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Rotavirus viroplasms are cytosolic, electron-dense inclusions corresponding to the viral machinery of replication responsible for viral template transcription, dsRNA genome segments replication and assembly of new viral cores. We have previously observed that, over time, those viroplasms increase in size and decrease in number. Therefore, we hypothesized that this process was dependent on the cellular microtubular network and its associated dynamic components. Here, we present evidence demonstrating that viroplasms are dynamic structures, which, in the course of an ongoing infection, move towards the perinuclear region of the cell, where they fuse among each other, thereby gaining considerably in size and, simultaneouly, explaining the decrease in numbers. On the viral side, this process seems to depend on VP2 for movement and on NSP2 for fusion. On the cellular side, both the temporal transition and the maintenance of the viroplasms are dependent on the microtubular network, its stabilization by acetylation, and, surprisingly, on a kinesin motor of the kinesin-5 family, Eg5. Thus, we provide for the first time deeper insights into the dynamics of rotavirus replication, which can explain the behavior of viroplasms in the infected cell.

PURPOSE: To analyze rates of expression of karyopherin alpha 2 (KPNA2) in different prostate tissues and to evaluate the prognostic properties for patients with primary prostate cancer. EXPERIMENTAL DESIGN: Tissue microarrays containing 798 formalin-fixed, paraffin-embedded prostate tissue cores from two different institutes of pathology. TMAs were stained immunhistochemically for KPNA2 and NBS1. SiRNA technologies were used to inhibit KPNA2 expression in vitro, and the effect of this inhibition on cellular viability was determined. Efficency of knock down experiments was determined by Western blot analysis.RESULTS: KPNA2 expression was significantly upregulated in carcinomas of the prostate, especially in metastatic and castration-resistant prostate cancer samples. Positive nuclear KPNA2 immunoreactivity was identified as a novel predictor of biochemical recurrence after radical prostatectomy (n=348), and was independent of the well-established predictive factors preoperative PSA value, Gleason score, tumor stage and surgical margin status. These results were validated by analyzing a second and independent prostate cancer cohort (n=330). Further, in vitro experiments showed that the cell proliferation and viability of PC3 cells was significantly reduced when KPNA2 expression was inhibited. KPNA2 knockdown did not induce poly(ADP-ribose) polymerase (PARP) cleavage as marker for apoptosis. No significantly increased subG1 fraction could be found by FACS analysis. CONCLUSIONS: KPNA2 is a novel independent prognostic marker for disease progression after radical prostatectomy. This allows to identify patients who need more aggressive treatment. It can moreover be speculated that patients not suited for surveillance regimens might be identified at initial biopsy by a positive KPNA2 immunohistochemistry.

Using tissue microarrays (TMAs) we studied COX2/PPARG immunoreactivity in a broad spectrum of tumors focussing on clinicopathological correlations and the outcome of patients with malignant melanoma (MM). TMA-1 contained normal and tumor tissues (n = 3448) from 47 organs including skin neoplasms (n = 323); TMA-2 88 primary MM, 101 metastases, and 161 benign nevi. Based on a biomodulatory approach combining COX/PPAR-targeting with metronomic low-dose chemotherapy metastases of 36 patients participating in a randomized trial with metastatic (stage IV) melanoma were investigated using TMA-3. COX2/PPARG immunoreactivity significantly increased from nevi to primary MM and metastases; COX2 positivity was associated with advanced Clark levels and shorter recurrence-free survival. Patients with PPARG-positive metastases and biomodulatory metronomic chemotherapy alone or combined with COX2/PPARG-targeting showed a significantly prolonged progression-free survival. Regarding primary MM, COX2 expression indicates an increased risk of tumor recurrence. In metastatic MM, PPARG expression may be a predicitive marker for response to biomodulatory stroma-targeted therapy.

BACKGROUND: We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox's proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso. RESULTS: Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers. CONCLUSIONS: The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers.

Using tissue microarrays assembling 465 nevi, primary melanomas and metastases, we investigated whether expression of methylthioadenosine phosphorylase (MTAP), a recently suggested biomarker of malignant melanoma, has prognostic significance and may predict responsiveness to adjuvant interferon therapy in patients with melanoma. Because of its association with MTAP activity and interferon signalling pathways, signal transducer and activator of transcription 1 (STAT1) immunohistochemistry was analysed, too. MTAP expression was significantly reduced in melanomas and metastases compared with nevi (P < 0.001); STAT1 expression significantly increased. In melanomas, loss of MTAP expression was significantly related to Clark level (P < 0.05) and tumor thickness (P < 0.01); whereas STAT1 immunoreactivity was significantly related to gender (p < 0.05) and tumor thickness (P < 0.05). Interestingly, subgroup analysis of patients with a tumor thickness of 1.5-4.0 mm revealed a significant survival benefit from adjuvant interferon treatment regarding recurrence-free survival (RFS; P < 0.05) if MTAP expression was observed in the primary melanoma. Patients with STAT1-positive melanomas also tended to benefit from interferon concerning RFS (P = 0.074) and showed a significant benefit concerning overall survival (OS; P < 0.05). According to Cox analysis, MTAP expression in contrast to STAT1 was an independent positive prognostic marker for RFS and OS. In conclusion, MTAP represents a highly promising immunohistochemical marker for prognosis and interferon response of patients with malignant melanoma.

BACKGROUND: Expression of periostin is an indicator of epithelial-mesenchymal transition in cancer but a detailed analysis of periostin expression in prostate cancer has not been conducted so far. METHODS: Here, we evaluated periostin expression in prostate cancer cells and peritumoural stroma immunohistochemically in two independent prostate cancer cohorts, including a training cohort (n = 93) and a test cohort (n = 325). Metastatic prostate cancers (n = 20), hormone refractory prostate cancers (n = 19) and benign prostatic tissues (n = 38) were also analyzed. RESULTS: In total, strong epithelial periostin expression was detectable in 142 of 418 (34.0%) of prostate carcinomas and in 11 of 38 benign prostate glands (28.9%). Increased periostin expression in carcinoma cells was significantly associated with high Gleason score (p < 0.01) and advanced tumour stage (p < 0.05) in the test cohort. Whereas periostin expression was weak or absent in the stroma around normal prostate glands, strong periostin expression in tumour stroma was found in most primary and metastatic prostate cancers. High stromal periostin expression was associated with higher Gleason scores (p < 0.001). There was a relationship between stromal periostin expression and shortened PSA relapse free survival times in the training cohort (p < 0.05). CONCLUSIONS: Our data indicate that periostin up-regulation is related to increased tumour aggressiveness in prostate cancer and might be a promising target for therapeutical interventions in primary and metastatic prostate cancer.

Modern pathology broadly searches for biomarkers which are predictive for the survival of patients or the progression of cancer. Due to the lack of robust analysis algorithms this work is still performed manually by estimating staining on whole slides or tissue microarrays (TMA). Therefore, the design of decision support systems which can automate cancer diagnosis as well as objectify it pose a highly challenging problem for the medical imaging community. In this paper we propose Relational Detection Forests (RDF) as a novel object detection algorithm, which can be applied in an off-the-shelf manner to a large variety of tasks. The contributions of this work are twofold: (i) we describe a feature set which is able to capture shape information as well as local context. Furthermore, the feature set is guaranteed to be generally applicable due to its high flexibility. (ii) we present an ensemble learning algorithm based on randomized trees, which can cope with exceptionally high dimensional feature spaces in an efficient manner. Contrary to classical approaches, subspaces are not split based on thresholds but by learning relations between features. The algorithm is validated on tissue from 133 human clear cell renal cell carcinoma patients (ccRCC) and on murine liver samples of eight mice. On both species RDFs compared favorably to state of the art methods and approaches the detection accuracy of trained pathologists.

Modern pathology broadly searches for biomarkers which are predictive for the survival of patients or the progression of cancer. Due to the lack of robust analysis algorithms this work is still performed manually by estimating staining on whole slides or tissue microarrays (TMA). Therefore, the design of decision support systems which can automate cancer diagnosis as well as objectify it pose a highly challenging problem for the medical imaging community. In this paper we propose Relational Detection Forests (RDF) as a novel object detection algorithm, which can be applied in an off-the-shelf manner to a large variety of tasks. The contributions of this work are twofold: (i) we describe a feature set which is able to capture shape information as well as local context. Furthermore, the feature set is guaranteed to be generally applicable due to its high flexibility. (ii) we present an ensemble learning algorithm based on randomized trees, which can cope with exceptionally high dimensional feature spaces in an efficient manner. Contrary to classical approaches, subspaces are not split based on thresholds but by learning relations between features. The algorithm is validated on tissue from 133 human clear cell renal cell carcinoma patients (ccRCC) and on murine liver samples of eight mice. On both species RDFs compared favorably to state of the art methods and approaches the detection accuracy of trained pathologists.

BACKGROUND: Secreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease. METHODS: WIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fisher's exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses. RESULTS: WIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3-methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DK

PURPOSE: To evaluate molecular and immunohistochemical markers to develop a molecular grading of urothelial bladder cancer and to test these markers in voided urine samples.Experimental Design: 255 consecutive biopsies from primary bladder cancer patients were evaluated on a tissue microarray. The clinical parameters gender, age, adjacent carcinoma in situ, and multifocality were collected. UroVysion fluorescence in situ hybridization (FISH) was done. Expression of cytokeratin 20, MIB1, and TP53 was analyzed by immunohistochemistry. Fibroblast growth factor receptor 3 (FGFR3) status was studied by SNaPshot mutation detection. Results were correlated with clinical outcome by Cox regression analysis. To assess the predictive power of different predictor subsets to detect high grade and tumor invasion, logistic regression models were learned. Additionally, voided urine samples of 119 patients were investigated. After cytologic examination, urine samples were matched with their biopsies and analyzed for loss of heterozygosity (LOH), FGFR3 mutation, polysomy, and p16 deletion using UroVysion FISH. Receiver operator characteristic curves for various predictor subsets were plotted.RESULTS: In biopsies, high grade and solid growth pattern were independent prognostic factors for overall survival. A model consisting of UroVysion FISH and FGFR3 status (FISH + FGFR3) predicted high grade significantly better compared with a recently proposed molecular grade (MIB1 + FGFR3). In voided urine, the combination of cytology with LOH analysis (CYTO + LOH) reached the highest diagnostic accuracy for the detection of bladder cancer cells and performed better than cytology alone (sensitivity of 88.2% and specificity of 97.1%).CONCLUSIONS: The combination of cytology with LOH analysis could reduce unpleasant cystoscopies for bladder cancer patients. (Cancer Epidemiol Biomarkers Prev 2009;18(6):OF1-9).

PURPOSE: The paired box gene 2, PAX2, encodes for a transcription factor that is up-regulated during nephrogenesis and becomes silenced in mature epithelium of the glomeruli, the proximal, and distal tubules. Reactivation of PAX2 has been frequently observed in clear cell renal cell carcinoma (ccRCC), a tumor type characterized by loss of von Hippel-Lindau (VHL) tumor suppressor function. The regulation of PAX2 expression in ccRCC is unknown. EXPERIMENTAL DESIGN: We applied reporter gene assays to investigate PAX2 promoter regulation. Furthermore, PAX2 expression was determined in ccRCC cell lines under normoxic and hypoxic condition in a VHL wild-type and mutated background. PAX2 expression was also assessed in 831 human ccRCC and correlated with hypoxia-inducible factor alpha (HIFalpha) and clinical parameters. RESULTS: Here, we show that both loss of VHL protein (pVHL) function and hypoxia leads to strong PAX2 reexpression. Using luciferase reporter gene assays, no induction was obtained in spite of six hypoxia response element motifs identified in the promoter of PAX2. Comprehensive immunohistochemical analyses showed significant correlations between PAX2, HIF1alpha, and HIF2alpha-target CCND1 expression patterns in ccRCC patients. Notably, PAX2 expression was highly associated with early-stage, well-differentiated ccRCC and, consequently, better clinical outcome (P < 0.0001 each). Additional analyses indicated that PAX2 repressor WT1 and cancer-linked hypomethylation are not important for transcriptional regulation of PAX2 in ccRCC. CONCLUSION: We conclude that in ccRCC, PAX2 reactivation is driven by HIF-dependent mechanisms following pVHL loss.

BACKGROUND: Y-box binding protein-1 (YB-1) is the prototypic member of the cold shock protein family that fulfills numerous cellular functions. In the nucleus YB-1 protein orchestrates transcription of proliferation-related genes, whereas in the cytoplasm it associates with mRNA and directs translation. In human tumor entities, such as breast, lung and prostate cancer, cellular YB-1 expression indicates poor clinical outcome, suggesting that YB-1 is an attractive marker to predict patients' prognosis and, potentially, is suitable to individualize treatment protocols. Given these predictive qualities of YB-1 detection we sought to establish a highly specific monoclonal antibody (Mab) for diagnostic testing and its characterization towards outcome prediction (relapse-free and overall survival). METHODS: Hybridoma cell generation was carried out with recombinant YB-1 protein as immunogen and Mab characterization was performed using immunoblotting and ELISA with recombinant and tagged YB-1 proteins, as well as immunohistochemistry of healthy and breast cancer specimens. Breast tumor tissue array staining results were analyzed for correlations with receptor expression and outcome parameters. RESULTS: YB-1-specific Mab F-E2G5 associates with conformational binding epitopes mapping to two domains within the N-terminal half of the protein and detects nuclear YB-1 protein by immunohistochemistry in paraffin-embedded breast cancer tissues. Prognostic evaluation of Mab F-E2G5 was performed by immunohistochemistry of a human breast cancer tissue microarray comprising 179 invasive breast cancers, 8 ductal carcinoma in situ and 37 normal breast tissue samples. Nuclear YB-1 detection in human breast cancer cells was associated with poor overall survival (p = 0.0046). We observed a close correlation between nuclear YB-1 detection and absence of progesterone receptor expression (p = 0.002), indicating that nuclear YB-1 detection marks a specific subgroup of breast cancer. Likely due

Renal cell carcinoma (RCC) is one of the ten most frequent malignancies in Western societies and can be diagnosed by histological tissue analysis. Current diagnostic rules rely on exact counts of cancerous cell nuclei which are manually counted by pathologists. We propose a complete imaging pipeline for the automated analysis of tissue microarrays of renal cell cancer. At its core, the analysis system consists of a novel weakly supervised classification method, which is based on an iterative morphological algorithm and a soft-margin support vector machine. The lack of objective ground truth labels to validate the system requires the combination of expert knowledge of pathologists. Human expert annotations of more than 2000 cell nuclei from 9 different RCC patients are used to demonstrate the superior performance of the proposed algorithm over existing cell nuclei detection approaches.

Renal cell carcinoma (RCC) can be diagnosed by histological tissue analysis where exact counts of cancerous cell nuclei are required. We propose a completely automated image analysis pipeline to predict the survival of RCC patients based on the analysis of immunohistochemical staining of MIB-1 on tissue microarrays. A random forest classifier detects cell nuclei of cancerous cells and predicts their staining. The classifier training is achieved by expert annotations of 2300 nuclei gathered from tissues of 9 different RCC patients. The application to a test set of 133 patients clearly demonstrates that our computational pathology analysis matches the prognostic performance of expert pathologists.

BACKGROUND: FOXM1 regulates expression of cell cycle related genes that are essential for progression into DNA replication and mitosis. Consistent with its role in proliferation, elevated expression of FOXM1 has been reported in a variety of human tumour entities. FOXM1 is a gene of interest because recently chemical inhibitors of FOXM1 were described to limit proliferation and induce apoptosis in cancer cells in vitro, indicating that FOXM1 inhibitors could represent useful anticancer therapeutics. METHODS: Using immunohistochemistry (IHC) we systematically analysed FOXM1 expression in human invasive breast carcinomas (n = 204) and normal breast tissues (n = 46) on a tissue microarray. Additionally, using semiquantitative realtime PCR, a collection of paraffin embedded normal (n = 12) and cancerous (n = 25) breast tissue specimens as well as benign (n = 3) and malignant mammary cell lines (n = 8) were investigated for FOXM1 expression. SPSS version 14.0 was used for statistical analysis. RESULTS: FOXM1 was found to be overexpressed in breast cancer in comparison to normal breast tissue both on the RNA and protein level (e.g. 8.7 fold as measured by realtime PCR). We found a significant correlation between FOXM1 expression and the HER2 status determined by HER2 immunohistochemistry (P < 0.05). Univariate survival analysis showed a tendency between FOXM1 protein expression and unfavourable prognosis (P = 0.110). CONCLUSION: FOXM1 may represent a novel breast tumour marker with prognostic significance that could be included into multi-marker panels for breast cancer. Interestingly, we found a positive correlation between FOXM1 expression and HER2 status, pointing to a potential role of FOXM1 as a new drug target in HER2 resistant breast tumour, as FOXM1 inhibitors for cancer treatment were described recently. Further studies are underway to analyse the potential interaction between FOXM1 and HER2, especially whether FOXM1 directly activates the HER2 promoter.

BACKGROUND: The inter-alpha-trypsin inhibitors (ITI) are a family of plasma protease inhibitors, assembled from a light chain - bikunin, encoded by AMBP - and five homologous heavy chains (encoded by ITIH1, ITIH2, ITIH3, ITIH4, and ITIH5), contributing to extracellular matrix stability by covalent linkage to hyaluronan. So far, ITIH molecules have been shown to play a particularly important role in inflammation and carcinogenesis. METHODS: We systematically investigated differential gene expression of the ITIH gene family, as well as AMBP and the interacting partner TNFAIP6 in 13 different human tumor entities (of breast, endometrium, ovary, cervix, stomach, small intestine, colon, rectum, lung, thyroid, prostate, kidney, and pancreas) using cDNA dot blot analysis (Cancer Profiling Array, CPA), semiquantitative RT-PCR and immunohistochemistry. RESULTS: We found that ITIH genes are clearly downregulated in multiple human solid tumors, including breast, colon and lung cancer. Thus, ITIH genes may represent a family of putative tumor suppressor genes that should be analyzed in greater detail in the future. For an initial detailed analysis we chose ITIH2 expression in human breast cancer. Loss of ITIH2 expression in 70% of cases (n = 50, CPA) could be confirmed by real-time PCR in an additional set of breast cancers (n = 36). Next we studied ITIH2 expression on the protein level by analyzing a comprehensive tissue micro array including 185 invasive breast cancer specimens. We found a strong correlation (p < 0.001) between ITIH2 expression and estrogen receptor (ER) expression indicating that ER may be involved in the regulation of this ECM molecule. CONCLUSION: Altogether, this is the first systematic analysis on the differential expression of ITIH genes in human cancer, showing frequent downregulation that may be associated with initiation and/or progression of these malignancies.