Visualizing and analyzing large-scale cancer genomic datasets
cBioPortal is an interactive open-source platform designed to visualize and and analyze genomic data. cBioPortal aims to give researchers and clinicians an insight in large scale cancer genomics datasets and to help them with selecting treatment for patients based on their genomic profile. Originally developed by Memorial Sloan Kettering Cancer Center (MSKCC) in New York, cBioPortal was made Open Source in 2015 under the AGPL 3 license.
The Hyve is a prominent member of the cBioPortal community and we have been working to support a variety of organizations, including Dana Farber Cancer Institute (DFCI), the American National Cancer Institute (NCI), the Netherlands Cancer Institute (NKI) and a number of major pharma companies, with deployment, data loading, development, consulting and training.
Open-source community around cBioPortal
The community around cBioPortal is one of the best examples of a young yet well functioning open-source community. It started in 2015, and The Hyve joined the community soon after cBioPortal became open-source. Since then the community has been growing, and at the moment we collaborate with MSKCC, Dana Farber Cancer Institute, MD Anderson Cancer Center, Princess Margaret Cancer Center, and many more.
How is cBioPortal relevant for you
If you work in the field of oncology, you have to deal with genomics data on a daily basis. In the era of Next Generation Sequencing personalized cancer medicine and personalized cancer treatment are getting more and more use. A lot of hospitals and cancer centers have a standard to sequence DNA of every new admitted patient, and generating such large cohorts of samples unleashes the potential of genomic data and allows multi-sample or even multi-cancer comparison and analysis.
But large-scale genomic data can be difficult to get around, especially if you are not a trained bioinformatician. cBioPortal can help you dig through the large amounts of cancer samples without any knowledge of bioinformatics! Its user-friendly and intuitive design, along with extensive functionality and new features being added all the time, allow you to investigate, analyze, interpret your data, generate hypotheses and perform both scientific and medical research. You can look at a lot of samples, the whole cohort, at once, compare gene expression, find common or unique mutations, identify mutations specific for a patient or a cancer type — all without going to the command line, analyzing anything manually or asking your bioinformatician to help. You can look at your own patients or compare their genomic profiles and clinical traits with patients from other cohorts, look at the relationship between a certain phenotypic feature/clinical parameter and genetic profile.
cBioPortal functionality that you did not know about
Install cBioPortal locally and compare your samples to TCGA: install cBioPortal locally, add your own samples to it and compare them with The Cancer Genome Atlas (TCGA) or the Cancer Cell Line Encyclopedia (CCLE) data
- View all clinical traits at once: go to Study view to get an overview of clinical traits in your samples (histology, stage of cancer, age of patients) — any characteristic that you wish
- Explore gene expression: the Oncoprint view shows you is a gene is up- or down-regulated in your samples (you will also see CNA events and mutations in the same view)
- Explore mutations: see mutations location on the gene and in the protein structure, compare the expression of the reference and the alternative alleles, discover what is known about the mutation in databases like OncoKB, Civic or COSMIC (screenshot mutations).
- analyze your data: find co-expressed genes, see the correlation between age and gene expression, and more!
- See the timeline of a patient: duration of treatment(s), dates of surgery, dates when a specimen was taken — cBioPortal places this information on a timeline and gives you an aggregated overview of time-related measurements, as well as the information about mutations and CNA events in all samples of a patient