uu.seUppsala University Publications
Change search
Link to record
Permanent link

Direct link
BETA
Bergman, Julia
Publications (7 of 7) Show all publications
Gremel, G., Djureinovic, D., Niinivirta, M., Laird, A., Ljungqvist, O., Johannesson, H., . . . Pontén, F. (2017). A systematic search strategy identifies cubilin as independent prognostic marker for renal cell carcinoma. BMC Cancer, 17, Article ID 9.
Open this publication in new window or tab >>A systematic search strategy identifies cubilin as independent prognostic marker for renal cell carcinoma
Show others...
2017 (English)In: BMC Cancer, ISSN 1471-2407, E-ISSN 1471-2407, Vol. 17, article id 9Article in journal (Refereed) Published
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.

Keywords
Cubilin, Renal cell carcinoma, Independent prognostic biomarker, Immunohistochemistry
National Category
Cancer and Oncology Clinical Laboratory Medicine
Identifiers
urn:nbn:se:uu:diva-315058 (URN)10.1186/s12885-016-3030-6 (DOI)000391341500003 ()28052770 (PubMedID)
Funder
Swedish Cancer SocietyKnut and Alice Wallenberg Foundation
Available from: 2017-03-03 Created: 2017-03-03 Last updated: 2017-11-29Bibliographically approved
Bergman, J. (2017). Aspects of Gene Expression Profiling in Disease and Health. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
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
Bergman, J., Botling, J., Fagerberg, L., Hallström, B. M., Djureinovic, D., Uhlén, M. & Ponten, F. (2017). The human adrenal gland proteome defined by transcriptomics and antibody-based profiling. Endocrinology, 158(2), 239-251
Open this publication in new window or tab >>The human adrenal gland proteome defined by transcriptomics and antibody-based profiling
Show others...
2017 (English)In: Endocrinology, ISSN 0013-7227, E-ISSN 1945-7170, Vol. 158, no 2, p. 239-251Article in journal (Refereed) 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.

National Category
Endocrinology and Diabetes Clinical Laboratory Medicine
Research subject
Pathology
Identifiers
urn:nbn:se:uu:diva-312934 (URN)10.1210/en.2016-1758 (DOI)000397101700008 ()27901589 (PubMedID)
Funder
Knut and Alice Wallenberg Foundation
Available from: 2017-01-16 Created: 2017-01-16 Last updated: 2019-03-29Bibliographically approved
Gremel, G., Bergman, J., Djureinovic, D., Edqvist, P.-H., Maindad, V., Bharambe, B. M., . . . Pontén, F. (2014). A systematic analysis of commonly used antibodies in cancer diagnostics. Histopathology, 64(2), 293-305
Open this publication in new window or tab >>A systematic analysis of commonly used antibodies in cancer diagnostics
Show others...
2014 (English)In: Histopathology, ISSN 0309-0167, E-ISSN 1365-2559, Vol. 64, no 2, p. 293-305Article in journal (Refereed) 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.

Keywords
biological tumour markers, differential diagnosis, immunohistochemistry, surgical pathology, tissue microarray analysis
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-216054 (URN)10.1111/his.12255 (DOI)000328347800012 ()
Note

De två (2) första författarna delar förstaförfattarskapet.

Available from: 2014-01-20 Created: 2014-01-17 Last updated: 2018-02-01Bibliographically approved
Rexhepaj, E., Agnarsdóttir, M., Bergman, J., Edqvist, P.-H., Bergqvist, M., Uhlen, M., . . . Pontén, F. (2013). A Texture Based Pattern Recognition Approach to Distinguish Melanoma from Non-Melanoma Cells in Histopathological Tissue Microarray Sections. PLoS ONE, 8(5), e62070
Open this publication in new window or tab >>A Texture Based Pattern Recognition Approach to Distinguish Melanoma from Non-Melanoma Cells in Histopathological Tissue Microarray Sections
Show others...
2013 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 5, p. e62070-Article in journal (Refereed) Published
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.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-203296 (URN)10.1371/journal.pone.0062070 (DOI)000319107900004 ()
Available from: 2013-07-08 Created: 2013-07-08 Last updated: 2017-12-06Bibliographically approved
Kampf, C., Bergman, J., Oksvold, P., Asplund, A., Navani, S., Wiking, M., . . . Ponten, F. (2012). A tool to facilitate clinical biomarker studies - a tissue dictionary based on the Human Protein Atlas. BMC Medicine, 10, 103
Open this publication in new window or tab >>A tool to facilitate clinical biomarker studies - a tissue dictionary based on the Human Protein Atlas
Show others...
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.

Keywords
Antibody-based proteomics, cancer biomarkers, tissue and cell dictionary, immunohistochemistry, protein expression, histology, pathology
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-192083 (URN)10.1186/1741-7015-10-103 (DOI)000312389800001 ()
Available from: 2013-01-16 Created: 2013-01-16 Last updated: 2017-12-06Bibliographically approved
Nodin, B., Fridberg, M., Jonsson, L., Bergman, J., Uhlen, M. & Jirstrom, K. (2012). High MCM3 expression is an independent biomarker of poor prognosis and correlates with reduced RBM3 expression in a prospective cohort of malignant melanoma. Diagnostic Pathology, 7, 82
Open this publication in new window or tab >>High MCM3 expression is an independent biomarker of poor prognosis and correlates with reduced RBM3 expression in a prospective cohort of malignant melanoma
Show others...
2012 (English)In: Diagnostic Pathology, ISSN 1746-1596, E-ISSN 1746-1596, Vol. 7, p. 82-Article in journal (Refereed) Published
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.

National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:uu:diva-182551 (URN)10.1186/1746-1596-7-82 (DOI)000308435100001 ()
Available from: 2012-10-11 Created: 2012-10-11 Last updated: 2017-12-07Bibliographically approved
Organisations

Search in DiVA

Show all publications