Translational Case Histories

1. Translational Case Histories Harvard Medical School Center for Biomedical Informatics i2b2 National Center for Biomedical Computing Isaac S. Kohane, MD, PhD John Glaser, PhD Susanne Churchill, PhD

3. First signal: • 1 year after Celecoxib • 8 months after Rofecoxib

4. For every million prescriptions, 0.5% increase in MI (95%CI 0.1 to 0.9) • 50.3% of the deviance explained

5. Effect on patient age • Negative association between mean age at MI and prescription volume • Spearman correlation -0.67, P<0.05

6. I2B2: Test RelNet Project • Correlate available GEO expression data for GPL96 platform containing expressions for more than 22K human genes • Number of gene pairs for this gene chip: ~ 250 Million • Multi-threaded application to run on the high-performance Cluster environment from HP • Bottleneck: the back-end Database • Current, fine-tuned version of the application takes about 2-3 months to complete one data set calculation

9. Obesity

10. Recurrent Themes • Access to large numbers of phenotyped specimens • Inadequacy of informatics at the cutting edge • Inadequacy of software solutions alone • A persistent multidisciplinary requirement

11. Overall Remission Rate with Citalopram = 32.9% QIDS: Quick Inventory of Depressive Symptoms, self report N = 943/2876 No depression Mildsymptoms Moderatesymptoms Severesymptoms Very severe symptoms Percent (%) Last QIDS-SR Score Trivedi MH, et al. Am J Psychiatry 2006;163:28-40.

12. Aims: • Identify a cohort of patients with TRD, and a matched cohort with SSRI-responsive MDD. • Data-mining tools • Natural language processing • Conduct the first genomewide association study of TRD.

14. Scan computerized medical records (DataMart) • ICD9 RA x 3 plus one of: • CCP or RF • Erosions on x-ray • DMARD treatment • Crimson “discarded” blood samples (cases and controls) • CCP on all samples (and bank serum) • DNA on all samples for genetic studies adds >95% specificity www.i2b2.org/disease/arthritis.html

16. Association in population samples Affecteds Controls SNP frequency in cases compared to controls Positive controls: MHC, PTPN22, STAT4, TRAF1-C5, TNFAIP3

17. …...acgt…ggaatac…... …...acgt…ggaatac….. Allele ‘A’ NspI NspI NspI NspI …...acgt…ggattac….. ......acgt…ggattac…… Allele ‘B’ NspI NspI NspI NspI _ B _ _ ‘B’ methylated both unmethylated ‘B’ expressed ‘A’ expressed both expressed MSRE digested Allele ‘A’ Allele ‘B’ A B A _ control (no digestion) ‘A’ methylated

18. Methodology Dev

19. Making the numbers better

20. Gene Network Enrichment Analysis Microarray data Protein protein interaction network Molecular Function Biological Process

21. Diabetes Genome Anatomy Project:Mouse Models of Insulin Resistance, Insulin Deficiency and Obesity • Knockouts • Insulin receptor • Insulin receptor substrates • Leptin • PGC1A • Environmental • High fat diets • Drug treatments (Streptozotocin) Tissues 67 Conditions Total

22. Three Functional Sets Are Consistently Over-represented In Disease Models • Insulin signaling, interleukins, and nuclear receptors. • Insulin signaling is consistent with the given disease models. Was not identified using standard techniques. • Interleukins and nuclear receptors consistent with the inflammation and disordered metabolism associated with type 2 diabetes. Insulin signaling NuclearReceptors Nuclear receptors: 31 of 67. Interleukins: 38 of 67. Insulin signaling: 45 of 67. Interleukins

23. Early evidence of signature from WBC

24. Predicting CAG Length in HD

25. Thank you