Portal FAQs

What is the cBioPortal for Cancer Genomics?

The portal stores genomic data from large scale, integrated cancer genomic data sets. It allows explorative data analysis (e.g.: Is my gene of interest altered in a specific cancer type? How frequently is EGFR amplified in glioblastoma? Do mutations of BRCA1 and BRCA2 in ovarian cancer co-occur?) and provides simple download of small data slices (user-defined gene and sample sets, no need to download entire data sets).

How do I get started?

We have recently posted two mini-tutorials to get you up and running.

What data types are in the portal?

The portal currently stores DNA copy-number data (putative, discrete values per gene, e.g. "deeply deleted" or "amplified", as well as log2 levels), mRNA and microRNA expression data, non-synonymous mutations, protein-level and phosphoprotein level (RPPA) data, DNA methylation data, and limited clinical data related to survival. For a complete breakdown of available data types per cancer study go to the Data Sets Page.

Can I use figures from the Portal in my publications or presentations?

Yes, you are free to use any of the figures from the portal in your publications or presentations (many are available as PDFs for easier scaling and editing). When you do, please cite Cerami et al., Cancer Discov. 2012 and Gao et al. Sci. Signal. 2013.

When using TCGA data in your publications, please adhere to the TCGA publication guidelines.

How do I cite the portal?

You can cite the following portal papers:

  • Cerami et al. The cBio Cancer Genomics Portal: An Open Platform for Exploring Multidimensional Cancer Genomics Data. Cancer Discovery. May 2012 2; 401. Abstract.
  • Gao et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013). Reprint.

How is the cBioPortal for Cancer Genomics different from the TCGA Data Portal?

The cBio portal is an exploratory analysis tool for exploring large-scale cancer genomic data sets. You can quickly view genomic alterations across a set of patients, across a set of cancer types, perform survival analysis and perform network analysis. By contrast, the TCGA Data Portal aims to be the definitive place for full-download and access to all data generated by TCGA. If you want to explore a pathway of interest in one or more cancer types, the cBio portal is probably where you want to start. However, if you want to download raw mRNA expression files or full segmented copy number files, the TCGA Data Portal is probably where you want to start.

Why do some cancer studies have mutation data and others do not?

We store mutation data for published cancer studies. We do not, however store mutation data for provisional cancer data sets generated by TCGA. This is because provisional studies contain preliminary somatic mutations, which per NCI guidelines cannot be redistributed until they have been validated. As each cancer study is published and finalized by the TCGA, we will import the corresponding mutation data.

Does the portal contain cancer study X?

Check out the Data Sets Page for the complete set of cancer studies currently stored in the portal. If you do not see your specific cancer study of interest, please contact us directly, and we will let you know if it's in the queue.

What kind of clinical data is stored in the portal?

The portal currently stores overall and disease-free survival data, when available.

Does the portal store raw or probe-level data?

No, the portal only contains gene-level data. Data for different isoforms of a given gene are merged. Raw and probe-level data for all date sets is available via NCBI GEO or through the TCGA Data Portal. See the cancer type description on the main query page for links to the raw data.

Which methylation probe is used for genes with multiple probes?

For genes with multiple probes, we only include methylation data from the probe with the strongest negative correlation between the methylation signal and the gene's expression.

What are OncoPrints?

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.

Example OncoPrint

Can I change the order of genes in the OncoPrint?

The order of genes in the OncoPrint is determined by the order entered into the initial query field. Simply change the initial gene order, resubmit your query, and the change will be reflected in the OncoPrint.

Does the portal work on all browsers and operating systems?

We support and test on the following web browsers: Internet Explorer 9.0 and above, Firefox 3.0 and above, Safari and Google Chrome. If you notice any other incompatibilities, please let us know.

How can I query phosphoprotein levels in the portal?

You need to input special IDs for each phosphoprotein/phopshosite such as AKT_pS473 (which means AKT protein phosphorylated at serine residue at position 473). You could also input aliases such as phosphoAKT1 or phosphoprotein, and the portal will ask you to select the phosphoprotein/phosphosite of your interest.

How can I query microRNAs in the portal?

You can input either precusor or mature miRNA IDs. Since one precusor ID may correspond to multiple mature IDs and vise versa, the portal creates one internal ID for each pair of precursor ID and mature ID mapping. For example, an internal ID of MIR-29B-1/29B stands for precursor microRNA hsa-mir-29b-1 and mature microRNA hsa-miR-29b. After entering a precusor or mature ID, you will be asked to select one internal ID for query and that internal ID will also be displayed in the Oncoprint.

What are mRNA and microRNA Z-Scores?

For mRNA and microRNA expression data, we typically compute the relative expression of an individual gene and tumor to the gene's expression distribution in a reference population. That reference population is all samples that are diploid for the gene in question (by default for mRNA), or normal samples (when specified), or all profiled samples . The returned value indicates the number of standard deviations away from the mean of expression in the reference population (Z-score). This measure is useful to determine whether a gene is up- or down-regulated relative to the normal samples or all other tumor samples.

Are there any normal samples available through cBioPortal?

No, we currently do not store any normal data in our system.

What is GISTIC? What is RAE?

Copy number data sets within the portal are generated by GISTIC or RAE algorithms. Both algorithms attempt to identify significantly altered regions of amplification or deletion across sets of patients. Both algorithms also generate putative gene/patient copy number specific calls, which are then input into the portal.

For TCGA studies, the table in all_thresholded.by_genes.txt (which is the part of the GISTIC output that is used to determine the copy-number status of each gene in each sample in cBioPortal) is obtained by applying both low- and high-level thresholds to to the gene copy levels of all the samples. The entries with value +/- 2 exceed the high-level thresholds for amps/dels, and those with +/- 1 exceed the low-level thresholds but not the high-level thresholds. The low-level thresholds are just the 'amp_thresh' and 'del_thresh' noise threshold input values to GISTIC (typically 0.1 or 0.3) and are the same for every thresholds.

By contrast, the high-level thresholds are calculated on a sample-by-sample basis and are based on the maximum (or minimum) median arm-level amplification (or deletion) copy number found in the sample. The idea, for deletions anyway, is that this level is a good approximation for hemizygous given the purity and ploidy of the sample. The actual cutoffs used for each sample can be found in a table in the output file sample_cutoffs.txt. All GISTIC output files for TCGA are available at: gdac.broadinstitute.org.

What do "-2", "-1", "0", "1", and "2" mean in the copy-number data?

These levels are derived from the copy-number analysis algorithms GISTIC or RAE, and indicate the copy-number level per gene. "-2" is a deep loss, possibly a homozygous deletion, "-1" is a shallow loss (possibly heterozygous deletion), "0" is diploid, "1" indicates a low-level gain, and "2" is a high-level amplification. Note that these calls are putative.

What are the sources of biological network data?

The sources of biological network data are listed here.

How do I get updates on new portal developments and new data sets?

Please subscribe to our low-traffic news mailing list or follow us on Twitter.

What if I have other questions / comments?

Please contact us at cbioportal@googlegroups.com. Previous discussions about cBioPortal are available on the user discussion mailing list.

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Data Sets

Example Queries

RAS/RAF alterations in colorectal cancer

BRCA1 and BRCA2 mutations in ovarian cancer

POLE hotspot mutations in endometrial cancer

TP53 and MDM2/4 alterations in GBM

PTEN mutations in GBM in text format

BRAF V600E mutations across cancer types

Patient view of an endometrial cancer case

What People are Saying

  "Whenever bench scientists ask me how they can look at TCGA data, I've never had a good answer for them. Now I do. The cBio Portal meets a critical need--it is the interface that the cancer research community needs to access the wealth of TCGA. Even as a computational biologist, I use it to follow-up on genes of interest. It makes querying the data much less painful."

– Postdoctoral Fellow, Oregon Health & Science University

  "I would like to congratulate you and the team of the cBio portal. It's just an amazing tool to work with, and we at Mass General really appreciate it."

– Research Fellow at Massachusetts General Hospital

  "As a bench biologist with primary aim of determining gene aberrations in GBM, I found your site absolutely fantastic! Thank you! I have to reiterate how awesome and user-friendly your group has made this site - finally accomplishing the goal of having data easily accessible and meaningful."

– Sr. Research Associate at Knight Cancer Institute/OHSU

  "Thank you for your incredible resource that has helped greatly in accessing the TCGA genomics data."

– Postdoctoral Fellow, Johns Hopkins University School of Medicine, Dept Radiation Oncology and Molecular Radiation Sciences

  "I have been enjoying the ease with which TCGA data can be extracted in R using your CGDS package. Very nice work!"

– Sr. Software Engineer, Institute for Systems Biology

  "Thank you for generating such an excellent software. It is very useful for our research."

– Research Fellow, Memorial Sloan-Kettering Cancer Center

  "Thank you very much for providing and maintaining this great resource."

– Scientist, Discovery Bioinformatics, Biotechnology Company

  "I want to thank you for the nice, useful and user-friendly interface you have generated and shared with the community."

– Postdoctoral Fellow, Harvard Medical School, Children's Hospital Boston

  "This portal is truly the greatest thing since sliced bread. I am making discoveries with it not only in glioblastoma, my primary focus, but in other cancers as well -- it's all so easy with this fantastic tool. And I am enjoying showing it to my colleagues, whose jaws also drop. Thank you a thousand times over for this beautiful public resource. I am looking forward to citing this soon in an upcoming paper..."

– Associate Professor, University of Virginia