EuroBioForum 2014, 3rd Annual Conference on Personalised Medicine, held in Tallinn Estonia on 22 + 23 September

1. Andres Metspalu
Director of the Estonian Genome Center
of the University of Tartu, Estonia
National perspective Estonia

2. Estonian Biobank and Personalised Medicine
Andres Metspalu, M.D., Ph.D
Estonian Genome Center
University of Tartu
EuroBioForum, Tallinn
September 22, 2014

3. Estonian Biobank
•Estonian Genome Center, University of Tartu
•A prospective, longitudinal, population-based database with health records and biological materials
•52,000 participants - 5% of the adult population of Estonia
•Individuals are recruited by GPs, physicians in the hospitals and medical personnel in the EGCUT recruitment offices
•Estonian Human Genes Research Act (HGRA)

4. § 3. Chief processor of Gene Bank
•(1) The chief processor of the Gene Bank is the University of Tartu, whose objectives as the chief processor are to:
•1) promote the development of genetic research;
•2) collect information on the health of the Estonian population and genetic information concerning the Estonian population;
•3) use the results of genetic research to improve public health.

5. § 11. Other rights of gene donors
•Gene donors have the right not to know their genetic data
•Gene donors have the right to access personally their data stored in the Gene Bank
•Gene donors do not have the right to access their genealogies
•Gene donors shall not be charged for accessing their data stored in the Gene Bank
•Gene donors have the right to genetic counseling upon accessing their data stored in the Gene Bank
•Gene donors have the right to submit additional information on themselves to the chief processor
•Gene donors have the right to prohibit the supplementation, renewal and verification of descriptions of their state of health stored in the Gene bank.

6. •A registered individual has the right to receive, upon request, information concerning his or her health as determined based on a sample. When providing information determined based on the sample, the person must be provided with an opportunity to receive an account of the significance of the information. A fee may be charged for clarifying the significance of the information that, at maximum, corresponds to the expenses incurred by providing the clarification.
688/2012 Biobank Act 1. Sept. 2013 Finland

7. EGCUT cohort vs Estonian population
182025303540455055606570758085+ EGCUT malesEGCUT femalesEstonian populationAgeNo of gene donors by age groupEstonian population by age group (01.01.2008) 70050030010010030050070015000100005000050001000015000

8. Public opinion and awareness of the EGCUT 2001-2013

9. -0.05
-0.03
-0.01
0.01
0.03
0.05
0.07
-0.05 -0.03 -0.01 0.01 0.03 0.05
P21 (4.68%)
PC1 (8.65%)
Austria Bulgaria Czech Republic
Estonia Finland (Helsinki) Finland (Kuusamo)
France Northern Germany Southern Germany
Hungary Northern Italy Southern Italy
Latvia Lithuania Poland
Russia Spain Sweden
Switzerland
-0.1
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
0.1
-0.12 -0.07 -0.02 0.03
PC2 23.8%
PC1 36.6%
Europe
CHB
JPT
YRI
CEU

10. Per Med Definition in EU documents
•Personalised medicine refers to a medical model using molecular profiling for tailoring the right therapeutic strategy for the right person at the right time, and/or to determine the predisposition to disease and/or to deliver timely and targeted prevention
This is the EU H2020 definition of the Per Med.

11. What do we need for implementing personalized medicine?
-Deep understanding variations and its meaning in the population
-E-Health system, which covers the whole country and all providers
-Technology to analyze genomes
-Automated decision support software in order to help primary care providers and others
-Database for genomic variations and related phenotypes (disease risk alleles and variants causing the adverse drug reaction)
-Educated medical workers, executives, med students and teachers
-Public acceptance

12. Medical genetics (Mendelian Diseases?)
-newborn screening
-microarray analysis (InfiniumDx CytoSNP-12 BeadChip)
-exome sequencing All covered by Estonian Health Care Insurance Fund
What is going on today?
Medical genomics (Complex Common Diseases? - biobank - eHealth
-microarray analysis
-full genome sequencing
-seases

13. Marta Garcia Martinez de Lecea and Michael Rossbach. Translational genomics in personalized medicine – scientific challenges en route to clinical practice. The HUGO Journal 2012, 6:2; http://www.thehugojournal.com/content/6/1/2
Carbamazepine
e.g. Carbamazepine

14. CLIPMERGE: How it works
When you are with a patient that has enrolled in the CLIPMERGE study
and you prescribe a medication, this is what happens:
CRAE
(CLIPMERGE Risk Assessment
Engine)
1. Physician prescribes a medication and signs order in Epic
2. A medication message is generated by Epic and sent to CLIPMERGE Risk Assessment
Engine (CRAE)
3. CRAE contains the patient’s genotype data, some clinical data and rules associating
particular genotypes and particular medications or clinical features with an alert (BPA)
4. If a rule is fulfilled, CRAE generates an alert Message and sends it back to Epic
5. The alert is displayed within Epic as a BPA and SmartSet
6. If the BPA is acknowledged or the original order is changed, Epic sends another
message to CRAE to let it know
Prof. E. Böttinger

15. What is Clinical Decision Support Engine (CDSE)?
•CDSE provides clinicians with knowledge presented at appropriate times
•It encompasses a variety of tools such as computerized alerts, clinical guidelines, and order sets
•CDSE has the potential provide the necessary level of personalized guidance to providers at the point of care that will be necessary in the era of genomic medicine

16. PHARMACIS AND FAMILY DOCTORS 2009
X-Roads, ID-card, State IS Service Register
HEALTH CARE BOARD - Health care providers - Health professionals - Dispensing chemists
STATE AGENCY OF MEDICINES
- Coding Centre
- Handlers of medicines
POPULATION REGISTER
PHARMACIS
2010 january
BUSINESS REGISTER
HOSPITALS
2009
FAMILY DOCTORS 2009
SCHOOL NURSES
2010 september
EMERGENCY MEDICAL SERVICE
2013
NATIONAL HEALTH INFORMATION SYSTEM 2008 december
PRESCRIPTION CENTRE 2010 january
PATIENT PORTAL 2009
XROADS GATEWAY SERVICE 2009
IT Architectural “Big picture”

17. Documents total – 10.8 mio
1.1 mio persons medical data (growth 20% during 2012)
National Patient Portal

18. Do we have enough information to start?

19. Known risk factors for many common complex diseases
•BMI
•Alcohol consumtion
•Smoking
•Already existing diseases
•Family history
•Biomarkers

20. What does genetics add?
•Add valuable information to traditional risk factors (1P)
•Genetic risk can be determined at an early age (1P)
•Markers remain the same during the lifetime (1P)
•Can motivate more to change the lifesryle (1P)
4 P Medicine !

21. Prevented deaths of CHD in Finland
By treating 2144 persons who have >20% risk when the genetic information is known 135 deaths would be prevented in 10 years!
Tikkanen, E. 2013. Genetic risk profiles for coronary heart disease

22. Polygenic risk score

23. Polygenic risk scores
•Genome-wide association studies (GWAS) have discovered a large number of genetic variants that are associated with common complex diseases.
•The effect of each individal marker (SNP) is in most cases not strong enough to justify their use in clinical practice
•Combining several markers across the genome leads to polygenic risk scores that already explain a large proportion of heritability of the trait and are a promising tool to aid personalized disease prediction

25. Economic burden of diabetes (GER) 2000-2007
•Prevalence of treated diabetes rose continuously from 6.5 to 8.9% (+36.8%)
•Direct costs per patient with diabetes rose from € 5 197 to € 5 726 (+10.2%)
•Total direct cost burden of diabetes in Germany grew from € 27.8 billion to € 42.0 billion (+51.1%)
•per-capita costs were € 2 400 in 2000 and € 2 605 in 2007 (+8.5%)
Exp Clin Endocrinol Diabetes 2011; 119(6): 377-385

26. Survey of primary care physicians
•Survey sent to 130 PCPs collaborating with the EGCUT, 65 responded
–96.3% - either strongly agree or agree that a training program in genetics and genomics is necessary
–75% - reported having patients show interest in their genomic health data
–96.4% - believe that predictive genetic testing will improve health care
–73.3% - believe that predictive genetic testing will be used in their practice in the next 5 years
–35.8% - believe that individualized medicine will be used in their practice in the next 5 years

27. Estonian Reform Party and Social Democratic Party coalition = current government POLICY PAPER:
•Social security, labour market, healthcare
•25. We will establish a course towards personalised medicine based on modern gene technology.

28. Proposal

29. Challenges and issues
•Awareness executives, doctors and patients
•New technologies and data empower patient with more possibilities to manage own health
•Ethical issues
–Right to know and right not to know
–Treatable and non-treatable conditions
–Big data
•Not enough knowledge about associations between DNA variants and diseases
•Large work-load to keep database of known risk markers updated

30. Are we ready for the personalized medicine?
•E-health, EMR and other e-based infrastructures are in place
•Genome analysis technology can do the task
•Scientific and medical expertiese is here
•Genomic variations are excisting and they have an effect on human health and behavior
•People are interested in personalized, genome based health realted information

31. Conclusions
•Estonia has great potential to plan and implement personalized medicine solutions for the whole country, starting with the pilot project for 50 000 gene donors
–Genetic research with 5% of population genetic and continuously updated phenotype information
–Nation wide Health Information Exchange platform
–10 years of experience of national level e-services (PKI, X-Road, ID-card, security framework)
–High level public trust and acceptance

32. Thank you!
www.biobank.ee