Genomic and Genetic Medicine in the Clinical Setting

Objectives

• Define genomic and genetic medicine

• Introduce molecular genetic testing• Outline clinical applications including

those for:–Mendelian disease– Common complex disease– Pharmacogenetics

What does it mean to say genomic or genetic medicine?

“an emerging medical discipline that involves using genomic information about an individual as part of their clinical care (e.g., for diagnostic or

therapeutic decision-making)”

Precision medicine is not a new concept

• Tailor anti-hypertension therapy to the individual

• Prescribe the appropriate antibiotic for a given infection (or choose not to prescribe)

• Vaccinate based on an individual’s age and risk factors

• Screen for cancer based on age, family history and risk factors

Precision Medicine

• Clinical history• Physical Exam• Data collection through

laboratory or imaging studies• Family history

• Evaluate for mendelian conditions

• Genomic risk assessment for common disease

Medicine

• Evaluate for variation in drug metabolism

Genomic profile

Pharmaco-genomics

Why Now?

Human genome has

been sequenced

Biomedical analytics have

improved

Management of large

datasets is feasible

DISCOVER. INSPIRE. GROW.

Why Now?

Human genome

has beensequenced

Biomedical analytics

have improved

Management of large

datasets is feasible

Costs declining

How can we get genomic information?

• Genotyping

• Sequencing– Single Gene Sequencing– Panel-based Sequencing– Whole exome sequencing– Whole genome sequencing

Genotyping vs Sequencing

• Genotyping– detection of genetic variants at specific

locations in the genome– “comparing difference between two books”

• Sequencing– determines exact sequence of a given

length of DNA– “reading the entire book”

Whole exome vs. whole genome

• Whole exome sequencing– Evaluates only the

protein coding regions of the genome (exons)

• Whole genome sequencing– Evaluates entire

genetic code (introns and exons)

Variant Interpretation

• Evaluation of peer-reviewed literature related to variant

• Comprehensive review of available databases

• Application of in-silico prediction models

Databases reviewed Population databases:▪ 1000 Genomes (http://browser.1000genomes.org)▪ NHLBI GO Exome Sequencing Project(ESP) (http://evs.gs.washington.edu/EVS)▪ dbSNP (http://www.ncbi.nlm.nih.gov/snp)▪ dbVar (http://www.ncbi.nlm.nih.gov/dbvar)

Disease databases: ▪ ClinVar (http://www.ncbi.nlm.nih.gov/clinvar)▪ ClinVitae (http://clinvitae.invitae.com)▪ Online Mendelian Inheritance in Man (OMIM) (http://www.omim.org)▪ Human Gene Mutation Database (HGMD) (http://www.hgmd.org)▪ Human Genome Variation Society (HGVS) (http://www.hgvs.org)▪ Leiden Open Variation Database (LOVD) (http://www.lovd.nl)▪ DECIPHER (http://decipher.sanger.ac.uk)

Sequence databases:▪ NCBI genome (http://www.ncbi.nlm.nih.gov/genome)▪ RefSeq Gene (http://www.ncbi.nlm.nih.gov/refseq/rsg▪ Locus Reference Genomic (LRG) (http://www.lrg-sequence.org)▪ MitoMap (http://www.mitomap.org/MITOMAP/HumanMitoSeq)

Five-tier classification systemPathogenic: This sequence change directly contributes to the development of disease.

Likely pathogenic: This sequence change is very likely to contribute to the development of disease; however, the scientific evidence is currently insufficient to prove this conclusively.

Uncertain significance: There is not enough information at this time to support a more definitive classification of this sequence change.

Likely benign: This sequence change is not expected to have a major effect on disease; however, the scientific evidence is currently insufficient to prove this conclusively.

Benign: This sequence change does not cause disease.

Categories of Genomic Medicine

• Mendelian conditions– Single Mendelian variants of large effect

• Polygenic complex conditions– Multiple risk variants each with small effect

• Pharmacogenetic variation– Genetic variants of drug metabolism

• Cancer genomics– Sequence variation between tumor and normal cells

Variant Frequency

Effe

ct S

ize

Very rare Common

Small

Large

• Common disease• Common variants• Small effect size

• Mendelian disease• Family-based

approach• Mendelian disease• Carrier testing

• Common disease• Intermediate

frequency and effect size

Spectrum of Genetic Disease

Modified from: Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature 2009;461:747-753

Variant Frequency

Effe

ct S

ize

Very rare Common

Small

Large

Common coronary artery disease

• Mendelian disease• Family-based

approachFamilial

Hypercholesterolemia

Coronary Artery Disease

Modified from: Manolio TA, Collins FS, Cox NJ, et al. Finding the missing heritability of complex diseases. Nature 2009;461:747-753

Ø Three known genes PCSK9, APOB, LDLRØ Early onset CADØ May be resistant to typical lipid therapyØ Genetic testing can identify affected, inform:

Ø ScreeningØ TreatmentØ Testing of at-risk family members

Ø ~ 60 genetic risk variants knownØ PCSK9 included

Ø Later onset CADØ Lifestyle significant contributorØ Genomic risk may inform:

Ø ScreeningØ Treatment

Categories of Genetic Medicine

• Mendelian conditions

• Polygenic complex conditions

• Pharmacogenetic variation

MENDELIAN CONDITIONS

Rare Disease in the U.S.

• 30 million people in the United States are living with rare diseases

• ~1 in 10 Americans or 10% of the U.S. population– 80% of rare diseases are genetic– 50% are adults

Globalgenes:AlliesinRareDisease,https://globalgenes.org/rare-diseases-facts-statistics/

Genetic conditions with common phenotype

• Familial Hypercholesterolemia: 1 in 200

• Lynch Syndrome: 1 in 440– Individuals with colon cancer: 1 in 30

• BRCA Mutation: 1 in 400– Women with breast cancer (any age): 1 in 50– Women with breast cancer (< 40 years): 1 in 10 (10%)– Men with breast cancer (any age): 1 in 20 (5%)– Women with ovarian cancer (any age): 1 in 8 to 1 in 10

(10%–15%)

FAMILIAL HYPERCHOLESTEROLEMIA

• Inherited disorder characterized by markedly elevated LDL cholesterol leading to premature cardiovascular disease

• Multiple genes with both homozygous and heterozygous disease states

Untreated FH leads to 20X increase

In heart disease risk

• Largely under recognized in the United States• Presentation can vary depending on whether an

individual is homozygous or heterozygous for a mutation in LDLR, APOB, or PCSK9

• LDL levels can vary widely in people with same mutation

• Cascade screening may reveal carriers of mutation who don’t meet clinical criteria• Treatment still appropriate due to

lifetime exposure to LDL

Approach to genetic testing

1. Consider retesting the person who best meets diagnostic criteria when new genes are known2. Provide feedback to genetic testing laboratory when segregation testing results are available.3. See screening guidelines for healthy at-risk individualsFrom GeneReviews Hypertrophic Cardiomyopathy 2016.

How do we find these people?

• Family history – Early onset conditions–Multiple generations affected with similar

condition• Individuals who present with– Early onset conditions–Multisystemic condition

Referrals to Adult Genetics Clinic• Cancer

– Breast, ovarian, endometrial, colorectal, pancreatic, kidney

• Neurological disorders– Ataxia, myopathy, muscular

dystrophy, sensory and/or motor neuropathy, motor neuron disease, dementia

• Gastrointestinal disorders– Polyposis, pancreatitis

• Cardiovascular disorders– Connective tissue, cardiomyopathy,

hyperlipidemia• Reproductive concerns

– Miscarriage, infertility, pre-conception, pre-natal

• Other– PM&RS, Nephrology, Pulmonology,

Eye, Pharmacy, Dermatology, Dental, Pathology, Radiology

Primarycare/InternalmedicineWomen’shealthGastroenterologySurgeryNeurologyHematology-oncologyCardiologyEndocrinologyMentalhealthRheumatology

COMMON COMPLEX DISEASE

CommonDiseaseComplexity

http://www.fac.org.ar/scvc/llave/epi/marian/mariani.htm; Nat Rev Neurol 2010 Nature Publishing

Epigenetic,post-genomicandregulatoryeventsGenerearrangementsSomaticmutations

MessengerRNAsplicingMethylation

RetroviralSignalsMicroRNAs

EnvironmentPathogensChemicalsSmokingDiet

Sunexposure

GenomevariantsSinglenucleotidepolymorphism

CopynumbervariationInsertion-deletionpolymorphisms

DiseasemodifiergenesDiseasesusceptibilitygenes

GreatestRisk

ElevatedRisk

ElevatedRisk

ElevatedRisk

Genetics of common disease• Substantial heritable components exist for

common disease (eg. CHD)

• Identifying genetic variants that quantify that heritability could help with screening and prevention

• Genome wide association (GWA) studies have identified many of these variants

What is GWA?

• Genome Wide Association – analysis of the entire genome at one time with fine resolution, VERY fast

• Technology makes available the ability to genotype 700,000 to 2,500,000 SNPs per subject at $50-$175

• Computer methods to analyze a dataset of this size have become available

Manolio TA. N Engl J Med 2010;363:166-176.

The Genomewide Association Study

Manolio TA. N Engl J Med 2010;363:166-176.

Meta-Analysis of Genomewide Association Studies

GWAS Catalogue

Why does findings the genes help?

Ø Developing specific therapies

Ø Identify prevention approaches

Understanding mechanisms

Genetic screening to identify high risk

individuals

GENE IDENTIFICATION

Coronary heart disease• Family history is a well-established independent risk

factor for CHD– Framingham:

• Men: 2.4x increased risk for heart disease• Women: 2.2x increased risk for heart disease

– Interheart: 1.5x increased risk for heart disease

• Twin and family studies have proven 40-50% of susceptibility to CHD is heritable– The frequency of CHD in monozygotic twins is ~ 44% vs 14% in dizygotic

twins based on the Danish twin registry study

Peden JF, Farrall M. Thirty-five common variants for coronary artery disease: the fruits of much collaborative labour. Hum Mol Genet. 2011 Oct 15;20(R2):R198-205.Roden, John. Cardiovascular Genetics and Genomics. Hoboken: John Wiley & Sons, 2009.

GWAS have led to success in identification of CHD SNPs

CHD-related SNPs by Mechanism

LDLHDL

TGMI

HTNUnknown

0

10

20

30

40

50

60

70

CHD-related SNPs by Year

Roberts R. A genetic basis for coronary artery disease. Trends Cardiovasc Med. 2015 Apr;25(3):171-8.CARDIoGRAMplusC4D C. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013;45:25-33

Quantifying Genomic Risk

• Genetic risk score–Genotype for known risk SNPs– Calculate a score based on the presence

of the risk SNP weighted by associated risk

CHD genomic variants as additional screening tool

• 15-20% of myocardial infarctions occur in individuals considered to be at low risk based on conventional risk factors

• Improved screening with genomic variants has the potential to improve risk prediction

Thanassoulis G, Vasan RS. Genetic cardiovascular risk prediction: will we get there? Circulation. 2010 Nov 30;122(22):2323-34.

Higher GRS associated with increased risk for coronary heart disease

Articles

4 www.thelancet.com Published online March 4, 2015 http://dx.doi.org/10.1016/S0140-6736(14)61730-X

and 1·72 (1·55–1·92, p<0·0001), respectively (fi gure 1). Data for individual quintiles 1–5 were similar (see appendix pp 6–7).

Baseline LDL cholesterol and HDL cholesterol levels were similar across genetic risk score categories within each trial, as were the absolute and percentage changes with statin therapy (appendix pp 8–10). Analyses were done to investigate the clinical benefi t of statin therapy across genetic risk score. The benefi t of statin versus placebo in the primary and secondary prevention trials with a total of 806 events is presented across genetic risk score categories in table 3 and genetic risk score quintiles in appendix pp 11–12. The relative risk reductions were 34% in low, 32% in intermediate, and 50% in high genetic risk score categories in the primary prevention trials, and 3% in low, 28% in intermediate, and 47% in high genetic

risk score categories in the secondary prevention trials. When the data were combined, the gradient of relative risk reductions with statin therapy across low, intermediate, and high genetic risk score categories were 13%, 29%, and 48%, respectively (p value for trend=0·0277, fi gure 2).

Similarly, in terms of the absolute risk reductions, a graded increase in the benefi t of statin therapy across the genetic risk score categories (from low risk to high risk) was evident in both the primary prevention trials (JUPITER and ASCOT) and the secondary prevention trials (CARE and PROVE IT-TIMI 22; table 3, fi gure 3). Correspondingly, the number needed to treat to reduce coronary heart disease events with statin therapy diff ered depending on genetic risk score. With a focus on the primary prevention trials, in JUPITER, the number needed to treat to prevent one coronary event in 10 years was 66 for those individuals with a low genetic risk score, 42 for those with an intermediate score, and 25 for those with a high score. In ASCOT, the number needed to treat to prevent one coronary heart disease event in 10 years was 57, 47, and 20, respectively, across the three genetic risk score categories.

Calculation of the diff erence in absolute risk reduction in each trial as a function of genetic risk score category and combination of the data from the trials showed a consistent and signifi cant gradient, with greater absolute risk reductions recorded in those individuals in higher genetic risk score categories (p=0·0101, appendix p 17). The regression coeffi cient of 0·71 indicates that for each 1% absolute risk reduction achieved with statin therapy in the intermediate genetic risk category, a 1·71% absolute

Events (n), control group

Individuals (n), control group

Event rate, control group (%)

Events (n), statin group

Individuals (n), statin group

Event rate, statin group (%)

HR ARR (%)

Primary prevention populations

JUPITER (108 events, 2·37 years of follow-up, 20 612 person-years of follow-up)

Low genetic risk 10 865 1·16% 7 878 0·80% 0·68 0·36%

Intermediate genetic risk 43 2621 1·64% 28 2605 1·07% 0·68 0·57%

High genetic risk 14 864 1·62% 6 878 0·68% 0·41 0·94%

ASCOT (149 events, 6·07 years of follow-up, 25 609 person-years of follow-up)

Low genetic risk 13 432 3·00% 8 412 1·94% 0·64 1·06%

Intermediate genetic risk 48 1187 4·04% 37 1344 2·75% 0·68 1·29%

High genetic risk 28 426 6·57% 15 418 3·59% 0·54 2·98%

Secondary prevention populations

CARE (320 events, 4·94 years of follow-up, 13 623 person-years of follow-up)

Low genetic risk 26 278 9·35% 22 297 7·41% 0·79 1·94%

Intermediate genetic risk 119 877 13·57% 92 850 10·82% 0·79 2·75%

High genetic risk 38 277 13·72% 23 299 7·69% 0·54 6·03%

PROVE IT-TIMI 22* (229 events, 2·03 years of follow-up, 3769 person-years of follow-up)

Low genetic risk 20 213 9·39% 21 186 11·29% 1·24 –1·90%

Intermediate genetic risk 88 605 14·55% 55 595 9·24% 0·63 5·31%

High genetic risk 28 188 14·89% 17 212 8·02% 0·51 6·87%

HR=hazard ratio. ARR=absolute risk reduction. *In PROVE IT-TIMI 22, the control group is moderate-intensity statin therapy (pravastatin 40 mg) and the statin group is high-intensity statin therapy (atorvastatin 80 mg).

Table 3: Risk of coronary heart disease associated with statin therapy across genetic risk score categories

Figure 1: Summary of risk of coronary heart disease across genetic risk score categories in primary and secondary prevention populationsThe boxes indicate the point estimates and the horizontal lines are 95% CIs.

p valueHazard ratio (95% CI)

Hazard ratio (95% CI)

Reference

1·34 (1·22–1·47)

1·72 (1·55–1·92)

<0·0001

<0·0001

Genetic risk score category

Low risk

Intermediate risk

High risk

Lower risk Higher risk

1·00·80 1·25 2·0

Mega J et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet. 2015 Jun 6;385(9984):2264-71.

Number Needed to Treat Declines with Increasing GRS

• NNTdeclinedinindividualswithincreasingGRS

• Targettreatmenttothosewhoderivegreatestbenefit

Mega J et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet. 2015 Jun 6;385(9984):2264-71.

NumberNeededtoTreat

GRS Jupiter ASCOT

Low 66 57

Intermediate 42 47

High 25 20

Barriers to implementation• Clinical guidelines do not exist to use this

type of screening• Most variants have been identified in

Caucasian males– Additional studies in more diverse

populations required• Risk prediction has not exceeded

performance of existing clinical predictors– Reclassification likely a better measure

PHARMACOGENETICS

=Howgenesaffectaperson’s

responsetodrugs

Pharmacogenetics

• Nearly every pathway of drug metabolism, transport and action is influenced by genetic variation

• FDA requires genetic info on package inserts for ~160 medications

• Prescription guidelines based on genetic information exist for ~20 medications

CPIC• Clinical Pharmacogenetics Implementation

Consortium (CPIC)• Goal is to address some of the barriers to

implementation of pharmacogenetic tests into clinical practice

• CPIC guidelines are peer-reviewed and published in a leading journal

• CPIC provides guidelines that enable the translation of genetic laboratory test results into actionable prescribing decisions for specific drugs

• Guidelines center on genes or around drugs

FromMarshandMcLeod.Hum.Molec.Genet.15:R89-93,2006

Types of Metabolizers

Extensivenormalmetabolizer(EM)

Ultrarapidmetabolizer(UM)

Intermediatemetabolizer(IM)

Poormetabolizer(PM)

Genotypingtoidentifyanindividual’smetabolismcanhelptominimizeadverseeventsandincreasedrugefficacy

Expectedresponse

Lackofresponse

Exaggeratedresponse

Adverseeffects

Forsomeantidepressants…

The Plavix Story…

• Bioactivated by cytochrome P450 2C19

• CYP2C19 is the gene encoding this cytochrome

• Variation in this gene can affect how well the enzyme works

• 1 abnormal copy = reduced enzyme activity

• 2 abnormal copies = no enzyme activity

• ~ 20% have at least one abnormal copy = “Plavix non-responder”

Primaryefficacyoutcome=deathfromcardiovascularcauses,MIorstroke

Prospective Clinical Implementation of CYP2C19 Genotype Guided

Antiplatelet Therapy After PCI: a Multi Site Investigation of MACE Outcomes in

a Real-World SettingIGNITE Pharmacogenetics Interest Group

Major adverse cardiovascular events (MACE): CV death, MI, or stroke

Compare risk for major adverse cardiovascular events (MACE)

in post-PCI patients using CYP2C19 guided antiplatelet

therapy

AIM:

Prospective multi-center investigation of clinical

CYP2C19 genotype-guided antiplatelet therapy post-PCI

••Alternative antiplatelet therapy recommended in CYP2C19 Loss of function (LOF)

••No genotype-guided recommendations in NON-LOF

••7 sites contributed data on patients who underwent PCI and genotyping for primary analysis

••Sanford Health under the leadership of Dr. Russ Wilke and Dr. Eric Larson

MACE stratified by genotype and treatment of choice

Log-rank p=0.016Unadjusted HR: 2.30CI (1.17–4.52), p = 0.015

LOF=Lossoffunction

Genotype-guided approach to antiplatelet therapy in the real world is feasible

In patients with CYP2C19 LOF, CV outcomes can be improved when clinical genotype made available and alternative therapy prescribed early after PCI

Barriers to implementation

• Clinical guidelines for many drug-gene interactions are still evolving

• Clinical outcomes data does not yet exist for many drug-gene interactions

Genomic Medicine in the Clinic• Genomic information is becoming more

readily available

• Diagnostic tools have improved our ability to diagnose rare disease

• Several applications to use genomics in the clinical setting are emerging

• There are limitations, thus we need to proceed with caution when using this data

QUESTIONS?