Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal - 图文

NIH Public AccessAuthor ManuscriptSci Signal. Author manuscript; available in PMC 2014 September 10.Published in final edited form as:Sci Signal. ; 6(269): pl1. doi:10.1126/scisignal.2004088.NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptIntegrative Analysis of Complex Cancer Genomics and ClinicalProfiles Using the cBioPortalJianjiong Gao1, Bülent Arman Aksoy1, Ugur Dogrusoz2, Gideon Dresdner1, BenjaminGross1, S. Onur Sumer1, Yichao Sun1, Anders Jacobsen1, Rileen Sinha1, Erik Larsson3,Ethan Cerami1,4, Chris Sander1, and Nikolaus Schultz11Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065,USA2Computer Engineering Department, Bilkent University, 06800 Ankara, Turkey3Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology, University ofGothenburg, S-405 30 Gothenburg, Sweden4Blueprint Medicines, Cambridge, MA 02142, USAAbstractThe cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource forexploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reducesmolecular profiling data from cancer tissues and cell lines into readily understandable genetic,epigenetic, gene expression, and proteomic events. The query interface combined with customizeddata 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. Theportal provides graphical summaries of gene-level data from multiple platforms, networkvisualization and analysis, survival analysis, patient-centric queries, and software programmaticaccess. The intuitive Web interface of the portal makes complex cancer genomics profilesaccessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitatingbiological discoveries. Here, we provide a practical guide to the analysis and visualization featuresof the cBioPortal for Cancer Genomics.IntroductionLarge-scale cancer genomics projects, such as The Cancer Genome Atlas (TCGA) and theInternational Cancer Genome Consortium (ICGC) (1), are generating an overwhelmingamount of cancer genomics data from multiple different technical platforms, making itincreasingly challenging to perform data integration, exploration, and analytics, especiallyfor scientists without a computational background. The cBioPortal for Cancer Genomics(http://cbioportal.org) (2) was specifically designed to lower the barriers of access to theCorrespondence should be addressed to cbioportal@cbio.mskcc.org; user support is available at cbioportal@googlegroups.com.Competing interests: The authors declare that they have no competing interests.Gao et al.Page 2

complex data sets and thereby accelerate the translation of genomic data into new biologicalinsights, therapies, and clinical trials.

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptThe portal facilitates the exploration of multidimensional cancer genomics data by allowingvisualization and analysis across genes, samples, and data types. Users can visualize patternsof gene alterations across samples in a cancer study, compare gene alteration frequenciesacross multiple cancer studies, or summarize all relevant genomic alterations in an

individual tumor sample. The portal also supports biological pathway exploration, survivalanalysis, analysis of mutual exclusivity between genomic alterations, selective datadownload, programmatic access, and publication-quality summary visualization.

Genomic data types integrated by cBioPortal include somatic mutations, DNA copy-numberalterations (CNAs), mRNA and microRNA (miRNA) expression, DNA methylation, proteinabundance, and phosphoprotein abundance. Currently, the portal contains data sets from 10published cancer studies (3–10), including the Cancer Cell Line Encyclopedia (CCLE) (10),and more than 20 studies that are currently in the TCGA pipeline (table S1). For each tumorsample, data may be available from multiple genomic analysis platforms. The portal'ssimplifying concept is to integrate multiple data types at the gene level and then query forthe presence of specific biological events in each sample (for example, genetic mutation,gene homozygous deletion, gene amplification, increased or decreased mRNA or miRNAexpression, and increased or decreased protein abundance). This allows users to querygenetic alterations per gene and sample and test hypotheses regarding recurrence andgenomic context of gene alteration events in specific cancers.

Equipment

A personal computer or computing device with an Internet browser with Javascriptenabled

Note: We support and test the following browsers: Google Chrome, Firefox 3.0 andabove, Safari, and Internet Explorer 9.0 and above.Adobe Flash player

Note: This browser plug-in is required for visualizing networks on the networkanalysis tab. It can be downloaded from http://get.adobe.com/flashplayer/. Thisrequirement is to be removed by mid-2013.Java Runtime Environment

Note: This application is needed for launching the Integrative Genomics Viewer(IGV). It can be downloaded from http://www.java.com/getjava/.Adobe PDF Reader

Note: This is necessary for viewing the Pathology Reports and for viewing many ofthe downloadable files. It can be downloaded from http://get.adobe.com/reader/.Vector graphic editor

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Note: This is necessary for visualizing and editing the SVG file of OncoPrints

downloaded from the cBioPortal. Examples of software supporting SVG are AdobeIllustrator (http://www.adobe.com/products/illustrator.html) and Inkscape (http://inkscape.org/).

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptInstructions

The genomic data sets in the cBioPortal for Cancer Genomics (http://cbioportal.org) can bequeried or downloaded by using an interactive Web interface or can be accessed

programmatically. Users have the option of querying a single cancer study or queryingacross cancer studies. They can also view relevant genomic alterations in individual cancersamples.

Querying Individual Cancer Studies

In a single-cancer query, users can explore and visualize genomic alterations in a selectedset of genes, including the relationship between alterations in these genes across all selectedsamples and the relationship between different data types for the same gene. There are foursteps to performing a query of a single-cancer study (Fig. 1). The general process isdescribed along with the specific query used to generate the results shown.

Users can select from one of more than 25 cancer studies. When selecting genomic profiles,mutations and CNAs are specified by default. When available, relative mRNA or miRNAexpression or relative protein and phosphoprotein abundance data can also be selected.Protein and phosphoprotein data are based on reverse phase protein array (RPPA)

experiments. For mRNA or miRNA data and protein and phosphoprotein data, z scores areprecomputed from the expression values, and users can specify the threshold or use thedefault setting (2 SDs from the mean). The z scores for mRNA expression are determinedfor each sample by comparing a gene's mRNA expression to the distribution in a referencepopulation that represents typical expression for the gene. If expression data are availablefor normal adjacent tissues, those data are used as the reference population; otherwise,expression values of all tumors that are diploid for the gene in question in the cancer studyare used. The z scores for miRNA expression or protein abundance are determined for eachsample by comparing with all samples with miRNA or protein data, respectively.

When defining case sets for analysis, the default option is set to match the selected genomicprofiles. For example, cases with sequencing data will be selected if querying for mutationsonly. However, the user can change this selection by choosing from the drop-down list ofcase sets defined by the available data (for example, tumors with mutations, CNA data, geneexpression, or RPPA data) or by known tumor subtypes. Users may also input specific casesof interest by selecting “User-Defined Case List” or build a customized case set based onclinical attributes in the “Build Case Set” dialog.

When entering gene sets for analysis, users can manually enter HUGO gene symbols, EntrezGene identifiers, and gene aliases or select from predefined gene sets or pathways ofinterest. If lists of recurrently altered genes are available for a given cancer study—forexample, recurrently mutated genes from MutSig or genes with recurrent CNAs from

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GISTIC (11)—then users can also select genes from these lists and either build the gene setby using these lists or add to the set of manually entered genes by selecting from these lists.

The Onco Query Language (OQL) can be used to refine the query (Table 1). OQL can beused in single- and cross-cancer queries. Once OQL is used in the initial query, this

refinement is reflected in results, such as the OncoPrint. Users can define alterations for fourdata types: CNAs, mutations, mRNA or miRNA expression changes, and protein orphosphoprotein abundance changes (Table 1). CNA and mutation events have discretesettings, whereas mRNA, miRNA, and protein abundance events have continuous settings.Expression values are converted to z scores to facilitate comparison and the definition ofalteration thresholds.

1.

General: Select a cancer study from the drop-down menu.Specific example: Select “Gliobastoma (TCGA, Nature 2008).”2.

General: Select the genomic profiles.

Specific example: Use the default setting with “Mutations” checked and “CopyNumber data” checked and “Putative copy-number alterations (RAE, 203 cases)”selected.

Note: Mutations and copy-number alterations are selected by default. Otheroptions are presented when the data are available. For mRNA or miRNA dataand protein and phosphoprotein data, the default z score threshold can beoptionally modified to a user-defined positive value. When both microarrayand RNA-Seq data are available, the RNA-Seq data set is preferred.

3.

General: Select a patient/case set from the drop-down menu or using the optionspresented in “Build Case Set.”

Specific example: Select “Tumors with sequence and aCGH data” from the drop-down menu.

Note: To enter a user-defined case list, this option must be selected from thedrop-down menu; then, enter the case ID separated by a space in the box thatappears.

4.

General: Enter genes of interest manually or by selecting from predefined lists.Specific example: Enter “CDKN2A CDK4 RB1” with spaces separating the genesand without any punctuation.

Note: Queries may be refined using Onco Query Language (OQL) (Table 1).

5.

General: Select the “Download Data” tab and select the desired data option toobtain a copy of the data in text format.

Specific example: Perform the following query from the Download Data tab:“CDKN2A CDK4 RB1” Select “Gliobastoma (TCGA, Nature 2008),”

“Mutations,” and “CDKN2A CDK4 RB1,” and press submit. Copy and paste thedisplayed data into a spreadsheet or choose “Save as” from the File menu in thebrowser.

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Note: Only data from one genomic profile can be selected for each downloadquery.

Viewing and Interpreting the Results

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptOn the basis of the query criteria, the portal classifies each gene in each sample as altered ornot altered, and this classification is used for all analysis and visualizations in the portal,each of which is represented on a separate tab. We describe the results shown in each tabbelow, using example queries. The query parameters representing the first four stepsoutlined in the previous section are shown on the figure associated with each example.Results Tab 1: OncoPrint—An OncoPrint is a concise and compact graphical summaryof genomic alterations in multiple genes across a set of tumor samples. Rows representgenes, and columns represent samples. Glyphs and color coding are used to summarizedistinct genomic alterations including mutations, CNAs (amplifications and homozygousdeletions), and changes in gene expression or protein abundance. Additional details areavailable by mousing over the event indicated on the gene and include the case ID (eachcase represents a patient sample or cell line), linked to the patient view page. For mutationevents, this also displays amino acid changes. By default, cases are sorted according toalterations. Users can also restore original case orders (alphabetical order by case ID for apredefined case lists, or the same order for a customized case list). Users also have theoption to remove unaltered cases from the visualization. By visualizing gene alterationsacross a set of cases, OncoPrints help identify trends such as mutual exclusivity or co-occurrence between genes within a gene set.

In addition to the OncoPrint, this results tab also includes information about the genes

queried that is available in the Sanger Cancer Gene Census and links to the Gene database inNCBI.

We use the OncoPrint from a query for alterations in the retinoblastoma (RB) pathway genesCDKN2A (encoding the cyclin-dependent kinase inhibitor p16), CDK4 (encoding cyclin-dependent kinase 4), and RB1 in glioblastoma multiforme (GBM) as an example (Fig. 2).From the OncoPrint, 65 cases (71%) have an alteration in at least one of the three genes,with the frequency of alteration in each of the three selected genes shown. For CDKN2A,most of the alterations are homozygous deletions, and there are a few mutations. The

alterations in CDK4 are amplifications. Events associated with RB1 included a deletion andseveral mutations (3). The alterations in these three genes are distributed in a nearly

mutually exclusive way across samples, which can be statistically analyzed and visualizedwith the Mutual Exclusivity tab.

1.Perform the query as specified in Fig. 2. Once the “submit” button is pressed, theOncoPrint result is displayed automatically.

2.Use the horizontal scroll bar if the genes do not fit the window.

3.

To make an OncoPrint more compact, there are three options available from the“Customize” button: (i) scale the OncoPrint by using the “Zoom” bar; (ii) remove

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cases without an alteration by selecting “Remove Unaltered Cases”; and (iii) select“Remove Whitespace” to eliminate the gaps between samples.

4.To restore the original case order (alphabetically by case ID or as defined by theuser in the original query), select “Restore Case Order” in the “Customize” options.5.To export the OncoPrint, choose to download the OncoPrint as an XML file inscalable vector graphic (SVG) format by pressing the SVG button.

6.To obtain additional information, mouse over the indicated alteration on the gene.7.

To modify or start a query, choose “Modify Query” above the tabs for the results.

Results Tab 2: Mutual Exclusivity—Biological processes or pathways in cancer areoften deregulated through different genes or by multiple different mechanisms. The conceptof mutual exclusivity can be exploited to identify previously unknown mechanisms thatcontribute to oncogenesis and cancer progression (12). In mutual exclusivity, events ingenes associated with a specific cancer tend to be mutually exclusive across a set of tumors—that is, each tumor is likely to have only one of the genetic events. The opposite situation(co-occurrence) is when genetic alterations occur in multiple genes in the same cancersample. The portal computes a set of simple statistics to identify patterns of mutual

exclusivity or co-occurrence. For each pair of query genes (G1 and G2), the portal calculatesan odds ratio (OR) (Eq. 1) that indicates the likelihood that the events in the two genes aremutually exclusive or co-occurrent across the selected cases:

(1)

Where A = number of cases altered in both genes; B = number of cases altered in G1 but notG2; C = number of cases altered in G2 but not G1; and D = number of cases altered inneither genes.

It then assigns each pair to one of five categories that are indicative of a tendency towardmutual exclusivity, of a tendency toward co-occurrence, or of no association. A legend isprovided with the analysis. To determine whether the identified relationship is significant foreach gene pair, the portal performs a Fisher's exact test.

Using the same query used for describing OncoPrints, the mutual exclusivity analysis showsthat events in the three selected genes tended to occur in a mutually exclusive way, but thepattern was only statistically significant for CDKN2A and CDK4, and for CDKN2A andRB1, but not for CDK4 and RB1, which may be due to the small sample size (Fig. 3). Thisfits with what is known about RB signaling in GBM, which can be deactivated by

inactivation of RB1 itself (through mutation or deletion), by activation of CDK4 (a CDKthat inhibits RB1 activity) through amplification, or by inactivation of the CDK inhibitorp16, which is encoded by CDKN2A, through deletion or mutation. Thus, a single alterationin one of these genes is sufficient to deactivate the pathway, and this is what the mutualexclusivity analysis showed.

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1.

Perform the query as specified in Fig. 3. Once the “submit” button is pressed, theOncoPrint result is displayed automatically.2.

Select the Mutual Exclusivity tab.

Note: This tab will only show if more than one gene is selected in the query.

Results Tab 3: Correlation Plots—The cBioPortal offers several different ways ofvisualizing discrete genetic events (CNAs or mutations) and continuous events, such as dataregarding mRNA or protein abundance, or DNA methylation.

For each gene specified in the query, the portal can generate various plots, depending on thedata available. The mRNA versus copy-number option displays a box-and-whisker plot toshow mRNA expression from user-selected data sources of a gene plotted in relation to itscopy-number status in each sample. Copy-number status can be homozygously deleted,heterozygously deleted, diploid, gained (meaning an amplification event with relatively fewcopies), or amplified (meaning an amplification event with many copies). The mRNA-versus-DNA methylation option displays a scatter plot of mRNA expression compared withDNA methylation data of a gene across all selected samples. A methylation beta-value is anestimate for the methylation level of a CpG locus using the ratio of intensities between

methylated and unmethylated alleles. The RPPA protein level versus mRNA option displaysa scatter plot of protein abundance compared with mRNA abundance for a gene across allselected samples.

Genes and data types are selected by using drop-down menus, and only those options forwhich data are available are provided in the menus. All plots can be exported as PDFdocuments for use in publications.

The example query to illustrate this type of analysis is a query of ERBB2 (a known proto-oncogene encoding an epidermal growth factor receptor) in colon and rectum

adenocarcinoma. ERBB2 is amplified in a subset of colorectal cancer samples (8). ThecBioPortal results show that ERBB2 mRNA is increased in the samples in which ERBB2 isamplified (Fig. 4A) and that the tumors with the highest amount of ERBB2 mRNA had thehighest amount of ERBB2 protein (Fig. 4B).

1.Perform the query shown in Fig. 4. Once the “submit” button is pressed, theOncoPrint result is displayed automatically.2.Select the Plots tab.

3.Select “mRNA expression (microarray)” from the first Data Types menu.4.Select “Putative copy-number alternations from GISTIC” from the second DataTypes menu.

5.Select “mRNA v. Copy Number” from the Plot Type menu.6.Press the arrow button to generate the graph shown in Fig. 4A.7.To export as a PDF, click the PDF link at the top near the graph title.8.

Select “RPPA protein level v. mRNA” from the Plot Type menu.

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9.Press the arrow button to generate the graph shown in Fig. 4B.

Note: If a combination that cannot be plotted is selected, an error message isdisplayed.

Results Tab 4: Mutations—The Mutations tab provides details as both a graphicalsummary and a customizable table about all nonsynonymous mutations identified in eachquery gene. The graphical summary shows the position and frequency of all mutations in thecontext of Pfam protein domains (13) encoded by the canonical gene isoform. All DNAmutations are standardized to the canonical RefSeq isoform (using Oncotator, http://www.broadinstitute.org/oncotator/). When a DNA mutation only affects noncanonicalisoforms, the mutations are not included in the graphical summary. Future versions of theportal will provide this information in a separate table.

Below the graphical summary is a table of all nonsynonymous mutations. This table, whichcan be sorted and filtered, provides the following information if the data are available: caseID for each sample (hyperlinked to the patient view page of the specific sample containingthe mutation); amino acid change; type of mutations (missense, nonsense, splice site,

frameshift insertion or deletion, in-frame insertion or deletion, nonstop, nonstart); number ofmutations at this position in COSMIC (Catalogue Of Somatic Mutations In Cancer) (14);predicted functional impact of missense mutations [with hyperlinks to Mutation Assessor(15) for the specified mutation and a multiple sequence alignment]; link to a 3D structurewith the mutation highlighted (with hyperlinks to Mutation Assessor); mutation status(somatic or germline–germline mutations are currently only provided for BRCA1 and

BRCA2 in some studies); validation status (valid or unknown); the sequencing center wherethe sample was sequenced and the mutation identified; variant allele frequency in the tumor;variant allele frequency in the matched normal sample; exact genomic position

(chromosome, start, end, reference allele, variant allele); variant and reference allele counts(the number of variant and reference alleles found in the sequencing results of tumor andnormal samples); and information about the affected isoform. The last three are not shownby default but may be displayed. Users can perform a search for any text in the table withthe search option.

The example query to illustrate this type of analysis is a query of ERBB2 in colon andrectum adenocarcinoma using only sequenced tumors (Fig. 5). The graphical summary ofthe mutations associated with this query showed that there are 10 ERBB2 nonsynonymousmutations in colorectal cancer samples, and four of them are V842I in the kinase domain(Fig. 5), suggesting that this is a hotspot for protein activation. From the table, the kinasedomain mutations at amino acids 755, 777, and 842 have been observed in several othercancer studies before (6, 8, and 2 COSMIC entries, respectively) (Fig. 5B).

1.Perform the query shown in Fig. 5.2.Select the Mutations tab.

3.

Mouse over the colored regions representing protein domains to view details aboutthe domain and its starting and ending residues in the protein sequence.

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4.

Mouse over the circles above the protein sequence diagram to see the specificmutation. The height of the line connecting the circle to the protein is indicative ofthe frequency of the mutation. The most frequent mutation is labeled with its aminoacid change.

5.

Customize the data displayed in the table using the “Show/hide columns” menu.Select those to display. Deselect those to hide.

Note: The following columns are hidden by default: Exact genomic position(chromosome, start, end, reference, variant allele); variant and reference alleleread counts in tumor and normal samples; and information about the affectedisoform.

6.Use the up and down arrowheads to sort the data according to the column values.7.Follow the hyperlinked Case ID to get details about the tumor sample containingthe mutation.

8.Use the browser back button to return to the Mutations tab.

9.

Mouse over the values in the COSMIC column to get details about the frequencyand specific mutations at that residue.

10.Mouse over the values in the FIS column to follow hyperlinks to the Mutation

Assessor or a Multiple Sequence Alignment.11.Click the 3D link to view 3D protein structures with the mutated amino acid

highlighted and return to the Mutations tab by using the browser back button.12.Enter “V842I” (without quotations) in the search box to display V842I mutations

only.

Note: The search options in tables in the cBioPortal support free text search onthe table content.

13.Delete the search text to return to the complete results.

Results Tab 5: Protein Changes—Protein and phosphoprotein data are available fromthe Protein Changes tab. Currently, large-scale proteomics data from the RPPA (16)platform are available in the portal for 12 TCGA cancer studies (table S1). As already

described, scatter plots of protein abundance versus mRNA expression for query genes canbe generated if both data types are available (Fig. 4B, Plots tab).

For each query, the portal also performs differential analysis for all available RPPA proteindata and identifies protein and phosphoprotein events that correlate with genomic alterationsin the query genes. It is not necessary to select “RRPA proten/phosphoprotein level” fromthe query screen. If the data are available, then this analysis can be performed. For eachavailable protein or phosphoprotein, cBioPortal performs a two-sided, two-sample Student'st test to identify differences in protein abundance between tumor samples that have at leastone event (alteration) in one of the query genes, and those that do not. The results aredisplayed as a list of proteins or phosphoproteins, ranked by their difference in abundancebetween altered and unaltered samples. The table includes the following information: the

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target protein recognized by the antibody; the residue phosphorylated or modified (for

example, by cleavage); the average protein abundance z scores in the tumors with alterationsand those without (unaltered); the P value; and an option to plot the results, which are shownby default. The RPPA ID and the absolute difference between the unaltered and alteredsamples' average z scores are optional columns. For each protein or phosphoprotein, the zscores of the RRPA data between the unaltered and altered samples can be displayed as abox plot.

A query of glioblastoma cancers for mutations and CNAs associated with the tumorsuppressor and lipid phosphatase encoded by PTEN illustrate this analysis (Fig. 6). Forexample, PTEN loss (mutation or copy number deletion) in glioblastoma cancer is tightlycorrelated with increased phosphorylation of AKT (pT308 and pS473) (Fig. 6).

1.Perform the query shown in Fig. 6.2.Select the Protein Changes tab.

3.Use the drop-down menu for “Antibody Type” to specify data collected usingantibodies that detect the total protein or the phosphoprotein.

4.Customize the data displayed in the table using the “Show/hide columns” menu.Select those to display. Deselect those to hide.

5.

Press the + symbol in the Plot column to display the boxplot comparing the z scoresfor abundance between the samples with alterations and those without alterations inthe queried gene (or genes).

6.Enter “ERBB” (without quotations) in the search box to display ERBB2 andERBB3 phosphoprotein changes.

7.

Delete search text to return to the complete results.

Results Tab 6: Survival—If survival data are available, overall survival and disease-freesurvival differences are computed between tumor samples that have at least one alteration inone of the query genes and tumor samples that do not. The results are displayed as Kaplan-Meier plots with P values from a logrank test.

A query for BRCA1 and BRCA2 mutations in ovarian cancer is used to illustrate theseresults. The analysis showed a significantly better overall and disease-free survival ofpatients with either a BRCA1 or BRCA2 mutation (Fig. 7).

1.Perform the query shown in Fig. 7.2.Select the Survival tab.

3.View the results for overall survival analysis and disease-free survival analysis.4.

Click the PDF link at the top near the title of each graph to download a PDFversion of the plot.

Results Tab 7: Network—The Network tab provides interactive analysis and

visualization of networks that are altered in cancer. The network consists of pathways and

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interactions from the Human Reference Protein Database (HPRD) (17), Reactome (18),National Cancer Insititue (NCI)–Nature (19), and the Memorial Sloan-Kettering CancerCenter (MSKCC) Cancer Cell Map (http://cancer.cellmap.org), as derived from the opensource Pathway Commons Project (20). By default, the network that is automatically

generated contains all neighbors of all query genes. If more than 50 neighbor genes exist inthe network, they are ranked by genomic alteration frequency within the selected cancerstudy, and only the 50 neighbors with the highest alteration frequency in addition to thequery genes are shown. This provides an effective means of managing network complexityand automatically highlights the genes most relevant to the cancer type in question. The full,nonpruned network can be downloaded in the SIF (simple integration file) and GraphMLformats for visualization and analysis in Cytoscape (21). By default, the portal automaticallycolor codes edges by interaction type and overlays multidimensional genomic data onto eachnode, highlighting the frequency of alteration by mutation, CNA, and mRNA up- or down-regulation. The data that are shown depend on the settings used in the query and the datathat are available for the selected genomic profiles. Various options for filtering the networkare available, and the network can be searched by gene symbol. Various options for alteringthe display of the network and the layout of the network are available. Legends explainingthe network symbols are provided. Details about the alterations found in the genes and theinteractions between the genes are viewed by clicking on the node or the edge, respectively.Interaction types are derived from the BioPAX to SIF inference rules (20). For example, “InSame Component” indicates that Genes A and B are involved in the same biological

component, such as a complex. “State Change” indicates that Gene A causes a state change,such as a phosphorylation change, within Gene B. “Other” is used to indicate all other typesof inter actions, including protein-protein interactions derived from HPRD. “Targeted byDrug” indicates a drug-target interaction.

The portal contains gene-centric drug-target information from the following resources:

DrugBank (22), KEGG Drug (23), NCI Cancer Drugs (http://www.cancer.gov/cancertopics/druginfo/alphalist), and Rask-Andersen et al. (24). Drugs are hidden from the network

display by default but can be added to the network by using the Genes & Drugs menu. Usershave the option of displaying U.S. Food and Drug Administration (FDA)–approved drugs,cancer drugs defined by NCI Cancer Drugs, or all drugs targeting the query genesNew networks can be generated by selecting genes in the current network and thensubmitting those genes as a new query.

For example, to identify genomic alterations in epidermal growth factor receptor (EGFR)signaling networks in serous ovarian cancer, we used EGFR and ERBB2 as the query genesand explored the resulting network (Fig. 8). Using the color-coding as a guide, connectedgenes with alterations in this cancer are obvious. For the EFGR and ERBB2 network MYC,a known downstream effector of ERBB2 (25), is colored more intensely red because it isamplified in 30% of the TCGA ovarian cancer samples (Fig. 8).

By adding the drug data, gefitinib and erlotinib, which are tyrosine kinase inhibitors thattarget the catalytic domain of EGFR, and cetuximab and trastuzumab, which are monoclonal

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antibodies that target the extracellular domain of EGFR and ERBB2, respectively, showwith edges connecting them to their targets (Fig. 8A) (26, 27).

1.Perform the query shown in Fig. 8.2.Select the Network tab.

3.Select “Show all Drugs” from the Genes & Drugs tab.

4.From the Layout button, select “Layout Properties” and set the maximum distanceto 100 to shorten the length of the edges.5.From the Layout button, select “Perform Layout.”

6.To automatically perform layout changes after filtering the network, select “Autolayout on changes.”

7.Set the “Filter Neighbors by Alteration” to 10.

8.Rearrange nodes by single clicking and repositioning nodes for better layout.9.

Double click the MYC node to view genomic profile details.

10.From the View menu, select “Highlight neighbors,” then select “Remove

highlights” to restore all nodes and edges.11.View and filter interaction types and sources in the Interactions tab.12.Double click the line connecting Flavopiridol to EGFR to view details.

13.Deselect “Merge Interactions” to show multiple edges of different interaction types

between nodes.14.From the View button, select “Always Show Profile Data” to visualize the

alteration frequencies of different genomic profiles around each gene. Deselect toremove.15.Use the options from the “Topology” button to hide or show only selected nodes or

remove disconnected nodes from the network.16.Select EGFR, ERBB2, and MYC from the Genes & Drugs tab and click the arrow

button to submit a new query.17.Use the browser back button to return to the previous result.

18.Download GraphML or SIF for further analysis in other tools such as Cytoscape.Results Tabs 8-10: IGV, Download, Bookmark—The Download tab provides allgenomic data and per-sample alteration events for download. Users can download tab-delimited text files with all data for the query genes or simply copy event information intoan external spreadsheet application for further analysis. The tab-delimited text files are

available in two formats: (i) a data matrix of genes (rows) versus samples (columns) and (ii)a transposed matrix of samples (rows) versus genes (columns).

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Users can also visualize copy number details by choosing to launch a Web start version ofthe IGV (28). IGV will open the segmented copy-number data of the current cancer studyand display the copy-number status of all query genes.

The Bookmark tab allows users to save or bookmark a specific query (the entire query canbe stored in a URL) or share their results with collaborators by generating a short URL(using bit.ly).

1.2.

Perform any query.

From the IGV tab, click the “Launch” button to load the data and start the viewer.

Note: The segmented copy-number data for all samples are visualized in IGV,regardless of which cases are selected for querying in the cBioPortal.

3.

From the Download tab, to obtain the data in tab-delimited format, click the

hyperlinks to view the file desired or open the URL in a new tab or window. Then“select all” to copy into a spreadsheet or select “File,” then “Save Page as” to saveas a text file.

From the Download tab, to place the data into a spreadsheet or create a filemanually, copy and paste the data in each text box into the program of choice.From the Bookmark tab, right-click (on a PC) the link shown and paste into abrowser to create a personal bookmark or to store the link to the specified query.From the Bookmark tab, press the “Shorten URL” button to create a shorter URLfor the specified query using bit.ly.

Note: Clicking on the short link or the long version will reload the Bookmarktab page for the specified query.

Performing Cross-Cancer Queries

Cross-cancer queries allow users to assess alteration frequencies and mutation data forindividual genes or combinations of genes across multiple different cancer types. Cross-cancer queries of mRNA expression or protein abundance data are not yet available. Theportal will automatically limit the studies searched to match the query parameters so thatonly data with mutation information is included for a mutation-only query and only datawith CNA information is included for a CNA-only query. The results are presented as ahistogram: (i) one showing the frequency of the alterations in the cancers, which can bepresented in descending order; or (ii) one showing the absolute number of samples with andwithout alterations in each cancer study, which can be presented in order of decreasing

number of cases with alterations. If multiple genes are queried, then the histograms representthe combined alterations or alteration frequency in all of the selected genes. Detailsregarding the queried genes in the form of OncoPrints for each cancer study are alsoprovided. This enables the results for each selected gene to be visualized for each cancerstudy.

A cross-cancer query of TP53, which encodes the tumor suppressor and transcriptionalregulator p53, illustrates this feature of the cBioPortal (Fig. 9A).

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript4.5.6.

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1.

General and Specific: Select “All Cancer Studies” from the main query page(Home).

General: Select data types.Specific: Select “Only Mutation.”

Note: This will automatically limit the query to only those cancer studies withmutation data.

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript2.

3.General: Enter genes of interest.Specific: Enter TP53.

4.5.6.

Press “Submit.”

Press the “Sort” link to organize the data from cancers with the most to those withthe least frequently occurring mutations in the query gene (Fig. 9B).

To view the data as the absolute number of altered and unaltered samples, select“Show number of altered cases (studies with mutation data)” from the drop-downmenu.

Mouse over any bar in the histogram to view a summary of the results.

Click the arrowhead beside any of the listed cancer studies to view the OncoPrintsfor the selected genes.

Click “View Cancer Study Details” to execute the query in the selected cancerstudy, which enables access to all of the results listed for a single study query.

7.8.9.

10.Use the browser back button to return to the cross-cancer query results.11.Click the “Export” link to download the data as an SVG file.

Viewing Cancer Study Summary Data

In addition to performing specific gene queries, the cBioPortal provides access to summaryinformation about each cancer study included in the portal. The data available includevarious clinical details about the patients (survival and age at diagnosis), details about thetumor (histology, stage, grade), and summaries of the genomic data (number of

nonsynonymous mutations and fraction of genome altered), details about the recurrentlymutated genes, and details about recurrent CNAs. The clinical data are presented bothgraphically and in table format (Fig. 10). The mutated gene and CNA data are presented intables. All tables have a search option. The search queries all content (case IDs, genesymbols, and clinical attributes) in the table containing the searched term or phrase.

1.2.3.4.

Select “Uterine Corpus Endometroid Carcinoma (TCGA, Provisional)” from thedrop-down menu in the main query page (HOME).Press the “View details” button.

Press the “more?” button to see additional graphical summaries.Mouse over the data in the graphical summaries for details.

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5.

Sort the data in the clinical data table by clicking the arrowheads next to eachcolumn. Use the scroll bars to move up and down or across the table.

Search for deceased patients by typing “Deceased” (without quotations) into thesearch box.

Note: Searching the table of patient data below the graphical summaries willnot update the graphical data for the selected patient.

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript6.

7.8.9.

Restore the full list of cases by deleting the search text from the search box.Click the tab “Copy Number Alterations” to access a list of chromosomal regionsand genes with CNAs.

Click the tab “Mutated Genes” to access the list of recurrently mutated genes.

10.Click any of the listed genes to execute a new query for mutations of the selected

gene in the selected cancer study.11.Use the browser back button to return to the cancer study summary, which displays

the “Clinical Data” results.12.Click the “Serous” pie in the Histology pie chart to update other plots and the table

to reflect the results of only those cases that are of the serous type.13.Click the “Clear selection” button to restore all plots and table.

Viewing Genomic Alterations in a Single Tumor: Patient View

Because there are potentially hundreds or thousands of genomic alterations in any singletumor sample, it is crucially important to select, for inspection and analysis, alteration eventsthat most likely contribute to oncogenesis or affect the response to therapy. Therefore, inaddition to gene-by-gene alteration maps across many samples and across diverse tumortypes and the cancer study summary data, users can also view genomic alterations inindividual tumor samples in an interactive patient view page. Links to these pages are

available from the OncoPrint (through the mouse-over details for each genomic event), theMutations tab, and the cancer study summary page.

The patient view summarizes and visualizes all relevant data about a tumor, includingclinical characteristics, summaries of the extent of mutations and copy-number alterations,as well as details about mutated, amplified, and deleted genes (Fig. 11). The results aredisplayed in tabbed displays. Genomic alterations in the summary tab are filtered by thefollowing criteria: recurrence of mutations or CNAs across the tumor cohort (from MutSigand GISTIC), mutation occurrence in COSMIC (14), and cancer gene annotation [fromresources, such as the Sanger Cancer Gene Census (29)]. The patient view also providesinformation about drugs that target the altered genes and lists relevant clinical trials fromhttp://www.cancer.gov/.

1.2.3.

Click the “DATA SETS” button at the top of the navigation pane.Click “Uterine Corpus Endometrioid Carcinoma (TCGA, Provisional).”Enter “TCGA-FI-A2D2” in the search box above the table.

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4.5.

Click on the case ID to access the patient view.

Mouse over the “More about this patient” link to see a summary of clinical details.Mouse over the column titles in the Mutations and CNA tables to learn more abouteach column.

Mouse over the numbers in the “Allele Freq (T)” column to see the variant andtotal allele counts for each mutation.

Mouse over the graph in the “Cohort” column to see the number and fraction ofcases in the cohort that have the same mutated gene and the same specific mutation.To view all of the mutations, either click the “Mutations” tab or click the “Show all42 mutations” below the mutations of interest table on the summary display.

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author Manuscript6.7.8.9.

10.To return to the summary, click the “Summary” tab.

11.To view all CNAs, either click the “Copy Number Alterations” tab, or click the

“Show all 557 CNAs” below the CNA of interest table on the summary display.12.To return to the summary, click the “Summary” tab.

13.Mouse over the graph in the top right of the summary display to see an enlarged

view of the scatter plot of mutation count versus fraction of genome altered for thecohort with the current patient highlighted in red.14.To view drugs that target genes with mutations or CNAs in this patient, click the

“Drugs” tab.15.To view clinical trials that may be relevant to this patient, click the “Clinical

Trials” tab.

Note: Results may be filtered by drug, which are clinical trials on the drugslisted in the Drugs tab, or by cancer type, which are clinical trials for the samecancer type the patient has.

16.To view a PDF of the pathology report for the tumor, click the “Pathology Report”

tab.

Note: This view requires Adobe PDF Reader other PDF viewer. For AdobePDF Reader, additional options for zooming and printing and saving appearwhen the mouse is placed near the top or bottom of the PDF.

Programmatic Access

REST-Based Web Service Interface—The cBioPortal Web service interface providesdirect programmatic access to all genomic data and metadata stored within the database.This enables client applications to access cancer genomic data in the portal through anyprogramming languages that can process HTTP requests and responses, such as Java,Python, Perl, R, and MATLAB. The REST-based Web service can be queried by clientapplications using URLs consisting of one or more parameters. The server responds with atab-delimited text format. A summary of valid CGDS (Cancer Genomics Data Server)commands is provided in Table 2.

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1.2.

Click the “Web API” button at the top of the navigation pane.

Follow the instructions and examples as described on the page to retrieve the dataof interest.

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptR and MATLAB Packages—The CGDS-R package provides direct access to all portaldata within the R framework for statistical computing and graphics. The package is availablefor download from CRAN (the Comprehensive R Archive Network). Similar to the

functionality of the Web API, the CGDS-R package provides functions to easily retrievedata and metadata about available cancer types, genetic data profiles, and case sets in thedatabase. Data are returned in a standard R data frame and is immediately ready forsubsequent visualization and statistical analysis by use of the R framework.

Like the R package, the MATLAB CGDS Cancer Genomics Toolbox provides a set offunctions for direct retrieval of cBioPortal data from within the MATLAB (MathWorks)environment. The toolbox can be downloaded from the MATLAB Central File Exchange.Each toolbox function has a direct counterpart in the portal Web API. Data are returned asstructured arrays in a format that is easy to interpret and ready for subsequent visualizationand statistical analysis. An included tutorial (“showdemo cgdstutorial”) shows how to use allthe functions as well as how to make basic plots.

1.2.

Click the “R/MATLAB” button at the top of the navigation pane.

Follow the instructions and examples as described on the page to retrieve the dataof interest.

Notes and Remarks

Complementary Data Sources and Analysis Options

Table S2 compares the cBioPortal to other cancer genomics data and analysis resources,including the TCGA data portal (https://tcga-data.nci.nih.gov/tcga/), the ICGC data portal(http://dcc.icgc.org/), the Broad Institute's Genome Data Analysis Center (GDAC) Firehose(http://gdac.broadinstitute.org), the IGV (28), the University of California, Santa Cruz(UCSC) Cancer Genomics Browser (30), IntOGen (31), Regulome Explorer (http://explorer.cancerregulome.org), and Oncomine (Reserach Edition) (32). The cBioPortal

provides a resource for exploratory analysis of cancer genomics data, with an intuitive Webinterface, biologically relevant abstraction of genetic alterations at the gene level, integrativeanalysis of genomic data sets and clinical attributes, interactive network analysis, and

patient-centric summaries. It was designed to complement existing tools and resources, suchas genome browsers. The cBioPortal does not store raw data, which are available from dataportals, such as TCGA, ICGC, and Gene Expression Omnibus (GEO) (http://

www.ncbi.nlm.nih.gov/geo/). A portion of the data in the cBioPortal is retrieved from theBroad Institute's GDAC Firehose (http://gdac.broadinstitute.org), an analysis pipeline thatautomatically performs standard processing and analyses on TCGA data sets. The

cBioPortal currently only supports analysis of correlations between alterations in querygenes. To explore more complex correlations among genes, including mRNA expression,Regulome Explorer and Oncomine can be used. To visualize and analyze multiple data types

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on the genome (23 chromosomes), the IGV and the UCSC Cancer Genome Browser can beused.

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptFuture Directions

The cBioPortal project is under active development. We anticipate several new features inthe near future, including the addition of more cancer studies; support for genomic

methylation events and their visualization in OncoPrints; addition of clinical attributes toOncoPrints; improvements to the network view using SBGN (Systems Biology GraphicalNotation) (33); queries to Pathway Commons for causative links (activation, inhibition);patient view improvements (more clinical attributes and treatment data, tumor tissue imageviews, gene expression events, and information about altered pathways in a single patient);and batch download of complete data sets.

We intend to build an active community of researchers and software engineers involved indevelopment of the portal software. We welcome industry involvement by mutual

agreement with MSKCC. Parties who are interested in obtaining a copy of the cBioPortalsoftware with or without the source code should contact us via cbioportal@cbio.mskcc.org.

Supplementary Material

Refer to Web version on PubMed Central for supplementary material.

Acknowledgments

We thank R. Sheridan (Sander Lab, MSKCC), J. Barlin (Levine Lab, MSKCC), and P. Jelinic (Levine Lab,

MSKCC) for invaluable feedback to improve the usability of the portal. We thank our collaborators at MSKCC andin the TCGA and the Stand Up To Cancer (SU2C) research networks, including D. Levine, D. Solit, C. Brennan(MSKCC); B. S.Taylor (UCSF); G. Mills (MD Anderson); and K. Shaw (NCI), for generous feedback and links tothe cancer genomics community. We thank G. Bader and M. Franz (University of Toronto) for support withCytoscape Web and the entire Pathway Commons team (MSKCC and University of Toronto) for developing thePathway Commons Web Application Programming Interface (API) and the network download facility. We thank J.Zhu (UCSC) and N. Lopez-Bigas (University Pompeu Fabra) for feedback regarding the UCSC Cancer GenomeBrowser and IntOGen. E.C. is now at Blueprint Medicines in Cambridge. B.A.A. is in the Tri-Institutional TrainingProgram in Computational Biology and Medicine, a joint graduate program of MSKCC, Cornell University, andWeill Cornell Medical College.

Funding: The cBioPortal for Cancer Genomics is supported by NCI as part of the TCGA Genome Data AnalysisCenter grant, NCI-U24CA143840, and NCI-R21CA135870. Funding for a separate Stand Up To Cancer (SU2C)instance of the cBioPortal is provided by a Stand Up To Cancer Dream Team Translational Research Grant, a

Program of the Entertainment Industry Foundation (SU2C-AACR-DT0209). Funding for network visualization andanalysis within the portal is provided by the National Resource for Network Biology (NIH National Center forResearch Resources grant numbers P41 RR031228 and GM103504). Funding for MutationAssessor is from theNIH NCI R01 CA132744. Funding for the integration with the Integrative Genomics Viewer (IGV) is provided bythe Starr Cancer Consortium (I5-A500).

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Fig. 10. The cancer study summary view

The example shows an overview of clinical attributes and a scatter plot of mutation countversus fraction of genome altered for each case in the TCGA endometrial cancer study.

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Fig. 11. The cBioPortal patient view

The example shows the relevant genomic alterations and clinical data of an endometrialcancer sample with mixed histology from the TCGA study.

Gao et al.Page 33

Table 1

OQL data types and functionality.

Data typeCopy number

KeywordCNA

CodeAMPHOMDELGAINHETLOSS

Mutations

MUT

MUTMUT = x

Description

Show amplified cases.

Show homozygously deleted cases.Show single copy gained cases.Show heterozygously deleted cases.Show mutated cases.

Show cases with specific mutations ormutation types.

CCNE1: MUT

BRAF: MUT = V600E; TP53: MUT =MISSENSE; TP53: MUT = NONSENSE;TP53: MUT = NON-START; TP53: MUT= NONSTOP; TP53: MUT =FRAMESHIFT;TP53: MUT=

INFRAME;TP53: MUT = SPLICE; TP53:MUT = TRUNCTP53: EXP > 1.5Example

CCNE1: AMP; CCNE1: CNA > GAIN;CCNE1:GAIN AMP

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptmRNA/miRNAexpression z scores

EXPEXP < xEXP > x

Show all underexpressed cases, less thanx standard-deviations from the mean.Show all overexpressed cases, greaterthan x standard-deviations from themean.

Show all protein-level underexpressedcases, less than x standard from themean.

Show all protein-level overexpressedcases, greater than x standard from themean.

Protein-level z scoresPROTPROT < xERBB2: PROT < ?2

PROT > x

Sci Signal. Author manuscript; available in PMC 2014 September 10.

Gao et al.Page 34

Table 2

Summary of Web service commands.

CommandgetCancerStudiesgetGeneticProfilesgetCaseLists

Description

Retrieves metadata regarding all cancer studies stored on the server.

Retrieves metadata regarding all genetic profiles—for example, mutation or copy-number profiles—stored about aspecific cancer type.

Retrieves metadata regarding all case lists stored about a specific cancer type. For example, within a particular study,only some cases may have sequence data, and another subset of cases may have been sequenced and treated with aspecific therapeutic protocol.

Retrieves genomic profile data for one or more genes.

Retrieves the full set of annotated mutation data. This includes validation status, sequencing center, the amino acid

change that results from the mutation, and the predicted functional consequence of each mutation, as predicted by http://mutationassessor.org.

Retrieves informations on antibodies used by RPPA to measure protein and phosphoprotein abundance.Retrieves the abundance of proteins, phosphoproteins, or both measured by RPPA.

Retrieves de-identified clinical data, including overall survival, disease-free survival and age at diagnosis.

NIH-PA Author ManuscriptNIH-PA Author ManuscriptNIH-PA Author ManuscriptgetProfileDatagetMutationData

getProteinArrayInfogetProteinArrayDatagetClinicalData

Sci Signal. Author manuscript; available in PMC 2014 September 10.

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