Next Generation Sequencing in Pharmacogenomics

1. Next Generation Sequencing in Pharmacogenomics • Gerry Higgins, Ph.D., M.D. • Vice President, Pharmacogenomic Science • AssureRx Health, Inc.

2. TOPICS • Explosive Growth in Sequence Data • The ‘Big Data’ Problem • The ‘Diminishing Discovery’ Problem • Human Genome Variation and Pharmacogenomics • Evolution of next generation sequencing (NGS) technology • Future Trends

3. Big Data

4. Explosive Growth in Sequence Data As the cost of DNA sequencing falls, the growth of human genome data becomes exponential

5. The ‘Big Data’ Problem Lee Hood, IOM February 27, 2012

6. The ‘Big Data’ Problem • “The world is shifting to an innovation economy and nobody does innovation better than America.” • —President Obama, 12/6/2011 • Pillers of Bioeconomy R&D: • 1) Synthetic Biology • 2) Proteomics • 3) Information Technology— Bioinformatics & Computational Biology

7. The ‘Diminishing Discovery’ Problem

8. The ‘Diminishing Discovery’ Problem FDA’s Solution: Adaptation in the Pre-Competitive Space SCREENING TRIAL Investigational drugs Promising drug candidate & associated PGx markers & associated PGx marker Achieve surrogate end point predictive of clinical outcome CONFIRMATORY TRIAL Promising drug candidate & associated PGx marker Achieve clinical outcome (regulatory standard for FDA approval) Replicate surrogate end point FDA APPROVAL Full drug approval Accelerated drug approval with approval of PGx biomarker *Slide adapted , with permission, from Janet Woodcock and Issam Zineh, CDER, FDA

9. The ‘Diminishing Discovery’ Problem Pre-Competitive Collaboration: Solution for Pharma • Share use cases/questions – gaps in current tools • Identify common solutions & options • Share development risk/costs • Build interoperability standards into platforms • Publicly share experiences - good & bad • PPP (public-private-partnership) infrastructure • Build portable talent base/experts across sites • Compile innovations from participating groups • Follow European model – share trial participants • Faster path for FDA drug approval

10. The ‘Diminishing Discovery’ Problem tranSMART: Bioinformatics & shared data analytics platform • tranSMART is an open source informatics software platform that allows pharmaceutical, diagnostic and medical device companies to share “pre-competitive” data and a set of common tools for analysis of data. The license protects the intellectual property of all stakeholders. • Dr. Eric Perakslis, now CIO and Chief Scientist (Informatics) at the FDA, originally developed tranSMART when he served as a research scientist at Johnson & Johnson. tranSMART is based on the i2b2 informatics platform. • tranSMART has been adopted more broadly in Europe than in the U.S. An example of a study where “pre-competitive” data were shared (KM: Knowledge Management): U-BIOPRED (Unbiased BIOmarkers in PREDiction of respiratory disease outcomes)1 1Bel EH et al. Diagnosis and definition of severe refractory asthma: an international consensus statement from the Innovative Medicine Initiative (IMI). Thorax. 2011 66(10):910

11. One Mind Integrative Informatics Platform Genome Proteome Signaling Phenome Disease Integrative Analyses Managed Thru Cloud-Based Portal One Mind PortalTM Builds off of tranSMART Data Knowledge Management System

12. Genome Variation and Pharmacogenomics

13. Human Genome Variation as determined by NGS “The ability of sequencing to detect a site that is segregating in the population is dominated by two factors: Whether the non-reference allele is present among the individuals chosen for sequencing, and; The number of high quality and well mapped reads that overlap the variant site in individuals who carry it. Simple models show that for a given total amount of sequencing, the number of variants discovered is maximized by sequencing many samples at low coverage. This is because high coverage of a few genomes, while providing the highest sensitivity and accuracy in genotyping a single individual, involves considerable redundancy and misses variation not represented by those samples.”1 • Genome variants of different types, determined by low coverage sequencing of individuals, trios (e.g., mother, father and daughter) and exons. These data are derived from the 1000 genomes project.1 • Note that they did not attempt to resolve Copy Number Variants (CNVs) or Variable Number of Tandem Repeats (VNTRs), which convey inter-individual variation. • Note the large percentage of novel SNPs that were discovered by NGS. 1Durbin et al. A map of human genome variation from population-scale sequencing. 2010. Nature 467: 1061-1073.

14. Genome Variation and Pharmacogenomics • Some important points about Single Nucleotide Polymorphisms (SNPs) : • All methods to determine human genome variation contain error. • So-called “common” SNPs, with a frequency of >0. 5%, have yielded modest effects in genome-wide association scans (GWAS) for determination in complex diseases. • Early results from pharmacogenomic GWAS appear to indicate a greater ability to discover SNPs with substantial effect size. Nevertheless, they do not explain the full extent of human genome variation and drug response. Pharmacogenomic GWAS are limited in power by small cohort sizes.1 • Although each human genome may have ~3 M SNPs, only some of these variants are deleterious. • SNPs have been the easiest genomic variant to measure, but other variants, such as Copy Number Variants (CNVs), may be more important determinants of drug response.2 • Most variants that impact individual drug response have not yet been identified.3* • 1Guessous, I., Gwinn, M. & Khoury, M.J. Genome-wide association studies in pharmacogenomics: untapped potential for translation. Genome Med 1, 46 (2009); Group, S.C. et al. SLCO1B1 variants and statin-induced myopathy—a genome wide study. N Engl J Med 359, 789-799 (2008). Sato, Y. et al. A new statistical screening approach for finding pharmacokinetics related genes in genome-wide studies. Pharmacogenomics J 9, 137-146 (2009); Crowley, J.J., Sullivan, P.F. & McLeod, H.L. Pharmacogenomic genome-wide association studies: lessons learned thus far. Pharmacogenomics 10, 161-163 (2009). • 2Rasmussen H B et al. Genome-wide identification of structural variants in genes encoding drug targets: possible implications for individualized drug therapy. Pharmacogenetics and Genomics. July 2012. 22 (7): 471-483. • 3Durbin et al. A map of human genome variation from population-scale sequencing. 2010. Nature 467: 1061-1073. *FDA.

15. Genome Variation and Pharmacogenomics Allele-Specific PCR cannot accurately detect SNPs1: Unknown SNP 1Favis, R. Applying next generation sequencing to pharmacogenomics studies in clinical trials. Unknown SNP

16. Genome Variation and Pharmacogenomics High throughput genotyping platforms cannot accurately resolve allelic variants of the CYP2D6 superfamily1: Genome-wide arrays, some that are specifically configured to examine pharmacogene variants, were poor at discriminating CYP2D6 alleles: 1Gamazon ER et al. The limits of genome-wide methods for pharmacogenomics testing. Pharmacogenetics and Genomics. 2012. 22:261–272.;

17. Genome Variation and Pharmacogenomics • Some important points about Next Generation Sequencing (NGS): • All methods to determine human genome variation contain error. • All ‘short read’ NGS methods rely on the use of a “reference genome” as ground truth, when the various reference genomes have been shown to have unusual variation1. • Short read NGS technology is fraught with errors, and thus either requires 60-100 fold coverage for a single individual, or low coverage whole genome sequence data from a large popoulation2. The most accurate results have been obtained from sequencing the whole genomes of closely-related individuals, along with inclusion of other data related to family medical history1,3. • Short read NGS technology is especially poor at calling variants in GC-rich regions of the genome such as CpG islands. • The real value is provided by long read technology, which has been implemented by Complete Genomics, but they have a backlog of genomes to sequence under contract (~27,354 as of 6/12). • So-called ‘clinical’ or bench-top sequencers, such as Illumina’s MiSeq or Life Technologies Ion Torrent, manifest all the problems associated with short read technology, including extensive pre-processing of tissue samples and complex data analysis. • 1Dewey et al. Phased whole-genome genetic risk in a family quartet using a major allele reference sequence. PLoS Genet. 2011 September; 7(9): e1002280. • 2Durbin et al. A map of human genome variation from population-scale sequencing. 2010. Nature 467: 1061-1073. • 3Patel C J et al. Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease. Bioinformatics. 2012 Jun 15;28(12):i121-i126.

18. Genome Variation and Pharmacogenomics Whole genome sequencing & analysis has been able to resolve pharmacogene variation on a genome-wide level, including the various alleles of the CYP2D6 superfamily1: 1Black JL et al. Frequency of undetected CYP2D6 hybrid genes in clinical samples: Impact on phenotype prediction. Drug Metab Dispos June 2012 40:1238; Patents: United States Patent Application 20120088247;

19. Trends in Next Generation Sequencing and Clinical Interpretation

20. Trends in Next Generation Sequencing 20102013

21. Trends in Next Generation Sequencing • 2nd Generation NGS - Short read archive: • Hardware and Service Companies – Market Share– Ilumina and Complete Genomics sequenced over 90% of all genomes as of 10/1/111 • Concordance of variant calls – Illumina versus Complete Genomics short read1 • 1Lam HL et al. Performance comparison of whole-genome sequencing platforms. Nature Biotech. 2012. 30: 78-82.

22. Next Generation Sequencing – Update 6/12

23. NGS – Complete Genomics, Inc.

24. NGS – Long Read Nanopore Solutions Their most recent technology involves combining a very high speed CCD (charge-coupled display) camera with each DNA base tagged with a fluorochrome coming through a nanopore. •They have achieved 500Kb read lengths, claim error rate is “I missed base call variant every 500Kb” – Lee Hood. •They have been able to resolve phased maternal and paternal chromosomes •They can resolve distributed repeats (e.g. pseudogenes) •However, their in-house, pre- and post-processing steps are very complex and time-consuming, their turnaround time for a human genome with a coverage of 10-fold is 72 days, and they now have a backlog of 25,000 genomes. Complete Genomics Extract and fragment DNA Each base (A, C, G, T) tagged with a different fluorochrome Multi-planar graphene array High-speed CCD camera – can capture every base per pixel with DNA traveling at ~10 base pairs per m-second.

25. NGS – Long Read Nanopore Solutions • Rosenstein et al1 latest device can accurately sequence 1 million base pairs of double-stranded DNA without error. • Unlike most researchers interested in using nanopores to directly sequence DNA that have slowed the DNA velocity in the nanopore translocation stage through adding an enzyme ratchet such as Oxford Nanopore Technology to accommodate the low bandwidths available, these researchers used complementary metal-oxide semiconductor (CMOS) processing and integrated circuits technology. • They have been able to redesign their system to increase the bandwidth above 50MHz, with a very low signal-to-noise ratio to sequence an entire human genome with very little sample preparation in 20 minutes. Ideal System1 Extract DNA. Pass “naked” DNA through graphene nanopore array. High bandwidth CMOS pre-amplifier positioned under every pore. Solid state silicon nitride membrane chip mounted in the fluid cell. 1Rosenstein JK et al. Integrated nanopore sensing platform with sub-microsecond temporal resolution. Nature Methods. 2012. 9 (5): 487-492.

26. WGA – Clinical Interpretation Software • Whole Genome Analysis - “The $1,000 genome and the $1M interpretation.” • 3 major approaches: • Filter data followed by complex analysis – Used by Cypher Genomics and Illumina • Apply proprietary natural language processing algorithms against whole genome or whole exome data – Used by Silicon Valley Biosystems • Genomic best linear unbiased prediction (GBLUP) method to evaluate predictive ability by cross-validation. GBLUP approaches take into account the covariance structure inferred from the genomic data. Best predictive accuracy1,2 • 1Ober U et al. Using whole-genome sequence data to predict quantitative trait phenotypes in Drosophila melanogaster. PLoS Genetics. May 2012. 8 (5): 1-14. • 2Jones B. Predicting phenotypes. Nature Reviews Genetics. 2012. 13. doi:10.1038/nrg3267

27. WGA – Clinical Interpretation Software Whole Genome Analysis - Example from Cypher Genomics

28. WGA – Clinical Interpretation Software Whole Genome Analysis - Example from Cypher Genomics

29. THE END – Thank-you!

30. AssureRx Health Overview Corporate Offices: Mason, Ohio Founded: 2006 • A personalized medicine company focused on behavioral health and dedicated to helping clinicians determine the optimal medications for individual patients suffering from psychiatric disorders. • Privately-held with key investments from Claremont Creek Ventures, Sequoia Partners, CincyTech and other notable investment groups • Cincinnati Children’s Hospital Medical Center and Mayo Clinic are AssureRx Health’s official technology collaborators for the GeneSightRx testing program

31. Lab & Technology Operations • Lab • Results delivered within one business day of receipt of a patient’s DNA sample • CLIA certified • CAP accredited • NY State Department of Health certified • Technology • Advanced bioinformatics • World-class data center operations • Secure Internet protocols • HIPAA compliant architecture • Data integration with Facility Health Information Management Systems