empirical data on the path to genomic medicine€¦ · novel lof medical exome >1% gene...

Empirical Data on the Path to Genomic Medicine

Robert C. Green, MD, MPH@RobertCGreen

director, genomes2peopleResearch Program in Translational Genomics and Health Outcomes

Division of Genetics, Department of Medicine

Brigham and Women’s Hospital

Partners HealthCare Center for Personalized Genetic Medicine

Broad Institute and Harvard Medical School

Current NIH Grant Funding

U01 HG006500 (Green) U01 AG024904 (Weiner)

R01 HG002213 (Green) R01 HG006615 (Holm)

R01 HG005092 (Green) P60 AR047782 (Kats/Karlson)

U19 HD077671 (Green/Beggs) P50 HG003170 (Church)

R01 HG06379 (Kullo) R01 HG007063 (Phillips)

R21 HG00603 (Wang) R01 CA154517 (Petersen/Koenig/Wolf)

T32 GM007748 (Morton) U41 HG006834 (Rehm/Ledbetter,

Nussbaum/Martin/Mitchell)

Disclosures

Research Grants: NIH, DOD, Illumina

Collaborations (uncompensated): Pathway, 23andMe

Speaking (compensated): Illumina

Advisory (compensated): Invitae, Prudential, Arivale, Helix

Equity: Arivale, Helix

“Genomes2People”

Research Studies in Translational Genomics and Health Outcomes

Genomes to PatientsGenomes to Physicians

Genomes in Public Health

Returning Risk Results of Common Variationin a Single Gene

Returning Risk Results of Common Variationin a Single Gene

The REVEAL StudyHG002213 (2000-2014)

3/3 (67%)

2/3 (8%)

2/2 (1%)

3/4 (20%)

4/4 (2%)

2/4 (3%)

Green et al., NEJM, 2009

0%

10%

20%

30%

40%

50%

60%

APOE ε4+ APOE ε4- Control

0%

5%

10%

15%

20%

25%

30%

% e

nd

ors

ing

ch

an

ge

Health Life Disability LTC

Control E4 Negative E4 Positive

Can genotyping be used to screen for risk of common diseases and what will people do with this

information?

Can genotyping be used to screen for risk of common diseases and what will people do with this

information?

Impact of Personal Genomics (PGen) Study

HG005092 (2010-2014)

My Results

Green and Farahany, Nature, 2013

Genome SequencingGenome Sequencing

Using Genome Sequencing for Undiagnosed Diseases in the Practice of Medicine

Using Genome Sequencing for Undiagnosed Diseases in the Practice of Medicine

Making a Diagnosis in a 5 year old boy with

Undiagnosed Bowel Disease

Sequencingrevealed unsuspectedXIAP Mutation

Biesecker and Green, NEJM, 2014

The problem and opportunity of incidental findings

The problem and opportunity of incidental findings

• “Minimum list”

• Standardized search and reporting

• Consistent with practice of medicine and patient expectation

Green, et al., Genetics in Medicine, 2013

ACMG List of

IFs

Inherited Cancer DisordersHereditary Breast and Ovarian CancerLi-Fraumeni SyndromePeutz-Jeghers SyndromeLynch SyndromeFamilial adenomatous polyposisMYH-Associated Polyposis; Adenomas, multiple colorectal, FAP type 2; Colorectal adenomatous polyposis, autosomal recessive, with pilomatricomasVon Hippel Lindau syndromeMultiple Endocrine Neoplasia Type 1Multiple Endocrine Neoplasia Type 2Familial Medullary Thyroid Cancer (FMTC)PTEN Hamartoma Tumor SyndromeRetinoblastomaHereditary Paraganglioma-Pheochromocytoma SyndromeWT1-related Wilms tumorNeurofibromatosis type 2Tuberous Sclerosis Complex

Cardiac DisordersEDS - vascular typeMarfan Syndrome, Loeys-Dietz Syndromes, and Familial Thoracic Aortic Aneurysms and DissectionsHypertrophic cardiomyopathyDilated cardiomyopathyCatecholaminergic polymorphic ventricular tachycardiaArrhythmogenic right ventricular cardiomyopathyRomano-Ward Long QT Syndromes Types 1, 2, and 3, Brugada Syndrome

Other: Malignant hyperthermia susceptibility, Familial hypercholesterolemia

Incidental Findings:

What is the right analogy?

Can genome sequencing be used to screen for risk of rare Mendelian conditions?


The MedSeq Project

HG006500 (2013-2017)

Project 2 WorkflowThe MedSeq Project

U01 HG006500 (2012-2016)

Physician reviews family history information and discloses results from Genome Report Patient’s electronic medical record

Physician reviews family history information and discloses results from Genome Report Patient’s electronic medical record

Medical Record ReviewMedical Record Review

Standard of Care +

Family History Review

Standard of Care +


Standard of Care +


+ Genome Report

Standard of Care +


+ Genome Report

Standard of Care +


+Genome Report

Standard of Care +


+Genome Report

Standard of Care+


Standard of Care+


Primary care physicians and their healthy middle-aged patients

Randomize each patient to receive

Primary care physicians and their healthy middle-aged patients


Cardiologists and their patients with cardiomyopathy


Cardiologists and their patients with cardiomyopathy


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Examining the Impact of Genomic Medicine

Medical

Behavioral

Economic

What is the impact upon individual and public health?

What is the impact upon physician and patient behavior?

What is the impact upon the healthcare system?

Variant Classification is Critical (and Difficult)Variant Classification is Critical (and Difficult)

Robert Green’s Exome

Robert Green’s Sequence

>5 million variants

≥10% in WGS Cases

HGMD ClinVar >5%

NovelLOF

Medical exome

>1%

Gene exclusions

Variant exclusions

~200-3

00 va

riants <60

variants 20-40 variants

10-30 variants

Data Set A ≥ 10% MAF WGS Cases� Excludes common technical FPs� Common indels wrong nomenclature� Exceptions FV, HFE, SERPINA1

Data Set B - Gene Exclusions• Evidence for gene-disease association

= none, limited, or disputed• Non medically relevant phenotype

Data Set C - Variant Exclusions• Benign interpretation• LOF but LOF not disease

mechanism• GWAS or PGx association only

Original filters

Curated Exclusion Datasets

A B C

MedSeq Genome Filtering Approach

71

31

11

2

611

Pathogenic

Likely Pathogenic

VUS-Favor Pathogenic

Other

Not reported

Not reported variants: 82%• VUS, Likely Benign, Benign• False positive variants

Reported variants: 18%

C

5%

Assessed

13%

A

69%

B

13%

Rules for Reporting on the Genome Report

1 Page Summary…

• Disease causing variants • Carrier variants• Pharmacogenomic variants• Blood groups

• Additional Pages… • Structured variant data• Variant evidence• Disease/inheritance • Supporting references

MedSeq Project “Genome Report”

Reported findings from MedSeq Project analysis of variants in ~4600 genes

MendelianDisease

Risk SFs

Carrier Status

SFs

Diagnostic Findings in the

Cardiology Cohort

# of patients 21/100(21%)*

92/100(92%)

24/50(48%)

Mean reported variants per patient

.21 2.3 0.54

Range of reported variants per patient

0-1 0-7 0-2

*1/90 (1%) by ACMG list

Examples of Reported MedSeq FindingsGene Variant Disease Classification Inheritance Notes

ELN c.1150+1G>A Supravalvular aortic stenosis Pathogenic AD

LHX4 c.452-2A>CCombined pituitary hormone

deficiencyPathogenic AD

PPOX p.Leu67X Variegate porphyria Pathogenic AD

RDH5 p.Trp95X Fundus albipunctatus Pathogenic AR Homozygous

HFE p.Cys282Tyr Hereditary hemochromatosis Pathogenic AR 3 biallelic cases

CHEK2 c.1100del CHEK2-related cancer risk Pathogenic AD

F5 p.Arg534Gln Factor V Leiden thrombophilia Risk allele Multi-factorial 3 cases

ANK2 p.Glu1458Gly Ankyrin-B related cardiac arrhythmia Likely pathogenic AD

COL2A1 p.Thr1439MetSpondyloepiphyseal dysplasia

congenitaLikely Pathogenic AD

EYA4 c.1739-1G>A Postlingual hearing loss Likely Pathogenic AD

KCNQ1* p.Ser276ProfsX13 Romano-Ward syndrome Likely Pathogenic AD

SQSTM1 p.Pro392Leu Paget disease of the bone Likely Pathogenic AD 2 cases

APP p.Ala713Thr Alzheimer’s disease, late onset VUS - Favor Pathogenic AD

ARSE p.Gly137Ala Chondrodysplasia punctata VUS – Favor Pathogenic XL

PDE11A p.Thr58ProfsX41Primary pigmented micronodular

adrenocortical diseaseVUS – Favor Pathogenic AD

TNNT2* p.Arg278Cys Hypertrophic cardiomyopathy VUS – Favor Pathogenic AD

Tales From MedSeq:

• Likely Pathogenic ANK2 Mutation (Ankyrin-B Related Cardiac Arrhythmia)

– History: Two syncopal episodes; No personal or family history suggestive of clinical torsades de pointe or LongQT

– MedSeq Outcome: Cardiology referral• No prolonged QT on 12-lead ECG

• Additional testing was ordered: Holter monitor (normal) and exercise treadmill test (ordered but not yet completed)

Tales From MedSeq (cont.):• Pathogenic PTPN11 Mutation (LEOPARD

syndrome)

– History: • 66 yrs. dx with HCM by echo ~20 yrs• Negative HCM testing in 2009- Most comprehensive panel available

(11 genes)• Family history (limited but no HCM)

– Daughter diagnosed with mild co-arctation at 12 yrs; Other daughter normal work-up

– “Heart attacks” in relatives

– MedSeq Outcome:• Physical Exam (proband): multiple lentigines, Noonan facial features • Adult Daughters Re-evaluated

– 34 yo.- mild coarc & seizure disorder, hypertelorism, nasal widening, lentigenes, no cardiac finding. Likely Noonan

– 35 yo- unremarkable evaluation

Tales From MedSeq (cont.):

• Homozygous Pathogenic RDH5 variant (AR Fundus Albipunctatus)

– Disclosure: • Patient reports that ophthalmologist noted "white dots"

on exam, potentially consistent with typical retinal findings (whitish subretinal spots); long-standing delayed adaptation to the dark, needs more light to read

– MedSeq Outcome: • Explanation for known symptoms advised to share with

their Ophthalmologist

38

GeneInsight Clinic® | Surfacing Alerts

All Genome Reports will be available in the EHR and GeneInsight

Physicians are alerted of variant reclassifications

Blood Group Typing Through Sequencing

• Traditional serologic phenotyping methods are:• Labor intensive• Costly• Sometimes unreliable• Reagents not always available

• Could the blood bank reliably predict complex blood group systems using WGS instead?• Only a minor added cost• Prevent adverse outcomes for patients

• 34 Blood Group Systems• 339 Serologic Phenotypes• >1,100 known alleles

The first demonstration of comprehensive RBC and platelet

antigen prediction using WGS data!

*variants relative to human ref genome

Genes avg. Bases cov. Sequenced RBC 45 34x 1,183,314 bp Plt 6 38x 323,222 bp

18

7

15

4

12

9

6

3

Whole Genome SequenceBased Antigen Prediction

AntigenGenes

RBC/Plt

10x

60x

50x

40x

30x

0x

20x

Seq C

overa

ge

Poor Seq

Known Novel Antigens Vars* Vars* Predicted RBC 20 1 217 Plt 290 30 34

1

2

5

11

17

19

22

X

Full sequencing and predictionof all known RBC Plt antigen genes

• All 100 individuals had RBC and Platelet antigens successfully predicted.

• Several (~ 5) individuals with rare antigen phenotypes identified for RBC, platelet and plasma donation.

• Serologic confirmation done for the 22 most commonly tested RBC antigens

• Total of 1760 serologic confirmations with no unresolvable discrepancies.

Is Sequencing “Useful”?Is Sequencing “Useful”?

Before sequencing, patients vary in the anticipated utility of WGS results

Enthusiasts, high utility (21%)

Intermediates, variable utility (60%)

Skeptics, low utility (19%)

Self-Reported Health Behavior Changes at 6 Weeks

Changes by Number of Variants

Blout, et al. ASHG 2015

17%26%

57%

11%23%

66%

Disagree/StronglyDisagree

Neutral Agree/StronglyAgree

All: Genetic Information Should be Part of a Standard MR

Baseline (n=201) 6-weeks (n=143)

14%

30%

55%

8%15%

77%

Not at all to Not VeryComfortable

Slightly Comfortable Very Comfortable

WGS Arm: Comfort with Genetic Information Going into MR

Baseline (n=76) 6-Weeks (n=78)

Comfort with Information in the Medical Record

Impact on Psychological Distress

HADS scale cutpoints:0-7 Normal

8-10 Borderline 11-21 Abnormal

All mean scores fall within Normal range; No significant differences by Randomization arm

Lee, et al. ASBH, 2015

Impact on Positive Psychological Responses

“Nothing serious was identified by the genome analysis” 203-P13

“Better to know about this then not to. My children and family can be tested.” 035-C02

“I started modifying my exercise and eating habits to promote a healthier lifestyle.” 083-P14

Lee, et al. ASBH, 2015

Examples of PCP decision-making in the first 38 WGS disclosure visits

ARM PATIENT’S RESULT TEST ORDERED

Primary Care(023-P05)

MONOGENIC RESULTKCNQ1 c.826delTLikely PathogenicRomano-Ward syndrome

EKG(And, referral to Cardiovascular Geneticist)


CARRIER STATUSHFE c.845G>APathogenicHereditary Hemochromatosis

Iron/ferritin studies


MONOGENIC RESULTPPOX c.199delCPathogenicVariegate porphyria

Repeat genetic testing for variegate porphyria at Mt. Sinai to confirm findings


CARDIOVASCULAR RISK ALLELES- Coronary heart disease- Abdominal aortic aneurysm

- Exercise stress tests - Abdominal ultrasound

Utility and cost effectiveness of population-based sequencing in healthy adults

Early disease

prevention and

detection

Cascade of harmful medical

interventions

Clinicalutility

Cost

Costeffectiveness

Vassy et al. ASHG 2015

PCP Cohort Results: Clinical Actions at Disclosure

Patients FH (n=49) FH+GS (n=48) p

n % n %

Laboratory Tests 3 6 7 15 0.20

Imaging Tests 0 0 2 4 0.24

Cardiology Tests 0 0 7 15 0.01

Referrals 6 12 6 13 >0.99

Medication Changes 1 2 7 15 0.03

“Did you order any __ because of the family history and/or genome reports?”


6-Month Healthcare Utilization and Costs- PCP Cohort

FH (n=32) FH+GS (n=32) p

Total Per Patient Total Per Patient

Laboratory Tests 74 2.31 87 2.72 0.82

Imaging Tests 30 0.94 22 0.69 0.75

Cardiology Tests 5 0.16 9 0.28 0.31

PCP Visits 18 0.56 21 0.66 0.63

Non-PCP Visits 61 1.91 73 2.28 0.51

Total cost $825 $1161 0.49


Short-term costs of integrating genome sequencing into clinical care: Preliminary results from the MedSeq Project

Length of disclosure sessions

Mean: 16 min vs 31 min, p<0.001

Medical costs over 6 months

Mean: $2,957 vs $3,699

Dukhovney, et al. ASHG 2015

Is Opportunistic Screening the same as Population-Based Screening?

Opportunistic

Infrastructure in place

Relatively cost neutral

Recommendations exist

Follows medical model

Population

Infrastructure not in place

Adds cost

No recommendations

Public health model

Penetrance of Actionable Incidental Findings in the Framingham Heart Study

Gold, Bick et al. Abstract 2369T presented at

2014 American Society of Human Genetics

Penetrance of Actionable Incidental Findings in the Framingham Heart Study

Gold, Bick et al. Abstract 2369T presented at

2014 American Society of Human Genetics

Gene Variant Amino Acid AssociatedCondition

Phenotype Age Sex

BRCA2

BRCA2

c.5213_5216del

c.4398_4402del

p.Thr1738Ilefs*2

p.Leu1466Phefs*2

Breast/ovarian cancer


Breastcancer

Prostate cancer

27-60

48-75

F

M

MUTYH c.536A>G p.Tyr179Cys Colon cancer No history of cancer

33-67 F

GLA c.427G>A p.Ala143Thr Fabry; HCM Normal echo 26-59 F

MYBPC3

MYBPC3

c.1504C>T

c.26-2A>G

p.Arg502Trp

p. ?

HCM

HCM

Echo showed HCM

Normal echo

41-71

38-73

M

M

APOB c.6240T>A p.Tyr2080*High

cholesterol/blood pressure

LDL: 33 mg/dL, on no cholesterol medications

23-29 M

LDLR c.429C>A p.Cys143* High cholesterol


35-68 F

2068 Variants Blindly Evaluated in 462 Framingham Subjects

Gene Variant Amino Acid AssociatedCondition

Phenotype Age Sex

BRCA2

BRCA2

c.5213_5216del

c.4398_4402del

p.Thr1738Ilefs*2

p.Leu1466Phefs*2



Breastcancer

Prostate cancer

27-60

48-75

F

M

MUTYH c.536A>G p.Tyr179Cys Colon cancer No history of cancer

33-67 F

GLA c.427G>A p.Ala143Thr Fabry; HCM Normal echo 26-59 F

MYBPC3

MYBPC3

c.1504C>T

c.26-2A>G

p.Arg502Trp

p. ?

HCM

HCM

Echo showed HCM

Normal echo

41-71

38-73

M

M

APOB c.6240T>A p.Tyr2080*High

cholesterol/blood pressure


23-29 M

LDLR c.429C>A p.Cys143* High cholesterol


35-68 F

Suggestive Clinical Findings with Pathogenic Variants

Gold et al., ASHG Annual Meeting, 2014

62%

38%

8 subjects with pathogenic

variants in an ACMG gene

13%

87%

454 subjects without a pathogenic variant in an ACMG gene

Classification of ACMG genes

Subjects with pathogenic

variant, + SCF

Subjects without a pathogenic

variant, + SCF

Cancer 66.7% (2/3) 5.3% (0.16/3)

Cardiovascular 60.0% (3/5) 16.8% (0.84/5)

With a suggestive clinical feature (SCF)

Without a suggestive clinical feature

Gold et al., ASHG Annual Meeting, 2014

IF you believe that screening is advantageous…IF you believe that screening is advantageous…

The BabySeq ProjectA Randomized Controlled Trial

HD077671 (2013-2018)

Alan Beggs/Robert Green (PIs)

Peter Park, Heidi Rehm, PankajAgrawal, Richard Parad, Ingrid Holm, Amy McGuire (co-Pis)

Project 2 Workflow

Study MDs/GCs disclose results from Genome Report to pediatricians and parentsInfant’s electronic medical record

Study MDs/GCs disclose results from Genome Report to pediatricians and parentsInfant’s electronic medical record

Medical Record ReviewMedical Record Review

Standard of Care NBS +

Family History


Family History


Family History+

Genome Report+

Indication-based Genome Results


Family History+

Genome Report+

Indication-based Genome Results


Family History+

Genome Report


Family History+

Genome Report

Standard of Care NBS+

Family History

Standard of Care NBS+

Family History

240 Healthy Newborns at BWH


240 Healthy Newborns at BWH


240 NICU infants at BCH


240 NICU infants at BCH

Randomize each patient to receive Ph

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Pediatricians discuss results from Family History and Genome Report with parentsPediatricians discuss results from Family History and Genome Report with parents

The BabySeq Project

1504 genes curated so far

898 genes meet all GNSR reporting criteria

Should they be reported in GNSR?

Moderate evidence/penetrance

but “clinically actionable” in

childhood

Carriers at risk for adult-onset

disease

Mixed presentation

Will be reported Will not be reportedNeed to discuss!

Childhood-onsetStrong evidenceHigh penetrance

Adult-onsetLimited evidenceLow penetrance

BabySeq Project: FDA and IRB

Outcomes from Sequencing Healthy IndividualsPilot study in UYG participants…

Outcomes from Sequencing Healthy IndividualsPilot study in UYG participants…

Total Number of UYG Alumni

N=453

Total Number of Genomes

SequencedN=413

Total Invitations Sent = 292

Initial Invitation – Monday/Tuesday, October 20-21

Reminder 1 – Monday, October 27

Reminder 2 – Thursday, October 30

Reminder 3 – Monday, November 3

Total Log-Ins=37Consented=27

Refused=1Partials=1

Complete=25

Sent to N=255


Refused=2Partials=2

Complete=40

Sent to N=246

Sent to N=225


Refused=3Partials=3

Complete=61


Refused=3Partials=5

Complete=75

Removedindividuals who either: 1) did not

wish to be contacted, or 2)

submitted a research genome

At time data was pulled for analysis:

31% of invited UYG alumni had logged into the survey (N=91)

25% of invited UYG alumni consented to participate in the study (N=74)

24% of invited UYG alumni completed the survey (N=70)

CharacteristicsNumber of Participants (%)

Total N=70

Age (years)Mean ± SDRange

53 ± 1226-91

SexMaleFemale

50 (71%)20 (29%)

ResidenceIn the United StatesOutside of the United States

Australia

41 (59%)29 (41%)14 (20%)

Self Reported RaceWhite/EuropeanAsianOther

63 (90%)4 (6%)3 (4%)

Self Reported Hispanic 0 (0%)

Educational BackgroundDoctorate Degree/Professional DegreeDoctor of MedicineMaster’s DegreeSome College/College/Some Graduate School

36 (52%)18 (26%)10 (14%)

6 (9%)

Household Income< $100,000$100,000 - $500,000

> $500,000No answer

6 (9%)43 (61%)17 (24%)

4 (6%)

Demographics of UYG participants

UYG participants were most motivated by…

MotivationsNumber of

Participants (%)Total N=70

1) Personal interest in genetics in generalVery/somewhat important 70 (100%)

2) Curiosity about my genetic make-upVery/somewhat important 69 (99%)

3) Desire to learn more about genome sequencing as part of my professional activitiesVery/somewhat importantNot applicable

68 (97%)2 (3%)

What are people doing with their results?

21% of respondents (15 of 70) made an appointment with a medical professional

4% (2 of 70) more stated they planned to make an appointment

0

1

2

3

4

5

6

7

Types of medical professionals participants had (or planned) to make an appointment with

Num

ber

of

part

icip

ants

Nine respondents reported having a test,

medical exam or procedure based on their

sequencing resultsOther (N=2)

VariantConfirm

ation (N=3)

Diagnostic test or procedure

(N=8)

*Respondents could select more than one option

How interested are UYG participants in sharing their data?

90% (N=63) were very or

somewhat

comfortable sharing their genome data

• 33% (N=23) – I would share publicly even with my identity attached

• 50% (N=35) – I would share publicly if my identity were anonymous

• 11% (N=8) - I would share with select individuals confidential

• 4% (N=3) – I would not consider sharing my genome data

90% (N=63) of participants agreed to share

a copy of their clinical test report with the study team

What are the outcomes of Personal Genome Sequencing (PGS)?

Personal Genome Sequencing Consortium

Outcomes Study (PeopleSeq)

Illumina UYG

500+ participants

CEO/MD/PhD Genome Projects(Thomas Caskey)

150 participants

Harvard Personal Genome Project(George Church)

200+ participants

Mount Sinai HealthSeq Study

(Eric Schadt)

40 participants

Nevada Pers. Med(Martin Schiller)

100+ projected

Pioneer 100(Lee Hood)

100 participants

MedSeqProject

(Robert Green)

50 participants

“PeopleSeq” Personal Genome

Outcomes Consortium 2015

Illumina UYG

3,400 participants

CEO/MD/PhD Genome Projects(Thomas Caskey)

150 participants

Harvard Personal Genome Project(George Church)

1,000 participants

Mount Sinai Wellness Study(Eric Schadt)

500 participants

Nevada Pers. Med(Martin Schiller)

300 participants

Pioneer 100(Lee Hood)

3,000 participants

MedSeq/BabySeqProjects

(Robert Green)

175 participants

“PeopleSeq” Personal Genome

Outcomes Consortium 2016

Thank You !!!

Email: [email protected]: genomes2people.orgTwitter: @RobertCGreen

empirical data on the path to genomic medicine€¦ · novel lof medical exome >1% gene...

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