Validation of proteomic-based discovery with tissue microarrays

Protein microarray applications: Autoantibody detection and posttranslational modification

The discovery of DNA microarrays was a major milestone in genomics; however, it could not adequately predict the structure or dynamics of underlying protein entities, which are the ultimate effector molecules in a cell. Protein microarrays allow simultaneous study of thousands of proteins/peptides, and various advancements in array technologies have made this platform suitable for several diagnostic and functional studies. Antibody arrays enable researchers to quantify the abundance of target proteins in biological fluids and assess PTMs by using the antibodies. Protein microarrays have been used to assess protein-protein interactions, protein-ligand interactions, and autoantibody profiling in various disease conditions. Here, we summarize different microarray platforms with focus on its biological and clinical applications in autoantibody profiling and PTM studies. We also enumerate the potential of tissue microarrays to validate findings from protein arrays as well as other approaches, highlighting their significance in proteomics.

Prediction of individual response to anticancer therapy: historical and future perspectives

Since the introduction of chemotherapy for cancer treatment in the early 20th century considerable efforts have been made to maximize drug efficiency and at the same time minimize side effects. As there is a great interpatient variability in response to chemotherapy, the development of predictive biomarkers is an ambitious aim for the rapidly growing research area of personalized molecular medicine. The individual prediction of response will improve treatment and thus increase survival and life quality of patients. In the past, cell cultures were used as in vitro models to predict in vivo response to chemotherapy. Several in vitro chemosensitivity assays served as tools to measure miscellaneous endpoints such as DNA damage, apoptosis and cytotoxicity or growth inhibition. Twenty years ago, the development of high-throughput technologies, e.g. cDNA microarrays enabled a more detailed analysis of drug responses. Thousands of genes were screened and expression levels were correlated to drug responses. In addition, mutation analysis became more and more important for the prediction of therapeutic success. Today, as research enters the area of -omics technologies, identification of signaling pathways is a tool to understand molecular mechanism underlying drug resistance. Combining new tissue models, e.g. 3D organoid cultures with modern technologies for biomarker discovery will offer new opportunities to identify new drug targets and in parallel predict individual responses to anticancer therapy. In this review, we present different currently used chemosensitivity assays including 2D and 3D cell culture models and several -omics approaches for the discovery of predictive biomarkers. Furthermore, we discuss the potential of these assays and biomarkers to predict the clinical outcome of individual patients and future perspectives.

A 'waterfall' transfer-based workflow for improved quality of tissue microarray construction and processing in breast cancer research

A major focus in cancer research is the identification of biomarkers for early diagnosis, therapy prediction and prognosis. Hereby, validation of target proteins on clinical samples is of high importance. Tissue microarrays (TMAs) represent an essential advancement for high-throughput analysis by assembling large numbers of tissue cores with high efficacy and comparability. However, limitations along TMA construction and processing exist. In our presented study, we had to overcome several obstacles in the construction and processing of high-density breast cancer TMAs to ensure good quality sections for further research. Exemplarily, 406 breast tissue cores from formalin-fixed and paraffin embedded samples of 245 patients were placed onto three recipient paraffin blocks. Sectioning was performed using a rotary microtome with a "waterfall" automated transfer system. Sections were stained by immunohistochemistry and immunofluorescence for nine proteins. The number and quality of cores after sectioning and staining was counted manually for each marker. In total, 97.1 % of all cores were available after sectioning, while further 96 % of the remaining cores were evaluable after staining. Thereby, normal tissue cores were more often lost compared to tumor tissue cores. Our workflow provides a robust method for manufacturing high-density breast cancer TMAs for subsequent IHC or IF staining without significant sample loss.

When tissue antigens and antibodies get along: revisiting the technical aspects of immunohistochemistry--the red, brown, and blue technique

Once focused mainly on the characterization of neoplasms, immunohistochemistry (IHC) today is used in the investigation of a broad range of disease processes with applications in diagnosis, prognostication, therapeutic decisions to tailor treatment to an individual patient, and investigations into the pathogenesis of disease. This review addresses the technical aspects of immunohistochemistry (and, to a lesser extent, immunocytochemistry) with attention to the antigen-antibody reaction, optimal fixation techniques, tissue processing considerations, antigen retrieval methods, detection systems, selection and use of an autostainer, standardization and validation of IHC tests, preparation of proper tissue and reagent controls, tissue microarrays and other high-throughput systems, quality assurance/quality control measures, interpretation of the IHC reaction, and reporting of results. It is now more important than ever, with these sophisticated applications, to standardize the entire IHC process from tissue collection through interpretation and reporting to minimize variability among laboratories and to facilitate quantification and interlaboratory comparison of IHC results.

Tissue microarrays as a tool in the discovery and validation of predictive biomarkers

The tissue microarray (TMA) is the embodiment of high-throughput pathology. The platform combines tens to hundreds of tissue samples on a single microscope slide for interrogation with routine molecular pathology tools. TMAs have enabled the rapid and cost-effective screening of biomarkers for diagnostic, prognostic, and predictive utility. Most commonly applied to the field of oncology, the TMA has accelerated the development of new biomarkers, and is emerging as an essential tool in the discovery and validation of tissue biomarkers for use in personalized medicine. This chapter provides an overview of TMA technology and highlights the advantages of using TMAs as tools toward rapid introduction of new biomarkers for clinical use.

Histo-proteomic profiling of formalin-fixed, paraffin-embedded tissue

In the functional proteome era, the proteomic profiling of clinicopathologic-annotated tissues is an essential step for mining and evaluating candidate biomarkers for disease. For many diseases, but especially cancer, the development of predictive biomarkers requires performing assays directly on the diseased tissue. The last decade has seen the explosion of both prognostic and predictive biomarkers in the research setting but few of these biomarkers have entered widespread clinical use. Previously, application of routine proteomic methodologies to clinical formalin-fixed and paraffin-embedded tissue specimens has provided unsatisfactory results. In this paper, we will discuss recent advancements in proteomic profiling technology for clinical applications. These approaches focus on the retention of histomorphologic information as an element of the proteomic analysis.

Requirements for the valid quantification of immunostains on tissue microarray materials using image analysis

Antibody-based proteomics applied to tissue microarray (TMA) technology provides a very efficient means of visualizing and locating antigen expression in large collections of normal and pathological tissue samples. To characterize antigen expression on TMAs, the use of image analysis methods avoids the effects of human subjectivity evidenced in manual microscopical analysis. Thus, these methods have the potential to significantly enhance both precision and reproducibility. Although some commercial systems include tools for the quantitative evaluation of immunohistochemistry-stained images, there exists no clear agreement on best practices to allow for correct and reproducible quantification results. Our study focuses on practical aspects regarding (i) image acquisition (ii) segmentation of staining and counterstaining areas and (iii) extraction of quantitative features. We illustrate our findings using a commercial system to quantify different immunohistochemistry markers targeting proteins with different expression patterns (cytoplasmic, nuclear or membranous) in colon cancer or brain tumor TMAs. Our investigations led us to identify several steps that we consider essential for standardizing computer-assisted immunostaining quantification experiments. In addition, we propose a data normalization process based on reference materials to be able to compare measurements between studies involving different TMAs. In conclusion, we recommend certain critical prerequisites that commercial or in-house systems should satisfy in order to permit valid immunostaining quantification.