Talk given at Tech Forum 2012 at The University of Melbourne

1. From Personalised Medicine to
Personal Health
Fernando J. Martin-Sanchez
Professor and Chair of Health Informatics
Melbourne Medical School
Faculty of Medicine, Dentistry & Health Sciences
&
Director, IBES Health and Biomedical Informatics Research Lab.

2. Outline
• Current challenges in Medicine
• Personalised Medicine
• Personal Health
• The role of Health Informatics
• Conclusions

3. Current
challenges in
medicine

4. Current challenges in Medicine
• Need of earlier diagnosis
• More personalized therapies Personalised
• Clinical trials and the development of new medicine
drugs need to be faster and more effective
• Improve disease classification systems Preventive
• Risk profiling, disease prediction and medicine
prevention
• Control health system costs
Personal
• Citizens should take more responsibility for Health
the maintenance of their own health.
àEmphasis on prevention, not cure

5. Personalised
medicine

6. Definition
• Personalized medicine uses an
individual's genetic (and molecular)
profile and individual information
about environmental exposures to
guide decisions made in regard to
(risk profiling) and the prevention,
diagnosis, and treatment of
disease.
(Adapted from F. Collins, Director NIH)

7. Clinical applications of genomic information
• Pharmacogenetics –
Personalized Medicine
Coalition - 72 drugs in 2011
• Cystic fibrosis – successful
clinical trial for a specific
mutation
• Identification of metabolic
diseases

8. The Digitalization of Medicine
• Digital
revolu-on
in
other
domains
(banking,
insurance,
leisure,
government,…)
• The
incorpora-on
of
digital
systems
in
healthcare
is
lagging
behind
other
sectors:
– Reasons:
complexity,
privacy,
volume
of
data,
lack
of
demand
– It
has
greatly
affected
healthcare
at
the
hospital
or
research
centre
level.
– The
digital
revolu-on
has
not
yet
reached
medicine
at
the
pa-ent/
ci-zen
level
• BUT
THIS
IS
STARTING
TO
HAPPEN
NOW
!!!

9. Personal
Health
Regina Holliday

10. E-patients
• Gimme my damn data!
• The patient will see you now…
• Let patients help
• Nothing about me without me!
• Dave de Bronkart
• Regina Holliday
• Hugo Campos
• Salvatore Iaconesi
• Marian Sandmaier

11. Professionals (Clinicians and researchers)

12. Government
NIH
Australian
PCEHR

13. Personal (Participatory) Health - Technologies
à Patients empowered, informed and involved in
decision making, prevention and learning
self tracking devices
Social networks
games
Participatory Health
mobile Internet of things
sensors PCEHR

14. The role of
health informatics

15. Data collection from sensors
Environmental sensors Genomic sensors
Phenomic sensors
Environmental risk factors Genome biomarkers (DNA sequence,
(pollution, radiation, toxic agents, …) Phenome variation, regulation)
Genome
Exposome Physiological, biochemical parameters
(cholesterol, temperature, glucose, heart rate…)
Integrated personal health record

16. Data integration: Human Phenome Ontology

17. Data integration: Phenomizer

18. Data Interpretation: First personal longitudinal OMICS
profiling exercise
• Combined analysis of genomic, transcriptomic,
proteomic, metabolomic and immunological
profiles from a single individual (one of the
authors- Prof. Michael Snyder), over a 14 month
period. More than 3 billion measurements.
• This study shows that diseases are a product of
an individual’s genetic profile as well as
interaction with the environment and that disease
can be treated based on molecular information.
(Chen et al, Cell 148, 1293-1307 March 16 2012 )

19. Data Interpretation: Comprehensive molecular
information analysis
• genomic DNA
copy number Comprehensive
arrays molecular
• DNA portraits of
methylation human breast
• exome tumours
sequencing The Cancer
• microRNA Genome Atlas
sequencing Nature 490, 61–
• reverse-phase 70
protein arrays (04 October
2012)

20. Data interpretation: Measuring the exposome
Environment-Wide
Association Study
on Type 2
Diabetes Mellitus
266 environmental
Factors
Future: combined
GWAS-EWAS?
(Patel et al. 2010 PloS One)

21. Adapted from: Stead et al. 2011, Acad. Med.

22. From personalized medicine to personal health:
Genome supercomputer to enhance interpretation

23. Interpretation of personal genome

24. Management of personal health data: Apps for
health – the ‘Appatient’
Stress
Glucose
ECG
Heart rate
temperature
Diet
Saturation
Drug reminder
LifeWatch V

25. Self tracking / self quantifying / self monitoring
• The belief that gathering and analysing data can help them improve their lives!
• QS’ers doubling every year.– 6000 members, 50 meet-up groups

26. Shared decision making

27. Conclusions

28. Conclusions
• Similarities
– Need of system approaches
– Integration of multiple sources of data
– Advances in analytical technologies
– Big data / data driven
• Differences
Personalised medicine Personal Health
o Clinician-focus o Patient-centred
o Focus molecular data o Focus environmental
o Curing o Prevention

29. Conclusions
Personal computing Personal sequencing Personal health?

30. Conclusions
Pros Cons
• Motivation • Privacy
• Deepening understanding • Security
of their health • Education
• Self-improvement • Cyberchondria
• Risk profiling • Equity
• Prevention • Regulation, accreditation
• Shift terciary à secondary • Role of the clinician
à primary à home care • Infrastructure needs
• Data donors for research • Therapeutic gap (ethics)

31. Thank you for your attention!
© Copyright The University of Melbourne 2012