The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource
for interactively exploring multidimensional cancer genomics data sets. It provides simple and intuitive integrated access to cancer genomics data, including copy number, mutation, mRNA and microRNA expression, methylation and protein and phosphoprotein data, on more than 5,000 tumor samples from 20 cancer studies (including 16 TCGA cancer types).
During the past year, we have added network visualization and analysis features to
the cBio Portal. These new features enable researchers to analyze genomic alterations in the context of known biological pathways and interaction networks, and to more easily mine data generated by the TCGA. A network of interest is derived from the Pathway Commons project, based on the query genes specified by the user. Multidimensional genomic data are overlaid onto each node of the network, highlighting the frequency of somatic mutation and copy number alteration (and optionally mRNA up/down-regulation). Users can manage the complexity of the network by filtering by total alteration frequency of genes or by type and source of the interactions. This provides an effective means of managing network complexity, while automatically highlighting those genes most directly relevant to the cancer type in question. In addition, drugs and drug target data can optionally be shown in relation to the network of interest. In this talk, we would like to illustrate the main network analysis features using data from the TCGA project. We will also discuss our future plans for the network view.
1. Network Visualization and Analysis
in the cBio Cancer Genomics Portal
U. Dogrusoz, S.O. Sumer, S. Sonlu, J. Gao, B.A. Aksoy,
B.E. Gross, N. Schultz, E. Cerami, C. Sander
2. cBio Cancer Genomics Portal
Goal:
Make complex genomic data available through an intuitive interface
Allow explorative data analysis / hypothesis testing / visualization
Cerami et al. 2012, Cancer Discovery
8. Hairball Problem
Complete network for TP53, MDM2, MDM4
& CDKN2A (463 interacting neighbors)
This complete network can be downloaded in SIF or GraphML in cBio Portal.
9. Pre-filtered network based on alteration frequencies
The network below contains 54 nodes, including
your 4 query genes and the 50 most frequently
genes out 463
altered neighbor genes(out of 463).
Query genes: CDKN2A MDM2 MDM4 TP53
12. Genomic data overlaid on interaction network
Alteration frequency: the % of samples that were altered on the gene
13. Genomic data overlaid on interaction network
Color gradient
•white to red
•based on alteration
frequency
Genomic data
•mutation
•copy number
•mRNA expression
14. Further filtering by genomic alterations
Filter genes by alteration frequency
Slide to
threshold Or type
value threshold
value
15. Further filtering by genomic alterations
Gene with total alteration frequency
12% or less filtered out
16. Filtering by genes of interest
Hide selected genes
from the network
Search genes
by name
Select genes from
canvas or gene list
under Genes tab
17. Filtering by interaction type & source
Interactions Interactions
merged by
default
Type (color-coded)
& source shown in
interaction details
Shown
individually
22. Drugs of New cBioPortal features
specified genes
Drug – gene targeting
• Data source is DrugBank database of detail:
Level
(http://www.drugbank.ca) -None
Inspect details targeting specified-FDA approved only
• Only drugs genes shown
including: -All
-Targeted genes
-Corresponding
DrugBank page
23. Other new cBioPortal features
Network visualization service for IGV
• Glioblastoma, RB pathway
27. Other new cBioPortal features
Many new cancer studies
• 21 (5 of them published, rest provisional)
cancer studies
28. Work in progress and future plans
• SBGN-compliant viewer (GSoC)
– Networks presented in SBGN Process Description
language
• Construct Network-of-Interest from Genes-of-
Interest using linker paths (Dogrusoz et al, 2009, BMC Bioinf)
• Roundtrip analysis (query, modify, re-query, …)
• Better/incremental layout
• Use Cytoscape Web 2 (html5 version)
29. Acknowledgements
• Bilkent University • MSKCC
– S. Onur Sumer – S. Onur Sumer
– Sinan Sonlu – Jianjiong Gao
– Naim Kucukdemirci – B. Arman Aksoy
– Istemi Bahceci – Benjamin E. Gross
– Nikolaus Schultz
– Ethan Cerami
– Chris Sander
Hinweis der Redaktion
B) The RB Oncoprint, alterations of genes in this pathway tend to be mutually exclusiveC) Many RB1 mutations may have strong functional consequences as predicted by MutationAccessor.orgD) CDK4 mRNA expression is elevated in amplified casesE) Cases with an RB pathway alteration have worse overall survival than cases without an RB pathway alterationF) Network tab provides interactive analysis and visualization of networks altered in the chosen cancer study
The network of interest built from genes of interest consists of pathways and interactions derived from the open-source Pathway Commons project.
PC provides a central, convenient point of access to multiple publicly available pathway and interaction databases. It does so by integrating data sources formatted in the BioPAX or PSI–MI standards, and making all data sets available via a unified web site, a single web service interface and a batch download site.
Glioblastoma: TP53 Pathway (4 genes)
Links to IGV for visualization of DNA copy-number changes (through Web Start)
Links to IGV for visualization of DNA copy-number changes (through Web Start)
Cross-cancer functionality enables users to query across all cancer studies in our database
OncoPrints are compact means of visualizing distinct genomic alterations, including somatic mutations, copy number alterations, and mRNA expression changes across a set of cases. They are extremely useful for visualizing gene set and pathway alterations across a set of cases, and for visually identifying trends, such as trends in mutual exclusivity or co-occurence between gene pairs within a gene set. Individual genes are represented as rows, and individual cases or patients are represented as columns.
Mutation Diagrams show mutations in the context of protein domains.
