Arno G. Motulsky Endowed Chair Medicine and Genome Sciences - - PowerPoint PPT Presentation

Gail P. Jarvik, M.D., Ph.D.

Arno G. Motulsky Endowed Chair Medicine and Genome Sciences Professor and Head, Medical Genetics University of Washington, Seattle

Challenges in the implementation of genomic medicine

Genomic Medicine Obstacles

• Lack of practice guidelines / Lack of insurance

coverage / reimbursement

• Need for evidence base
• When it helps
• How best to do it
• What do all those variants mean?
• Regulatory climate / New legislation?
• Lack of non-geneticist provider training
• Patient/consent issues, family communication
• Postmortem genome-sharing

Genomic medicine is another new technology

§ Study required to implement safely and

effectively

§ Harm of implementing both too fast and too slowly § Need to evaluate best practices § Understand economics (and convince insurers) § We have learned that evidence trumps “logic”

p Think hormone replacement therapy

CSER Ongoing Outcomes Efforts: Steps to access to genomic medicine

Insurance Coverage Practice Guidelines Evidence base Research

Contemp Clin

• Trials. 2014.

PMID: 24997220 Genet Med.

• 2014. PMID:

25394171

Next generation sequencing panels for the diagnosis of colorectal cancer and polyposis syndromes: a cost- effectiveness analysis. Gallego, Shirts, Bennette, et al.

J Clin Onc.

• 2015. in press

Regence Insurance Policy 64

effective (7/1/14)

§ “The following genetic panels are

considered investigational because the current scientific evidence is not yet sufficient to establish how test results from panels which include a broad number

• f genes may be used to direct treatment

decisions and improve health outcomes associated with all components of the panels.”

§ List of ALL panels, including cystic fibrosis

32 mutation panel

Exomes can save money without changing management: a case

• Patient in teens
• Movement disorder early in life
• Saw 12 experts in centers from Vancouver to

Texas without a diagnosis

• PE: choreoathetosis and dystonia of limbs, most

prominent at rest; progressed to include facial twitches and mild dysarthria

• Exome: de novo R418W (c.1252C>T) in ADCY5
• Familial Dyskinesia with Facial Myokymia
• Ended diagnostic odyssey

Chen et al, Annals of Neurology.

Twitter #hail_CSER

Explore, within an active clinical setting, the application of genomic sequence data to the care of patients.

> 200 clinicians involved

NHGRI’s Genomic Medicine Research Program

Program Goal Σ $M Years

eMERGE II Use biorepositories with EMRs and GWA data to incorporate genomics into clinical research and care 31.1 FY11-14 eMERGE- PGx Apply PGRN’s validated VIP array for discovery and clinical care in ~9,000 patients 9.0 FY12-14 CSER Explore infrastructure, methods, and issues for integrating genomic sequence into clinical care 66.5 FY12-16 RoR Investigate whether/when/how to return individual research results to pts in genomic research studies 5.7 FY11-13 ClinGen Develop and disseminate consensus information on variants relevant for clinical care 25.0 FY13-16 IGNITE Develop and disseminate methods for incorporating patients’ genomic findings into their clinical care 32.3 FY13-16 NSIGHT Explore possible uses of genomic sequence information in the newborn period 10.0 FY13-16 UDN Diagnose rare and new diseases by expanding NIH’s Undiagnosed Diseases Program 67.9 FY13-17

CSER U Award Study Populations

More interactive data-sets available online at

http://cser-consortium.org/impact

Enrollment & Germline Findings

Cases Path/LP VUS Other Yield

Cancer (Adult) 164 7 30 127 4% Cancer (Pediatric) 150 24 120 6 16% Dysmorphology 88 16 16 56 18% Heart Disease 117 27 44 46 23% Bilateral sensorineural hearing loss 34 8 12 14 24% Neurological Diagnosis 211 37 41 133 18% Retinal 55 18 13 24 33% Preconception (Carrier) 45 31 14 69%

Diagnostic Yield Varies by CSER Study Population

UW Randomized control trial

• N=220 over 3.5 years

Research Visit + 2 weeks + 1 month, + 4 months, +7 months, +10 months Various surveys Clinic Visit 2 + 2 weeks 2 Sets of Surveys Clinic Visit 2 + 1 month 1 Set of Surveys 3 Sets of Surveys ~2 Months ~14 Months Clinic Visit 1 (Baseline) Clinical CRCP Genetic Counseling Consent Discussion Blood Draw Randomize ~6 Weeks Clinic Visit 2 (WES) Clinical and exome CRCP Genetic Test Results Clinic Visit 2 (UC) Clinical CRCP Genetic Test Results Research Visit (WES) Discuss Incidental Findings Research Visit (UC) Review Family History Usual Care Exome

Comparing whole exome sequencing to usual care for adult patients having clinical genetic testing for hereditary colorectal cancer/polyps

So many variants, so little time

§ Average persons WGS has >3.5 million

variants from a reference sequence*

§ 0.6 million are rare or novel § 400 GENES have rare or novel, nonsynonymous (coding) variants in conserved regions

§ Guessing < 10K variants with well

established pathogenic role

§ Need annotations!

§ Pathogenic, Likely pathogenic, VUS, Likely benign, benign

*Kohane, Tsing, Kong, Genet Med 2012, PMID 22323072

ClinVar National Database

• f variants

http://www.clinvar.com/

Submitter Variants Genes Expert Consortia and Professional Organizations

International Society for Gastrointestinal Hereditary Tumours (InSiGHT) 2362 8 Clinical and Functional Translation of CFTR (CFTR2) 133 1 American College of Medical Genetics and Genomics (ACMG) 23 1

Clinical Laboratories

International Standards For Cytogenomic Arrays Consortium Laboratories 14441 >14000 Partners Healthcare Laboratory for Molecular Medicine 12133 222 University of Chicago Genetic Services Laboratory 7129 600 GeneDx 6757 574 Emory University Genetics Laboratory 5192 537 Ambry Genetics 4150 47 Sharing Clinical Reports Project for BRCA1 and BRCA2 2147 2 Laboratory Corporation of America (LabCorp) 1390 140 ARUP Laboratories 1304 7 InVitae 1102 35

• U. Washington CSER Program with Northwest Clinical Genomics Laboratory

625 76 University of Washington Collagen Diagnostic Laboratory 411 1 Children's National Medical Center GenMed Metabolism Laboratory 317 1 Pathway Genomics 166 17 Baylor College of Medicine Medical Genetics Laboratories 155 12 BluePrint Genetics 123 56 Counsyl 112 2 University of Pennsylvania School of Medicine Genetic Diagnostic Laboratory 68 1

Research Programs and Locus-Specific Databases

Breast Cancer Information Core (BIC) 3734 2 Royal Brompton Hospital Cardiovascular Biomedical Research Unit 1381 10 Muilu Laboratory, Institute for Molecular Medicine Finland 840 41 ClinSeq Project, National Human Genome Research Institute, NIH 425 36 Lifton Laboratory, Yale University 389 279 PALB2 Leiden Open Variation Database 242 2 Dept of Ophthalmology and Visual Sciences, Kyoto University Hospital 171 59 Dept Zoology, M.V. Muthiah Government College, India 58 3

Aggregate Databases

Online Mendelian Inheritance in Man (OMIM) 24973 3606 GeneReviews 4035 459 Total variant submissions to ClinVar* 149354

Total variant submissions to ClinVar with clinical assertions*

102418 Total unique variant submissions to ClinVar with clinical assertions * 76606 Total genes, in submissions with assertions, with variants in one gene* 6801 Total genes, in submissions with assertions, with variants across multiple genes* 17763

Total unique variant submissions to ClinVar with clinical assertions = 76606 (Jan 20, 2015) http://www.ncbi.nlm.nih.gov/clinvar/submitters/

Cross-CSER Annotation of 6 Variants

Site MSH6 c.2731C>T; p.Arg911* RYR1 c.1840C>T; p.Arg614Cys FBN1 c.4270C>G; p.Pro1424Ala TSC2 c.736A>G; p.Thr246Ala TNNT2 c.732G>T; p.Glu244Asp LDLR c.967G>A; p.Gly323Ser

1 Pathogenic Likely pathogenic VUS VUS VUS VUS 2 Pathogenic Pathogenic Likely pathogenic/ VUS VUS VUS VUS 3 Pathogenic Pathogenic VUS VUS VUS VUS 4 Pathogenic Pathogenic VUS VUS Likely pathogenic VUS 5 Pathogenic Likely pathogenic Likely pathogenic/ VUS Likely pathogenic VUS VUS 6 Pathogenic Likely pathogenic Pathogenic/ Likely pathogenic Likely pathogenic VUS Likely pathogenic/ VUS

Amendola et al, Genome Research, in press

Infrastructure Needs: Get annotations into ClinVar

§ UW has multiple molecular labs

§ UW Exome Lab pipelines to ClinVar § UW Lab Med molecular tests: 1000’s of cancer variants not submitted § UW Collage Diagnostic Lab: 1000 of variants not submitted

§ Outside lab reports to Clinical Service

§ Completed submitting BRCA1/2 § Hundreds more not submitted

§ Insufficient resources

What if the EHR pushed annotated variants to ClinVar?

Regulatory Changes: Data Sharing, FDA

Regulatory Changes: FDA Oversight of Lab developed tests

• Proposed FDA approval of tests
• Freezes the test for each approval
• Includes
• Analysis pipeline
• Which variants have clinical utility
• Applies to research
• Response to non-genetic test failures
• Amer Assoc for Cancer Research: “must

be FDA-approved”

• Assoc for Molec Path: “Stifle innovation

and freeze tests in time”

FDA regulations?

§ For genomics where is the public health

cost-benefit?

§ Is it reasonable for FDA to decide what

variants mean?

§ Use ClinVar (now ~77K annotated variants)

§ Mostly research labs put in annotations § Generally only few positives and limited phenotypes § ~228,000 genomes sequenced by researchers (2014) § In future most genomes will be done privately

§ Wrong/outdated tool?

§ Premarket vs. postmarket

Bigger idea: database of all genomic data generated

§ Similar to Sentinel System

§ drug safety data for 190 million Americans § Post-market data

§ Genetic surveillance

§ Collect whole genome data and broad phenotype data § Follow for new disorders § Public health use overcomes HIPAA

• Goals
• What is the chance that a random person having a

genomic test will find a genetic change VERY likely to cause a preventable/actionable disease?

• 6503 adults (4300 European Ancestry and 2203 African Ancestry)
• Contribute to national databases of variants (ClinVar)

Anything important in my genome?

• Definition of “actionable” gene-disease pair:
• Specific, evidence-based medical

recommendations expected to improve health

• utcomes
• Sufficient benefit
• e.g. Lynch syndrome variant -> screening
• Find all changes in 6503 people’s genomes

with literature (~700)

• Read all the literature to see if they cause

disease

Steps

MEMBER EXPERTISE(s) Arno Motulsky, MD Medical Genetics, pharmacogenetics Benjamin Wilfond, MD Pediatrics, bioethics Brian Shirts, MD PhD Molecular pathology Carlos Gallego, MD Medical genetics Debbie Nickerson, PhD Genomics Fuki Hisama, MD Medical Genetics, Neurology, Pediatrics, Adult Gail Jarvik, MD PhD Medical genetics, Internal Medicine, genomics, bioethics James P Evans, MD PhD Medical genetics, Internal Medicine, genomics Jeff Murray, MD PhD Medical genetics, Pediatrics Jerry Kim, MD Anesthesiology, genetics Jonathan Berg, MD PhD Medical genetics, cancer and adult genetics Katherine Leppig, MD Medical genetics, cytogenetics, eMERGE RORC Laura Amendola, MS CGC Genetic counselor, cancer genetics Michael Dorschner, PhD Molecular diagnostics , Genomics Mitzi Murray, MD Medical genetics, collagen/vascular, molecular diagnostics Peter Byers, MD PhD Medical genetics, collagen/vascular, molecular diagnostics Robin Bennett, MS CGC Genetic counselor, cancer genetics Ron Scott, MD Medical genetics, biochemical genetics

• S. Malia Fullerton, PhD

Bioethics, eMERGE RORC Thomas Bird, MD Neurogenetics, Neurology Virginia Sybert, MD Medical & Dermatological Genetics, Turner syndrome Wendy Raskind, MD PhD Medical Genetics, General Int. Med, cancer William Grady, MD Gastroenterology, Cancer, genetics Wylie Burke, MD PhD Medical genetics, internal medicine, bioethics

ROR committee

NEXT Medicine Return of Results Committee, September 2012

Genes with Actionable Variants relevant to Adults

=112 Total Genes

(a)Highlighted genes are recommended for return by the American College of Medical Genetics and Genomics guidelines.

Dominant X-Linked

ACTA2a KCNQ1 RBM20 DMD ACTC1 KIT RET EMD ACVRL1 LDLR RYR1 GLA APC LMNA RYR2 OTC BMPR1A MAX SCN5A BRCA1 MEN1 SDHAF2

Recessive

BRCA2 MET SDHB ATP7B CACNA1C MLH1 SDHC BCHE CACNA1S MLH3 SDHD BLM CACNB2 MSH2 SERPINC1 CASQ2 CDC73 MSH6 SGCD COQ2 CDH1 MUTYH SMAD3 COQ9 CNBP MYBPC3 SMAD4 CPT2 COL3A1 MYH11 SMARCB1 F5 DMPK MYH7 STK11 GAA DSC2 MYL2 TGFB2 HAMP DSG2 MYL3 TGFB3 HFE DSP MYLK TGFBR1 HFE2 ENG NF2 TGFBR2 IDUA EPCAM PDGFRA TMEM127 LDLRAP1 FBN1 PKP2 TMEM43 PAH FH PLN TNNI3 PCBD1 FLCN PMS2 TNNT2 PTS GCH1 PRKAG2 TP53 QDPR HMBS PRKAR1A TPM1 SERPINA1 KCNE1 PROC TSC1 SLC25A13 KCNE2 PROS1 TSC2 SLC37A4 KCNH2 PTCH1 VHL SLC7A9 KCNJ2 PTEN

Pathogenic Segregation* in >= 2 unrelated families OR 2 of 3:

• 1. Segregation * in 1 family
• 2. Identified in >= 3 unrelated individual
• 3. De novo event in trio

OR Protein truncation known to cause disease AND Below allele frequency cut off Likely pathogenic Identified in >= 3 unrelated individuals OR Segregation* in 1 family OR De novo event in trio AND Below allele frequency cut off

Classification criteria (strict for IFs)

*1/16 probability cut-off to define segregation

Expected rate of actionable variants:

Exome Variant Server (EVS) Results by Ancestry Group

Participants with classification European ancestry* N=4300 African ancestry N=2203

Disease causing variants (known) 30 (0.7%) 6 (0.3%) Likely disease causing variants (known) 52 (1.2%) 13 (0.6%) Disease causing variants (novel disruptive) 6 (0.1%) 6 (0.3%) Total (Includes LP) 88 (2.0%) 25 (1.2%)

*Caveats: No CNV included; only 3% Ashkenazi

Amendola et al, Genome Research, 2015. PMID: 25637381

§ Blind double/triple review for

QC.

§ Step 1: Random 25% of 615:

§ 83/156 (53%) of all variants discrepant

§ Step 2: All pathogenic &

likely pathogenic variants: § 44/79 (56%) discordant; § 42/44 (95%) overcalled (final call VUS)

§ Of 52 reviewers, a few made

systematic errors -> all

• recalled. Errors at all

expertise levels.

Variant Classification QC: Overcalling

P & LP Variants Double Reviewed 79 Discordant Classification 44 Concordant Classification 35 VUS 42 P 2 Revised Classification

Final CSER calls match other experts § 45/45 (100%) match with Sharing Clinical

Reports Project (SCRP)

§ 97/99 (98%) match with Partners

Laboratory for Molecular Medicine (LMM)

CADD

Kircher et

• al. Nat

Genet 2014, PMID 24487276

GERP++ Davydov et al. Plos Comput Biol 2010, PMID 21152010

GERP vs. CADD scores of pathogenic & likely pathogenic dominant variants

(excluding disruptive variants)

Expected rate of actionable variants:

6503 Exome Variant Server (EVS) Results by Ancestry Group

Participants with classification European ancestry* N=4300 African ancestry N=2203

Disease causing variants (known) 30 (0.7%) 6 (0.3%) Likely disease causing variants (known) 52 (1.2%) 13 (0.6%) Disease causing variants (novel disruptive) 6 (0.1%) 6 (0.3%) Total 88 (2.0%) 25 (1.2%)

*Caveats: No CNV included, HIGHER in Ashkenazi

Amendola et al, Genome Research, in press

Physician Education Vignette

§ CYP2C9 c.430C>T warfarin sensitivity result returned

to a research participant (affects starting dose)

§ Email ~1 month after return

“The NEXT study indicated I have a moderate sensitivity to Warfarin, the generic Coumadin. With that information, I switched to Rivaroxaban (Xarelto), one of the new blood thinner drugs, to mitigate that sensitivity. The NEXT study provided this useful information that I acted on… I feel very fortunate to have been able to have been able to be a part of the NEXT study and benefit from its results”

§ Plan: interview primary care physician re experience

Patient/Consent issues

§ Explain test / Incidental findings § EHR communication § Family communication

Why is a genetic test different?

§ The genetic results on 1

family member can affect the medical care of other family members

§ Other family members may

share the same genetic alterations

§ You may be able to show

that the other family members do NOT share concerning alleles

§ Need to test affected person § Most benefit if information is

shared

A real case

§ Patient with >1 cancer at early age seen by

genetics (large family, but no kids)

§ Genetic test agreed to and ordered for next

blood draw

§ Patient unexpectedly decompensates, is not

conscious, and not expected to survive > 24 hrs

§ 7 siblings and 2 geneticists converge in hospital § Family meeting: Do test? Who do you tell?

A real case (continued)

§ Large family meeting + 2 geneticists + nurse

§ >>1 hour § Genetics communicates patient desire to share this information with family § Family uniformly interested § Family elects single representative to interact with genetics

§ Blood drawn hours before patient died without

gaining consciousness

§ Test results shared with contact member

What is good about this case?

§ Everyone knew the patient had a bad disease § Most knew it might be genetic (no family history) § We could educate most of the family on what we

were doing and why

§ General consensus to test § General consensus on who should be the main

point of contact

§ Each of these could have gone the other way

Why ask about preferences?

Everyone is different and its not easy to predict their wishes

Wide Range of Preferences (for IFs)

§ Some wanted to know all information

§ “I’d want to know everything. I’d want no sugar coating at all.”

§ Other interested in findings that are ‘actionable’ (broadly

construed) or certain § “I think if I could be treated I would want to know, but if it’s something that they may not be able to treat or if it’s something that they can’t guarantee that I’m going to get or the percentage is like 50/50, then I have to just live wondering about this.”

§ Others wary of genomic information

§ “I just think you could go nuts treating all these little

• possibilities. I mean it just seems like there would be no end

to, I don’t know, trying to research what you should be eating

• r not eating for this condition. I would go crazy.”

§ Preferences dependent on prior test results?

Consent form language

Research Participant’s Authorization: I am a participant in the NEXT (New ExomeTechnology in) Medicine study. I authorize the Principal Investigator, Gail P. Jarvik, MD to allow for the disclosure of my genetic research results to the named person below in the event that I should die or become incapacitated before receiving all genetic research results myself. I understand that my genetic records may contain DNA test results related to my risk to develop genetic conditions and/or may explain why I have a genetic disease. The results are termed “genetic incidental findings” that derive from whole exome sequencing of my DNA. Genetic results information is important for my blood relatives to learn as we share similar genetic material. This form will be destroyed when all study related genetic results from the NEXT Medicine study are given to me personally. I give my specific authorization for my genetic results derived from usual care tests or whole exome sequencing tests to be released to the recipient named below: Yes___ No___ (initial your choice) This authorization is valid until _________ ( date) OR until the end of the NEXT Medicine study___________

Should we return genomic results to someone if you are deceased?

§ 75 enrolled, 44 male, 31 female § 69 yes ( 92%) vs. 6 no return

§ If no:

p 4 no biological family or contact p 2 (3% of 75) want to decide after results are returned (TRUE NO)

Who to return genomic results to, if not me?

Participan t Return to spouse N (%) Return to male relative N (%) Return to female relative N (%) Total N Female 16 (61.5%) 2 (7.7%) 8 (30.8%) 26 Male 29 (67.4%) 3 (7.0%) 11 (26.6%) 43 Total 45 (65.2%) 5 (7.3%) 19 (27.5%) 69

Who should we return to?

§ Of yes: 45/69 (65%) to spouse, 24/69 (35%) to first/second degree relatives § Of those 45 selecting spouse

p 24 had kids age <=25 yo p 6 no kids p 11 at least 1 kid > 25 yo p 4 data pending p Spouse: could ‘handle’ the information best and make sure it gets

to the right person(s), although it has no impact on the spouses person’s health

p Spouse can manage information for children

Return to non-spouse

§ Of 24 naming a first/second-degree relative

§ 19 named women (p=0.0025)

p 10 sister, 6 daughter, 2 mother, 1 grandmother

§ 9 had spouses/partners—all selected women § 5 named a man (2 brother, 3 son)

p 2 of 5 are women p 4 of 5 did not have a daughter p 4 of 5 had deceased mothers p 2 of 5 selected men over women (son v. daughter, brother v. sister)

§ Most but not all want to share § Usual medical care rarely seeks out

family members to share information- genetics may differ

§ Next of kin CAN access medical records, but may not know there is valuable information there. § They may not have access to research data.

§ May not be selecting legal next-of-kin

• r executor

Postmortem return implications

Genomic Medicine Obstacles

• Lack of practice guidelines / Lack of

insurance coverage

• Need for evidence base
• When it helps
• How best to do it
• What do all those variants mean?
• Regulatory climate / New legislation?
• Lack of non-geneticist provider training
• Patient/consent issues, family

communication

• Address postmortem return

Gail Jarvik Debbie Nickerson David Veenstra Wylie Burke Malia Fullerton Michael Dorschner Donald Patrick Peter Byers Dean Regier Fuki Hisama Peter Tarczy-Hornoch Patrick Heagerty Brian Browning Barbara Evans, JD Carlos Gallego Chris Nefcy Clinical review committee

Thank you, UW Next Medicine Team

Laura Amendola Martha Horike-Pyne Sue Trinidad Bryan Comstock Amber Burt David Crosslin Jerry Kim Daniel Kim Sara Carlson Jane Ranchalis Emily Turner Josh Smith Brian Shirts Robin Bennett Adam Gordon Sara Goering Carrie Bennette Elizabeth Hopley Bryon Paeper Jeff Furlong Peggy Robertson Katie Igartua Debbie Olson

Funded by NIH,NHGRI & NCI (U01HG006507, U01HG006375, 5T32GM007454), WA State Life Sciences Discovery Fund (265508, NWIGM); EVS data supported by NHLBI

Elisabeth Rosenthal Kelly Jones Mari Tokita Jimmy Bennett Colin Pritchard Tom Walsh Wendy Raskind

Learning Health Care System, e.g.

• UW Return of Results Committee
• How to classify challenging variants
• What IFs are returned (Gene-dz pairs)
• EHR report formatting
• EHR clinical decision support
• Content
• Usage
• All variant classifications pushed to

ClinVar

e.g. (in packet) Dorschner MO, Amendola LM, Shirts BH, Kiedrowski L, Salama J, Gordon AS, Fullerton SM, Tarczy‐Hornoch P, Byers PH, Jarvik GP. 2014. Refining the structure and content of clinical genomic reports. Am J Med Genet Part C Semin Med Genet 166C:85–92.