Genome wide expression profiling identifies specific deregulated pathways in meningioma.

Genome-wide expression signatures improve the understanding of tumor biology. We performed expression profiling of 24 meningioma including 8 of each WHO grade and 2 dura controls analyzing 55.000 transcripts including 18.300 known genes. We compared expression in meningioma vs. dura, expression of low grade (WHO I) vs. higher-grade (WHO II and WHO III) tumors and expression of meningothelial and syncytial meningioma vs. fibroblastic meningioma. Overall expression was significantly decreased in meningioma compared to dura and in meningothelial and syncytial compared to fibroblastic meningioma. Gene expression was exemplarily confirmed by immunohistochemistry using independent samples. Applying our statistical gene set analysis toolkit "GeneTrail", we identified significantly deregulated biochemical pathways using Kyoto encyclopedia of genes and genomes and Transpath databases. Kyoto encyclopedia of genes and genomes pathways with decreased expression in meningioma included cell adhesion molecules (p<0.0001) and cytokine-cytokine receptor interactions (p<0.0001). Pathways with increased expression included several metabolic pathways. Extended expression profiling by a novel statistical gene set enrichment identified pathways that have previously not been associated with meningioma.

Pattern of serum autoantibodies allows accurate distinction between a tumor and pathologies of the same organ.

PURPOSE: Recent studies impressively showed the diagnostic potential of seroreactivity patterns for different tumor types, offering the prospect for low-cost screening of numerous tumor types simultaneously. One of the major challenges toward this goal is to prove that seroreactivity profiles do not only allow for identifying a tumor but also allow for distinguishing tumors from other pathologies of the same organ. EXPERIMENTAL DESIGN: We chose glioma as a model system and tested 325 sera (88 glioma, 95 intracranial tumors, 60 other brain pathologies, and 82 healthy controls) for seroreactivity on a panel of 35 antigens. RESULTS: We were able to discriminate between glioma and all other sera with cross-validated specificity of 86.1%, sensitivity of 85.2%, and accuracy of 85.8%. We obtained comparably good results for the separation of glioma versus nontumor brain pathologies and glioma versus other intracranial tumors. CONCLUSION: Our study provides first evidence that seroreactivity patterns allow for an accurate discrimination between a tumor and pathologies of the same organ even between different tumor types of the same organ.

GeneTrail--advanced gene set enrichment analysis.

We present a comprehensive and efficient gene set analysis tool, called 'GeneTrail' that offers a rich functionality and is easy to use. Our web-based application facilitates the statistical evaluation of high-throughput genomic or proteomic data sets with respect to enrichment of functional categories. GeneTrail covers a wide variety of biological categories and pathways, among others KEGG, TRANSPATH, TRANSFAC, and GO. Our web server provides two common statistical approaches, 'Over-Representation Analysis' (ORA) comparing a reference set of genes to a test set, and 'Gene Set Enrichment Analysis' (GSEA) scoring sorted lists of genes. Besides other newly developed features, GeneTrail's statistics module includes a novel dynamic-programming algorithm that improves the P-value computation of GSEA methods considerably. GeneTrail is freely accessible at http://genetrail.bioinf.uni-sb.de.

SePaCS--a web-based application for classification of seroreactivity profiles.

Immunogenic antigen sets possess high potential for minimally invasive disease detection and monitoring. For various diseases, including cancer, appropriate antigen sets have already been detected in blood sera of patients. Typically, a large number of sera from diseased and unaffected persons is screened for the antigens of interest. Sophisticated statistical learning approaches are trained on the resulting data set to classify sera as either tumor or normal sera. We developed a web-based application, called 'Seroreactivity Profile Classification Service' (SePaCS) that enables clinical groups to carry out analyzes of training sets and predictions of unclassified seroreactivity profiles with minimal effort. SePaCS provides a broad range of classification methods: four versions of a Naïve Bayes Classifier, Support Vector Machines with a radial basis function kernel, Linear Discriminant Analysis, and Diagonal Discriminant Analysis. The computed results are summarized in a PDF file. We demonstrate the functionality of SePaCS exemplarily for meningioma, a generally benign intracranial tumor. As a second example, we evaluated SePaCS on glioma, a malignant brain tumor. SePaCS is freely available at http://www.bioinf.uni-sb.de/sepacs.

Frequent overexpression of the genes FXR1, CLAPM1 and EIF4G located on amplicon 3q26-27 in squamous cell carcinoma of the lung.

Previously, we reported gene amplification at chromosome 3q26-27 in more than one third of squamous cell carcinomas of the lung. Frequent amplification of eukaryotic translation initiation factor 4G on 3q27.1 indicated a possible role of this amplification in translation initiation. The analysis of 61 squamous cell lung carcinomas shows that the percentage of carcinomas with a 3q27.1 amplification increases in higher malignant tumors. Non-invasive (T1) and minimal-invasive (T2) tumor stages showed similar percentages of amplified and non-amplified tumors, whereas locally-invasive (T3) tumors revealed a statistically significant (p < 0.05) increased percentage of amplified tumors. Microarrays were used to analyze the expression pattern of genes mapping in the amplified domain and its flanking regions (3q25-28) as well as the expression of genes directly or indirectly associated with translation initiation in squamous cell carcinoma, large cell carcinoma, adenocarcinoma and small cell carcinoma. Three genes, namely FXR1, CLAPM1 and EIF4G, are most frequently overexpressed in the center of the amplified domain in squamous cell carcinomas. The eukaryotic translation initiation factors 4A1, 2B and 4B as well as the poly(A)-binding protein PABPC1 where found to be overexpressed in all lung cancer entities. We found, however, no overexpression of eIF4E. Our results contribute to the understanding of the frequent amplification processes in squamous cell carcinomas of the lung and to the understanding of the translation initiation that appears not to require eIF4E in lung carcinogenesis.

A minimally invasive multiple marker approach allows highly efficient detection of meningioma tumors.

BACKGROUND: The development of effective frameworks that permit an accurate diagnosis of tumors, especially in their early stages, remains a grand challenge in the field of bioinformatics. Our approach uses statistical learning techniques applied to multiple antigen tumor antigen markers utilizing the immune system as a very sensitive marker of molecular pathological processes. For validation purposes we choose the intracranial meningioma tumors as model system since they occur very frequently, are mostly benign, and are genetically stable. RESULTS: A total of 183 blood samples from 93 meningioma patients (WHO stages I-III) and 90 healthy controls were screened for seroreactivity with a set of 57 meningioma-associated antigens. We tested several established statistical learning methods on the resulting reactivity patterns using 10-fold cross validation. The best performance was achieved by Naïve Bayes Classifiers. With this classification method, our framework, called Minimally Invasive Multiple Marker (MIMM) approach, yielded a specificity of 96.2%, a sensitivity of 84.5%, and an accuracy of 90.3%, the respective area under the ROC curve was 0.957. Detailed analysis revealed that prediction performs particularly well on low-grade (WHO I) tumors, consistent with our goal of early stage tumor detection. For these tumors the best classification result with a specificity of 97.5%, a sensitivity of 91.3%, an accuracy of 95.6%, and an area under the ROC curve of 0.971 was achieved using a set of 12 antigen markers only. This antigen set was detected by a subset selection method based on Mutual Information. Remarkably, our study proves that the inclusion of non-specific antigens, detected not only in tumor but also in normal sera, increases the performance significantly, since non-specific antigens contribute additional diagnostic information. CONCLUSION: Our approach offers the possibility to screen members of risk groups as a matter of routine such that tumors hopefully can be diagnosed immediately after their genesis. The early detection will finally result in a higher cure- and lower morbidity-rate.

Complex humoral immune response against a benign tumor: frequent antibody response against specific antigens as diagnostic targets.

There are numerous studies on the immune response against malignant human tumors. This study was aimed to address the complexity and specificity of humoral immune response against a benign human tumor. We assembled a panel of 62 meningioma-expressed antigens that show reactivity with serum antibodies of meningioma patients, including 41 previously uncharacterized antigens by screening of a fetal brain expression library. We tested the panel for reactivity with 48 sera, including sera of patients with common-type, atypical, and anaplastic meningioma, respectively. Meningioma sera detected an average of 14.6 antigens per serum and normal sera an average of 7.8 antigens per serum (P = 0.0001). We found a decline of seroreactivity with malignancy with a statistical significant difference between common-type and anaplastic meningioma (P < 0.05). We detected 17 antigens exclusively with patient sera, including 12 sera that were reactive against KIAA1344, 9 against natural killer tumor recognition (NKTR), and 7 against SRY (sex determining region Y)-box2 (SOX2). More than 80% of meningioma patients had antibodies against at least one of the antigens KIAA1344, SC65, SOX2, and C6orf153. Our results show a highly complex but specific humoral immune response against a benign tumor with a distinct serum reactivity pattern and a decline of complexity with malignancy. The frequent antibody response against specific antigens offers new diagnostic and therapeutic targets for meningioma. We developed a statistical learning method to differentiate sera of meningioma patients from sera of healthy donors.

GraBCas: a bioinformatics tool for score-based prediction of Caspase- and Granzyme B-cleavage sites in protein sequences.

Caspases and granzyme B are proteases that share the primary specificity to cleave at the carboxyl terminal of aspartate residues in their substrates. Both, caspases and granzyme B are enzymes that are involved in fundamental cellular processes and play a central role in apoptotic cell death. Although various targets are described, many substrates still await identification and many cleavage sites of known substrates are not identified or experimentally verified. A more comprehensive knowledge of caspase and granzyme B substrates is essential to understand the biological roles of these enzymes in more detail. The relatively high variability in cleavage site recognition sequence often complicates the identification of cleavage sites. As of yet there is no software available that allows identification of caspase and/or granzyme with cleavage sites differing from the consensus sequence. Here, we present a bioinformatics tool 'GraBCas' that provides score-based prediction of potential cleavage sites for the caspases 1-9 and granzyme B including an estimation of the fragment size. We tested GraBCas on already known substrates and showed its usefulness for protein sequence analysis. GraBCas is available at http://wwwalt.med-rz.uniklinik-saarland.de/med_fak/humangenetik/software/index.html.

Integrative analysis of cancer-related data using CAP.

The development of human cancer is a highly complex process and can be considered the result of several combined events, such as genetic alterations, disturbance of signal transduction, or failure of immunological surveillance. Cancer-related databases usually focus on specific fields of research, e.g., cancer genetics or cancer immunology, whereas the complexity of cancer genesis requires an integrated analysis of heterogeneous data from several sources. Here we present the cancer-associated protein database (CAP), a novel analysis system for cancer-related data. CAP integrates data from multiple external databases, augments these data with functional annotations, and offers tools for statistical analysis of these data. We have employed CAP to analyze genes that have been found to cause an autoimmune response in cancer. In particular, we explored the connection between the autoimmune response, mutations, and overexpression of these genes. Our preliminary results suggest that mutations are not significant contributors to raising an antibody response against tumor antigens, whereas overexpression seems to play a more important role. We hereby demonstrate how different types of data can be integrated and analyzed successfully, providing interesting results. As the amount of available data is growing rapidly, a combined analysis will play an important role in exploring the genetic and immunological basis of cancer. CAP is freely available at the following web site: http://www.bioinf.uni-sb.de/CAP/.

Toward a more complete recognition of immunoreactive antigens in squamous cell lung carcinoma.

There is very limited knowledge about the antibody response against tumor-expressed antigens in lung cancer. To arrive at a more complete picture of lung cancer antigens, we generated 2 cDNA libraries from squamous cell lung carcinoma and isolated 15 immunogenic antigens using autologous sera. Among the antigens most frequently identified were the lymphoid blast crisis oncogene (LBC), an unknown hypothetical protein and the p53-binding protein (TP53 BP), which have already been associated with tumor development. Of the immunogenic antigens, 6 map to chromosomes that are frequently altered in squamous cell lung carcinoma. SEREX database analysis showed that 7 of the identified immunogenic antigens have been associated with the humoral immune response in other human tumors. Screening with heterologous sera of patients with lung carcinoma identified 4 antigens, including human protein kinase C and TP53 BP, which have also been found by autologous screening. Only 1 of the 15 identified antigens reacted with any of the 36 control sera, which were taken from individuals without known disease. Sera from adenocarcinoma and large cell carcinoma of the lung were not reactive for the antigens. In summary, using 2 newly established cDNA libraries, we isolated 15 novel antigens, which were subsequently evaluated for the frequency of their corresponding antibodies in autologous, normal and heterologous sera; their chromosomal localization; and their correlation with survival after surgery.

MGEA6 is tumor-specific overexpressed and frequently recognized by patient-serum antibodies.

Tumorigenesis of meningioma has been associated with chromosome 22, most notably the NF2 gene, but additional genes have also been implicated in meningioma development. Previously, we have cloned the cDNAs for the meningioma expressed antigen 6 (MGEA6) and its splice variant MGEA11. Here, we show that antibodies against recombinantly expressed MGEA6/11 are found in 41.7% (10/24) of the sera from meningioma patients and in 2/8 sera of glioblastoma patients, whereas no response was seen in 12 sera from healthy persons. Western-blot analyses using generated polyclonal antibodies, revealed overexpression in meningioma and glioma tumor samples compared to normal brain. Immunohistochemical staining of tissue sections confirms reactivity in meningioma tumor cells and tumor cells of glial origin. We found no reactivity to normal astrocytes and only faint reactivity to normal leptomeninges. Sequence analysis predicted membranic localization of MGEA6/11, that was confirmed by cell fractionation. The immune response to MGEA6/11 is frequent in both meningioma and glioma patients and may likely be attributed to overexpression of the MGEA6/11 protein in the tumor cells.