1. The Realization of Genomic Medicine HL7 Work Group September 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 ™ Mut. IDGeneDiseaseSubst.State CM094237WNT10AEctodermal Dysplasiap.Phe228Ilehet CM001280SLC26A4Pendred syndromep.Thr193Ilehet CM990677GALTGalactosemiap.Met336LeuHet CM094615SEC23BDyserythropoietic anemiap.Val426IleHet CM012456RP2XL retinitis pigmentosap.Arg282Trphemi

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