A user guide to cBioPortal - The Fred Hutch instance -

Introduction to the Fred Hutch instance of cBioPortal and a demonstration of its usage in the context of research data.

Learning Objectives

After reading this article, you will learn:

What cBioPortal is and how it can help with your research.
What the Fred Hutch instance of cBioPortal is and steps to take to upload your data onto the instance.
Ways to format your data for upload into cBioPortal.

What can you do with cBioportal?

There are innumerable ways you can leverage the vast amount of data on cBioPortal. In the example below, we demonstrate how you can harness some of the capabilities of cBioPortal to facilitate your own research.

Note: The examples below are demonstrated using the public instance of cBioPortal but can be replicated in the Fred Hutch instance as well)

As an example, let us say you identified KRAS as an important gene in cancer. Let’s see how we can use cBioPortal to expand this observation leveraging publicly available datasets.

Question 1: How often is KRAS mutated in cancers?

Using cBioPortal, you can explore the frequency of KRAS mutations in different cancers, for example here we look at colorectal, lung, and pancreatic cancers. This analysis shows that KRAS is mutated in 26% of patients across these studies. You can similarly investigate other studies (over 400 publicly available datasets) on cBioPortal.
With cBioPortal, you can explore the different types of mutations in KRAS, such as single-nucleotide variations, insertions, deletions, and copy number changes. These can be visualized in OncoPrint format like above (showing mutations in genes for each sample), lollipop format like below (showing where mutations occur on the protein), and many others.
Additionally, cBioPortal’s direct integration with external databases such as ClinVar, COSMIC, etc. allows you to evaluate the impact of these genomic alterations. Here you can see each mutation in KRAS and its prediction of pathogenicity by OncoKB and COSMIC databases.

Thus with cBioPortal, exploring published multi-omics data becomes easier without needing to download raw data files, process them, and manually create visualizations. It also provides you with a one-stop shop to evaluate specific mutations by referencing external databases as well. The platform makes the entire process seamless and user-friendly.

Question 2: Are mutations in KRAS associated with any clinical parameters such as sex, age, etc.?

With cBioPortal, you can visualize genomic data, such as mutations, alongside clinical factors like sex, age at diagnosis, or smoking history. This allows for a deeper understanding of how these clinical traits may correlate with specific genetic changes. In the example below, we can add tracks representing patient sex, age at diagnosis, and history of smoking in subjects with mutations in KRAS. You can see that KRAS mutations are frequent in individuals with a history of smoking.
You can also correlate if specific clinical covariates are associated with certain mutation types or expression like in the screenshot below. Even if you have a particular tool in mind that you’re more comfortable with, users can download the underlying data and analyze it outside of cBioPortal.

Overlaying clinical data with genomic data is crucial for discovering novel associations between genetic mutations and patient outcomes. This integration can help you identify patterns that may not be apparent from analyzing genetic data alone. By combining both types of data, it becomes easier to uncover critical insights that can lead to better predictions of survival and improved treatment strategies.

Question 3: Do KRAS mutations co-occur with other kinds of mutations?

You can also utilize the cBioPortal platform to identify if specific genes co-mutate with KRAS. Alternatively, you could assess if they are mutually exclusive to KRAS mutations.

These analyses can reveal alternative therapeutic targets by identifying genes that often co-occur with mutations (co-occurrence) or show mutually exclusive patterns (synthetic lethal interactions). Ultimately, these findings can improve predictions for patient outcomes and guide research direction to identify personalized, effective therapies.

Question 4: Do KRAS mutations affect survival or disease progression?

Using cBioportal, you can explore the relationship between KRAS mutations and overall survival in the three cohorts. In the analysis below, it appears the KRAS mutant tumors have a worse overall survival compared to those cancer patients with unaltered KRAS.
You can also explore if KRAS mutations affect disease free survival. In this case, we see KRAS mutant tumors have a lower percentage of disease-free survival which is an important metric when considering oncogenic targets for therapeutic intervention.

cBioPortal provides a powerful platform for assessing overall survival and disease-free progression by integrating genomic alterations with clinical outcomes. The platform allows you to visualize survival curves, stratify patients by genetic features, and correlate these with clinical variables like treatment response. This helps in identifying potential biomarkers that could predict prognosis or guide personalized treatment strategies, making cBioPortal an invaluable resource for cancer research and clinical decision-making.

Other examples of what you can explore with cBioPortal:

Pathway-level analysis: Investigate how genetic changes affect key biological pathways using the “Pathways” tab, helping you understand their impact and find potential drug targets. For instance, you could explore how KRAS mutations influence its signaling pathway and look at mutation frequencies in other related genes.
Copy number alterations: Explore large-scale genomic changes, such as amplifications or deletions, using cBioPortal’s “CN Segments” tab. For example, you could compare copy number profiles of KRAS mutant tumors to non-mutant ones to identify patterns linked to KRAS mutations.
Create custom cohorts: Build groups based on genetic mutations or clinical data relevant to your research. For example, you could focus on lung adenocarcinoma patients with KRAS mutations and a history of smoking to study genetic and clinical patterns.
Tumor evolution: Examine how tumors change over time by analyzing multiple samples from the same patient. For instance, you could investigate whether KRAS mutant tumors become more genetically unstable over time, thus potentially affecting survival.
Immuno-genomics: Study the relationship between genetic changes and immune-related features like tumor mutational burden or immune cell infiltration. For example, you might explore if KRAS mutant tumors have a higher mutational burden, suggesting a more immune-responsive environment.

How do I prepare my data for upload into cBioportal?

Before you begin

Start by creating a folder to store all of your relevant study files. The name of the folder must be the exact study ID that was provided to you by the cBioPortal team. If any other name is provided, the automation steps will refuse upload since the altered study ID would not be on the list of approved values. The cancer_study_identifier value in your meta files should also be this same study ID. Note: the study ID can be updated if desired, just reach out to the Data Governance team with your preferred ID.
Once your study files are close to complete, you can test out your file formatting using one of two validation methods:
1. If you’ve received official approval for upload, try directly uploading it into the fh-dasl-cbio bucket. As mentioned above, the automated upload process will send you a validation report via email outlining any issues that may have popped up.
2. You could also validate your study folder by launching a local instance of cBioPortal and troubleshooting the errors (if any) in your study data.

Required/Optional Study Files

There are a few files that are required while all other files are optional. Below is an overview of the required files and some optional files Note: Version 6 of cBioportal currently also requires in the least 1 non-clinical file to be uploaded as well. See instructions below on where to find a dummy table that you can modify in case you are only uploading clinical data.

Type	Requirement	Filename Example	Required Format	Purpose	Detailed Instructions	Example
Cancer Study	Required	meta_study.txt	Text file	Overall information about the study	Readme	Example
Cancer Type	Optional	meta_cancer_type.txt	Text file	A meta file with information about the file with new cancer type. Required if your cancer type does not exist in the database.	Readme	Example
Cancer Type	Optional	cancer_type.txt	Tab Separated Value (TSV)	Details about a new cancer type not found in the cBioPortal database. Required if your cancer type does not exist in the database.	Readme	Example
Clinical Sample	Required	meta_clinical_sample.txt	Text file	A meta file with information about the clinical samples	Readme	Example
Clinical Sample	Required	data_clinical_sample.txt	Tab Separated Value (TSV)	File with the sample-level clinical covariates/metadata	Readme	Example
Clinical Patient	Optional	meta_clinical_patient.txt	Multi-line text file	A meta file with information about the clinical patient	Readme	Example
Clinical Patient	Optional	data_clinical_patient.txt	Tab Separated Value (TSV)	File with the sample-level clinical covariates/metadata	Readme	Example
Panel	Optional	meta_gene_panel_matrix.txt	Multi-line text file	A meta file for describing the gene panel matrix file	Readme	Example
Panel	Optional	data_gene_panel_matrix.txt	Tab Separated Value (TSV)	Sample level details of the gene panel used for the different samples	Readme	Example
Mutation	Optional	meta_mutations.txt	Multi-line text file	A meta file describing information about the mutation file.	Readme	Example
Mutation	Optional	data_mutations.txt	Tab Separated Value (TSV)	File with mutation data	Readme	Example
Case Lists	Required	case_lists/cases_sequenced.txt	Multi-line text file	It helps cBioPortal identify which samples have data. Required if uploading data files beyond clinical data.	Readme	Example
Structural Variant	Optional	meta_sv.txt	Multi-line text file	A meta file for describing the structural variant data file	Readme	Example
Structural Variants	Optional	data_sv.txt	Tab Separated Value (TSV)	File with structural variant data	Readme	Example
Generic Assays: Arm-level CNA	Optional	meta_armlevel_CNA.txt	Multi-line text file	A meta file for arm-level copy number alteration data	Readme	Example
Generic Assays: Arm-level CNA	Optional	data_armlevel_CNA.txt	Tab Separated Value (TSV)	Arm-level copy number alteration data	Readme	Example

Publicly available tools for data formatting

There are many publicly available formatting tools that may help with the formatting process. When deciding which one works best for you, it ultimately depends on what tools you’re comfortable with and what kinds of data you’re uploading, but here are a few options that might help get you started:

Tool Name	Description	Advantages	Disadvantages	Fred Hutch Repository Link
Data-processor	Formats clinical data tables in multi-tab Excel files to cBioportal format	- Useful for varied clinical data fields. - Supports multi-tab Excel files. - Easy terminal execution	- Does not seem to work to generate clinical data files. - Requires adherence to specific clinical data variable names from [cBioPortal Clinical Data Dictionary]	Data_processor
cbpManager	An R-based Shiny App that allows users to create and upload cBioPortal-formatted studies.	- A relatively easy to run R based (Shiny) App. - Allows you to create clinical data files, timeline related files, and mutation files. - Allows users to run the validation of their formatted study folders	- Currently only helps to create clinical and mutation data related files. - If using the app to create the files then can only update one patient at a time	cbpManager
CaisisTools (a Fred Hutch tool)	Takes clinical data in the form of an excel workbook and converts to cBioportal format	- Helpful for processing clinical data. - Can be used for data from RedCap	- Data either must be obtained from Caisis or should be in the same format	CaisisTools
Varan 2.0	Takes genomic data and existing study folder to process and upload into cBioportal.	- Useful for validating an existing cBioportal study folder. - Can concatenate from multi-sample vcf files. - Can be used to do filtering of genomic files	- Has several local dependencies (vcf2maf, VEP, and samtools). - Folder preparations restricted to CNV, SNV, SV, and clinical data	Varan
cBioPortal-BS-Lab	Helpful scripts to take data from RedCap to convert to clinical data files	- Good for demonstrating how to take data stored in RedCap and format	- Mostly would be useful for clinical data files	cBioPortal-BS-Lab
cBioPortal_Importer	Python script to prepare data for uploads into cBioportal. Mostly genomics data.	- Helpful scripts to transform specific data types into cBioportal format	- Accepts very specific output files. - Requires threshold setting for copy number data, etc.	cBioPortal_Importer
cbpConverter	R Shiny App to convert Excel sheets into cBioportal format	- Seems like a simple Shiny app to convert clinical data into cBioportal format	- Looks untested but might have helpful scripts. - Again only clinical data	cbpConverter
gdc-et-pipeline	Converts data from the GDC repository to cBioportal format	- If data is available on GDC, this might be useful	- Written in Java. - File formats have to be in the GDC format. - Folder preparations restricted to CNV, SNV, Expression, and clinical data	gdc-et-pipeline
kf-cbioportal-etl	Specific to this study: CAVATICA and Data Warehouse	- Helpful scripts that can be leveraged.	- These scripts might be specific to the format of files found in this study.	kf-cbioportal-etl
mutational-signature-converter	Very specifically converts the mutational signature data into cBioportal format	- Helps to convert mutational signature data into cBioPortal format	- Has not been updated in a few years. - Simple python script	mutational-signature-converter
shah-cbioportal-tools	Specifically for formatting Copy Number Data expects a seg file and TITAN output	- Could potentially be used for tools other than TITAN that generate a seg file	- Specific for TITAN outputs	shah-cbioportal-tools

Quick Links

Help

Report bugs or issues

To report bugs or issues with the Fred Hutch instance of cBioPortal, please file an issue here. For questions about using the tool and formatting your data for upload, schedule a data house call using the link above.