The document summarizes a presentation on the Norwegian clinical genetics analysis platform "genAP". Key points include: - "genAP" aims to develop an ICT infrastructure for centralized storage of human genome data to allow distributed use nationally and potentially internationally. - It seeks to efficiently analyze sensitive genome data through existing tools and integrate genome data into clinical diagnostics and treatment. - Challenges include standardizing analyses, conveying complex information to clinicians, ensuring data security and privacy, and navigating research versus clinical applications. - Examples provided automation of variant analysis, a decision support system for drug dosages, and plans for future clinical pilots.

1. Norwegian clinical genetics analysis
platform ”genAP”
VERDIKT Conference
October 15, 2013
T. Grünfeld og T. Håndstad

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, direktør 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 Retterstøl, M.D. Ph.D (lab doctor)
Tim Hughes, Ph. D. (variant calling)
Eidi Nafstad, MSc. (lab engineer)

24. 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?

25. Faser
1
Klinisk
bruk
2
3
Tilsvarende dagens SNV-genotyping
(utvidet med økende
kunnskapsbase/datamengd
e)
Krav til
genetisk
kompetanse
Integrering med
pasientjournal
Høy
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. våtlab)
Dager/timer
Sekunder?
Fungerende klinisk løsning
Beskrivelser, pilotløsninger