Despite improvements in the clinical management of soft tissue sarcomas (STS), 50% of patients will die of metastatic disease that is largely unresponsive to conventional chemotherapeutic agents. The aims of this study were to identify genes and pathways that are dysregulated in progressive and metastatic STS. In addition to this, cell lines from fresh tumours were initiated and established, thus increasing the repository of cell lines available for functional studies. Recent advances in the understanding of the molecular biology of STS have thus far not resulted in the use of molecular markers for clinical prognostication. Identifying novel genes and pathways will lead to molecular diagnostic methods to better stratify prognostic groups and could identify cellular targets for more efficacious treatments. Gene expression profiling of sarcoma cell lines of increasing metastatic potential revealed over-expression of genes involved in the epidermal growth factor (EGF) and transforming growth factor beta (TGFb) pathways. Factors involved in invasion and metastasis such as integrins and MMPs were over-expressed in the cell lines with higher metastatic potential. The developmental Notch pathway and cell cycle regulators were also dysregulated. NDRG1 was significantly over-expressed in the high grade sarcoma cell line, a novel finding in sarcomas. The expression of EGFR, NDRG1 and other genes from the above pathways was validated using quantitative RT-PCR in real time (qRT-PCR). A tissue microarray (TMA) comprising STS of varying tumour grades was constructed for high throughput assessment of target proteins. EGFR, its activated form and its signal transducers were investigated using immunohistochemistry (IHC). Activated EGFR (HR 2.228, p < 0.001) and phosphorylated Akt (HR 2.032, p = 0.003) were found to be independent predictors of overall survival and both correlated with tumour grade. Of the several STS cultures initiated and maintained, two of these cell lines were fully characterised in terms of cytogenetics, telomerase and alternate lengthening of 5 telomeres (ALT) status, KIT and TP53 mutation and the expression of certain biomarkers using both qRT-PCR and IHC. In summary, transcript profiling identified several potential biomarkers of tumour progression and metastasis in STS. Crucially, activated EGFR and pAkt were found in a cohort of STS samples to correlate with clinical outcome, identifying them as potential diagnostic and therapeutic targets in the treatment of STS. Activated EGFR can be used as a diagnostic marker for patient selection, as well as for target effect monitoring. Furthermore, the cell lines established in this project will serve as valuable tools in future preclinical studies.

In this thesis, methods to study gene and protein expression in cells and tissues were developed and utilized in combination with protein-specific antibodies, with the overall objective to attain greater understanding of protein function. To analyze protein expression in in vitro cultured cell lines, a cell microarray (CMA) was developed, facilitating antibody-based protein profiling of cell lines using immunohistochemistry (IHC). Staining patterns in cell lines were analyzed using image analysis, developed to automatically identify cells and immunohistochemical staining, providing qualitative and quantitative measurements of protein expression. Quantitative IHC data from CMAs stained with nearly 3000 antibodies was used to evaluate the adequacy of using cell lines as models for cancer tissue. We found that cell lines are homogenous with respect to protein expression profiles, and generally more alike each other, than corresponding cancer cells in vivo. However, we found variability between cell lines in regards to the level of retained tumor phenotypic traits, and identified cell lines with a preserved link to corresponding cancer, suggesting that some cell lines are appropriate model systems for specific tumor types. Specific gene expression patterns were analyzed in vitiligo vulgaris and malignant melanoma. Transcriptional profiling of vitiligo melanocytes revealed dysregulation of genes involved in melanin biosynthesis and melanosome function, thus highlighting some mechanisms possibly involved in the pathogenesis of vitiligo. Two new potential markers for infiltrating malignant melanoma, Syntaxin-7 and Discs large homolog 5, were identified using antibody-based protein profiling of melanoma in a tissue microarray format. Both proteins were expressed with high specificity in melanocytic lesions, and loss of Syntaxin-7 expression was associated with more high-grade malignant melanomas. In conclusion, the combination of antibody-based proteomics and microarray technology provided valuable information of expression patterns in cells and tissues, which can be used to better understand associations between protein signatures and disease.

To construct a complete map of the human proteome landscape is a vital part of the total understanding of the human body. Such a map could enrich the mankind to the extent that many severe diseases could be fully understood and hence could be treated with appropriate methods. In this study, immunohistochemical (IHC) data from ~6000 proteins, 65 cell types in 48 tissues and 47 cell lines has been used to investigate the human proteome regarding protein expression and localization. In order to analyze such a large data set, different statistical methods and algorithms has been applied and by using these tools, interesting features regarding the proteome was found. By using all available IHC data from 65 cell types in 48 tissues, it was found that the amount of tissue specific protein expression was surprisingly small, and the general impression from the analysis is that almost all proteins are present at all times in the cellular environment. Rather than tissue specific protein expression, the localization and minor concentration fluctuations of the proteins in the cell is responsible for molecular interaction and tissue specific cellular behavior. However, if a quarter of all proteins are used to distinguish different tissues types, there are a proportion of proteins that have certain expression profiles, which defines clusters of tissues of the same kind and embryonic origin. The estimation of expression levels using IHC is a labor-intensive method, which suffers from large variation between manual annotators. An automated image software tool was developed to circumvent this problem. The automated image software was shown to be more robust then manual annotators, and the quantification of expressed protein levels of the stained imaged was in the same range as the manual annotations. A more thorough investigation of the stained image estimations made by the automated software revield a significant correlation between the estimated protein expression and the cell size parameters provided by the automated software. To make it feasible to compare protein expression levels across different cell lines, without the cell line size bias, a normalization procedure was implemented and evaluated. It was found that when the normalization procedure was applied to the protein expression data, the correlation between protein expression values and cell size was minimized, and hence comparisons between cell lines regarding protein expression is possible. In addition, using the normalized protein expression data, an analysis to investigate the degree of correlation between mRNA levels and proteins for 1065 gene products was performed. By using two individual microarray data sets for estimation of RNA levels, and normalized protein data measured by the automated software as estimation of the protein levels, a mean correlation of ~0.3 for was found. This result indicates that a significant proportion of the manufactured antibodies, when used in IHC setup, are indeed an accurate measurement of protein expression levels. By using antibodies directed towards human proteins, plasma samples were investigated regarding metabolic dysfunctions. Since plasma is a complex sample, an optimization regarding protocol for quantification of expressed proteins was made. By using certain characteristics within the dataset, and by using a suspension bead microarray, the protocol could be evaluated. Expected characteristics within the dataset were found in the subsequent analysis, which showed that the protocol was functional. Using the same experimental outline will facilitate future applications, e.g. biomarker discovery. / QC 20100728 / Human Proteome Resource

Breast cancer is the most common cancer in Taiwanese women and the invasive ductal carcinoma (IDC) is the most common type. Increasing evidence shows that cancer stem cells (CSCs) have been implicated in tumorigenesis, tumor progression, and drug-resistance. In addition, four reprogramming factors (Octamer-binding Protein 4 (Oct4), Sex-determining Region Y (SRY)-related Box 2 (Sox2), Nanog and Lin28) employed to induce induced pluripotent stem (iPS) cells are associated with CSCs formation. The purpose of this study was to investigate the relationship of the protein expression levels of the reprogramming factors (Oct4, Sox2, Nanog and Lin28) with the tumorigenesis, clinicopathologic outcomes and prognosis of breast IDC patients. Immunohistochemistry (IHC) assay of tissue microarrays, made by 309 IDC and 20 breast fibrosis paraffin embedded samples, were used to examine the protein expression levels of Oct4, Sox2, Nanog and Lin28 in normal mammary ductal tissues, tumor adjacent normal mammary ductal tissues, ductal carcinoma in situ (DCIS), IDC and recurrence tissues. Our IHC results showed that Sox2 and Lin28 were expressed in half of breast IDC patients¡¦ tumor tissue (49.6% and 49.7%, respectively), but Oct4 and Nanog are less expressed (13.5% and 24.7%, respectively). The protein expression levels of the four proteins were positively correlated with each other. In addition, the expression levels of the four proteins were upregulated in tumor adjacent normal tissue as compared to breast fibrosis pateints¡¦ normal mammary ductal tissue. To compare the expression levels of the four proteins in different tissues; such as tumor adjacent normal, DCIS, IDC and recurrence tissues, the expression levels of the four protiens gradually decreased when tumor developed and progressed. However, their expression levels were comparable between IDC and recurrence tissues. Additionally, the high expression levels of four proteins were high in two good clinicopathological characteristics and a biomarker of breast cancer; such as nuclear Sox2 and Lin28 in those with pathology stage I; nucleus expression of the four proteins in those with well and moderate cell differentiation; and Sox2 in those with positive estrogen receptor. However, the four proteins¡¦ expression levels were not correlated with IDC patients¡¦ survival. In conclusion, the reprogramming factors: Oct4, Sox2, Nanog and Lin28 may play an important role in tumorigenesis of breast IDC, but their impacts on tumor progression were quite small.

The human genome contains tens of thousands of gene loci which code for an even greater number of protein and RNA products. The highly complex temporal and spatial expression of these genes makes possible all the biological processes of life. Altered gene expression by mutation or deregulation is fundamental for the development of many human diseases. The ultimate aim of this thesis was to identify gene expression changes relevant to cancer. The advent of genome-wide expression profiling techniques, such as microarrays, has provided powerful new tools to identify such changes and researchers are now faced with an explosion of gene expression data. Processing, comparing and integrating these data present major challenges. I approached these challenges by developing and assessing novel methods for cross-platform analysis of expression data, scalable subspace clustering, and curation of experimental gene regulation data from the published literature. I found that combining results from different expression platforms increases reliability of coexpression predictions. However, I also observed that global correlation between platforms was generally low, and few gene pairs reached reasonable thresholds for high-confidence coexpression. Therefore, I developed a novel subspace clustering algorithm, able to identify coexpressed genes in experimental subsets of very large gene expression datasets. Biological assessment against several metrics indicates that this algorithm performs well. I also developed a novel meta-analysis method to identify consistently reported genes from differential expression studies when raw data are unavailable. This method was applied to thyroid cancer, producing a ranked list of significantly over-represented genes. Tissue microarray analysis of some of these candidates and others identified a number of promising biomarkers for diagnostic and prognostic classification of thyroid cancer. Finally, I present ORegAnno (www.oreganno.org), a resource for the community-driven curation of experimentally verified regulatory sequences. This resource has proven a great success with ~30,000 sequences entered from over 900 publications by ~50 contributing users. These data, methods and resources contribute to our overall understanding of gene regulation, gene expression, and the changes that occur in cancer. Such an understanding should help identify new cancer mechanisms, potential treatment targets, and have significant diagnostic and prognostic implications.

Specific antibodies are invaluable tools for proteomics research. The availability of thoroughly validated antibodies will help to improve our understanding of protein expression, localization and function; fundamental processes and features of all living organisms. The objectives of the studies in this thesis were to develop high-throughput methods to facilitate the generation and purification of monospecific antibodies, and to address problems associated with antigen selection for difficult target proteins and subsequent validation issues. In the first of the studies, it was demonstrated that antibodies specific to human proteins could be generated in a high-throughput manner using protein epitope signature tags (PrESTs) as both antigens and affinity ligands. A previously developed purification process was adapted to a high-throughput format and this, in combination with the development of a protein microarray assay, resulted in monospecific antibodies that were used for profiling protein expression in 48 human tissues. Data obtained in these analyses suggest that a complete Human Protein Atlas should be attainable within the next ten years. In order to reduce the number of animals needed for such a massive project, and improve the cost-efficiency of antibody generation, a multiplex immunization strategy was developed in a further study. Antisera from rabbits immunized with mixtures of two, three, five and up to ten different PrESTs were successfully purified and analyzed for specificity using protein arrays. Almost 80% of the animals immunized with up to three PrESTs yielded antibodies towards all the PrESTs administered, and they yielded comparable immunohistochemical staining patterns (of consecutive human tissue sections) to those of antibodies obtained from traditional single PrEST immunizations. Proteins with highly similar sequences to other proteins present a major challenge for the proteome-wide generation of antibodies. In another study, Cytokeratin-17 which displays high sequence similarity to closely related members of the intermediate filament family, was used as a model and the specificity and cross-reactivity of antibodies generated against this target were investigated using epitope mapping in combination with comparative IHC analyses. Antibodies identified by epitope mapping as binding to the most unique parts of the Cytokeratin-17 PrESTs also showed the most Cytokeratin-17-like staining pattern, thus further supporting the strategy of using sequence identity scores as the main criteria for PrEST design. An alternative antigen design strategy was investigated for use in raising antibodies towards G-proteincoupled receptors (GPCRs). The extracellular loops and N-terminus of each of three selected GPCRs were assembled to form single antigens and the resulting antibodies were analyzed by flow cytometric and confocal microscopic analyses of cell lines over-expressing the respective receptors. The results from both flow cytometric and immunofluorescence analyses showed that the antibodies were able to bind to their targets. In addition, the antibodies were used successfully for the in situ analysis of human brain and pancreatic islet cells. / QC 20100727

The human genome contains tens of thousands of gene loci which code for an even greater number of protein and RNA products. The highly complex temporal and spatial expression of these genes makes possible all the biological processes of life. Altered gene expression by mutation or deregulation is fundamental for the development of many human diseases. The ultimate aim of this thesis was to identify gene expression changes relevant to cancer. The advent of genome-wide expression profiling techniques, such as microarrays, has provided powerful new tools to identify such changes and researchers are now faced with an explosion of gene expression data. Processing, comparing and integrating these data present major challenges. I approached these challenges by developing and assessing novel methods for cross-platform analysis of expression data, scalable subspace clustering, and curation of experimental gene regulation data from the published literature. I found that combining results from different expression platforms increases reliability of coexpression predictions. However, I also observed that global correlation between platforms was generally low, and few gene pairs reached reasonable thresholds for high-confidence coexpression. Therefore, I developed a novel subspace clustering algorithm, able to identify coexpressed genes in experimental subsets of very large gene expression datasets. Biological assessment against several metrics indicates that this algorithm performs well. I also developed a novel meta-analysis method to identify consistently reported genes from differential expression studies when raw data are unavailable. This method was applied to thyroid cancer, producing a ranked list of significantly over-represented genes. Tissue microarray analysis of some of these candidates and others identified a number of promising biomarkers for diagnostic and prognostic classification of thyroid cancer. Finally, I present ORegAnno (www.oreganno.org), a resource for the community-driven curation of experimentally verified regulatory sequences. This resource has proven a great success with ~30,000 sequences entered from over 900 publications by ~50 contributing users. These data, methods and resources contribute to our overall understanding of gene regulation, gene expression, and the changes that occur in cancer. Such an understanding should help identify new cancer mechanisms, potential treatment targets, and have significant diagnostic and prognostic implications.

Despite improvements in the clinical management of soft tissue sarcomas (STS), 50% of patients will die of metastatic disease that is largely unresponsive to conventional chemotherapeutic agents. The aims of this study were to identify genes and pathways that are dysregulated in progressive and metastatic STS. In addition to this, cell lines from fresh tumours were initiated and established, thus increasing the repository of cell lines available for functional studies. Recent advances in the understanding of the molecular biology of STS have thus far not resulted in the use of molecular markers for clinical prognostication. Identifying novel genes and pathways will lead to molecular diagnostic methods to better stratify prognostic groups and could identify cellular targets for more efficacious treatments. Gene expression profiling of sarcoma cell lines of increasing metastatic potential revealed over-expression of genes involved in the epidermal growth factor (EGF) and transforming growth factor beta (TGFb) pathways. Factors involved in invasion and metastasis such as integrins and MMPs were over-expressed in the cell lines with higher metastatic potential. The developmental Notch pathway and cell cycle regulators were also dysregulated. NDRG1 was significantly over-expressed in the high grade sarcoma cell line, a novel finding in sarcomas. The expression of EGFR, NDRG1 and other genes from the above pathways was validated using quantitative RT-PCR in real time (qRT-PCR). A tissue microarray (TMA) comprising STS of varying tumour grades was constructed for high throughput assessment of target proteins. EGFR, its activated form and its signal transducers were investigated using immunohistochemistry (IHC). Activated EGFR (HR 2.228, p < 0.001) and phosphorylated Akt (HR 2.032, p = 0.003) were found to be independent predictors of overall survival and both correlated with tumour grade. Of the several STS cultures initiated and maintained, two of these cell lines were fully characterised in terms of cytogenetics, telomerase and alternate lengthening of 5 telomeres (ALT) status, KIT and TP53 mutation and the expression of certain biomarkers using both qRT-PCR and IHC. In summary, transcript profiling identified several potential biomarkers of tumour progression and metastasis in STS. Crucially, activated EGFR and pAkt were found in a cohort of STS samples to correlate with clinical outcome, identifying them as potential diagnostic and therapeutic targets in the treatment of STS. Activated EGFR can be used as a diagnostic marker for patient selection, as well as for target effect monitoring. Furthermore, the cell lines established in this project will serve as valuable tools in future preclinical studies.

Despite improvements in the clinical management of soft tissue sarcomas (STS), 50% of patients will die of metastatic disease that is largely unresponsive to conventional chemotherapeutic agents. The aims of this study were to identify genes and pathways that are dysregulated in progressive and metastatic STS. In addition to this, cell lines from fresh tumours were initiated and established, thus increasing the repository of cell lines available for functional studies. Recent advances in the understanding of the molecular biology of STS have thus far not resulted in the use of molecular markers for clinical prognostication. Identifying novel genes and pathways will lead to molecular diagnostic methods to better stratify prognostic groups and could identify cellular targets for more efficacious treatments. Gene expression profiling of sarcoma cell lines of increasing metastatic potential revealed over-expression of genes involved in the epidermal growth factor (EGF) and transforming growth factor beta (TGFb) pathways. Factors involved in invasion and metastasis such as integrins and MMPs were over-expressed in the cell lines with higher metastatic potential. The developmental Notch pathway and cell cycle regulators were also dysregulated. NDRG1 was significantly over-expressed in the high grade sarcoma cell line, a novel finding in sarcomas. The expression of EGFR, NDRG1 and other genes from the above pathways was validated using quantitative RT-PCR in real time (qRT-PCR). A tissue microarray (TMA) comprising STS of varying tumour grades was constructed for high throughput assessment of target proteins. EGFR, its activated form and its signal transducers were investigated using immunohistochemistry (IHC). Activated EGFR (HR 2.228, p < 0.001) and phosphorylated Akt (HR 2.032, p = 0.003) were found to be independent predictors of overall survival and both correlated with tumour grade. Of the several STS cultures initiated and maintained, two of these cell lines were fully characterised in terms of cytogenetics, telomerase and alternate lengthening of 5 telomeres (ALT) status, KIT and TP53 mutation and the expression of certain biomarkers using both qRT-PCR and IHC. In summary, transcript profiling identified several potential biomarkers of tumour progression and metastasis in STS. Crucially, activated EGFR and pAkt were found in a cohort of STS samples to correlate with clinical outcome, identifying them as potential diagnostic and therapeutic targets in the treatment of STS. Activated EGFR can be used as a diagnostic marker for patient selection, as well as for target effect monitoring. Furthermore, the cell lines established in this project will serve as valuable tools in future preclinical studies.

Despite improvements in the clinical management of soft tissue sarcomas (STS), 50% of patients will die of metastatic disease that is largely unresponsive to conventional chemotherapeutic agents. The aims of this study were to identify genes and pathways that are dysregulated in progressive and metastatic STS. In addition to this, cell lines from fresh tumours were initiated and established, thus increasing the repository of cell lines available for functional studies. Recent advances in the understanding of the molecular biology of STS have thus far not resulted in the use of molecular markers for clinical prognostication. Identifying novel genes and pathways will lead to molecular diagnostic methods to better stratify prognostic groups and could identify cellular targets for more efficacious treatments. Gene expression profiling of sarcoma cell lines of increasing metastatic potential revealed over-expression of genes involved in the epidermal growth factor (EGF) and transforming growth factor beta (TGFb) pathways. Factors involved in invasion and metastasis such as integrins and MMPs were over-expressed in the cell lines with higher metastatic potential. The developmental Notch pathway and cell cycle regulators were also dysregulated. NDRG1 was significantly over-expressed in the high grade sarcoma cell line, a novel finding in sarcomas. The expression of EGFR, NDRG1 and other genes from the above pathways was validated using quantitative RT-PCR in real time (qRT-PCR). A tissue microarray (TMA) comprising STS of varying tumour grades was constructed for high throughput assessment of target proteins. EGFR, its activated form and its signal transducers were investigated using immunohistochemistry (IHC). Activated EGFR (HR 2.228, p < 0.001) and phosphorylated Akt (HR 2.032, p = 0.003) were found to be independent predictors of overall survival and both correlated with tumour grade. Of the several STS cultures initiated and maintained, two of these cell lines were fully characterised in terms of cytogenetics, telomerase and alternate lengthening of 5 telomeres (ALT) status, KIT and TP53 mutation and the expression of certain biomarkers using both qRT-PCR and IHC. In summary, transcript profiling identified several potential biomarkers of tumour progression and metastasis in STS. Crucially, activated EGFR and pAkt were found in a cohort of STS samples to correlate with clinical outcome, identifying them as potential diagnostic and therapeutic targets in the treatment of STS. Activated EGFR can be used as a diagnostic marker for patient selection, as well as for target effect monitoring. Furthermore, the cell lines established in this project will serve as valuable tools in future preclinical studies.