| 1. |
- Danielsson, Frida, et al.
(författare)
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Majority of differentially expressed genes are down-regulated during malignant transformation in a four-stage model
- 2013
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Ingår i: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 110:17, s. 6853-6858
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Tidskriftsartikel (refereegranskat)abstract
- The transformation of normal cells to malignant, metastatic tumor cells is a multistep process caused by the sequential acquirement of genetic changes. To identify these changes, we compared the transcriptomes and levels and distribution of proteins in a four-stage cell model of isogenically matched normal, immortalized, transformed, and metastatic human cells, using deep transcriptome sequencing and immunofluorescence microscopy. The data show that similar to 6% (n = 1,357) of the human protein-coding genes are differentially expressed across the stages in the model. Interestingly, the majority of these genes are down-regulated, linking malignant transformation to dedifferentiation. The up-regulated genes are mainly components that control cellular proliferation, whereas the down-regulated genes consist of proteins exposed on or secreted from the cell surface. As many of the identified gene products control basic cellular functions that are defective in cancers, the data provide candidates for follow-up studies to investigate their functional roles in tumor formation. When we further compared the expression levels of four of the identified proteins in clinical cancer cohorts, similar differences were observed between benign and cancer cells, as in the cell model. This shows that this comprehensive demonstration of the molecular changes underlying malignant transformation is a relevant model to study the process of tumor formation.
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| 2. |
- O'Leary, Patrick C., et al.
(författare)
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Systematic antibody generation and validation via tissue microarray technology leading to identification of a novel protein prognostic panel in breast cancer
- 2013
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Ingår i: BMC Cancer. - : Springer Science and Business Media LLC. - 1471-2407. ; 13
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Tidskriftsartikel (refereegranskat)abstract
- Background: Although omic-based discovery approaches can provide powerful tools for biomarker identification, several reservations have been raised regarding the clinical applicability of gene expression studies, such as their prohibitive cost. However, the limited availability of antibodies is a key barrier to the development of a lower cost alternative, namely a discrete collection of immunohistochemistry (IHC)-based biomarkers. The aim of this study was to use a systematic approach to generate and screen affinity-purified, mono-specific antibodies targeting progression-related biomarkers, with a view towards developing a clinically applicable IHC-based prognostic biomarker panel for breast cancer. Methods: We examined both in-house and publicly available breast cancer DNA microarray datasets relating to invasion and metastasis, thus identifying a cohort of candidate progression-associated biomarkers. Of these, 18 antibodies were released for extended analysis. Validated antibodies were screened against a tissue microarray (TMA) constructed from a cohort of consecutive breast cancer cases (n = 512) to test the immunohistochemical surrogate signature. Results: Antibody screening revealed 3 candidate prognostic markers: the cell cycle regulator, Anillin (ANLN); the mitogen-activated protein kinase, PDZ-Binding Kinase (PBK); and the estrogen response gene, PDZ-Domain Containing 1 (PDZK1). Increased expression of ANLN and PBK was associated with poor prognosis, whilst increased expression of PDZK1 was associated with good prognosis. A 3-marker signature comprised of high PBK, high ANLN and low PDZK1 expression was associated with decreased recurrence-free survival (p < 0.001) and breast cancer-specific survival (BCSS) (p < 0.001). This novel signature was associated with high tumour grade (p < 0.001), positive nodal status (p = 0.029), ER-negativity (p = 0.006), Her2-positivity (p = 0.036) and high Ki67 status (p < 0.001). However, multivariate Cox regression demonstrated that the signature was not a significant predictor of BCSS (HR = 6.38; 95% CI = 0.79-51.26, p = 0.082). Conclusions: We have developed a comprehensive biomarker pathway that extends from discovery through to validation on a TMA platform. This proof-of-concept study has resulted in the identification of a novel 3-protein prognostic panel. Additional biochemical markers, interrogated using this high-throughput platform, may further augment the prognostic accuracy of this panel to a point that may allow implementation into routine clinical practice.
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| 3. |
- Rexhepaj, Elton, et al.
(författare)
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A Texture Based Pattern Recognition Approach to Distinguish Melanoma from Non-Melanoma Cells in Histopathological Tissue Microarray Sections
- 2013
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 8:5, s. e62070-
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Tidskriftsartikel (refereegranskat)abstract
- 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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| 4. |
- Stadler, Charlotte, et al.
(författare)
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Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells
- 2013
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Ingår i: Nature Methods. - 1548-7091 .- 1548-7105. ; 10:4, s. 315-323
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Tidskriftsartikel (refereegranskat)abstract
- Imaging techniques such as immunofluorescence (IF) and the expression of fluorescent protein (FP) fusions are widely used to investigate the subcellular distribution of proteins. Here we report a systematic analysis of >500 human proteins comparing the localizations obtained in live versus fixed cells using FPs and IF, respectively. We identify systematic discrepancies between IF and FPs as well as between FP tagging at the N and C termini. The analysis shows that for 80% of the proteins, IF and FPs yield the same subcellular distribution, and the locations of 250 previously unlocalized proteins were determined by the overlap between the two methods. Approximately 60% of proteins localize to multiple organelles for both methods, indicating a complex subcellular protein organization. These results show that both IF and FP tagging are reliable techniques and demonstrate the usefulness of an integrative approach for a complete investigation of the subcellular human proteome.
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