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VERDIKT conference 2013
Citation preview
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?