Scientific Excellence Innovation Collaboration “Big Data and How It Is Being Used to Transform the Pharmaceutical Business Model” Guna Rajagopal, VP & Global Head, Computational Sciences Pharmacogenomics Scientific Excellence Innovation Collaboration

Integrating the digital universe of data to deliver precision medicine Eric Schadt, MSSM

Anon. The Big Data Challenge Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it … Anon.

Opportunities & Challenges Growing confidence in ability to leverage ‘Big Data’ for healthcare despite critical challenges involving : sustainability, harmonization Indemnification. Increasing computational power and cheaper sequencing costs Multiple academic, government and private company efforts initiated Rich databases under development Multi ‘omics and metadata ‘Normal’ and disease populations Link genotype/phenotype Key issues of privacy, confidentiality and sharing of data to advance R&D still unresolved. Cost depending on duration, # of individuals, type of data collected Study subject retention easier in clinical trials cf. prospective studies Biobanking quality, # of samples, heterogeneity Sampling Errors sample size, incomplete/noisy data Bioinformatics Signal/Noise issues Experimental and/or study design nature and scope of query, ease of acquiring high-quality samples and associated phenotype data

Example – Application to Pharmacogenomics The Question: How do we identify new genetic markers and mechanisms for response/non- response for patients in ((anti-TNF treatment for RA)) using samples and phenotypic data collected in a clinical study? This question addressed in 3 phases – we outline Phase 1 Collaboration: In partnership with R&D IT and Immunology colleagues and external collaborator (Nic Schork, JCVI), Amazon and SDSC experts.

Our Challenge – Integrating Big Data, Cloud, HPC platforms & Analytics RA WGS FASTQ, BAM, VCF, GFF, report 90 TB Encryption Factory FASTQ/BAM SDSC HPC AWS tranSMART Amazon S3 FASTQ-> BAM-> VCF subset BAM Glacier (135 TB) Clinical/biomarker data VCF Janssen Scripps Feinstein Critical support from R&D IT and SDSC staff

Big Data Needs Big Compute! 257 TB Lustre scratch used at peak 5,000 cores (30% of Gordon) in use at once Partnership with Intel/IMEC/SDSC to optimize code/HPC performance

Limitations - Meaningfulness of Answers A risk with “Big Data Analytics” is that one can “discover” patterns that are meaningless => Check, Double Check & Verify!

REFINING THERAPEUTIC DECISIONS & PREDICTING DRUG EFFICACY NR R AE