© 2015 Cognizant © 2015 Cognizant

June 8, 2015

Biomarker and Target Analyst: A new platform using data science to improve Pharma R&D outcomesCamille Diges, PhD

© 2015 Cognizant

Personalized medicine is driving rapid biomarker market growth

Data from Frost & Sullivan, 2013;Markets and Markets, 2014

IT R&D AND DISCOVERY R&D TEAMS MUST ADAPT TO NEW

MARKET NEEDS

IT R&D CHALLENGES• ‘OMICS TECHNOLOGIES CREATING MORE DATA

AT FASTER RATES EVERY YEAR

• NEW INFRASTRUCTURE REQUIRED TO HANDLE DATA ANALYSIS QUICKLY

• KEEPING ALL INTERNAL AND EXTERNAL DATA SOURCES UP TO DATE

DISCOVERY R&D CHALLENGES• DATA ANALYSIS REQUIRES SPECIALIZED

PROGRAMMING ABILITIES

• DIFFICULT TO PLACE RESULTS IN BIOLOGICAL CONTEXT

• DIFFICULT TO INTEGRATE AND VISUALIZE DATA GENERATED FROM DIFFERENT EXPERIMENTS

© 2015 Cognizant 3

Biomarker Analyst: helping make personalized medicine a reality

METABOLITES

BIOMARKERANALYST

PLATFORMPROTEINCHANGES

EPIGENETICS

GENEEXPRESSION

GENOMESEQUENCE

miRNAEXPRESSION

• IMPROVED DATA ANALYSIS

• INCREASED DATA ACCESSIBILITY

• DATA INTEGRATION ACROSS EXPERIMENTS

• FUNCTIONAL FRAMEWORK FOR RESULTS

PLATFORM CAPABILITIES

BUSINESS VALUE• FASTER TIME TO MARKET FOR NEW DRUGS

• REDUCED FAILURE RATE IN CLINICAL TRIALS

• BIOMARKER AND TARGET IDENTIFICATION

• BETTER UNDERSTANDING OF DISEASE

• NEW RESEARCH DISCOVERIES

© 2015 Cognizant

Biomarker Analyst: Combining experimental analysis with functional information

Data Sources

Gene expression

DNA Sequencing

Epigenetic Data

Pathway Databases

Biomarker Analyst

Proteomic Data

© 2015 Cognizant

General Biomarker Analyst infrastructure overview

Storage Layer

Data Sources

StructuredData

UnstructuredData

Publications

NCBI

Reactome

PathwayDatabasesGenome

SequencesMass

Spectra

ChIPData

ExternalData

Local FileSystem

Big Data Storage & Processing Consumption LayerAnalytics &

Visualization

R&D Scientists

Biomarker Analyst democratizes data analysis, provides access to the most up-to-date analysis packages and scientific results, and enables the integration of data from different types of experiments to provide a holistic and functional view of the disease state.

© 2015 Cognizant

Proof of Concept: Identifying new biomarkers for breast cancer relapse

© 2015 Cognizant 7

Application: Linking gene expression to phenotype to identify biomarkersClient Challenge: • “Biomarkers” routinely identified, but fail at various stages of drug

development. Costly failures ($MM).• Gene expression data is time consuming and statistically challenging

to analyze. Misinterpretation is common. No functional information.• Biomarker success dependent on understanding its function.

Cognizant Solution: • Cognizant coupled gene expression analysis to signaling pathway

data to identify functional biomarkers for breast cancer relapse• Differential gene expression algorithms implemented to add rigor to

data analysis• Hadoop and R used to decrease processing time from days to hours

Client Benefit: • Differential gene expression analysis reduced candidate biomarkers

from 2,300+ genes to 10 gene pairs• 7 potential biomarkers identified for breast cancer relapse when placed

in functional context

IndustryPharmaceutical

EnvironmentHadoopR, RstudioNCBI

ChallengeData normalizationStatistical analysisIdentification of important gene pairs

Cognizant SolutionAutomate process to shorten analysis time from days to minutes

Client BenefitBM IdentificationFunctional analysis of BMCompanion testsCompanion diagnostics

© 2015 Cognizant

Biomarker Analyst places results in a functional context

Microarray Data from

GEO

Pathway Data

Data Collection

Generating Possible Pairs on Pathway

List of Differential Gene Paris

List of Gene Pairs on inter-

connected Pathways

Joint Lists of Gene Pairs Map Pairs on Pathway MapBio-Marker Network Charts

Grouping Normalization

Name Matching

ANOVA Test Filter

Differential Genes

Correlation for all pairs of differential

genes

Keep strongly related gene

pairs only

• Use the Differential Genes to form pairs• Examine pairs in the functional context of Reactome Pathways

Phase1

Phase2

Phase 3

Note: • Gene Selection, Pearson’s Correlation in Phase 1 AND Generating Pairs on Pathway in Phase 2 improved by applying Hadoop• If desired, Gene Selection Step may be taken out from process to keep more genes after Hadoop applied.

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Down-regulated gene pairs correlated to high relapse rates of breast cancer

“Kinome Expression Profiling and Prognosis of Basal Breast Cancers”, Sabatier et al, Molecular Cancer, 2011

• Our analysis identified 7 key proteins that are significantly down-regulated in relapsed breast cancer patients

• All seven genes are part of the “Immune Metagene”

• 51% of patients with down-regulation of the Immune Metagene will have breast cancer relapse in 5 years compared to only 9% with no changes in gene expression.

© 2015 Cognizant 10

Biomarker Analyst Platform: Additional Features

• Multiple data sources for analysis to reflect complexity of experimental landscape

• DNA sequencing, metabolomic and proteomic data, CHiP-Seq and RNA-Seq

• Capable of working with any signaling pathway databases• Ingenuity IPA, GeneGo, Pathway Studio, KEGG, etc.

• Integrate results from different experiment types• Not possible currently due to data handling, analytical, and statistical challenges

• Customize visualization of results• Designed to meet scientific needs

• Phase 2 will automatically connect results to key publications using semantic technology

• Provide instant scientific context for results

© 2015 Cognizant

Camille DigesCamille.Diges@Cognizant.com