Cancer Genomics

Cancer Genomics Data Portal

The cBio Cancer Genomics Portal is an open-access resource for interactively exploring multidimensional cancer genomics data sets. It currently provides integrated access to cancer genomic data (including copy number, mutation, mRNA and microRNA expression, methylation and protein and phosphoprotein data) on more than 5000 tumor samples from 20 cancer studies.

With a focus on usability and ease of use, the cBio Portal specifically provides integrated access to multiple genomic data types, graphical summaries of genomic alterations, network analysis, survival analysis and predicted functional consequences of somatic mutations.

All features of the portal are available via a streamlined four-step web interface, enabling researchers to interactively explore gene sets and pathways, and dynamically broaden or limit the scope of their query. By integrating data on thousands of tumor samples, and providing a simple, yet powerful and flexible interface, the cBio Portal enables cancer researchers to translate genomic data into biological insights and clinical applications.

Mutation Assessor

Mutation Assessor predicts the functional impact of amino-acid substitutions in proteins, such as mutations discovered in cancer or nonsynonymous polymorphisms. The functional impact is assessed based on evolutionary conservation of the affected amino acid in protein homologs. The method has been validated on a large set (51k) of disease associated (OMIM) and polymorphic variants.


MEMo (Mutually Exclusive Modules) is a method for identifying mutually exclusive driver networks in cancer. The method uses correlation analysis and statistical tests to identify network modules by three criteria: (1) Member genes are recurrently altered across a set of tumor samples; (2) member genes are known to or are likely to participate in the same biological process; and (3) alteration events within the modules are mutually exclusive. The method is freely available below as a Java-based command line tool.

NetBox 1.0 Software

NetBox is a Java-based software tool for performing network analysis on human interaction networks. It is pre-loaded with a Human Interaction Network (HIN) derived from four literature curated data sources, including the Human Protein Reference Database (HPRD), Reactome, NCI-Nature Pathway Interaction (PID) Database, and the MSKCC Cancer Cell Map. It currently supports a command line interface for connecting genes into a network, identifying statistically significant “linker” genes, partitioning the network into modules, and executing two random background models. Results are then made available to the end user as an HTML web page and a series of network and attribute files, which can be loaded into Cytoscape for visualization and further analysis.

Cancer Genes

CancerGenes is an online resource for simplifying the process of gene selection and prioritization in large collaborative projects. CancerGenes combines gene lists annotated by experts with information from key public databases. Each gene is annotated with gene name(s), functional description, organism, chromosome number, location, Entrez Gene ID, GO terms, InterPro descriptions, gene structure, protein length, transcript count, and experimentally determined transcript control regions, as well as links to Entrez Gene, COSMIC, and iHOP gene pages and the UCSC and Ensembl genome browsers. The user-friendly interface provides for searching, sorting and intersection of gene lists. Users may view tabulated results through a web browser or may dynamically download them as a spreadsheet table.

The cBio Cancer Genomics Portal was recently profiled in The Scientist: Combing the Cancer Genome: A guided tour through the main online resources for analyzing cancer genomics data. March, 2012.

Ciriello G, Cerami E, Sander C, Schultz N., Mutual exclusivity analysis identifies oncogenic network modules. Genome Res. 2012 Feb. [Abstract]

Higgins ME, Claremont M, Major JE, Sander C, Lash AE., CancerGenes: a gene selection resource for cancer genome projects. Nucleic Acids Res. 2007 Jan. [Abstract]