Hypothesis-Generating Research and Predictive Medicine. Sanford Imagenetics Genomic Medicine Symposium October 10, 2014

Transcription

1 Hypothesis-Generating Research and Predictive Medicine Sanford Imagenetics Genomic Medicine Symposium October 10, 2014

2 Outline Research Hypothesis-generating v. hypothesis-testing Clinical care Diagnostic v. predictive Incidental findings & predictive medicine

3 Current Clinical Research Paradigm Gather background information Formulate hypothesis Phenotype subjects Apply a biologic assay to subject to test Interpret data, refine, and extend hypothesis

4 Hypothesis-Generating Research Assemble a cohort of subjects Generate large-scale dataset Parse genomic data for patterns, perturbations, etc. Hypothesis for how the omic attribute affects the subject Test the hypothesis with clinical research

5 ClinSeq Cohort Enrolled >1,000 subjects Primary phenotype atherosclerosis Consented for Full sequencing Downstream phenotyping - any >950 exomes Mb exome captures > 5 trillion bp >2,500,000 variants ~300,000 nonsense, frameshift, splice site, and non-synonymous Biesecker et al, Genome Res :

6 Combined Malonic & MethylMalonic Acidemia (CMAMMA) Organic acidosis, childhood onset with metabolic decompensation, CNS infarcts, coma Autosomal recessive Exome sequenced a single nuclear trio Filters 12 genes One (ACSF3) with mitochondrial leader 7 additional patients 6/7 with 2 rare variants Functional data, complementation Sloan et al. Nat Genet :

7 TYPE # TYPED HomRef HetRef HomNRef REF VAR AA# Damaging dbsnp SPL NonSyn L P 2-4 NonSyn R W 10 0 NonSyn A P 17-5 rs (g,c) FS NonSyn G S 64-7 NonSyn P A NonSyn P S NonSyn P L NonSyn R W NonSyn D N NonSyn R L NonSyn I V NonSyn E K NonSyn V M rs (g,a) NonSyn R P NonSyn V M NonSyn R Q NonSyn R W NonSyn A T Stop W * NonSyn R W

8 TYPE # TYPED HomRef HetRef HomNRef REF VAR AA# Damaging dbsnp SPL NonSyn L P 2-4 NonSyn R W 10 0 NonSyn A P 17-5 rs (g,c) FS NonSyn G S 64-7 NonSyn P A NonSyn P S NonSyn P L NonSyn R W NonSyn D N NonSyn R L NonSyn I V NonSyn E K NonSyn V M rs (g,a) NonSyn R P NonSyn V M NonSyn R Q NonSyn R W NonSyn A T Stop W * NonSyn R W

9 Clinical Evaluation 66 yo female Recent history 4 car accidents, memory difficulties, incontinence Serum and urine analysis MMA plasma 48 um (100x ULN), urine 70x ULN MA plasma 11 um (nl undetectable) Cranial MRI Multiple small high intensity signals, c/w infarcts

10 Conclusions Proof of causation for ACSF3 mutations Current understanding of phenotypic spectrum wrong Modifiers matter

11 Conclusions Hypothesis testing model leads to circular reasoning Unbiased ascertainment of phenotypes essential to knowing full spectrum Iterative clinical research essential requires thorough consent & motivated cohort

12 Predictive Medicine What is it? Predictive medicine, Personalized medicine, Precision medicine Precisely applying prevention and treatments Those who have highest risks of disease, complications, particular prognosis Maximize efficacy, minimize side effects

13 Current Clinical Practice Paradigm Chief complaint & history Physical examination Formulate differential diagnosis Select & apply clinical test(s) to patient Interpret result(s), refine differential, diagnose Treat

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15 Predictive Medicine Model Basic patient intake & consent Generate large-scale dataset Parse genomic data for patterns, perturbations, etc. Predict how the omic attribute affects the subject Test the hypothesis with clinical evaluation

16 Health Predictions Need ability to assay an attribute of patient that defines occult disease or future risk Physical signs on health maintenance visit Newborn screening

17 Scientific Predictions Prediction is very difficult, especially if it's about the future. Niels Bohr

18 Scientific Predictions Prediction is very difficult, especially if it's about the future. Niels Bohr Occurrences in this domain are beyond the reach of exact prediction because of the variety of factors in operation, not because of any lack of order in nature. Albert Einstein

19 Health Predictions Need ability to assay an attribute of patient that defines occult disease or future risk Physical signs on health maintenance visit Newborn screening Why not for all heritable disorders? Need assay to broadly assess risks Until recently it was technically impossible

20 Incidental Findings in ClinSeq Key objection Unknown penetrance of variants outside of positive family history context A phenotype-centric view Cardiomyopathy/dysrhythmia A genome-wide approach All null variants

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22 Evaluate This Question Cardiomyopathy/dysrhythmias 870 exomes Evaluate 63 known genes Set high thresholds for pathogenicity

23 Cardiomyopathy & Dysrhythmias MYH7 IVS8+1G>A Reinterpretation MRI > L ventricular non-compaction MYBPC3 p.arg495gln Concentric L ventricular hypertrophy MYBPC3 p.gly490arg Asymmetric septal hypertrophy

24 Cardiomyopathy & Dysrhythmias MYH7 IVS8+1G>A Reinterpretation MRI > L ventricular non-compaction MYBPC3 p.arg495gln Concentric L ventricular hypertrophy MYBPC3 p.gly490arg Asymmetric septal hypertrophy All abnormal imaging and family history

25 Cardiomyopathy & Dysrhythmias MYH7 IVS8+1G>A Reinterpretation MRI > L ventricular non-compaction MYBPC3 p.arg495gln Concentric L ventricular hypertrophy MYBPC3 p.gly490arg Asymmetric septal hypertrophy All abnormal imaging and family history Similar results with dysrhythmias (3/3)

26 Conclusions You can identify pathogenic variants from genomic data in unselected patients Need to use high thresholds for pathogenicity Current concerns may lead us to be too conservative Our positive prediction value for cardiac disease is 100%

27 A Genomic View How generalizable is this and how often can you make predictions from exomes? Search 950 exomes for all null variants Nonsense, frameshift, splice Known disease causing genes Haploinsufficiency genes Actual disease (not traits)

28 Null Variants - Positives (32) BRCA1 (2) BRCA2 (4) FLCN (2) HOXD13 KCNQ4 KRT16 LDLR (7) MSH6 PKD1 PMPP PMS2 PPARG PROS1 SALL4 SFTPC SGCE SLC4A1 (2) TGIF1

29 Null Variants - Positives KCNQ4 c.1725del p.ile576serfsx40 Mod-Severe S-N hearing loss PPARG p.glu499argfsx12 Familial partial lipodystrophy HOXD13 c.820c>t p.arg274x Limb anomalies FLCN c g>a p.trp306x Biopsy positive

30 Null Variants Negatives (32) ATP2C1 BRCA1 (2) BRCA2 COL10A1 (3) DMD DSG1 EPHA2 GLI2 GLI3 GNAS HSPB1 MSH6 MYH6 MYOC (4) OFD1 PMS2 (2) PRKCSH (2) RP1 RUNX1 SDHC SMAD3 TGIF1 TRPC6 PAX3

31 Null Variants - Negatives ARID1B Coffin-Siris syndrome / ID c.1762g>t, p.glu588x GLI3 Greig cephalopolysyndactyly syndrome c.45g>t, p.lys15x MYOC Open angle glaucoma c.1102c>t, p.q368x

32 Null Variant Screen Why negatives? Variant in far 3 of gene Truncated protein > hypomorphic Variant in far 5 of gene Alternative translation initiation Non-obligate exon Skipping of stop Incorrect gene model Incomplete penetrance / variable expressivity

33 Null Variant Screen About 50% yield of disease 2-3% of participants Most undiagnosed Probably underestimate Not all such genes known Subset of pathogenic mutations Conservative phenotyping

34 Bayesian Reasoning A major criticism of Precision Medicine If prior probability 1/50,000, 99.9% reliable test yields ~2% true positives, ~98% false positives Prior probability incorrect If using a genomic screen, then prior is sum of frequencies of ALL detectable disorders Our estimate suggests this is in 5-10% range

35 An Incidental Finding 56 yo man in heart disease sequencing study Fam Hx: 59 yo sister with ductal carcinoma No genetic testing Proband: BRCA1 c.68_69delag, p.glu23valfsx17 Shared with sister Tested, positive Underwent hysterectomy and ovariectomy

36 Serous Tubal Intraepithelial Carcinoma H&E BRCA1 p53 A B C D E F

37 An Incidental Finding 56 yo man in heart disease sequencing study Fam Hx: 59 yo sister w DCIS No genetic testing Proband: BRCA1 c.68_69delag, p.glu23valfsx17 Shared with sister Tested, positive Underwent hysterectomy and ovariectomy Back to OR for nodes & peritoneal wash Negative

38 Predictive Medicine Arguments Can t interpret most variants Some variants highly predictive

39 Predictive Medicine Arguments Can t interpret most variants Some variants highly predictive Don t know penetrance Crude estimates useful

40 Predictive Medicine Arguments Can t interpret most variants Some variants highly predictive Don t know penetrance Crude estimates useful No data on clinical utility Medical common sense works

41 Predictive Medicine Arguments Can t interpret most variants Some variants highly predictive Don t know penetrance Crude estimates useful No data on clinical utility Medical common sense works Why all the fuss?

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43 Genetic Exceptionalism Genetics is conceptually distinct Genetics practice distinct Geneticists more kind to patients

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45 History

46 Diagnostic-Therapeutic Gap

47 Reproductive Genetics

48 Conclusions Hypothesis-generating clinical research now possible Can discover things that hypothesis-testing research cannot Phenotype-driven Readily extracted from exomes Yield ~2% Genomics-driven search of null variants Undiagnosed disease ~50% positive predictive value

49 Conclusions Genome & exome seq not indicated for healthy people Can be very useful for patients with rare or unexplained diseases Incidental findings = predictive medicine Predictive medicine is just medicine

50 Mainstream Medicine

51 Thanks to Katie Lewis Jennifer Johnston David Ng Steven Gonsalves Wendy Rubenstein David Cooper Peter Stenson Charles Venditti