1. The PedcBioPortal & DiseaseXpress
CBTTC Investigator Meeting
Pichai Raman on behalf of D3B
Monday, May 8, 17

2. Downstream Analytics and Visualization
Focus on RNA-Seq with common standard of processing & normalization
DiseaseXpress
Raw Data
SBG Cavatica
PedcBioPortal
Processed Data
Takes all data types with a focus on integrative analysis & summary

3. Downstream Analytics and Visualization
PedcBioPortal
DiseaseXpress
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
Hypothesis driven analysis
cBioPortal Development now shared across 5 teams : DFCI, MSKCC, Princess Margaret, and CHOP, the Hyve
PedcBioPortal : CHOP Implementation has a focus on Pediatric cancer data sets
Data Mining
D3B Powered application that has greater than 20K samples all processed the same way
Leverages work performed by UCSD and the broad and builds upon it by providing web-api access and additional data sets
Accessible by R
CURL
DEXTER
Cohort analysis and summary
Machine Learning
Pan-cancer Analysis
Sample level views
What genes are most often mutated in my cohort? And what is the association of that mutation with survival?
What are some potential CAR therapy targets for Group 3 Medulloblastomas? Are these targets expressed in adult tumors as well?
What are the known actionable genetic lesions for this sample?
Are there gene expression signatures that are predictive of survival or relapse in brain cancers?
What are the hotspot mutations or domains most commonly mutated on this gene?

4. PedcBio - Key Functionality & Features
PedcBioPortal+ 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
Patient 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

5. PedcBio - Visualizations & Analytics - Gene
Determine mutational hotspots on a gene by looking at mutational landscape across the gene
Compare copy number vs mRNA or Risk/Stage vs mRNA
CNA vs mRNA
Mutation Mapper
Single Gene View
Look at relationships between genes and identify other potentially interesting candidates by association analysis
Determine if genes are mutually exclusive or recurrently associated. Find patients without canonical lesions.
OncoPrints
Network
Multi-Gene Views
Survival Analysis

6. PedcBio - Visualizations & Analytics - Patient
Global view of tumor sample lesions & CNA
PedcBio - Visualizations & Analytics - Patient
Relevant mutations
Copy Number Changes

7. PedcBio - Visualizations & Analytics – Study Summary
Survival Analysis
Relevant mutations
Cohort Characteristics

8. PedcBio - Current Development & Connectivity
Virtual Cohort Creation
Biorepository Integration
Synthetic or virtual cohorts can serve to bridge together data form different studies to focus on subsets of patients or samples. Examples Include
Patients with rare cancers & diseases
Patients belonging to specific population or demographic
Cases originating from a particular locale
Users can move seemlessly between visualization on the portal and samples in the biorepository applications
Connecting to other applications on the cloud currently

9. Downstream Analytics and Visualization
PedcBioPortal
DiseaseXpress
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
PedcBioPortal : CHOP Implementation has a focus on Pediatric cancer data sets
D3B Powered application that has greater than 20K samples all processed the same way
Leverages work performed by UCSD and the broad and builds upon it by providing web-api access and additional data sets
Accessible by R
CURL
DEXTER

10. DiseaseXpress - Overview

11. DiseaseXpress – Boxplots by Study / Disease
Collapse a study to represent one box
Set the collapsed study or other disease as reference box

12. DiseaseXpress – Scatterplot of 2 Genes
Disease specific
correlation estimates
And P-values

13. DiseaseXpress – Current Development & Connectivity
Cancer Pathway Analysis (single sample & cohort)
Addition of all available public RNA-Seq datasets (GEO, ArrayExpress)
Increasing speed of access
Enhancing annotation to include tags
Tie to variant, fusion, and copy number warehouses

14. Acknowledgements
Karthik Kalletla / Anthony Cros : Building DiseaseXpress datastore and engine
Komal Rathi : Developing DEXTER Shiny Application
DAMEU
Bo Zhang
Allison Heath
Yuankun Zhu
D3B Team Members

15. BACKUPS

16. Discovery Data Cavatica Mission Pipelines Hypotheses Visualizations
To develop a sustainable application eco-system that supports many of the aspects associated with basic & translational research.
To be the premier portal for pediatric disease research and serve as a central hub to promote collaborative research between investigators to support sharing and creation of …
Pipelines
Hypotheses
Data
Visualizations
Algorithms

17. Services Applications Users
Cavatica Eco-System
Services
Applications
Users
Bio Repository [ HARVEST, …]
Physical Specimens
Raw Data Storage & Processing [SBG, …]
FASTQ / BAM Files
Processed Data Informatics [GitHub LFS, …]
Matrices, Data Frames
Data Visualizations & Analytics [cBioPortal & DiseaseXpress]
Graphs, tables, summaries
Data Scientists
Data Tracker
Investigators / Post-Doc
User Management System
Data Engineers