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Norwegian clinical genetics analysis platform genAPVERDIKT Conference October 15, 2013 T. Grünfeld og T. Håndstad

Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and Tony Håndstad, Oslo Universitetssykehus

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VERDIKT conference 2013

Text of Norwegian clinical genetics analysis platform ”genAP”, Thomas Grünfeld and Tony Håndstad, Oslo...

  • 1. Norwegian clinical genetics analysis platform genAP VERDIKT Conference October 15, 2013 T. Grnfeld og T. Hndstad
  • 2. Agenda Drivers and development of individialized medicine Our areas of focus, and some challenges Selected examples
  • 3. Falling prices.
  • 4. Faster and faster, more and more. Increasing number of persons having their genome mapped
  • 5. Possible development of full whole genome sequencing Sport for the rich and famous The first scientists etc. Minisolution 23andme Offer for those Easier to Deployment in analyze all screening who really needs it than 3-5 genes (E.g. newborns) Patients with challengin diagnostics New causative genes Allows for reuse of sequencing (DNA static) Later in-silico analysis Simplifies existing methods Allows for reuse WGS integrated part of EPR Everybody routinely analyzed Integrated expert systems in EPR Fra Henry T. Greely, direktr Centre for Law and biosciences, Stanford University
  • 6. Some major challenges Do we want to know everything? Privacy issues Insurance companies Legal DNA registries Data security How do we know what to do?
  • 7. Agenda Drivers and development of individialized medicine Our areas of focus, and some challenges Selected examples
  • 8. Aim of the project The aim of the project is to (contribute to) develop an ICT infrastructure for central, secure storage of human genome data, which allows for dissemminated use nationally (And possibly internationally) Elucidate how to best analyze sensitive genome data through existing (internet based) tools Enable efficient (societally) deployment of genome data in diagnostics and treatment Vision of becoming an ICT platform for personalized medicine in Norway Key end products of the project are: (Pilots for) practical tools for the general clinician at their bedside activities An infrastructure and (organizatonal) methodology for expansion of the pilots to other clinical domains An ICT infrastructure that can use aggregated data in the continuous development of the solutions.
  • 9. Clinical vs. Research focus Research Cancer and New mutations immunology(?) Quality above newest technology Robustness above flexibility Diagnostics Known mutations Cancer; therapy Germlinemutations Somatic mutations Our success is clinical demand! Other legal limitations and framworks than research solutions User interface key: I.e. refinement of info to easily available knowledge/ decision support
  • 10. Overview of modules in the system
  • 11. Some key issues Quality and standardization of analyses How to convey the information Storage, ownership and datasecurity Involvement of relatives Research vs. Clinical diagnostics Are the lab. analyses of adequate quality? Major interpretation challenges 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 Dataconfidentiallity vs. Availability for usage: a trade off? Patient autonomy vs. Documentation requirements How to deal with tests that have direct implications for ? What when the patient dies? Increasingly blurry boarders between what is research (Common interest) and clinical diagnostics (Individual interest) Strong need to revise and update legislation and guidelines From Henry T. Greely, Head of Centre for Law and Biosciences, Stanford University
  • 12. A new field and language in medicine (Bass Hassan, Univ of Oxford) Research Clinical testing Clinical treatment BIOMEDISH Bioinformatics Medical informatics Informatics
  • 13. Challenges for HTS data analysis (Bass Hassan, Univ of Oxford) Challenge Details Rare skill which is difficult to find Bioinformatics competence Costly resources when found Experience invaluable Self teaching very challenging Often not intuitive (Demanding user interface) Software Demand for many different applications Requires often command line skills These are just about our experiences! Substantial amounts of data to handle ICT infrastructure Requirements for high performance computing (HPC) Resources are difficult to find and challenging to share Substantial cost of storage and maintenance
  • 14. Agenda Drivers and development of individialized medicine Our areas of focus, and some challenges Selected examples
  • 15. Automating variant analysis Sample volume is expected to grow rapidly. The task of analysing genetic variants constitute a bottleneck in the whole process. To speed up and increase the quality of the analysis, we seek to automate parts of it and provide decision support for the molecular biologists that analyse the variant data.
  • 16. Algorithm for variant analysis
  • 17. Evaluate frequency and inheritance DMG cancer workflow Add note (+class 2) Possible carrier/ comp heteroz With sufficient population size; not a patient pop YES Note: X-linked inheritance is a third possibility for other cases (esp. for general genetics) Extract var Heterozygous? YES/ NO Evaluate frequency: >~10% (lower for genot) Recessive Inheritance mode? Latest dbSNP build not in Alamut, checks web if variant not found Note: in addition to inheritance, frequency cut-off should be based on prevalence data and/or frequency of known pathogenic variants (adjusted for each gene/diagnosis) Common? NO Pat DB dbSNP Dominant Homozygous? (hemizygous?) YES Add note (+class 1) YES/ NO Evaluate frequency: >1% (lower for genot) With sufficient population size; not a patient pop YES Answer/add as class 1 Always check BIC Report
  • 18. DEMO
  • 19. Providing decision support for clinicians The average physician has little knowledge of genomics. For genomics to change clinical practice, the information must be translated into actionable recommendations, easily available in the form of a decision support system.
  • 20. Decision support prototype for Tacrolimus dosage tested on transplant surgeons
  • 21. Publication
  • 22. Acknowledgements Morten C. Eike, Ph.D. (post doc) Dag Undlien, prof. M.D. Ph.D. (project owner) Halvard Lerum, Ph.D. (EHR integration) Lars Retterstl, M.D. Ph.D (lab doctor) Tim Hughes, Ph. D. (variant calling) Eidi Nafstad, MSc. (lab engineer)
  • 23. Pilot: 2-5 pakker/systemer-opprinnelig Diagnostisk Farmakologisk Predikitv/ prognostisk Cardiomyopatier: 33 gener Cytostatika: CYP450? Nevrologi: Myopatier? Immunmoduleren de behandling v/organtransplantasjon? ~50 gener Brystkreft: BRCA1 BRCA2 ++? Nevropediatri: H ypotont barn? Muskeldystrofi?
  • 24. Faser 1 Klinisk bruk 2 3 Tilsvarende dagens SNV-genotyping (utvidet med kende kunnskapsbase/datamengd e) Krav til genetisk kompetanse Integrering med pasientjournal Hy Middels Lav Eksom, target (array-CGH?) Teknologi Helgenom (transkriptom, epigenom?) Geninteraksjon Funksjonell annotering Responstid Prosjekt Monogene tilstander/ enkeltmutasjoner Oligogene tilstander Polygen farmakologi Polygene tilstander/ farmakogoli? Ikke-synonyme SNVer, frameshift, stopp Synonyme SNVer, >insdels, CNV/strukturell, regulatorisk Uker (inkl. vtlab) Dager/timer Sekunder? Fungerende klinisk lsning Beskrivelser, pilotlsninger