Genomic and Genetic Medicine in the Clinical Settingacross genetic risk score. The benefi t of...
Transcript of Genomic and Genetic Medicine in the Clinical Settingacross genetic risk score. The benefi t of...
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?