The Realization of Genomic Medicine HL7 Work Group September 14, 2011

1. The Realization of Genomic MedicineHL7 Work GroupSeptember 14, 2011 Leslie G Biesecker, MD Chief and Senior Investigator Genetic Disease Research Branch National Human Genome Research Institute National Institutes of Health ™

2. Background

3. Assumptions • Substantial component of common disease is an amalgamation of rare processes • Genetic testing facilitates genotype-phenotype correlation • Prediction – ‘Personalized medicine’ • Limited to germline ™

4. Current Clinical Practice Paradigm • Gather history • Examine patient • Formulate differential diagnosis • Apply clinical test(s) to patient • Interpret result(s), refine differential, diagnose • Treat ™

5. Low Throughput Paradigms • Must frontload hypotheses, differentials and phenotyping because the assays or clinical tests are • Rate/time limiting • Expensive • Noisy ™

6. Low Throughput Paradigms • Must frontload hypotheses, differentials and phenotyping because the assays or clinical tests are • Rate/time limiting • Expensive • Noisy ™

7. ClinSeq™ Cohort • Enrolled >900 subjects • Primary phenotype atherosclerosis • Consented for • Full sequencing • Data sharing • Return of results • Downstream phenotyping - any • 572 exomes ™

8. Whole Genome Sequencing • Use read depth as a proxy for CNVs • Manually reviewed WGS data • (Note that software to do this is available) • 1.7 Mb Deletion of 17p12, including PMP22 gene -> Hereditary liability to Nerve and Pressure Palsies • Patient previously unaware of diagnosis ™

9. What about known disease genes? • Intersect all variants in a WGS data set with a database of ‘known’ disease-causing mutations

10. Results of HGMD intersection • 64 variants at positions said to cause disease in HGMD • 43 >> literature review suggests not causative of mendelian trait • 17 >> reference sequence has allele HGMD says is causative • 5 >> apparently causative ™

11. Five disease gene mutations ™

12. Clinical Analysis: Cancer • 37 Cancer genes • Cancer exome: 451 Total variants • Filtered • Frequency • Damaging • Literature review • Database review • Family history • Categorized by modified IARC scheme • 5: >99% prob. pathogenic • 2: 95-99% • 3: 5-95% • 2: .1-5% • 1: <.1% • 0 Poor quality sequence call ™

13. Clinical Example: BRCA2 Frameshift • p.T2766NfsX11 - known pathogenic allele • Not a high-risk pedigree • Note that this paradigm does not require that the patient or their relative has suffered or died of this disease • True preventive medicine ™

14. Genomics and Clinical Care I • Newborn exam – normal • Genome sequenced from cord blood • Using parent and clinician key, interrogate genome for NBS gene panel • Order recommended follow-up tests • Restrict diet • Consult to metabolic expert ™

15. Genomics and Clinical Care II • Patient presents to clinic for asthma • History and pertinent examination • Using patient &clinician key, interrogate genome for susceptibility & pharmacogenetics • Prescribe treatment ™

16. Genomics and Clinical Care III • Couple presents to clinic for preconceptual counseling • Using patient, partner, and clinician key, interrogate genomes for carrier states • If one hit each in a gene, refer for counseling and consideration of PND, PGD, etc. ™

17. Genomics and Clinical Care IV • Patient presents to clinic for routine healthcare evaluation • Patient reviews interactive educational tool on breast/ovarian cancer susceptibility • Using patient and clinician key, interrogate genome for susceptibility alleles • If abnormal allele identified, refer to cancer genetics clinic ™

18. Why Not? • Infrastructure to generate, store, distribute data • Clinical research to define utility of approach • Clinician-friendly analytic software & robust databases • Changing clinician training, attitudes, & practice ™

19. Why Not? • Infrastructure to generate, store, distribute data • Clinical research to define utility of approach • Clinician-friendly analytic tools & robust databases • Changing clinician training, attitudes, & practice • All of these are hard to do and will take time ™

20. Clinician-Friendly Algorithms • Most genetics and genomics should disappear into general and non-genetic subspecialty practice • Flag mutations for which it is essential to practice highest standard of non-directive counseling • Rare, atypical, outlier cases efficiently shunted to an expert ™

21. But We Are Stuck • Many components to develop and test • Need to find a way forward ™

22. Rare Disease Challenge • How many syndromes? • Syndromes Head & Neck • >2,500 entities • London Medical Database • >4,500 entities • Many rare, few with genes, few with natural history ™

23. Build Out From Rare Diseases • Build sequencing & data infrastructure • Start with specialists using informatics tools • Specialists teach generalists • Learn about many ‘incidental’ findings from these families ™

24. Data Infrastructure • Interrogate genome once • Prediction is that doing this once will be cheaper than lifetime cost of multiple interrogations • Secondary benefit is instant availability • Consequence is that one gets much more data than anyone needs or wants • Secure storage with ready accessibility • Robust database of correlation of variants with phenotypes ™

25. Prognostic Tool