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High expression of the RNA-binding motif protein 3 (RBM3) has been shown to correlate, with prolonged survival in several malignant diseases and with the benefit of platinum-based chemotherapy in ovarian cancer. The aim of this study was to evaluate RBM3 in metastatic colorectal cancer (mCRC) as a prognostic factor for overall survival and in relation to benefit of first-line chemotherapy.Immunohistochemical staining was conducted and evaluated in tumours from 455 mCRC patients. Kaplan-Meier analysis and Cox regression proportional hazards models were used to access the impact of RBM3 expression on overall survival (OS) and progression-free survival (PFS).High RBM3 expression, both nuclear and cytoplasmic, was an independent prognostic factor for prolonged OS (hazard ratio [HR] 0.67, 95% confidence interval [CI] 0.50-0.90 and HR 0.66, 95% CI 0.48-0.91, respectively). PFS was significantly longer in patients with high RBM3 expression who had received first-line oxaliplatin based treatment, compared to those who had received irinotecan based treatment, both regarding nuclear and cytoplasmic expression (p-value 0.020 and 0.022 respectively).High RBM3 expression is an independent predictor of prolonged survival in mCRC patients, in particular in patients treated with first-line oxaliplatin based chemotherapy.

The mammalian brain is a complex organ composed of many specialized cells, harboring sets of both common, widely distributed, as well as specialized and discretely localized proteins. Here we focus on the human brain, utilizing transcriptomics and public available Human Protein Atlas (HPA) data to analyze brain-enriched (frontal cortex) polyadenylated messenger RNA and long non-coding RNA and generate a genome-wide draft of global and cellular expression patterns of the brain. Based on transcriptomics analysis of altogether 27 tissues, we have estimated that approximately 3% (n=571) of all protein coding genes and 13% (n=87) of the long non-coding genes expressed in the human brain are enriched, having at least five times higher expression levels in brain as compared to any of the other analyzed peripheral tissues. Based on gene ontology analysis and detailed annotation using antibody-based tissue micro array analysis of the corresponding proteins, we found the majority of brain-enriched protein coding genes to be expressed in astrocytes, oligodendrocytes or in neurons with molecular properties linked to synaptic transmission and brain development. Detailed analysis of the transcripts and the genetic landscape of brain-enriched coding and non-coding genes revealed brain-enriched splice variants. Several clusters of neighboring brain-enriched genes were also identified, suggesting regulation of gene expression on the chromatin level. This multi-angle approach uncovered the brain-enriched transcriptome and linked genes to cell types and functions, providing novel insights into the molecular foundation of this highly specialized organ.

To better understand prostate function and disease, it is important to define and explore the molecular constituents that signify the prostate gland. The aim of this study was to define the prostate specific transcriptome and proteome, in comparison to 26 other human tissues. Deep sequencing of mRNA (RNA-seq) and immunohistochemistry-based protein profiling were combined to identify prostate specific gene expression patterns and to explore tissue biomarkers for potential clinical use in prostate cancer diagnostics. We identified 203 genes with elevated expression in the prostate, 22 of which showed more than five-fold higher expression levels compared to all other tissue types. In addition to previously well-known proteins we identified two poorly characterized proteins, TMEM79 and ACOXL, with potential to differentiate between benign and cancerous prostatic glands in tissue biopsies. In conclusion, we have applied a genome-wide analysis to identify the prostate specific proteome using transcriptomics and antibody-based protein profiling to identify genes with elevated expression in the prostate. Our data provides a starting point for further functional studies to explore the molecular repertoire of normal and diseased prostate including potential prostate cancer markers such as TMEM79 and ACOXL.

To understand functions and diseases of urinary bladder, it is important to define its molecular constituents and their roles in urinary bladder biology. Here, we performed genome-wide deep RNA sequencing analysis of human urinary bladder samples and identified genes up-regulated in the urinary bladder by comparing the transcriptome data to those of all other major human tissue types. 90 protein-coding genes were elevated in the urinary bladder, either with enhanced expression uniquely in the urinary bladder or elevated expression together with at least one other tissue (group enriched). We further examined the localization of these proteins by immunohistochemistry and tissue microarrays and 20 of these 90 proteins were localized to the whole urothelium with a majority not yet described in the context of the urinary bladder. Four additional proteins were found specifically in the umbrella cells (Uroplakin 1a, 2, 3a, and 3b), and three in the intermediate/basal cells (KRT17, PCP4L1 and ATP1A4). 61 of the 90 elevated genes have not been previously described in the context of urinary bladder and the corresponding proteins are interesting targets for more in-depth studies. In summary, an integrated omics approach using transcriptomics and antibody-based profiling has been used to define a comprehensive list of proteins elevated in the urinary bladder.

To understand renal functions and disease, it is important to define the molecular constituents of the various compartments of the kidney. Here, we used comparative transcriptomic analysis of all major organs and tissues in the human body, in combination with kidney tissue micro array based immunohistochemistry, to generate a comprehensive description of the kidney-specific transcriptome and proteome. A special emphasis was placed on the identification of genes and proteins that were elevated in specific kidney subcompartments. Our analysis identified close to 400 genes that had elevated expression in the kidney, as compared to the other analysed tissues, and these were further subdivided, depending on expression levels, into tissue enriched, group enriched or tissue enhanced. Immunohistochemistry allowed us to identify proteins with distinct localisation to the glomeruli (n = 11), proximal tubules (n = 120), distal tubules (n = 9) or collecting ducts (n = 8). Among the identified kidney elevated transcripts, we found several proteins not previously characterised or identified as elevated in kidney. This description of the kidney specific transcriptome and proteome provides a resource for basic and clinical research to facilitate studies to understand kidney biology and disease.

The pancreas is composed of both exocrine glands and intermingled endocrine cells to execute its diverse functions, including enzyme production for digestion of nutrients and hormone secretion for regulation of blood glucose levels. To define the molecular constituents with elevated expression in the human pancreas, we employed a genome-wide RNA sequencing analysis of the human transcriptome to identify genes with elevated expression in the human pancreas. This quantitative transcriptomics data was combined with immunohistochemistry-based protein profiling to allow mapping of the corresponding proteins to different compartments and specific cell types within the pancreas down to the single cell level. Analysis of whole pancreas identified 146 genes with elevated expression levels, of which 47 revealed a particular higher expression as compared to the other analyzed tissue types, thus termed pancreas enriched. Extended analysis of in vitro isolated endocrine islets identified an additional set of 42 genes with elevated expression in these specialized cells. Although only 0.7% of all genes showed an elevated expression level in the pancreas, this fraction of transcripts, in most cases encoding secreted proteins, constituted 68% of the total mRNA in pancreas. This demonstrates the extreme specialization of the pancreas for production of secreted proteins. Among the elevated expression profiles, several previously not described proteins were identified, both in endocrine cells (CFC1, FAM159B, RBPJL and RGS9) and exocrine glandular cells (AQP12A, DPEP1, GATM and ERP27). In summary, we provide a global analysis of the pancreas transcriptome and proteome with a comprehensive list of genes and proteins with elevated expression in pancreas. This list represents an important starting point for further studies of the molecular repertoire of pancreatic cells and their relation to disease states or treatment effects.

We explored the attachment of an H16N3 influenza virus to human, mallard, and gull tissues using virus histochemistry applied to tissue microarrays and employing human and mallard viruses as references. Of the viruses tested, the H16N3 gull virus most readily attached to the human respiratory tract and eye. These results underscore the need to assess the potential for gull influenza viruses to replicate in human tissues and further investigate the role of gulls in influenza virus ecology.

GABA (γ-aminobutyric acid) is the main inhibitory neurotransmitter in the CNS and is present in high concentrations in presynaptic terminals of neuronal cells. More recently, GABA has been ascribed a more widespread role in the control of cell proliferation during development where low concentrations of extrasynaptic GABA induce a tonic activation of GABA receptors. The GABA-A receptor consists of a ligand-gated chloride channel, formed by five subunits that are selected from 19 different subunit isoforms. The functional and pharmacological properties of the GABA-A channels are dictated by their subunit composition. Here we used qRT-PCR to compare mRNA levels of all 19 GABA-A channel subunits in samples of human glioma (n = 29) and peri-tumoral tissue (n = 5). All subunits except the ρ1 and ρ3 subunit were consistently detected. Lowest mRNA levels were found in glioblastoma compared to gliomas of lower malignancy, except for the θ subunit. The expression and cellular distribution of the α1, γ1, ρ2 and θ subunit proteins was investigated by immunohistochemistry on tissue microarrays containing 87 gliomas grade II. We found a strong co-expression of ρ2 and θ subunits in both astrocytomas (r = 0.86, p

Tissue localization of gene expression is increasingly important for accurate interpretation of large scale datasets from expression and mutational analyses. To this end, we have (1) developed a robust and scalable procedure for generation of mRNA hybridization probes, providing >95% first-pass success rate in probe generation to any human target gene and (2) adopted an automated staining procedure for analyses of formalin-fixed paraffin-embedded tissues and tissue microarrays. The in situ mRNA and protein expression patterns for genes with known as well as unknown tissue expression patterns were analyzed in normal and malignant tissues to assess procedure specificity and whether in situ hybridization can be used for validating novel antibodies. We demonstrate concordance between in situ transcript and protein expression patterns of the well-known pathology biomarkers KRT17, CHGA, MKI67, PECAM1 and VIL1, and provide independent validation for novel antibodies to the biomarkers BRD1, EZH2, JUP and SATB2. The present study provides a foundation for comprehensive in situ gene set or transcriptome analyses of human normal and tumor tissues.

RAS and BRAF mutations impact treatment and prognosis of metastatic colorectal cancer patients (mCRC), but the knowledge is based on trial patients usually not representative for the general cancer population. Patient characteristics, treatment and efficacy according to KRAS, BRAF and MSI status were analyzed in a prospectively collected unselected population-based cohort of 798 non-resectable mCRC patients. The cohort contained many patients with poor performance status (39% PS 2-4) and elderly (37% age>75), groups usually not included in clinical trials. Patients without available tissue micro array (TMA) (42%) had worse prognostic factors and inferior survival (all patients; 7m vs 11m, chemotherapy-treated;12m vs 17m). The 92 patients (21%) with BRAF mutation had a poor prognosis regardless of microsatellite instability, but receipt of 1-2nd chemotherapy was similar to wildtype BRAF patients. Median survival in this cohort varied from 1 month in BRAF mutated patients not given chemotherapy to 26 months in wildtype KRAS/BRAF patients

Human cytomegalovirus (HCMV) has been indicated being a significant oncomodulator. Recent reports have suggested that an antiviral treatment alters the outcome of a glioblastoma. We analysed the performance of commercial HCMV-antibodies applying the immunohistochemical (IHC) methods on brain sample obtained from a subject with a verified HCMV infection, on samples obtained from 14 control subjects, and on a tissue microarray block containing cores of various brain tumours. Based on these trials, we selected the best performing antibody and analysed a cohort of 417 extra- and intra-axial brain tumours such as gliomas, medulloblastomas, primary diffuse large B-cell lymphomas, and meningiomas. HCMV protein pp65 immunoreactivity was observed in all types of tumours analysed, and the IHC expression did not depend on the patient's age, gender, tumour type, or grade. The labelling pattern observed in the tumours differed from the labelling pattern observed in the tissue with an active HCMV infection. The HCMV protein was expressed in up to 90% of all the tumours investigated. Our results are in accordance with previous reports regarding the HCMV protein expression in glioblastomas and medulloblastomas. In addition, the HCMV protein expression was seen in primary brain lymphomas, low-grade gliomas, and in meningiomas. Our results indicate that the HCMV protein pp65 expression is common in intra- and extra-axial brain tumours. Thus, the assessment of the HCMV expression in tumours of various origins and pathologically altered tissue in conditions such as inflammation, infection, and even degeneration should certainly be facilitated.

AimsImmunohistochemistry 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 resultsMelanoma 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).ConclusionHere 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.