tranSMART’s Application to
Clinical Biomarker
Discovery in Sanofi
Sherry Cao Ph.D.
tranSMART Community Meeting
Nov. 6th, 2...
Outline
●
●
●
●

Challenges in clinical biomarker discovery
How Sanofi is meeting those challenges
Role of tranSMART
tranS...
Clinical Biomarker Discovery Process
Clinical Sample
Procurement
Clinical Information
• Patients
• Diseases
• Clinical Phe...
Challenges for Clinical Biomarker Discovery
●

High-throughput biological measurements generate
unprecedented amount of da...
Interdisciplinary team for Clinical Biomarker Research

Clinical
Statisticians

Clinicians
CBR
Team

Research
Scientists

...
Two Distinctive User Groups

Clinicians, Research
Scientists

Informatic Scientists &
Statisticians

Main Role

Hypothesis...
Informatics Systems Mapped onto Research
Flow
Data Capture

Discovery

Interpretation

Clinical Sample
Validation

Platfor...
Challenges for Clinical Biomarker Discovery
●

High-throughput biological measurements generate
unprecedented amount of da...
Two Distinctive User Groups

Clinicians, Research
Scientists

Informatic Scientists &
Statisticians

Main Role

Hypothesis...
Informatics Systems Mapped onto Research
Flow
Data Capture

Discovery

Interpretation

Clinical Sample
Validation

Platfor...
Role of TranSMART within Sanofi
●

●

●

Translational data hub - One stop shop for all data related to a
biomarker discov...
Clinical Biomarker Discovery Use Case 1
●
●
●
●

●

Business unit with established & active biomarker discovery
process
Sa...
tranSMART in Sanofi – Data Management

Global view of all the data available
From level 1 data (uncurated/raw files)
to le...
Data organization
●

Data is organized in a hierarchical structure:
Program

Study

File Folder*

Assay
Analysis
* A file ...
Program Explorer
Program Explorer box allows to navigate within Programs
Analysis or File Folders

, Studies

, Assays

|
...
Integrated search
New search function at the top of the screen. Any data (levels 1-4) can be searched.

Dropdown with a li...
Filter
A new Filter option can also be used for selections based on fields with a small
set of possible values.

1

2

The...
Search & filter in Analyze
Synchronized search & filter function in Analyze

|

18
Visualization of gene expression analysis
Creation of a template for loading and displaying gene expression analysis resul...
File export – Shopping Cart function
New concept of Shopping Cart for exporting files.

Note: If positive feedback from us...
Clinical Biomarker Discovery Use Case 2
●
●
●

●

Business unit with focused biomarker discovery program
Goal is to identi...
tranSMART in Sanofi – Data Integration
Current state
● Within study clinical & gene expression profiling data

End Point

...
tranSMART in Sanofi – Data Integration
●

In the pipeline
● Multi-modal profiling data support

Data types to be addressed...
tranSMART in Sanofi – Providing Analysis Tools
to Research Scientists
General Summary Statistics on Patient Cohorts
Baseline marker gene expression is correlated with
outcome at 52 weeks
Disease Signature Evaluation
Clinical Biomarker Discovery Use Case 3
●
●

Efficacy biomarker discovery for complex disease with 15,000
patients
Situati...
Conclusions
●
●

●
●

tranSMART can provide critical solutions for clinical biomarker
discovery needs
● Data management, i...
Question

Functionality

User Interface
Acknowledgement
●
●

●
●

Genzyme
● Jike Cui, Adam Palermo, Rena Baek, Petra Olivova, Leslie Jost, Rob
Pomponio, Allison M...
Dream Analysis Process
Define question
Identify patient cohort
Obtain relevant profile
& clinical data
Run analysis

Satis...
Upcoming SlideShare
Loading in...5
×

tranSMART Community Meeting 5-7 Nov 13 - Session 3: transmart’s application to clinical biomarker discovery in sanofi

777

Published on

tranSMART Community Meeting 5-7 Nov 13 - Session 3: tranSMART’s Application to Clinical Biomarker Discovery Studies in Sanofi
Sherry Cao, Sanofi
This presentation will discuss challenges we are encountering in clinical biomarker discovery
study and how we are using tranSMART to help to address them.

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
777
On Slideshare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
40
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

tranSMART Community Meeting 5-7 Nov 13 - Session 3: transmart’s application to clinical biomarker discovery in sanofi

  1. 1. tranSMART’s Application to Clinical Biomarker Discovery in Sanofi Sherry Cao Ph.D. tranSMART Community Meeting Nov. 6th, 2013
  2. 2. Outline ● ● ● ● Challenges in clinical biomarker discovery How Sanofi is meeting those challenges Role of tranSMART tranSMART in Sanofi
  3. 3. Clinical Biomarker Discovery Process Clinical Sample Procurement Clinical Information • Patients • Diseases • Clinical Phenotypes • Lab tests • Pathology reports • Drugs Data Capture Molecular Information • DNA • RNA • Protein • Lipid • Metabolites Discovery & Interpretation Biomarkers • Diagnostic • Prognostic • Efficacy Signatures • Molecular classifications • Patient stratifications Target ID/Credentialing • Molecular targets • Pathways • Clinical phenotypes Clinical Sample Validation Sample Sources • In house • Public Type • In silico • Experimental
  4. 4. Challenges for Clinical Biomarker Discovery ● High-throughput biological measurements generate unprecedented amount of data for each biological sample ● Chip based profiling technologies ● Exome, transcriptome & genomic sequencing technologies ● Data Management The complexity of disease biology requires large sample numbers to reach statistical significance ● GWAS studies for complex traits ● Molecular signature developments for patient stratification ● Heterogeneous data types & data sources ● Research & clinical ● Structured & non-structured data ● ● Data curation is a very critical & time consuming process Complex analysis & visualizations are needed to transform data to knowledge Integration & Analysis
  5. 5. Interdisciplinary team for Clinical Biomarker Research Clinical Statisticians Clinicians CBR Team Research Scientists Clinical Informaticians Research Informaticians 5
  6. 6. Two Distinctive User Groups Clinicians, Research Scientists Informatic Scientists & Statisticians Main Role Hypothesis generation, Mechanistic Interpretation Data analysis Statistical Analysis Type Single variable, correlative analysis Multi-variable complex analysis Very limited SAS, JMP, R Drag & Drop GUI API Data acquisition, Data analysis turnaround time Data acquisition, Data curation & reformatting, Not enough time to do real analysis Statistical Tool Access User Interface Major Complaints
  7. 7. Informatics Systems Mapped onto Research Flow Data Capture Discovery Interpretation Clinical Sample Validation Platform Specific System Data Management & Integration
  8. 8. Challenges for Clinical Biomarker Discovery ● High-throughput biological measurements generate unprecedented amount of data for each biological sample ● Chip based profiling technologies ● Exome, transcriptome & genomic sequencing technologies ● Data Management The complexity of disease biology requires large sample numbers to reach statistical significance ● GWAS studies for complex traits ● Molecular signature developments for patient stratification ● Heterogeneous data types & data sources ● Research & clinical ● Structured & non-structured data ● ● Data curation is a very critical & time consuming process Complex analysis & visualizations are needed to transform data to knowledge Integration & Analysis
  9. 9. Two Distinctive User Groups Clinicians, Research Scientists Informatic Scientists & Statisticians Main Role Hypothesis generation, Mechanistic Interpretation Data analysis Statistical Analysis Type Single variable, correlative analysis Multi-variable complex analysis Very limited SAS, JMP, R Drag & Drop GUI API Data acquisition, Data analysis turnaround time Data acquisition, Data curation & reformatting, Not enough time to do real analysis Statistical Tool Access User Interface Major Complaints
  10. 10. Informatics Systems Mapped onto Research Flow Data Capture Discovery Interpretation Clinical Sample Validation Platform Specific System Data Management & Integration
  11. 11. Role of TranSMART within Sanofi ● ● ● Translational data hub - One stop shop for all data related to a biomarker discovery project Data management & integration ● Clinical & research data ● Structured & non-structured data ● Fully curated data for integrated analysis & not-fully curated data Deliver critically needed statistical/informatics analysis tool to clinicians & research scientists ● Unit variant analysis ● Simple clustering analysis & heatmap generation Help informatics scientists to generate custom analysis data sets based on distinctive cohort definitions Data management & integration
  12. 12. Clinical Biomarker Discovery Use Case 1 ● ● ● ● ● Business unit with established & active biomarker discovery process Samples are routinely sent out for profiling at different platforms Data are generated routinely both from CRO & internal groups ● High throughput profiling data ● Low throughput imaging & assay data (IHC, ELISA, qPCR, etc.) Situation ● Biomarker team reps are overwhelmed by data management related questions with little time to do actual analysis Critical need ● How to organize data effectively? ● How to manage the low throughput data systematically with data from clinical & high throughput data? ● How to search & find the relevant data quickly?
  13. 13. tranSMART in Sanofi – Data Management Global view of all the data available From level 1 data (uncurated/raw files) to levels 3-4 data (analysis results, findings) Run analysis on subject-level data (former Dataset Explorer) Navigate within Programs > Studies > Assays , Analysis and File Folders (see next slide) Browse level 2 (processed) data – incl. clinical / preclinical / molecular data, etc. Search data using dictionaries Search subject-level data Create new Programs > Studies > Assays and Files Folders, and annotate (tag) them Select data subsets (cohorts) Export files Run basic statistical and genomic analyses on those subsets (standard features from tranSMART v1.0) Visualize gene expression analysis results Export out data subsets
  14. 14. Data organization ● Data is organized in a hierarchical structure: Program Study File Folder* Assay Analysis * A file folder can be created at any levels: program, study, assay… Each object (Program, Study, Assay, etc.) is tagged with metadata: – Provide information on the object – Enable queries using search Predefined annotation templates – Most fields use CV with pick-list or autocomplete functionalities. Examples of dictionaries used: MESH, WhoDD, some branches Nextbio Ontology. – Description field enables to capture free text | 14
  15. 15. Program Explorer Program Explorer box allows to navigate within Programs Analysis or File Folders , Studies , Assays | 15
  16. 16. Integrated search New search function at the top of the screen. Any data (levels 1-4) can be searched. Dropdown with a list of dictionaries + free-text search Autocomplete feature for values in dictionaries Analyze view: The system points you to level 2 data Browse view: The search returns Programs, Studies, Assays and/or Files that match your query | 16
  17. 17. Filter A new Filter option can also be used for selections based on fields with a small set of possible values. 1 2 The search returns Programs, Studies, Assays and/or Files that match your query. | 17
  18. 18. Search & filter in Analyze Synchronized search & filter function in Analyze | 18
  19. 19. Visualization of gene expression analysis Creation of a template for loading and displaying gene expression analysis results. | 19
  20. 20. File export – Shopping Cart function New concept of Shopping Cart for exporting files. Note: If positive feedback from users on this Shopping Cart concept, we may extend this feature in RC-2 to subject-level data. | 20
  21. 21. Clinical Biomarker Discovery Use Case 2 ● ● ● ● Business unit with focused biomarker discovery program Goal is to identify disease progression biomarkers than the current clinical functional test Situation at hand ● Researchers don’t have any appropriate analytical tools for correlative analysis ● A variety of profiling experiments are being planned • RNAseq, Proteomics, RBM, miRNA, Metabolomics ● Patient data at multiple time points are collected Critical need ● How to integrate all the data? ● How to enable clinical researchers to analyze and visualize data? ● How to analyze time series data more effectively?
  22. 22. tranSMART in Sanofi – Data Integration Current state ● Within study clinical & gene expression profiling data End Point ● Gene expression
  23. 23. tranSMART in Sanofi – Data Integration ● In the pipeline ● Multi-modal profiling data support Data types to be addressed ● ● ● ● ● RNAseq miRNA profiling (qPCR + seq) Metabolomics Proteomics RBM Protein Level ● Gene expression
  24. 24. tranSMART in Sanofi – Providing Analysis Tools to Research Scientists General Summary Statistics on Patient Cohorts
  25. 25. Baseline marker gene expression is correlated with outcome at 52 weeks
  26. 26. Disease Signature Evaluation
  27. 27. Clinical Biomarker Discovery Use Case 3 ● ● Efficacy biomarker discovery for complex disease with 15,000 patients Situation at hand ● A number of profiling experiments are being planned • RNAseq, RBM, Metabolomics ● Patients often manifest other disease symptons ● Critical issue ● How to load such a large dataset? ● How to analyze such a large sample numbers with multiple high dimensional data? ● How to analyze comorbidities?
  28. 28. Conclusions ● ● ● ● tranSMART can provide critical solutions for clinical biomarker discovery needs ● Data management, integration & analysis Two distinctive user groups for tranSMART through user interface and through API Different business units have different requirements for tranSMART Sanofi developed critical user interface and functionality improvements to meet sanofi and general clinical biomarker discovery needs
  29. 29. Question Functionality User Interface
  30. 30. Acknowledgement ● ● ● ● Genzyme ● Jike Cui, Adam Palermo, Rena Baek, Petra Olivova, Leslie Jost, Rob Pomponio, Allison McVie-Wylie, Steve Madden, Clarence Wang Diabetes ● Juergen Kammerer, Manfred Hendlich, Dan Crowther Oncology ● Mary Penniston, Jack Pollard Sanofi tranSMART development team ● Claire Virenque, Annick Peraux ● Angelo Decristofano, Lars Greiffenberg, Christophe Gibault, David Peyruc
  31. 31. Dream Analysis Process Define question Identify patient cohort Obtain relevant profile & clinical data Run analysis Satisfied Format! Export & publish results
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×