Genomics & Personalized Medicine: Analysis & Clinical - - PowerPoint PPT Presentation

Genomics & Personalized Medicine: Analysis & Clinical Implementation

Our vision

To create a borderless, complementary and synergistic research environment in southeast Wisconsin to translate discoveries into better health for our citizens; while simultaneously providing comprehensive educational and training programs to develop the next generation of clinical and translational researchers.

Our Principles of Operation

Creating a culture that fosters interdisciplinary innovation within and among institutions and contributes to the new discipline of clinical and translational science

Transformation

Providing core resources to assist investigators in the development, implementation, analysis, and dissemination

• f clinical and translational research

Facilitation

Training clinicians and basic scientists in the emerging discipline of Clinical and Translational Science

Education

Leveraging institutional strengths and resources to enhance trans-disciplinary clinical and translational research

Collaboration

CTSI-Cancer Center Workshop

Future Directions & Opportunities for Cancer-related Research Collaboration

Friday, December 16, 2011

Rehabilitation Research Workshop Wednesday, May 26, 2010 12:00 - 4:00 pm Marquette University

Nucleating Workshops & Conferences

Exploring the Metabolic Syndrome:

Basic Mechanisms, Clinical Implications and Community Impact

Wednesday, February 9, 2011

Brain Imaging Workshop University of Wisconsin - Milwaukee Wednesday, May 20, 2009

Drug Discovery and Repurposing: Form ing Partnerships May 31, 20 12 – Medical College of Wisconsin

Convergence of Disciplines 2012 Pilot Awards

• Medicine
• Biomedical Informatics
• Rehabilitation
• Psychology
• Economics
• Nursing
• Dentistry
• Public Health
• Computer Science
• Business
• Physical Therapy
• Exercise Science
• Biomedical Engineering
• Genetics
• Physics
• Chemistry
• Mechanical Engineering
• Psychiatry

Howard Jacob, PhD

• Dr. Jacob led a team of researchers at the

Medical College of Wisconsin who used NextGen sequencing to help diagnose the medical condition of Nicholas Volker.

• Dr. Jacob and his team have also built the

world’s first integrated Genomics Medicine Clinic.

• He earned his PhD in Pharmacology from

the University of Iowa followed by fellowships at MIT, Harvard and Stanford.

• He is the founding director of the Personalized Medicine Program and

Human and Molecular Genetics Center, and Professor in Physiology and Pediatrics at the Medical College of Wisconsin and Children’s Hospital of Wisconsin.

• Dr. Jacob currently serves as the Director of Biomedical Informatics for

CTSI

Who is Personalized Medicine?

Howard J. Jacob, Ph.D. http://www.mcw.edu/HMGC Jacob@mcw.edu

Personalized Medicine

• “products and services that leverage the

science of genomics and proteomics (directly

• r indirectly) and capitalize on the trends

toward wellness and consumerism to enable tailored approaches to prevention and care.”

PricewaterhouseCoopers The new science of personalized medicine

Goals for today

• Discuss six aspects of personalized medicine

– Data generation Implementation – Data analysis Ethics – Data knowledge Education

• Break out groups

– Meet, discuss, create opportunities for collaboration

• Setting the stage

How long is all the DNA in your Body?

2m X 100,000,000,000,000 cells = 2 x1014m

The earth to the sun is 150,000,000,000m (1.5x1011)

2x1014/1.5x1011 = 1333 trips Or 666.5 round trips

IS FAMILY HISTORY IMPORTANT?

Personalized Medicine

Adults: Take 2 aspirin

Family history is how we practice genetics in the clinic every day in 2012

370 Drugs

Nic Volker, gene found, treatment, doing well One in a Billion by Mark Johnson and Kathleen Gallagher

Clinical Care Team Sequencing Team Clinical Molecular Testing Data Assessment Team EthicsTeam Clinical Phenotypes & Family History BioInformatics & Electronic Medical Records Patient

Additional Reference Data Clinical Genomic Data Clinical Reference Data Diagnostic Test Results Molecular Reference Data

Expression profiling Proteomics profiling Genomic Medicine Genetics CNV profiling Epigenetic profiling Drug interactions Ref genomes Ref pathways Genotype to phenotype Family data Sequence data Ref genotypes Outcomes Clinical attributes Treatments Environmental exposures Radiology Phenotype data Molecular interactions Immunohistochemistry FACS Hematology Pathology Patent info Ref expression data Biomarkers Pharmacogenomics Biochemical Disease registry Diagnoses SNP typing Risk factors G x E associations

Who will pay for all this?

Letter from Insurance Group

6 out of 12 pre-authorizations

Personalized Medicine

Repeat for hundreds of diseases and treatments Repeat for thousands of patients Personalized Treatment

Individual Patient G + C + E

Predictive Model for Disease Susceptibility & Treatment Response State-of-the-Art Machine Learning Genetic, Clinical, & Environmental Data

Would you like your genome sequenced?

Categorical Model of Choice

• ptional

disclosures mandatory disclosures

1° diagnostic 2° none 2° not actionable childhood 2° treatable childhood 2° actionable adulthood 2° not actionable adulthood

Parental Decisions Utilizing Categorical Model

Proceeded with WGS None* Not Actionable Childhood Actionable Adult Not Actionable Adult Yes 12 1 10 10 7 No 1 11 2 2 5

*Mandatory disclosures include pathogenic and actionable childhood onset diseases.

Consumer Driven

Individual Health & Wellness Plan

Data and Knowledge Changes Age and Health Status Changes

1. Generate whole genome sequence 2. Analyze (de novo assembly) 3. Annotate the genome for all structures 4. Link to Electronic Medical Record 5. Link to all biomedical literature

• 6. Provide data to physician for clinical management

ALL IN ONE DAY Then provide prospective follow-up as new discoveries come “online”

Goal

Challenges

• Managing Data
• Analyzing Data

– Data Visualization

• Diagnosis (genomics and clinical presentation)
• Implementation
• Storing data
• Staying current as medical knowledge changes and

the patient changes

How do we help patients today?

Our CTSI is personalized medicine

Nic Volker, gene found, treatment, doing well One in a Billion by Mark Johnson and Kathleen Gallagher Andrew, gene(s) have not been found, no diagnosis Cracking your genetic code; NOVA Avery What if her DNA was sequenced at birth?

Current and Future Sequencing Technologies

Michael Tschannen

Molecular Genetics Technical Specialist Sequencing Core Manager Human and Molecular Genetics Center Medical College of Wisconsin

Many Choices of Technology

Sanger Sequencing – ABI3730xl

Specs: “Gold Standard” of Sequencing 800-1000bp read length 1.2Mb per 4 hour run Typical Applications: Clinical Variant Verification Amplicon Sequencing Gene Panel Sequencing Region Specific Sequencing

Roche GS-FLX+ (454)

Specs: 2 million reads per 2 day run 450-700 bp reads 600-800 Mb per run Typical Uses: Metagenomics (16S) Amplicon Sequencing Bacterial WGS Viral WGS Sequence Capture Gene Panel Sequencing

Ion Torrent – Life Technologies

Ion Personal Genome Machine Specs: Up to 200bp reads Up to 1Gb in 5 hour run Typical Uses: Amplicon Sequencing Sequence Capture Ion Proton Specs: Up to 200bp reads Up to 10Gb per 4 hour run Typical Uses: Whole Exome Sequencing Whole Genome Sequencing (future)

Illumina HiSeq 2000

Specs: 2.5 – 3 Billion reads per 12 day run 2x100 Paired End Sequencing 2 Flowcells per run 2 Human Genomes per flowcell 24 Exomes per flowcell Typical Uses: Human/Rat/Mouse Whole Genome Sequencing Exome Sequencing RNAseq – Gene Expression / Transcript Identification

Illumina MiSeq

Specs: 35 million reads per flowcell 1/5 of a single HiSeq Lane 2x250 PE reads 7-8Gb output per 36 hour run Upgrading to 15Gb output in 2013 Typical Uses: Whole Exome Sequencing HiSeq Library/Pool QC Amplicon Sequencing Gene Panel Sequencing

Pacific Biosciences PacBio RS

Specs: 120,000 Reads per SMRT Cell 150-250 Mb per 3 hour cell run 16 SMRT cells per day 3,000bp average read length New XL chemistry – up to 5kB Typical Uses: Bacterial Whole Genome Viral Whole Genome Amplicon Sequencing Sequencing of repeats Genomic gap filling

Oxford Nanopore

Specs: Protein Nanopore GridION – 2,000/8,000 pores – 150Gb/Day MinION – 500 pores – 25Gb/Day No set run time/read length Whole Human Genome -15 min - $10/Gb Uses: Still Unreleased Whole Genome Sequencing Metagenomics “Destination” Sequencing

The Future

Higher Throughput Faster Cheaper Longer Read Length More Accurate Easier Assembly Efficient Data Storage

Acknowledgements

Contact Info: Mike Tschannen mtschann@mcw.edu 414-456-8890

Genomic Medicine Big Data: Production, Analysis and Management

Liz Worthey Ph.D

Assistant Professor Clinical and Translational Genomic Analysis Lab Human and Molecular Genetics Center Pediatric Genomics, Department of Pediatrics Medical College of Wisconsin

Human Genome Sequencing

First Human Genome Sequence

• 10 years to complete
• $2.7 billion
• 100s ABI 3730 sequencing

machines

• 3 Gbases data total

Single Human Genome Sequence now

• 8 days to complete
• $5,000
• 1 Illumina HiSeq2500 machine
• 100 Gbases per day

The cost to sequence one base has dropped 100 million fold

Many technologies exist

Analysis phases

NGS RUN BASE CALLING Lane-level Q/C ALIGNMENT PRIMARY ALIGNMENT GENOTYPE CALLING DE-MULTIPLEX

SECONDARY TERTIARY

QC metrics Thresholds fastq

• bam
• other
• Vcf
• other

VARIANTS LIST NOVEL KNOWN ANNOTATE PRIORITIZE CLASSIFY CLINICAL REPORT

PRIMARY

ASSAY DESIGN SUB IND CNV SV

Derived from figure from Birgit Funke

Challenge: A variety of events lead to production of many sequencing errors

• Polymerase errors (~300,000 per genome)
• De-Phasing

– Occurs with step-wise addition methods when growing primers move out of synchronicity for any given cycle

• Dark Nucleotides causing false deletion errors

– The nucleotide does not contain a fluorescent label – Breakdown of dye-label nucleotides – Contamination with unlabeled nucleotides

Sequencing the same sample with known variants on different platforms produces different sequencing errors

Courtesy of Chris Mason and SeQC Consortium

• Illumina more likely to have error after ‘G’
• PCR-based methods miss GC- and AT-rich regions
• PolyA miscount errors for pyrosequencing methods

Secondary analysis – read mapping

p.L80P p.Q98X p.Q86X p.V146G p.R123W p.L153R p.R173G p.R175P p.Q183H p.R217G p.E240X p.Q239X p.Q236X p.L227P p.A288G p.D311N p.E315A p.R319Q p.A323P p.D406V p.X420W p.C417F p.E391X p.Q384X p.R359W p.Q290X p.R256X p.R62X p.Q45X p.E57K

NEMO MECP2 Many methods are available: Which to use?

• Speed
• Accuracy
• Computational

requirements

• Robustness
• Support

Some of these regions are of significant interest

RPS17 HEXB DAZ1 SFTPA2 C4A C4B SSX4 INPP5E F7 CES1 PMS2 SOX18 CCL3L1 CHST14 SLC7A9 ATP8B1 CYP2R1 ABCC2 MME LPA CSH1 OCLN HBA2 KRT81 CFHR3 HRAS DSG1 FCGR2C SMN2 F13A1 CYP2C19 TFE3 CR1 NCF1 STRC CFC1 D2HGDH EVC FKTN FCGR2B ANTXR2 OTOA ENAM

52 2,925 829 Actionable Poorly covered Gaps shared in 25 WGS

SFTPA1 SMN1 OPN1MW OPN1LW IKBKG NEB SLC6A8 CHRNA3 CFD

Amelogenesis imperfecta Systemic lupus erythematosus

>90 diseases

Ehlers-Danlos syndrome, musculocontractural type Ellis-van Creveld syndrome C4a deficiency

881 1117 1551 309 185 932 362 663 526 309

Clinically actionable Genes

Strategies for spanning gaps

• Some gaps are caused by overrepresentation of certain

genomic regions in the sequence output, with underrepresentation of others

• Sanger fill in – too expensive
• WGS plus WES strategy?
• Some gaps are caused by repeats that can’t be sequenced

through or cause issues with mapping – add longer read data from a PacBio or ?

• De Novo assembly?

Variant callers give different results on the same data

Differences between sequencing/analysis pipelines are between 4 and 14% of variants per sample Various mapping and variant calling algorithms used in each analysis

Rosenfeld, Mason, and Smith. PLoS One. 2012.

Concordance good for latest renditions of SNV callers, ok for indel callers, and poor for SVs Comparison of variants called from 32 HapMap genomes by Complete Genomics and 1K Genomes Project

Blue=CG Red=1KGP

Even calling of SNVs has issues

Patient 2F Patient 2C2

Concordance between Illumina pipeline and BWA/GATK SNV calls

Rosenfeld, Mason, and Smith. PLoS One. 2012.

But cannot simply exclude variants called by a single method

Transition mutations occur more frequently than transversions thus: (Ti/Tv >1) Sequencing errors tend to be more transversions thus: (Ti/Tv < 1)

• Elliott Margulies et. al. at Illumina have shown that only 95% of high

quality SNVs are called the same in 14 stringently performed replicates

• Solutions:
• Ongoing algorithm development
• Hold of until methods mature – we don’t yet “do” clinical SVs

Tertiary analysis

~250,000 ~1,800 ~1,200 ~50 ~10 0-5 ~4,800,000

Variant storage, analysis, prioritization, and reporting

Analytical considerations/challenges

• n the variant impact axis
• Existing WGS technologies produce many

sequencing errors

• Existing mapping algorithms in combination with

short read technology give rise to many mapping errors

• Bioinformatics limitations with variant calling

(especially indels and SV)

• Data is incorrect/outdated - Allele Frequencies
• Data must be used appropriately - nucleotide or

amino acid conservation scores

• Data must be understood - SIFT, PolyPhen, Condel

e.g. Allele frequency data cannot be relied upon without review

The allele frequency reported for a particular variant can vary widely amongst commonly used data sources These variants were not randomly selected – these variants are all associated with disease in HGMD Solution: compare multiple data sources and consider the sources in terms of possible source disease status as well as technological aspects etc.

• Many of the datasets being used were never intended to be

used in the way we are currently applying them

• We estimate that up to ~8% of genotype to phenotype data

in larger mutation databases is incorrect:

– Typos/human entry errors are common

• E.g. term breast cancer entered rather than cervical

– Old/Outdated

• <70% in PubMed is captured in DBs

– Many entries subsequently disproven or even retracted – These are not seen as part of DBs “job”

• It is important to understand the quality of the datasets and

to curate/track corrections or in house annotations

• Data sharing is hugely important
• Identifying a variant with a functional impact on a gene is not

the same as identifying the cause of the disease

Analytical considerations/challenges

• n the functional classification axis

e.g. Many reported “rare mutations” are not so rare polymorphisms

• HUGE curation task to ensure data is correct and up to date.
• Currently – never believe anything – many hours of

verification required

• Data sharing will be critical to our cumulative success!
• HEXB
• NPHS1
• LAMA2
• ADA
• MTHFR
• GALC
• MEFV
• CYP21A2
• ARSB
• CPT1A
• HESX1
• HGSNAT
• ACADM
• FKTN
• WNT10A
• PMM2
• MPL
• POLG
• BTD
• NTRK1
• ALG6
• DPYD
• HADHA
• GAA
• AHI1
• AMPD1
• ATP7B
• CDH23
• SBDS
• NEFL
• GLB1
• ATP7B
• ETFB
• NHLRC1
• IGHMBP2
• SERPINA1
• NPHS1
• SLC26A2
• MYO5A
• DPYD

Challenge: Data storage

Data storage costs currently ~$1,200 per year per patient per WGS (on high performance disks; without compression)

At 1 Tb per genome

2013: 100,000 genomes 1 Tb each = 100 PetaBytes WGS for all new U.S. babies/year: 4,000,000 genomes = 4 ExaBytes WGS for all U.S. citizens over 50: 310,000,000 genomes = 310 ExaBytes WGS for all: 7,000,000,000 genomes = 7 ZettaBytes

At 400 Gb per genome

2013: 100,000 genomes 100 Gb each = 40 TeraBytes WGS for all new U.S. babies/year: 4,000,000 genomes = 1.6 PetaBytes WGS for all U.S. citizens over 50: 310,000,000 genomes = 124 PetaBytes WGS for all: 7,000,000,000 genomes = 2.8 ExaBytes

http://www.allthingstechnology.net/2011/07/how-much-byte-make-yottabyte.html

How much data is this?

@1Tb/genome All = 7 Zb @400 Gb/genome All = 2.8 Eb

Solutions

• Development of and agreement on

sustainable retention guidelines

• Compression?

– By retaining only the data related to variant calls we can compress 50-100 fold. – But we need to keep in mind clinical use cases.

• Transparency of data used to determine that no variant

existed (no call)?

• Transparency of data that shows that a particular

region was not covered sufficiently to be sure of accuracy of the call – gap calls?

Performing WGS in a clinically appropriate timeline

Data generation/analysis/reporting steps HiSeq2000 (Current) HiSeq2500 (RapidRun) Barcode and Accessioning of samples 0.5 0.5 DNA extraction and Sequencing 289* 33.5 DNA Extraction 2 2 Sequencing Lib Prep and Quant 77* 4.5 Verification and Sequencing 210* 27 Secondary analysis 19 19 Tertiary analysis 5 5 Variant Annotation 3 3 Production of Clinical Report, Delivery 2 2 Interpretation and reporting 11 11 Interpretation 9 9 Preparation and review of final report 2 2 Total (hours) 324.5 69

* 3 samples processed simultaneously in these steps

Currently a $5,000 sequence, a $200 analysis, and a $1,250 interpretation +

• Analysis/Interpretation:
• Bioinformatician: loading, processing, report generation - $200
• Clinical Geneticist: interpretation - $1000
• Follow up:
• Technician: Sanger confirmation/analysis - $150
• Reporting:
• Analyst / Clinical geneticist: Report finalization - $100

+

Getting better but not good enough

• Capabilities – We are a diverse group of researchers with varied

backgrounds and expertise in genetics, genomics, physiology, computer science, clinical informatics, statistics, clinical research, clinical diagnostics, bioinformatics etc. etc.

• Needs – Progress is being made but many challenges remain in

the continued development of Genomic Medicine. In Informatics realm we have significant challenges in:

– Data storage and management, transfer and sharing – Secondary analysis – reference genome, mapping, variant calling – Tertiary analysis – annotation, prioritization, visualization – Clinical Interpretation – data mining, phenotype data extraction and analysis, genotype – phenotype correlation curation – Education – across the board

• Goals – Collaboration to further our joint capabilities:

– Identification of disease cohorts for collaborations in translational research – Collaborations to develop novel or reuse existing methodologies/algorithms addressing the challenges outlined – Identification of areas of expertise for submission of joint grants

Summary

• Families and Patients
• Referring Physicians
• MCW- HMGC
• Howard Jacob
• David Bick
• David Dimmock
• Mary Shimoyama
• Jill Northup
• Jenny Guerts
• Brad Taylor
• CHW Genetics Center
• Regan Veith
• Angela Pickart
• William Rhead
• AGEN-Seq Technicians
• Mike Tschannen
• Daniel Helbling
• Brett Chirempes
• Jayme Wittke
• Jamie Wendt-Andrae
• CHW
• Juliet Kersten
• Paula North
• Tara Schmit
• Jack Routes
• Altheia

Roquemore-Goins

• Gail Bernadi
• Michael Gutzeit
• Steven Leuthner
• Rodney Willoughby
• Thomas May
• Robert Kliegman
• Funding/Support:
• MCW Children’s

Research Institute

• Jeffrey Modell

Foundation

• Private Donors
• Bioinformatics/Curation

/Systems support

• Brandon Wilk
• Jeremy Harris
• Wendy Demos
• Arthur Weborg
• George Kowalski
• Weihong Jin
• Weisong Liu
• Jeff DePons
• Sharon Tsaih
• Oliver Hummel
• Stacy Zacher
• Marek Tutaj
• Greg McQuestion
• Kent Brodie
• Stan Laulederkind
• Victoria Petri
• Jennifer Smith
• Alex Stoddard
• Pushkala Jayaraman

Acknowledgements

Genomics & Personalized Medicine: Analysis & Clinical Implementation Breakout Sessions 1 & 2

Genomics and Science Education

• Dr. Neil Lamb

HudsonAlpha Institute for Biotechnology

• Dr. Tim Herman

Milwaukee School of Engineering

Teachers FIRST

From Interesting Research to Scientific Teaching

Tim Herman Center for BioMolecular Modeling Milwaukee School of Engineering

An NIH Science Education Partnership Award (SEPA) project

The MSOE Center for BioMolecular Modeling

…an instructional materials development laboratory, …with a science education outreach mission.

… recent advances in the technologies that have delivered the promise of genomic and proteomic medicine to clinics have completely outstripped the education of the public who stand to benefit from this new science ….. … as well as the very health care professionals who suddenly find themselves in a position to make use of this new technology.

The Problem…..

In the old days …..

• Zinc Finger Nucleases and Genome Editing
• The CCR5 Gene and Resistance to HIV
• Cytochrome p450s and Pharmacogenomics
• Beery Twins Story – Sepiapterin Reductase
• Nic Volker Story –XIAP.

Genes, Genomes and Personalized Medicine

…. Molecular Stories ….

Nic Volker Story –XIAP.

http://cbm.msoe.edu/stupro/so/module2012/xiapHome.html

Neil E. Lamb, Ph.D. Director of Educational Outreach HudsonAlpha Institute for Biotechnology

an online game about complex disease, risk assessment and prevention/treatment

funded through a 5-year NIH Science and Education Partnership Award (SEPA)

• goal: assess disease risks for the six-person crew and

pack the ship with supplies that reduce risk and/or provide treatment

• game can be played in single person or classroom format
• Students’ activities and decisions are available to the

teacher

• setting: preparing to launch the Argos1 - a 20-year

exploratory mission to Triton, a moon of Neptune

• accompanying website with background

information

What is Touching Triton?

Touching Triton Gameplay

select mission identify ‘combined disease risks’ choose crew member launch mission determine risks make packing recommendations

• bserve outcomes

family history genomic findings medical records

Crew Selection

Crew Member Dashboard

Medical Records

Genomic Data

MACULAR DEGENERATION

Family History

Final Risk Assessment

Packing

Advisor support

• grounded in research
• risk from genomic data - multiply odds ratios from each

SNP, compare to population risk

• risks from medical records and family history selected

using a slider bar

• verall risk assessment - slider bar
• bjective: understand how students perceive risks in the

context of complex disorders

• ‘consensus lifetime risk’ - determined from a survey of

medical geneticists, genetic counselors and clinical researchers - teachers can compare this to student risk estimates

About Risks

Project Timeline

Y1 Y2 Y3 Y4 Y5

discovery & planning phase prototype generation usability testing to assess functionality repeated testing & revision; generate teacher content & background in-class testing; focus

• n content

final beta testing; summer training for 30 early adopters final revisions & deployment; website complete embedded training in classrooms of 75 Alabama teachers

• nline

training module crafted & deployed assessment & broad dissemination

• Dec. 2012

Filming of Crew Scenes

Acknowledgements

• Adam Hott
• Kelly East
• Jennifer Carden
• Madelene Loftin
• JD Frey

HudsonAlpha

• Paul Chang
• Bryan Powell
• Nevin Langdon

Metabahn/PCD/HyCreative

• Camellia Sanford

Rockman et al.

This project supported by the National Center for Research Resources and the Division of Program Coordination, Planning and Strategic Initiatives of the National Institutes of Health through Grant Number R25OD010981

The U.S. Space and Rocket Center

Ethical, Legal, Social Implications (ELSI) related to Genomics (the brief version)

MCW Program in Genomics and Ethics and MCW Ctr. for Patient Care and Outcomes Research (nothing to disclose)

Mike Farrell, MD

• “Safety” ensuring more good than harm
• ELSI: techniques to ensure that genomics is

safe for patients, relatives, and society.

• Key: effective collaboration between

clinicians, lab, informatics & ELSI scholars

A “Safety” Paradigm for ELSI

Patient care TODAY Safer, wider practice TOMORROW ELSI Technological Advances

“What can we do?” “What should we do?” “How should we do it?”

• Distributive justice

– Socially just allocation of goods in society – Example question: should we spend money

• n genomics when people need access to

care, immunizations, mental health care, food, etc…

• This ELSI is commonly raised, but ignored

in this talk because of the assumption that genomic sequencing is here to stay.

Not included in this talk…

• When Genomics is implemented, what will

happen to…

ELSI example #1: Harm vs. Benefit

Patients? • Problems with understanding, anxiety, other emotions,

decision-making, self-image, stigmatization, etc…

Relatives? • Genomic information disseminates from the patient, exposing

relatives to risks for which they did not consent.

Children? • Especially vulnerable because identities are still forming.

• No right of refusal.
• The mis-paternity problem (“dad” is not biological father).
• Disrupted bonding between parents and children.

Healthcare? • Providers will be confused, and may require re-training to deal

with a huge volume of information.

• How will genomic data be treated by payors, employers?

Communities? • Will there be cultural disparities, stigmatization, or unique sorts

• f problems with misconceptions?

Society? • Will genetic risk factors change how we appraise each others’

value as individuals?

• Will genomics data worsen health disparities?

• Many ELSIs are phrased in the form of a

“lingering question.” Why is this so?

– Likelihood of the ELSI is unclear – It is hard to study a hypothetical innovation, or generalize from dissimilar situations – Some ELSIs are best described in works of imaginative fiction (e.g. the movie Gattaca) – fiction may raise awareness, or it may give the public an unrealistic idea of genomics ELSI example #1: Harm vs. Benefit cont’d

• Another problem: some genomic data has a

varying degree of uncertainty

• Example: Does a given variant matter?

ELSI example #1: Harm vs. Benefit cont’d

(Mike’s view about)

Association between a genomic variant and phenotype Published evidence of association between variant and …

• Disease
• Risk for disease
• Disease in animals
• Risk for disease in animals
• Disease in children vs. adults

Plausible association between variant and …

• Disease
• Risk for disease

Evidence or plausible association between variant and …

• Phenotype that does not relate

to disease (e.g. height) Uncertain significance

• Is genomic sequencing a test, or a

procedure?

• What constitutes adequate informed

consent? How much information needs to be provided?

• How do we ensure that patients can weigh

harms and goods for themselves.

• What are the boundaries between clinical

care and research?

ELSI example #2: Consent

• Given that sequencing is complicated,

and has risks and benefits…

–Should there be pre-requisites for credentials or training of providers before they can order sequencing? –Should patients be required to see a genetic counselor before and/or after sequencing? ELSI example #3: Professional issues

• Answer is not simple
• Solutions need to be crafted for individual

ELSIs, and the communities that may experience them.

• Some solutions have their own problems

– Genetic counselors: cost and supply limits – Consent: how much education is needed? – Best practices: do they matter if anybody can buy sequencing from a private lab?

• Addressing ELSI is thus a matter of expertise

and troubleshooting

OK, so what do we do about ELSIs?

• Expertise in ELSIs, communication, and research ethics
• Based in MCW’s Center for Bioethics and Medical

Humanities (itself based in Institute for Health & Society) Contact us to collaborate! kstrong@mcw.edu

Program in Genomics and Ethics

Kimberly Strong PhD Ryan Spellecy PhD Thomas May PhD Mike Farrell MD Art Derse MD JD Kaija Zusevics PhD (postdoc) Alison La Pean Kirschner MS CGC (genetic counselor)

Current State of Practice: Whole Genome Sequencing in clinical use today

David Bick, M.D.

Director of Advanced Genomics Laboratory at Children's Hospital of Wisconsin Professor of Pediatrics and Obstetrics & Gynecology Chief, Section of Genetics, Dept. of Pediatrics, Medical College of Wisconsin

Human and Molecular Genetics Center

Geno enomics & & Per erson

• nal

alized ed M Medi edicine: A Anal nalysis and and Clin Clinic ical I l Imple lementatio ion Clin Clinic ical & l & Transla lational Sc l Scie ience I Institute 12-17-12

Disclosures

• The following relationship(s) exist related to this

presentation:

– Children’s Hospital of Wisconsin (CHW) and Medical College of Wisconsin (MCW) provides fee for service whole genome sequencing (WGS) for clinical use. – WGS is not an FDA approved device, the FDA has determined that such approval is not necessary. – Testing is performed in CLIA/CAP approved laboratory.

Whole Genome Sequencing (WGS) & Whole Exome Sequencing (WES) is in clinical practice Children’s Hospital of Wisconsin & Medical College of Wisconsin offers whole genome sequencing:

Nature 478, 22-24 (2011) News Feature Human genetics: Genomes on prescription Brendan Maher

48 patients evaluated for the programme, 17 have been accepted, and their families have gone through six hours or more of genetic counselling before sequencing. Insurance companies have agreed to foot the bill for at least two of the cases.

Steps in clinical whole genome sequencing

• Patient selection
• Genetic counseling
• Generation of sequence data - WGS

vs WES

• Data analysis
• Writing the report
• How can we improve the process?

Patient selection

• Choose cases that are likely genetic

and rare

• Non-syndromic MR not a good choice
• Assure that all reasonable testing

already done & testing will advance clinical care

• As more gene connected to

phenotypes loosen restrictions

Genetic counseling

• Topics for counseling. 6 – 10 hrs.
• Report cause of patient’s problem and childhood onset

treatable disorders

• Patient/family choice: Incidental findings – Ex: BRCA1
• Insurance coverage

Exploration of expectations 16% Inheritance 14% Test Methodology 9% Categorical Model of Choice 18% Psychosocial Counseling 19% Follow-up Planning 10% Formal consent 8% Clinical genetics evaluation 9%

6-10 hours total

WES vs WGS

Advantages of WES

• Exome is 1% of

genome

– WES costs less than WGS – Deeper coverage of some areas compared to WGS

• Exome includes only

the coding region

– Tools to interpret changes best developed

Advantages of WGS

• WGS has better coverage of

some areas

– Exome capture array does not capture all exons

• Certain parts of introns and

regions between genes can be interpreted

• Encode project published 9/12

– more than 80% of the genome is functional!

• mutations miR-96 cause

autosomal dominant progressive hearing loss

Generation of sequence data - WGS vs WES

Laboratory Considerations in WGS/ WES

NGS RUN BASE CALLING Lane-level Q/C ALIGNMENT PRIMARY ALIGNMENT GENOTYPE CALLING DE-MULTIPLEX SECONDARY TERTIARY QC metrics Thresholds fastq

• bam
• other
• Vcf
• other

VARIANTS LIST NOVEL KNOWN ANNOTATE PRIORITIZE CLASSIFY CLINICAL REPORT

SUB IND CNV SV

PRIMARY ASSAY DESIGN

Prima mary analy lysis is – On machine chemistry and software – generates reads Seconda

• ndary anal

alysis – align-ment software and variant calling software – calls variants Ter ertiary anal analysis – variant annotation software – clinical relevance

ANALYSIS STEPS:

Analysis Challenges

• The reference human

genome (called Hg19 ) 250 gaps

• Much variation not in

Hg19 – WGS can end up with unmapped reads – Read with and insertion or deletion may not map to ref genome

• Ref genome &

database of variants improving

• Current NexGen devices

works well for single bp substitutions

• Partial list of genomic variants and

structures

– Single base-pair substitutions – Insertions – Deletions – Adjacent insertion and deletion – GC rich regions – Trinucleotide repeats – Copy number variants – Homopolymer tracts – Translocations – Inversions – Short tandem repeats – Pseudogenes – Highly polymorphic regions (e.g. HLA locus)

Tertiary analysis – annotation considerations

• Incomplete phenotypic description of patients
• Each variant evaluated for clinical relevance to

THAT PATIENT

– Known mutations

• Human Gene Mutation Database, Online Mendelian

Inheritance in Man, Gene specific databases

– Stop, readthough, missense

• NCBI , Ensembl
• SIFT, Polyphen2

– Splice-site

• GeneSplicer

– Evolutionary conservation

• PhastCons for nucleotides, AA conservation tools

– Novel /rare variants

• dbSNP, Exome Variant Server

A brief history of genetic testing

• Single disorder – Cystic Fibrosis

– Lab designs and validates test for this gene – MD orders test in patients with appropriate phenotype

• Gene panel - Cardiomyopathy

– Lab reviews literature, chooses genes assoc with cardiomyopathy, designs and validates test for the chosen genes – MD orders test in patients with appropriate phenotype

Patient Suspected Clinical Dx Final Diagnosis Genetic Counselor Physicia n Molecular Dx Laboratory Director Genetic Counselor

• Whole genome/exome

sequencing – Lab designs and validates test – MD orders test in patient

– Lab chooses genes associated with patient’s phenotype AFTER the test is run!

• Must confirm all

variants with Sanger

• Large number of

errors in research literature for “disease causing” variants

• Read original

paper before reporting!

Доверяй, но проверяй TRUS TRUST B T BUT V UT VERI ERIFY! FY!

Writing the report

How can we improve the process?

• Incidental findings

– How should these be handled by the lab?

• Variants of uncertain significance

– How can these be changed to benign variants or pathogenic variants? – Mary E. Shimoyama, PhD & Jack Routes, MD – extending clinical whole genome sequencing

• Families
• Referring Physicians
• CHW WGS Review

Group

• MCW Genetics
• David Dimmock
• Donald Basel
• William Rhead
• Stephanie Offord
• Esperanza Font-

Montgomery

• Gunter Scharer
• CHW Genetics Center
• Regan Veith
• Amanda Laedtke
• Heather Radtke
• Dania Stachiw
• Angela Pickart
• Linda Smith
• Jeff Kopesky
• Neha Sekhri
• Cristin Griffis
• LuAnn Weik
• Dimmock Lab
• Daniel Helbling
• CHW Laboratory

Medicine

• Paula North
• Tara Schmit
• Altheia

Roquemore-Goins

• Michelle Galalvez
• Children’s Research

Institute

• Juliet Kersten
• Illumina
• Tina Hambuch
• Marc Laurent
• Brad Sickler
• Funding:
• Children’s

Research Institute

• Jeffrey Modell

Foundation

• Private Donors
• AGEN-Seq
• Jaime Wendt
• Jayme Wittke
• Brett Chirempes
• MCW- HMGC
• Howard Jacob
• Liz Worthey
• Mary Shimoyama
• Stan Lauderkind
• Pushkala Jayaraman
• Jenny Geurts
• Victoria Petri
• Alison Sarkis
• Sasha Zeng
• Bryce Schuler
• Florence Yeo
• Lisa Armitage
• Bioinformatics/Systems

support

• Jeff DePons
• George Kowalski
• Wes Rood
• Sharon Tsaih
• Brad Taylor
• Stacy Zacher
• Greg McQuestion
• Kent Brodie

Acknowledgements – It takes a village

Practical Limitations of Current Practice

• Dr. Mary Shimoyama

Medical College of Wisconsin

• Dr. John Routes

Medical College of Wisconsin

Research Clinical Divide and Moving Research Software to the Clinic

Extending Current State of Practice

John M. Routes Chief, Section of Allergy and Clinical Immunology Co-Director, Clinical and Translational Science Institute of Southeast WI Professor of Pediatrics, Medicine, Microbiology and Molecular Genetics Medical College of Wisconsin

• Turnaround time
• Candidate genes with data sets difficult

to interpret or manipulate

• Candidate genes with unknown

function

• Lack of experimental models to test

function of mutant protein

Current Limitations

Mary Shimoyama, PhD

Assistant Professor

Human and Molecular Genetics Center

Moving Research Software to the Clinic

Current Limitations

• Multiple analysis tools
• Single function
• Lack of standards
• Developed for basic research
• Private – single use
• Lack of centralized access points
• Data scattered across sites
• Lack of data standards and integration
• Lack of software suitable for clinic use
• Not designed for clinic workflow
• Often require specialist knowledge
• Difficult to use

Leveraging Existing and Emerging Tools for Clinical Use

• Identifying needs
• Identifying tools – existing and

emerging

• Identifying potential collaborations
• Development and testing
• Implementation

Genomics & Personalized Medicine: Analysis & Clinical Implementation Breakout Sessions 3 & 4