With the availability of complete DNA sequences for many prokaryotic and eukaryotic genomes, and soon for the human genome itself, it is important to develop reliable proteome-wide approaches for a better understanding of protein function. As elementary constituents of cellular protein complexes and pathways, protein-protein interactions are key determinants of protein function. Here we have built a large-scale protein-protein interaction map of the human gastric pathogen Helicobacter pylori. We have used a high-throughput strategy of the yeast two-hybrid assay to screen 261 H. pylori proteins against a highly complex library of genome-encoded polypeptides. Over 1,200 interactions were identified between H. pylori proteins, connecting 46.6% of the proteome. The determination of a reliability score for every single protein-protein interaction and the identification of the actual interacting domains permitted the assignment of unannotated proteins to biological pathways.

Gene signatures can improve current stratification of patients with RMS but will require complex assays to be developed and extensive validation before clinical application. A significant majority of their prognostic value was encapsulated by the fusion gene status. A continuous risk score derived from the combination of clinical parameters with the presence or absence of PAX3/FOXO1 represents a robust approach to improving current risk-adapted therapy for RMS.

Purpose: Despite research efforts to develop more effective diagnostic and therapeutic approaches, malignant pleural mesothelioma (MPM) prognosis remains poor. The assessment of tumor response to therapy can be improved by a deeper phenotypical classification of the tumor, with emphasis on its clinicobiological heterogeneity. The identification of molecular profiles is a powerful approach to better define MPM subclasses and targeted therapies.Experimental Design: Molecular subclasses were defined by transcriptomic microarray on 38 primary MPM cultures. A three-gene predictor, identified by quantitative reverse transcription PCR, was used to classify an independent series of 108 frozen tumor samples. Gene mutations were determined in BAP1, CDKN2A, CDKN2B, NF2, and TP53. Epithelial-to-mesenchymal transition (EMT) markers were studied at the mRNA and protein levels.Results: Unsupervised hierarchical clustering on transcriptomic data defined two robust MPM subgroups (C1 and C2), closely related to prognosis and partly to histologic subtypes. All sarcomatoid/desmoplastic MPM were included in the C2 subgroup. Epithelioid MPM were found in both subgroups, with a worse survival prognosis in the C2 subgroup. This classification and its association with histologic subtypes and survival were validated in our independent series using the three-gene predictor. Similar subgroups were found after classification of other MPM series from transcriptomic public datasets. C1 subgroup exhibited more frequent BAP1 alterations. Pathway analysis revealed that EMT was differentially regulated between MPM subgroups. C2 subgroup is characterized by a mesenchymal phenotype.Conclusions: A robust classification of MPM that defines two subgroups of epithelioid MPM, characterized by different molecular profiles, gene alterations, and survival outcomes, was established. Clin Cancer Res; 20(5); 1323-34. Ó2014 AACR.