Norwegian clinical genetics analysisplatform ”genAP”

VERDIKT Conference

October 15, 2013

T. Grünfeld og T. Håndstad

Agenda

Drivers and development of individializedmedicine

Our areas of focus, and some challenges

Selected examples

Falling prices….

Faster and faster, more and more….

Increasing number ofpersons having theirgenome mapped

WGSintegrated

part of EPR

Deployment inscreening

(E.g. newborns)

Easier toanalyze all

than 3-5 genes

Offer for thosewho ”really

needs it”

Possible development of full ”whole genomesequencing”

”Sport” for ”therich and famous”

Fra Henry T. Greely, direktør Centre for Law and biosciences, Stanford University

•The firstscientists etc.•Minisolution”23andme”

•Patients withchallengindiagnostics•Newcausativegenes

•Allows for re-use ofsequencing(DNA static)•Later in-silicoanalysis

•Simplifiesexistingmethods•Allows for re-use

•”Everybody”routinelyanalyzed•Integratedexpert systemsin EPR

Some major challenges

Do we want toknow

everything?

Insurancecompanies

Data security

Privacy issues

Legal DNAregistries

How do we knowwhat to do?

Agenda

Drivers and development of individializedmedicine

Our areas of focus, and some challenges

Selected examples

Aim of the project

The aim of the project is to (contributeto) develop an ICT infrastructure forcentral, secure storage of humangenome data, which allows fordissemminated use nationally (Andpossibly internationally)

•Elucidate how to best analyze sensitivegenome data through existing (internetbased) tools

•Enable efficient (societally)deployment of genome data indiagnostics and treatment

•Vision of becoming an ICT platformfor ”personalized medicine” inNorway

Key end products of theproject are:•(Pilots for) practical toolsfor ”the general clinician” attheir bedside activities•An infrastructure and(organizatonal) methodologyfor expansion of the pilots toother clinical domains•An ICT infrastructure thatcan use aggregated data in thecontinuous development of thesolutions.

Clinical vs. Research focus

Research

Diagnostics

Germline-mutations

Somaticmutations

New mutationsCancer and

immunology(?)

Knownmutations

Cancer;therapy

•Quality above ”newesttechnology”

•Robustness aboveflexibility

•Other legal limitationsand framworksthan ”research solutions”

•User interface key: I.e.refinement of info toeasily availableknowledge/ decisionsupport

Our success is clinicaldemand!

Overview of modules in the system

Some key issues

From Henry T. Greely, Head of Centre for Law and Biosciences, Stanford University

Quality andstandardization of

analyses

How to convey theinformation

Storage, ownership anddatasecurity

Involvement of relatives

Research vs. Clinicaldiagnostics

•Are the lab. analyses of adequate quality?•Major interpretation challenges

•Increasingly blurry boarders between what is research (Commoninterest) and clinical diagnostics (Individual interest)•Strong need to revise and update legislation and guidelines

•How to deal with tests that have direct implications for ?•What when the patient dies?

•Dataconfidentiallity vs. Availability for usage: a trade off?•Patient autonomy vs. Documentation requirements

•Where is the limit for what should be conveyed (Significance, certainty etc)?•Who and how should info be conveyed (Degree of counseling, preparation)•How often must data be ”re-interpreted”, providing for new knowledge

A new field and language in medicine(Bass Hassan, Univ of Oxford)

Research

Bioinformatics

Informatics

Clinicaltesting Clinical

treatment

”BIO-

MEDISH”

Medicalinformatics

Challenges for HTS data analysis(Bass Hassan, Univ of Oxford)

Challenge Details

Bioinformaticscompetence

Software

ICTinfrastructure

•Rare skill which is difficult to find

•Costly resources when found

•Experience invaluable

•Self teaching very challenging

•Often not intuitive (Demanding user interface)

•Demand for many different applications

•Requires often ”command line” skills

•Substantial amounts of data to handle

•Requirements for high performance computing (HPC)

•Resources are difficult to find and challenging to share

•Substantial cost of storage and maintenance

These arejust about

ourexperiences!

Agenda

Drivers and development of individializedmedicine

Our areas of focus, and some challenges

Selected examples

Automating variant analysis

• Sample volume is expected to grow rapidly.

• The task of analysing genetic variantsconstitute a bottleneck in the whole process.

• To speed up and increase the quality of theanalysis, we seek to automate parts of it andprovide decision support for the molecularbiologists that analyse the variant data.

Algorithm for variant analysis

DMG cancer workflow Evaluate frequency and inheritance

Extractvar

Pat DB

Evaluatefrequency:

>~10%(lower for genot)

Common?

YES

Answer/addas class 1

Inheritancemode?

Homozygous?(hemizygous?)

Heterozygous?

Add note(+class 1)

YES/NO

Dominant

Recessive

YES/NO

Evaluatefrequency:

>1%(lower for genot)

dbSNP

YES

YES

Add note(+class 2)

Possible carrier/comp heteroz

NO

Report

With sufficientpopulation size;

not a patient pop

Latest dbSNP buildnot in Alamut,checks web if

variant not found

With sufficientpopulation size;

not a patient pop

Always check BIC

Note: in addition toinheritance, frequencycut-off should be based

on prevalence dataand/or frequency ofknown pathogenic

variants (adjusted foreach gene/diagnosis)

Note: X-linkedinheritance is athird possibilityfor other cases

(esp. for generalgenetics)

DEMO

Providing decision support for clinicians

• The average physician has little knowledgeof genomics.

• For genomics to change clinical practice,the information must be translated intoactionable recommendations, easilyavailable in the form of a decision supportsystem.

Decision support prototype for Tacrolimusdosage tested on transplant surgeons

Publication

Acknowledgements

• Morten C. Eike, Ph.D. (post doc)

• Dag Undlien, prof. M.D. Ph.D. (project owner)

• Halvard Lerum, Ph.D. (EHR integration)

• Lars Retterstøl, M.D. Ph.D (lab doctor)

• Tim Hughes, Ph. D. (variant calling)

• Eidi Nafstad, MSc. (lab engineer)

Pilot: 2-5 pakker/systemer-opprinnelig

Diagnostisk Farmakologisk Predikitv/prognostisk

Cardiomyopatier:33 gener

Cytostatika:CYP450?

Brystkreft:BRCA1BRCA2

++?Nevrologi:

Myopatier?

Nevropediatri: ”Hypotont barn”?Muskeldystrofi?

Immunmodulerende behandlingv/organtrans-plantasjon?~50 gener

1 2 3

Faser

Tilsvarende dagens SNV-genotypingIntegrering medpasientjournal

(utvidet med økendekunnskapsbase/datamengd

e)

Kliniskbruk

Høy

LavMiddels

Krav tilgenetisk

kompetanse

Eksom, target (array-CGH?)

Helgenom (transkriptom, epigenom?)Teknologi

Monogene tilstander/enkeltmutasjoner

Oligogene tilstanderPolygen farmakologi

Polygene tilstander/farmakogoli?

Geninter-aksjon

Ikke-synonyme SNVer, frameshift, stopp

Synonyme SNVer, >insdels, CNV/strukturell, regulatorisk

Funksjonellannotering

Fungerende klinisk løsningProsjekt

Beskrivelser, pilotløsninger

Uker (inkl. våtlab) Dager/timerResponstid Sekunder?