Confirmation of human protein interaction data by human expression data

Unravelling the complexity of signalling networks in cancer: A review of the increasing role for computational modelling

Cancer induction is a highly complex process involving hundreds of different inducers but whose eventual outcome is the same. Clearly, it is essential to understand how signalling pathways and networks generated by these inducers interact to regulate cell behaviour and create the cancer phenotype. While enormous strides have been made in identifying key networking profiles, the amount of data generated far exceeds our ability to understand how it all "fits together". The number of potential interactions is astronomically large and requires novel approaches and extreme computation methods to dissect them out. However, such methodologies have high intrinsic mathematical and conceptual content which is difficult to follow. This review explains how computation modelling is progressively finding solutions and also revealing unexpected and unpredictable nano-scale molecular behaviours extremely relevant to how signalling and networking are coherently integrated. It is divided into linked sections illustrated by numerous figures from the literature describing different approaches and offering visual portrayals of networking and major conceptual advances in the field. First, the problem of signalling complexity and data collection is illustrated for only a small selection of known oncogenes. Next, new concepts from biophysics, molecular behaviours, kinetics, organisation at the nano level and predictive models are presented. These areas include: visual representations of networking, Energy Landscapes and energy transfer/dissemination (entropy); diffusion, percolation; molecular crowding; protein allostery; quinary structure and fractal distributions; energy management, metabolism and re-examination of the Warburg effect. The importance of unravelling complex network interactions is then illustrated for some widely-used drugs in cancer therapy whose interactions are very extensive. Finally, use of computational modelling to develop micro- and nano- functional models ("bottom-up" research) is highlighted. The review concludes that computational modelling is an essential part of cancer research and is vital to understanding network formation and molecular behaviours that are associated with it. Its role is increasingly essential because it is unravelling the huge complexity of cancer induction otherwise unattainable by any other approach.

HAPPI-2: a Comprehensive and High-quality Map of Human Annotated and Predicted Protein Interactions

BACKGROUND

Human protein-protein interaction (PPI) data is essential to network and systems biology studies. PPI data can help biochemists hypothesize how proteins form complexes by binding to each other, how extracellular signals propagate through post-translational modification of de-activated signaling molecules, and how chemical reactions are coupled by enzymes involved in a complex biological process. Our capability to develop good public database resources for human PPI data has a direct impact on the quality of future research on genome biology and medicine.

RESULTS

The database of Human Annotated and Predicted Protein Interactions (HAPPI) version 2.0 is a major update to the original HAPPI 1.0 database. It contains 2,922,202 unique protein-protein interactions (PPI) linked by 23,060 human proteins, making it the most comprehensive database covering human PPI data today. These PPIs contain both physical/direct interactions and high-quality functional/indirect interactions. Compared with the HAPPI 1.0 database release, HAPPI database version 2.0 (HAPPI-2) represents a 485% of human PPI data coverage increase and a 73% protein coverage increase. The revamped HAPPI web portal provides users with a friendly search, curation, and data retrieval interface, allowing them to retrieve human PPIs and available annotation information on the interaction type, interaction quality, interacting partner drug targeting data, and disease information. The updated HAPPI-2 can be freely accessed by Academic users at http://discovery.informatics.uab.edu/HAPPI .

CONCLUSIONS

While the underlying data for HAPPI-2 are integrated from a diverse data sources, the new HAPPI-2 release represents a good balance between data coverage and data quality of human PPIs, making it ideally suited for network biology.

Phosphoproteomic analysis of protein phosphorylation networks in Tetrahymena thermophila, a model single-celled organism

Tetrahymena thermophila is a widely used unicellular eukaryotic model organism in biological research and contains more than 1000 protein kinases and phosphatases with specificity for Ser/Thr/Tyr residues. However, only a few dozen phosphorylation sites in T. thermophila are known, presenting a major obstacle to further understanding of the regulatory roles of reversible phosphorylation in this organism. In this study, we used high-accuracy mass-spectrometry-based proteomics to conduct global and site-specific phosphoproteome profiling of T. thermophila. In total, 1384 phosphopeptides and 2238 phosphorylation sites from 1008 T. thermophila proteins were identified through the combined use of peptide prefractionation, TiO2 enrichment, and two-dimensional LC-MS/MS analysis. The identified phosphoproteins are implicated in the regulation of various biological processes such as transport, gene expression, and mRNA metabolic process. Moreover, integrated analysis of the T. thermophila phosphoproteome and gene network revealed the potential biological functions of many previously unannotated proteins and predicted some putative kinase-substrate pairs. Our data provide the first global survey of phosphorylation in T. thermophila using a phosphoproteomic approach and suggest a wide-ranging regulatory scope of this modification. The provided dataset is a valuable resource for the future understanding of signaling pathways in this important model organism.

Extracting a few functionally reproducible biomarkers to build robust subnetwork-based classifiers for the diagnosis of cancer

In microarray-based case-control studies of a disease, people often attempt to identify a few diagnostic or prognostic markers amongst the most significant differentially expressed (DE) genes. However, the reproducibility of DE genes identified in different studies for a disease is typically very low. To tackle the problem, we could evaluate the reproducibility of DE genes across studies and define robust markers for disease diagnosis using disease-associated protein-protein interaction (PPI) subnetwork. Using datasets for four cancer types, we found that the most significant DE genes in cancer exhibit consistent up- or down-regulation in different datasets. For each cancer type, the 5 (or 10) most significant DE genes separately extracted from different datasets tend to be significantly coexpressed and closely connected in the PPI subnetwork, thereby indicating that they are highly reproducible at the PPI level. Consequently, we were able to build robust subnetwork-based classifiers for cancer diagnosis.

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

BACKGROUND

In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.

RESULTS

The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.

CONCLUSIONS

The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms.

Characterization of hNek6 interactome reveals an important role for its short N-terminal domain and colocalization with proteins at the centrosome

Physical protein-protein interactions are fundamental to all biological processes and are organized in complex networks. One branch of the kinome network is the evolutionarily conserved NIMA-related serine/threonine kinases (Neks). Most of the 11 mammalian Neks studied so far are related to cell cycle regulation, and due to association with diverse human pathologies, Neks are promising chemotherapeutic targets. Human Nek6 was associated to carcinogenesis, but its interacting partners and signaling pathways remain elusive. Here we introduce hNek6 as a highly connected member in the human kinase interactome. In a more global context, we performed a broad data bank comparison based on degree distribution analysis and found that the human kinome is enriched in hubs. Our networks include a broad set of novel hNek6 interactors as identified by our yeast two-hybrid screens classified into 18 functional categories. All of the tested interactions were confirmed, and the majority of tested substrates were phosphorylated in vitro by hNek6. Notably, we found that hNek6 N-terminal is important to mediate the interactions with its partners. Some novel interactors also colocalized with hNek6 and γ-tubulin in human cells, pointing to a possible centrosomal interaction. The interacting proteins link hNek6 to novel pathways, for example, Notch signaling and actin cytoskeleton regulation, or give new insights on how hNek6 may regulate previously proposed pathways such as cell cycle regulation, DNA repair response, and NF-κB signaling. Our findings open new perspectives in the study of hNek6 role in cancer by analyzing its novel interactions in specific pathways in tumor cells, which may provide important implications for drug design and cancer therapy.

Measuring the physical cohesiveness of proteins using physical interaction enrichment

Motivation: Protein–protein interaction (PPI) networks are a valuable resource for the interpretation of genomics data. However, such networks have interaction enrichment biases for proteins that are often studied. These biases skew quantitative results from comparing PPI networks with genomics data. Here, we introduce an approach named physical interaction enrichment (PIE) to eliminate these biases.

Methodology: PIE employs a normalization that ensures equal node degree (edge) distribution of a test set and of the random networks it is compared with. It quantifies whether a set of proteins have more interactions between themselves than proteins in random networks, and can therewith be regarded as physically cohesive.

Results: Among other datasets, we applied PIE to genetic morbid disease (GMD) genes and to genes whose expression is induced upon infection with human-metapneumovirus (HMPV). Both sets contain proteins that are often studied and that have relatively many interactions in the PPI network. Although interactions between proteins of both sets are found to be overrepresented in PPI networks, the GMD proteins are not more likely to interact with each other than random proteins when this overrepresentation is taken into account. In contrast the HMPV-induced genes, representing a biologically more coherent set, encode proteins that do tend to interact with each other and can be used to predict new HMPV-induced genes. By handling biases in PPI networks, PIE can be a valuable tool to quantify the degree to which a set of genes are involved in the same biological process.

Contact:i.sama@cmbi.ru.nl; m.huynen@cmbi.ru.nl

Supplementary information:Supplementary data are available at Bioinformatics online.

Dosage sensitivity shapes the evolution of copy-number varied regions

Dosage sensitivity is an important evolutionary force which impacts on gene dispensability and duplicability. The newly available data on human copy-number variation (CNV) allow an analysis of the most recent and ongoing evolution. Provided that heterozygous gene deletions and duplications actually change gene dosage, we expect to observe negative selection against CNVs encompassing dosage sensitive genes. In this study, we make use of several sources of population genetic data to identify selection on structural variations of dosage sensitive genes. We show that CNVs can directly affect expression levels of contained genes. We find that genes encoding members of protein complexes exhibit limited expression variation and overlap significantly with a manually derived set of dosage sensitive genes. We show that complexes and other dosage sensitive genes are underrepresented in CNV regions, with a particular bias against frequent variations and duplications. These results suggest that dosage sensitivity is a significant force of negative selection on regions of copy-number variation.

Searching for functional gene modules with interaction component models

BACKGROUND

Functional gene modules and protein complexes are being sought from combinations of gene expression and protein-protein interaction data with various clustering-type methods. Central features missing from most of these methods are handling of uncertainty in both protein interaction and gene expression measurements, and in particular capability of modeling overlapping clusters. It would make sense to assume that proteins may play different roles in different functional modules, and the roles are evidenced in their interactions.

RESULTS

We formulate a generative probabilistic model for protein-protein interaction links and introduce two ways for including gene expression data into the model. The model finds interaction components, which can be interpreted as overlapping clusters or functional modules. We demonstrate the performance on two data sets of yeast Saccharomyces cerevisiae. Our methods outperform a representative set of earlier models in the task of finding biologically relevant modules having enriched functional classes.

CONCLUSIONS

Combining protein interaction and gene expression data with a probabilistic generative model improves discovery of modules compared to approaches based on either data source alone. With a fairly simple model we can find biologically relevant modules better than with alternative methods, and in addition the modules may be inherently overlapping in the sense that different interactions may belong to different modules.

HAPPI: an online database of comprehensive human annotated and predicted protein interactions

Background

Human protein-protein interaction (PPIs) data are the foundation for understanding molecular signalling networks and the functional roles of biomolecules. Several human PPI databases have become available; however, comparisons of these datasets have suggested limited data coverage and poor data quality. Ongoing collection and integration of human PPIs from different sources, both experimentally and computationally, can enable disease-specific network biology modelling in translational bioinformatics studies.

Results

We developed a new web-based resource, the Human Annotated and Predicted Protein Interaction (HAPPI) database, located at . The HAPPI database was created by extracting and integrating publicly available protein interaction databases, including HPRD, BIND, MINT, STRING, and OPHID, using database integration techniques. We designed a unified entity-relationship data model to resolve semantic level differences of diverse concepts involved in PPI data integration. We applied a unified scoring model to give each PPI a measure of its reliability that can place each PPI at one of the five star rank levels from 1 to 5. We assessed the quality of PPIs contained in the new HAPPI database, using evolutionary conserved co-expression pairs called "MetaGene" pairs to measure the extent of MetaGene pair and PPI pair overlaps. While the overall quality of the HAPPI database across all star ranks is comparable to the overall qualities of HPRD or IntNetDB, the subset of the HAPPI database with star ranks between 3 and 5 has a much higher average quality than all other human PPI databases. As of summer 2008, the database contains 142,956 non-redundant, medium to high-confidence level human protein interaction pairs among 10,592 human proteins. The HAPPI database web application also provides …” should be “The HAPPI database web application also provides hyperlinked information of genes, pathways, protein domains, protein structure displays, and sequence feature maps for interactive exploration of PPI data in the database.

Conclusion

HAPPI is by far the most comprehensive public compilation of human protein interaction information. It enables its users to fully explore PPI data with quality measures and annotated information necessary for emerging network biology studies.

Predicting protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression

Background

Large-scale identification of the interrelationships between different components of the cell, such as the interactions between proteins, has recently gained great interest. However, unraveling large-scale protein-protein interaction maps is laborious and expensive. Moreover, assessing the reliability of the interactions can be cumbersome.

Results

In this study, we have developed a computational method that exploits the existing knowledge on protein-protein interactions in diverse species through orthologous relations on the one hand, and functional association data on the other hand to predict and filter protein-protein interactions in Arabidopsis thaliana. A highly reliable set of protein-protein interactions is predicted through this integrative approach making use of existing protein-protein interaction data from yeast, human, C. elegans and D. melanogaster. Localization, biological process, and co-expression data are used as powerful indicators for protein-protein interactions. The functional repertoire of the identified interactome reveals interactions between proteins functioning in well-conserved as well as plant-specific biological processes. We observe that although common mechanisms (e.g. actin polymerization) and components (e.g. ARPs, actin-related proteins) exist between different lineages, they are active in specific processes such as growth, cancer metastasis and trichome development in yeast, human and Arabidopsis, respectively.

Conclusion

We conclude that the integration of orthology with functional association data is adequate to predict protein-protein interactions. Through this approach, a high number of novel protein-protein interactions with diverse biological roles is discovered. Overall, we have predicted a reliable set of protein-protein interactions suitable for further computational as well as experimental analyses.

Global networks of functional coupling in eukaryotes from comprehensive data integration

No single experimental method can discover all connections in the interactome. A computational approach can help by integrating data from multiple, often unrelated, proteomics and genomics pipelines. Reconstructing global networks of functional coupling (FC) faces the challenges of scale and heterogeneity--how to efficiently integrate huge amounts of diverse data from multiple organisms, yet ensuring high accuracy. We developed FunCoup, an optimized Bayesian framework, to resolve these issues. Because interactomes comprise functional coupling of many types, FunCoup annotates network edges with confidence scores in support of different kinds of interactions: physical interaction, protein complex member, metabolic, or signaling link. This capability boosted overall accuracy. On the whole, the constructed framework was comprehensively tested to optimize the overall confidence and ensure seamless, automated incorporation of new data sets of heterogeneous types. Using over 50 data sets in seven organisms and extensively transferring information between orthologs, FunCoup predicted global networks in eight eukaryotes. For the Ciona intestinalis network, only orthologous information was used, and it recovered a significant number of experimental facts. FunCoup predictions were validated on independent cancer mutation data. We show how FunCoup can be used for discovering candidate members of the Parkinson and Alzheimer pathways. Cross-species pathway conservation analysis provided further support to these observations.

Activation of the osteopontin/matrix metalloproteinase-9 pathway correlates with prostate cancer progression

PURPOSE

Prostate cancer remains the second most frequent cause of tumor-related deaths in the Western world. Additional markers for the identification of prostate cancer development and progression are needed. Osteopontin (OPN), which activates matrix metalloproteinases (MMP), is considered a prognostic biomarker in several cancers. "In silico" and experimental approaches were used to determine whether OPN-mediated MMP activation may be a signal of prostate cancer progression.

EXPERIMENTAL DESIGN

Pearson correlation coefficients were computed for each OPN/MMP pair across seven publicly available prostate cancer gene expression data sets. Using Gene Set Enrichment Analysis, 101 cancer-related gene sets were analyzed for association with OPN and MMP-9 expression. OPN, MMP-9, MMP-2 tissue inhibitor of metalloproteinase-1 plasma levels, and MMP gelatinase activity were measured by ELISA and zymography in 96 and 92 patients with prostate cancer and benign prostatic hyperplasia, respectively, and 125 age-matched healthy men.

RESULTS

Computational analyses identified a significant correlation only between MMP-9 and OPN, and showed significant enrichment scores in "cell proliferation", "genes constituting the phosphoinositide-3-kinase predictor", "proliferation signature", and "tumor metastasis" gene sets in association with both OPN and MMP-9. Plasma analyses revealed a significant increase in OPN and MMP-9 levels and activity in patients with prostate cancer in association with clinical variables (prostate-specific antigen > 4 ng/mL and Gleason score > 7). Significant correlation between OPN and MMP-9 levels were also observed. Mean plasma levels of OPN and MMP-9 decreased in patients with prostate cancer within 6 months after prostatectomy.

CONCLUSIONS

The concordant computational and experimental data indicate that the extent of OPN pathway activation correlates with prostate cancer progression.

A new computational approach to analyze human protein complexes and predict novel protein interactions

A new approach to identifying interacting proteins based on gene-expression data uses hypergeometric distribution and Monte-Carlo simulations.

We propose a new approach to identify interacting proteins based on gene expression data. By using hypergeometric distribution and extensive Monte-Carlo simulations, we demonstrate that looking at synchronous expression peaks in a single time interval is a high sensitivity approach to detect co-regulation among interacting proteins. Combining gene expression and Gene Ontology similarity analyses enabled the extraction of novel interactions from microarray datasets. Applying this approach to p21-activated kinase 1, we validated α-tubulin and early endosome antigen 1 as its novel interactors.

PRINCESS, a protein interaction confidence evaluation system with multiple data sources

Advances in proteomics technologies have enabled novel protein interactions to be detected at high speed, but they come at the expense of relatively low quality. Therefore, a crucial step in utilizing the high throughput protein interaction data is evaluating their confidence and then separating the subsets of reliable interactions from the background noise for further analyses. Using Bayesian network approaches, we combine multiple heterogeneous biological evidences, including model organism protein-protein interaction, interaction domain, functional annotation, gene expression, genome context, and network topology structure, to assign reliability to the human protein-protein interactions identified by high throughput experiments. This method shows high sensitivity and specificity to predict true interactions from the human high throughput protein-protein interaction data sets. This method has been developed into an on-line confidence scoring system specifically for the human high throughput protein-protein interactions. Users may submit their protein-protein interaction data on line, and the detailed information about the supporting evidence for query interactions together with the confidence scores will be returned. The Web interface of PRINCESS (protein interaction confidence evaluation system with multiple data sources) is available at the website of China Human Proteome Organisation.

KIT and RAS signalling pathways in testicular germ cell tumours: new data and a review of the literature

Testicular germ cell tumours (TGCTs) are the leading cause of cancer deaths in young male Caucasians. Identifying changes in DNA copy number can pinpoint genes involved in tumour development. We defined the smallest overlapping regions of imbalance in TGCTs using array comparative genomic hybridization analysis. Novel regions, or regions which refined those previously reported, were identified. The expression profile of genes from 12p, which is invariably gained in TGCTs, and amplicons defined at 12p11.2-12.1 and 4q12, suggest KRAS and KIT involvement in TGCT and seminoma development, respectively. Amplification of these genes was not found in intratubular germ cell neoplasia adjacent to invasive disease showing these changes, suggesting their involvement in tumour progression. Activating mutations of RAS genes (KRAS or NRAS) and overexpression of KRAS were mutually exclusive events. These, correlations between the expression levels of KIT, KRAS and GRB7 (which encodes an adapter molecule known to interact with the KIT tyrosine kinase receptor) and other reported evidence reviewed here, are consistent with a role for activation of KIT and RAS signalling in TGCT development. In order to assess a role for KIT in seminomas, we modulated the level of KIT expression in TCam-2, a seminoma cell line. The likely seminomatous origin of this cell line was supported by demonstrating KIT and OCT3/4 overexpression and gain of 12p material. Reducing the expression of KIT in TCam-2 through RNA inhibition resulted in decreased cell viability. Further understanding of KIT and RAS signalling in TGCTs may lead to novel therapeutic approaches for these tumours.

Network-based prediction of protein function

Functional annotation of proteins is a fundamental problem in the post-genomic era. The recent availability of protein interaction networks for many model species has spurred on the development of computational methods for interpreting such data in order to elucidate protein function. In this review, we describe the current computational approaches for the task, including direct methods, which propagate functional information through the network, and module-assisted methods, which infer functional modules within the network and use those for the annotation task. Although a broad variety of interesting approaches has been developed, further progress in the field will depend on systematic evaluation of the methods and their dissemination in the biological community.

Large-scale mapping of human protein-protein interactions by mass spectrometry

Mapping protein–protein interactions is an invaluable tool for understanding protein function. Here, we report the first large-scale study of protein–protein interactions in human cells using a mass spectrometry-based approach. The study maps protein interactions for 338 bait proteins that were selected based on known or suspected disease and functional associations. Large-scale immunoprecipitation of Flag-tagged versions of these proteins followed by LC-ESI-MS/MS analysis resulted in the identification of 24 540 potential protein interactions. False positives and redundant hits were filtered out using empirical criteria and a calculated interaction confidence score, producing a data set of 6463 interactions between 2235 distinct proteins. This data set was further cross-validated using previously published and predicted human protein interactions. In-depth mining of the data set shows that it represents a valuable source of novel protein–protein interactions with relevance to human diseases. In addition, via our preliminary analysis, we report many novel protein interactions and pathway associations.

Identification of functional modules using network topology and high-throughput data

BACKGROUND

With the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data.

RESULTS

We describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity.

CONCLUSION

We have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.

Identification of functional modules using network topology and high-throughput data

Background

With the advent of systems biology, biological knowledge is often represented today by networks. These include regulatory and metabolic networks, protein-protein interaction networks, and many others. At the same time, high-throughput genomics and proteomics techniques generate very large data sets, which require sophisticated computational analysis. Usually, separate and different analysis methodologies are applied to each of the two data types. An integrated investigation of network and high-throughput information together can improve the quality of the analysis by accounting simultaneously for topological network properties alongside intrinsic features of the high-throughput data.

Results

We describe a novel algorithmic framework for this challenge. We first transform the high-throughput data into similarity values, (e.g., by computing pairwise similarity of gene expression patterns from microarray data). Then, given a network of genes or proteins and similarity values between some of them, we seek connected sub-networks (or modules) that manifest high similarity. We develop algorithms for this problem and evaluate their performance on the osmotic shock response network in S. cerevisiae and on the human cell cycle network. We demonstrate that focused, biologically meaningful and relevant functional modules are obtained. In comparison with extant algorithms, our approach has higher sensitivity and higher specificity.

Conclusion

We have demonstrated that our method can accurately identify functional modules. Hence, it carries the promise to be highly useful in analysis of high throughput data.

New potential ligand-receptor signaling loops in ovarian cancer identified in multiple gene expression studies

Based on the hypothesis that gene products involved in the same biological process would be coupled at transcriptional level, a previous study analyzed the correlation of the gene expression patterns of ligand-receptor (L-R) pairs to discover potential autocrine/paracrine signaling loops in different cancers (Graeber and Eisenberg. Nat Genet 2001; 29:295). By refining the starting database, a list of 511 L-R pairs was compiled, combined to eight data sets from a single pathology, epithelial ovarian cancer, and examined as a proof-of-principle of the statistical and biological validity of the correlation of the L-R gene expression patterns in cancer. Analysis revealed a Bonferroni-corrected significant correlation of 105 L-R pairs in at least one data set and, by systematic analysis, identified 39 more frequently correlated L-R pairs, 7 of which were already biologically confirmed. In four data sets examined for an L-R correlation associated with patient survival time, 15 L-R pairs were significantly correlated in short surviving patients in two of the data sets. Immunohistochemical analysis of one of the newly identified correlated L-R pairs (i.e., EFNB3-EPHB4) revealed the correlated expression of ephrin-B3 and EphB4 proteins in 45 of 55 epithelial ovarian tumor samples (P < 0.0001). Together, these data not only support the validity of cross-comparison analysis of gene expression data because known and expected correlations were confirmed but also point to the promise of such analysis in identifying new L-R signaling loops in cancer.

Activating mutations and/or expression levels of tyrosine kinase receptors GRB7, RAS, and BRAF in testicular germ cell tumors

Amplification and/or overexpression of genes encoding tyrosine kinase receptors KIT and ERBB2 have been reported in testicular germ cell tumors (TGCTs). These receptors can bind the adaptor molecule GRB7 encoded by a gene adjacent to ERBB2 at 17q12, a region also frequently gained in TGCTs. GRB7 binding may be involved in the activation of RAS signaling and KRAS2 maps to 12p, which is constitutively gained in TGCT and lies within a minimum overlapping region of amplification at 12p11.2-12.1, a region we have previously defined. RAS proteins activate BRAF, and activating mutations of genes encoding these proteins have been described in various tumors. Here we determine the relationships between expression levels and activating mutations of these genes in a series of 65 primary TGCTs and 4 TCGT cell lines. High levels of expression and activating mutations in RAS were mutually exclusive events, and activating mutations in RAS were only identified in the seminoma subtype. Mutations in BRAF were not identified. Increased ERBB2 expression was associated with differentiated nonseminoma histology excised from lymph nodes postchemotherapy. Mutation, elevated expression, and correlations between expression levels of KRAS2, GRB7, and KIT are consistent with their involvement in the development of TGCTs.