The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal

Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients' lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.

/pdf/integrated-genomic-analyses-of-ovarian-carcinoma-hbamiwnq4e.pdf

Integrated genomic analyses of ovarian carcinoma

A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients’ lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.

Gastric cancer was the world’s third leading cause of cancer mortality in 2012, responsible for 723,000 deaths1. The vast majority of gastric cancers are adenocarcinomas, which can be further subdivided into intestinal and diffuse types according to the Lauren classification2. An alternative system, proposed by the World Health Organization, divides gastric cancer into papillary, tubular, mucinous (colloid) and poorly cohesive carcinomas3. These classification systems have little clinical utility, making the development of robust classifiers that can guide patient therapy an urgent priority.

The majority of gastric cancers are associated with infectious agents, including the bacterium Helicobacter pylori4 and Epstein–Barr virus (EBV). The distribution of histological subtypes of gastric cancer and the frequencies of H. pylori and EBV associated gastric cancer vary across the globe5. A small minority of gastric cancer cases are associated with germline mutation in E-cadherin (CDH1)6 or mismatch repair genes7 (Lynch syndrome), whereas sporadic mismatch repair-deficient gastric cancers have epigenetic silencing of MLH1 in the context of a CpG island methylator phenotype (CIMP)8. Molecular profiling of gastric cancer has been performed using gene expression or DNA sequencing9–12, but has not led to a clear biologic classification scheme. The goals of this study by The Cancer Genome Atlas (TCGA) were to develop a robust molecular classification of gastric cancer and to identify dysregulated pathways and candidate drivers of distinct classes of gastric cancer.

/pdf/comprehensive-molecular-characterization-of-gastric-3jzhmi95fs.pdf

Comprehensive molecular characterization of gastric adenocarcinoma

The Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the US National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.

/pdf/pid-the-pathway-interaction-database-3duld874ov.pdf

PID: the Pathway Interaction Database.

The Pathway Interaction Database (PID, "http://pid.nci.nih.gov":http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the U.S. National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists, and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.

PID: The Pathway Interaction Database

The Pathway Interaction Database (*PID*, http://pid.nci.nih.gov) is a freely availablecollection of curated and peer-reviewed signaling pathways composed of humanbiomolecular interactions and cellular processes. Created in a collaboration between theU.S. National Cancer Institute and Nature Publishing Group, the database is a researchtool for cell biologists, biochemists, computational biologists and bioinformaticians.The PID offers a range of tools to facilitate pathway exploration. Users can browse thepre-defi ned set of pathways and also create interaction network maps centered ona single molecule of interest or an extensive list of molecules. In addition, users candownload complete data sets in extensible markup language (XML) and BiologicalPathway Exchange (BioPAX) Level 2 formats. The database is updated every month andsupplemented by a concise editorial section that provides synopses of recent noteworthypapers in cell signaling and specially commissioned articles on the practical uses ofother relevant online tools. Users can sign up for free email alerts or RSS feeds to receivedatabase updates.

The NCI-Nature Pathway Interaction Database: A cell signaling resource.

Inhibitory G proteins (GNAI/Gαi) bind to the scaffold G protein signaling modulator 2 (GPSM2) to form a conserved polarity complex that regulates cytoskeleton organization. GPSM2 keeps GNAI in a guanosine diphosphate (GDP)-bound state, but how GPSM2-GNAI is generated or relates to heterotrimeric G protein signaling remains unclear. We find that RGS12, a GTPase-activating protein (GAP), is required to polarize GPSM2-GNAI at the hair cell apical membrane and to organize mechanosensory stereocilia in rows of graded heights. Accordingly, RGS12 and the guanine nucleotide exchange factor (GEF) DAPLE are asymmetrically co-enriched at the hair cell apical junction, and Rgs12 mouse mutants are deaf. GPSM2 and RGS12 share GoLoco motifs that stabilize GNAI(GDP), and GPSM2 outcompetes RGS12 to bind GNAI. Our results suggest that polarized GEF/GAP junctional activity might dissociate heterotrimeric G proteins, generating free GNAI(GDP) for GPSM2 at the adjacent apical membrane. GPSM2-GNAI(GDP), in turn, imparts asymmetry to the forming stereocilia to enable sensory function in hair cells.

https://www.science.org/doi/pdf/10.1126/sciadv.abq2826?download=true

RGS12 polarizes the GPSM2-GNAI complex to organize and elongate stereocilia in sensory hair cells

Proceedings: AACR 101st Annual Meeting 2010‐‐ Apr 17‐21, 2010; Washington, DC

The NCI-Nature Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of professionally curated and expert-reviewed signaling and regulatory pathways composed of human molecular interactions, signaling events and cellular processes extracted from primary literature. Pathways selected for curation are based on potential drug targets, suggestions made by our users and reviewers, and other prominent cell signaling molecules. As of February 2010, the database contains 106 pathways encompassing 6696 interactions, 3231 proteins, 142 small molecules, 2622 complexes and 4843 peer-reviewed publications, and includes recent additions to both the p53 and EGFR pathways. Created in a collaboration between the U.S. National Cancer Institute and Nature Publishing Group, the PID is aimed at researchers interested in cell signaling pathways, such as molecular cell biologists, and bioinformaticians. The database offers a range of tools to facilitate pathway exploration. Users can browse the pre-defined set of pathways and create network maps centered on a single molecule or biological process of interest. The Batch query tool allows users to upload molecule lists, such as those derived from microarray data, and visualize the resulting molecular connectivity map. In addition, users can download lists of proteins, references used to create the pathway and complete database content in extensible markup language (XML) or Biological Pathways Exchange (BioPAX) format. The database is updated every month and supplemented by a concise editorial section that provides synopses of recent noteworthy papers in cell signaling and specially commissioned articles on the practical uses of other relevant Bioinformatics tools. Users can sign up for email alerts or RSS feeds to receive database updates.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr LB-130.

Matthew Day

Papers

PID: the Pathway Interaction Database.

PID: The Pathway Interaction Database

The NCI-Nature Pathway Interaction Database: A cell signaling resource.

RGS12 polarizes the GPSM2-GNAI complex to organize and elongate stereocilia in sensory hair cells

Abstract LB-130: The NCI-Nature Pathway Interaction Database: A cell signaling resource