clinicopathological features and high Ki67 expression. A significant inverse correlation was seen between expression of MCM3 and RBM3 (p = 0.025). High MCM3 expression was associated with a reduced DFS (HR = 5.62) and MSS (HR = 6.03), and these associations remained significant in multivariable analysis, adjusted for all other factors (HR = 5.01 for DFS and HR = 4.96 for MSS). RBM3 expression remained an independent prognostic factor for MSS but not DFS in the multivariable model.

    Conclusions: These findings provide validation of the utility of MCM3 expression as an independent biomarker for prognostication of patients with primary melanoma. Moreover, the inverse association and prognostic impact of MCM3 and RBM3 expression indicate a possible interaction of these proteins in melanoma progression, the functional basis for which merits further study.

  • 7.
    Rexhepaj, Elton
    et al.
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Agnarsdóttir, Margrét
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Bergman, Julia
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Edqvist, Per-Henrik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    Bergqvist, Michael
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för radiologi, onkologi och strålningsvetenskap, Enheten för onkologi.
    Uhlen, Mathias
    Gallagher, William M.
    Lundberg, Emma
    Pontén, Fredrik
    Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Medicinska fakulteten, Institutionen för immunologi, genetik och patologi, Molekylär och morfologisk patologi. Uppsala universitet, Science for Life Laboratory, SciLifeLab.
    A Texture Based Pattern Recognition Approach to Distinguish Melanoma from Non-Melanoma Cells in Histopathological Tissue Microarray Sections2013Inngår i: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, nr 5, s. e62070-Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Aims: Immunohistochemistry is a routine practice in clinical cancer diagnostics and also an established technology for tissue-based research regarding biomarker discovery efforts. Tedious manual assessment of immunohistochemically stained tissue needs to be fully automated to take full advantage of the potential for high throughput analyses enabled by tissue microarrays and digital pathology. Such automated tools also need to be reproducible for different experimental conditions and biomarker targets. In this study we present a novel supervised melanoma specific pattern recognition approach that is fully automated and quantitative. Methods and Results: Melanoma samples were immunostained for the melanocyte specific target, Melan-A. Images representing immunostained melanoma tissue were then digitally processed to segment regions of interest, highlighting Melan-A positive and negative areas. Color deconvolution was applied to each region of interest to separate the channel containing the immunohistochemistry signal from the hematoxylin counterstaining channel. A support vector machine melanoma classification model was learned from a discovery melanoma patient cohort (n = 264) and subsequently validated on an independent cohort of melanoma patient tissue sample images (n = 157). Conclusion: Here we propose a novel method that takes advantage of utilizing an immuhistochemical marker highlighting melanocytes to fully automate the learning of a general melanoma cell classification model. The presented method can be applied on any protein of interest and thus provides a tool for quantification of immunohistochemistry-based protein expression in melanoma.

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