1. Pichai Raman on behalf of cBioPortal Team Wednesday, May 25, 16
cBioPortal+ Cancer Visualization & Analytics Application for Research | Translational Science | Clinical Decisions
Pichai Raman on behalf of cBioPortal Team
Wednesday, May 25, 16

2. Outline History& Overview Data and Application usage
Key Functionality & Features
Security & Authentication
Coming Attractions

3. History & Overview
Original cBioPortal developed at MSKCC for TCGA data and other large-scale cancer profiling efforts
Lowers barrier to access and visualize complex genomic data for research
cBioPortal Development now shared across 5 teams : DFCI, MSKCC, Princess Margaret, and CHOP, the Hyve
cBioPortal+ : CHOP Implementation has a focus on Pediatric cancer data sets

4. History & Overview
Excels at visualization & presentation of multiple data types in an integrated manner

5. Data and Application Usage
Used Extensively at MSKCC with > 5000 Users a week
Type
Total
MSKCC
CHOP
Studies
113 91 22
Samples
24026
21334
2692
Data sets are being added on a weekly basis from various sources.
Processed through reproducible best practice pipelines.

6. Data and Application Usage
MSKCC currently houses a number of data sources.
Used Extensively at MSKCC with > 5000 Users a week
SU2C
CBTTC
BUT WE ARE ADDING MORE
TARGET
EGA/dbGaP

7. Key Functionality & Features
cBioPortal+ has a number of visualizations based on one of three entry points
Study View
Display of frequent / recurrent mutations or lesions within a study
When creating virtual co-horts of molecular subtypes will be able to quickly identify “potental” drivers
Sample View
Get an overview of all of a patients genetic lesions, connections to Path Reports, clinical trials, drugs, etc..
Has COSMIC data as well as internal statistics to aid in determining if a mutation is likely causal
Gene View
Look at gene data (mutation / expression etc..) across or within study
Correlate genes to other genes within a study or compare to normal tissue expression
Can be used to identify targets for immunotherapy

8. Key Functionality & Features
cBioPortal+ has a number of visualizations based on one of three entry points
Study View
Display of frequent / recurrent mutations or lesions within a study
When creating virtual co-horts of molecular subtypes will be able to quickly identify “potental” drivers
Sample View
Get an overview of all of a patients genetic lesions, connections to Path Reports, clinical trials, drugs, etc..
Has COSMIC data as well as internal statistics to aid in determining if a mutation is likely causal
Gene View
Look at gene data (mutation / expression etc..) across or within study
Correlate genes to other genes within a study or compare to normal tissue expression
Can be used to identify targets for immunotherapy

9. Mutation Lollipop View Recurrent hotspot identification
Height indicates frequency
Annotates with COSMIC, cBioPortal Frequencies, and predicts whether mutation event is damaing

10. Tumor vs Normal Immunotherapy Target Discovery
Color indicates significance
P-value Cutoff
P-value, Tumor vs Normal
Median Tumor Expression
Visualization for RNA-Seq or Microarray data, with ability to look at raw, log, or z-score normalizations and p-value showing differential

11. Other Gene View Visuals
Mutual Exclusivity
Correlation
PPI Networks

12. Key Functionality & Features
cBioPortal+ has a number of visualizations based on one of three entry points
Study View
Display of frequent / recurrent mutations or lesions within a study
When creating virtual co-horts of molecular subtypes will be able to quickly identify “potental” drivers
Sample View
Get an overview of all of a patients genetic lesions, connections to Path Reports, clinical trials, drugs, etc..
Has COSMIC data as well as internal statistics to aid in determining if a mutation is likely causal
Gene View
Look at gene data (mutation / expression etc..) across or within study
Correlate genes to other genes within a study or compare to normal tissue expression
Can be used to identify targets for immunotherapy

13. Descriptive statistics on cohort
Summary Page Overview
Recurrent CNV
Recurrent Mutations
Descriptive statistics on cohort

14. Selection of Samples Survival Plot
Select Genes and all samples with a mutation become a group for Survival Plot

15. Clinical Data Table Tabular view to find Samples
Clinical data sortable and searchable
Can click on sample to get to sample view

16. Key Functionality & Features
cBioPortal+ has a number of visualizations based on one of three entry points
Study View
Display of frequent / recurrent mutations or lesions within a study
When creating virtual co-horts of molecular subtypes will be able to quickly identify “potental” drivers
Sample View
Get an overview of all of a patients genetic lesions, connections to Path Reports, clinical trials, drugs, etc..
Has COSMIC data as well as internal statistics to aid in determining if a mutation is likely causal
Gene View
Look at gene data (mutation / expression etc..) across or within study
Correlate genes to other genes within a study or compare to normal tissue expression
Can be used to identify targets for immunotherapy

17. Clinical Trials and additional tabs
Patient Summary Page
Add to Harvest Cart
Mutation & CNA table
Clinical Trials and additional tabs
Genome View

18. Patient View Harvest Cart Integration
Clicking submit takes you to CBTTC Harvest application with desired samples loaded
Samples Added to the bucket can be accessed via the HARVEST CART tab

19. Gene Target & FDA approval also listed
Patient View Drugs Tab
Gene Target
Gene Target & FDA approval also listed

20. Other Patient View Visuals
Tissue Images
Pathology Report
Clinical Data

21. Security & Authentication
User
User Authentication Provided by Google
Group
CBTTC
SU2C
Public
Data Set 1
Data Set 2
Data Set 3
Data Set 4
Data

22. Coming Attractions
Timeline – Multiple samples per patient Support for PDX Variant Annotation & Prioritization More simplified clinical interface Isoform level information Connection to raw data and processing pipelines
30+ Active Developers from CHOP, MSKCC, DFCI, Princess Margeret, and the Hyve

23. Acknowledgements cBioPortal Consortium MSKCC DFCI Princess Margeret
The Hyve
CBTTC Collaborators
Adam Resnick
Alex Felmeister
Tyler Rivera
Jena Lilly
Angela Waanderers
Philip Allman
CHOP cBioPortal+ Team
Karthik Kalletla
Anna Lu
Kaitlyn Money
CHOP/DBHi Collaborators
Deanne Taylor
Asif Chinwalla
John Maris & SU2C

24. Thank You
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