Summary of activities - Health and Biomedical Informatics Centre @ The University of Melbourne

1. New sources of big data
for precision medicine:
are we ready?
Melbourne Academic Centre
Annual Research Symposium
19-June-2014
Fernando J. Martin-Sanchez
Professor and Chair of Health Informatics
Melbourne Medical School
&
Director, Health and Biomedical Informatics Centre (HaBIC)

2. 2014
FMDHS
Research
Review

3. Outline
• New sources of (Big) data
• Evolution of the concept -
Precision medicine
• Strategic initiatives in UK
and USA
• Relevant research
• Are we ready?
• Conclusions

4. New
sources of
(Big) data

5. Data generation in Biomedicine
• Human Genome
– first decoded in
2003 after one
decade of work
– nowadays takes one
day
• PubMed
– 5000 biomedical
research articles are
published daily
– Over 22 mill articles
• Global size of “Big Data” in Healthcare
stands at roughly 150 Exabytes (1018) in
2011, increasing at a rate between 1.2 and
2.4 Exabytes per year. (SAS)

6. Genome
regulation
Microbiome
Epigenome
Exposome
Inter and intra
individual
genetic variation
Phenome levels

7. Human Genetic Variation –
1000 Genomes project
Nature 491,
56–65 (01
November
2012)

8. Mapping the Epigenome
New York Times

9. ENCODE – Encyclopedia of DNA Elements
nature.com/encode

10. Inter e Intra-tumour heterogeneity

12. In 2008 the same team described the
chemical composition of human
cerebrospinal fluid and in 2011 they
determined the chemical composition of
human blood.

13. Human Microbiome
Nature 464, 59-65(4 March 2010)3.3 million microbial genes

14. Exposome
The exposome has been
defined as the life-long
exposure to environmental
factors of an individual.

15. GenomeExposome
Phenome
Biomarkers (DNA sequence, Epigenetics)Environmental risk factors
(pollution, radiation, toxic agents, …)
Anatomy, Physiological, biochemical parameters
(cholesterol, temperature, glucose, heart rate…)
Social media / Personal health record / EMRs
Availability of new sensors for data collection

16. Evolution of
the concept
Precision
Medicine

17. Origin
• In Ancient
Greece, Rufus of
Ephesus
suggested that
drugs have
different effects on
different people.

18. Origin
• In 1902, Archibald
Garrod suggested
individuals were
different not only
phenotypically,
but also at the
biochemical level.

19. Origin
• The term ‘personalised
medicine’ was coined in
1999 by Robert Langreth
and Michael Waldholz
(Wall Street Journal
reporters) in an article to
describe the development
by pharmaceutical
companies of:
“a cornucopia of personalized
medicines that will produce
huge profits into the next
century”.

20. Evolution
• Clinicians have always practiced personalized
medicine, using personal health information to make
decisions on diagnosis and treatment.
• When we refer to personal health information we are
including genetic, physiological, anatomical,
environmental risk factors, socio economic issues,
cultural and psychological aspects as well as the familial
and individual health history.

21. Evolution – Genomic Medicine
• However, our ability to collect
individual genetic and
environmental data has been very
limited until recently.
• One major milestone in overcoming
those barriers was the Human
Genome Project, which together
with advances in DNA sequencing
technologies, has facilitated a fast
and affordable access to personal
genetic variation data.
• This marked the beginning of the
Genomic Medicine age in the early
2000’s

22. Evolution – Molecular medicine
• Our increased knowledge
about the molecular causes
of complex diseases
represents another key
aspect of the advances that
we are witnessing in this field.
• Microarray-based
technologies opened the door
to the study of functional
aspects of DNA and protein
expression, which has been
referred to as molecular
medicine.
Comprehensive molecular portraits
of human breast tumours
The Cancer Genome Atlas
Nature 490, 61–70
(04 October 2012)

23. Evolution – P3 and P4 Medicine
• The term P4 medicine, coined by Leroy Hood, tries to
amalgamate most of these previous objectives, making
a greater emphasis on the importance of preventative
and participatory medicine.

24. Evolution P5, P6, …

25. P6 Medicine
PERSONALIZED PREDICTIVE PREVENTIVE
PARTICIPATORY PSYCHO-COGNITIVE
POPULATION-BASED

26. Evolution – Stratified medicine
• US President’s Council of S&T noted
that personalised medicine ‘refers to
the tailoring of medical treatment to
the individual characteristics of each
patient’.
• It does not literally mean the creation
of drugs or medical devices that are
unique to a patient, but rather the
ability to classify individuals into
subpopulations that differ in their
susceptibility to a particular disease or
their response to a specific treatment.

27. Precision medicine
• Precision Medicine is an approach to discover
and develop medicines, vaccines or routes of
intervention (behavior, nutrition, etc.) that enable
disease prevention and deliver superior
therapeutic outcomes for patients, by integrating
“Big Data”, clinical, molecular (multi-omics
including epigenetics), environmental and
behavioral information to understand the
biological basis of disease.
• This effort leads to better selection of disease
targets and identification of patient populations
that demonstrate improved clinical outcomes to
novel preventive and therapeutic approaches.
C.M. Christensen et al.. The innovator’s prescription a disruptive solution for health care.
McGraw-Hill, 2008

28. Evolution - Precision medicine
• Work in this area is
aimed at redefining
disease
classification,
identifying common
underlying causes
and representing
them into new
taxonomies.
Toward Precision Medicine:
Building a Knowledge Network for Biomedical Research
and a New Taxonomy of Disease (2011)

29. Academy of
Medical Sciences
2013

30. Personalised
Medicine
Data sources:
Precision
Medicine
Informal data sources
Exposome
(environmental data)
Metabolomics
Proteomics
Genomics (genomic
variants)
Phenotype (clinical
records)
Personalised vs Precision Medicine
PM combines the knowledge of the patient’s characteristics with traditional medical records
and environmental information to optimize health.
PM does not only rely on genomic medicine but also integrates any other relevant information
such as non-genomic biological data, clinical data, environmental parameters and the patient’s
lifestyle.
Servant N et al. Front Genet. 2014; 5: 152.

31. Personalised medicine
• Improving therapy
• Looking for the right drug for
the right people
• Companion diagnostics to
stratify patients
• Use of genomics data
• Static - “Snapshot”
Precision medicine
• Improving Diagnosis
• Looking for the right drug for
the right disease
• New taxonomy of disease and
disease reclassification
• New/refined diagnostics methods
• Use of molecular (-omics) and
other (i.e. exposome) data sources
• Dynamic stratification - Modelling
patient journeys
Personalised vs Precision Medicine

32. Strategic
initiatives in
UK and USA

33. UK NHS
• New CIO – Simon Stevens
• Urged the service to become a world leader in
personalised medicine.
• National Institute for Health Research
Health Informatics Collaboration (HIC)
• Five participating NHS trusts
• This initiative will deliver IT capability to
support the sharing and use of NHS data for
research.

34. http://www.mrc.ac.uk/research/
initiatives/health-and-
biomedical-informatics/

35. The Lancet Vol 383 March 29, 2014

36. UK
• The MRC, in partnership with nine other government departments,
research councils and charities, have committed over £90m in several
initiatives that are aimed at supporting health informatics research,
infrastructure and scientists. Their ultimate aim is to build a sustainable
capability in health and biomedical informatics in the UK.
Ø The Farr Institute of Health Informatics Research £39m was
awarded to four centres based at University College London, and the
universities of Manchester, Swansea and Dundee.
Ø UK Health Informatics Research Network (UKHIRN)
Ø Strategic Skills Fellowships
Ø Medical bioinformatics initiative - £32m - six strategic awards to
combine clinical, health and bioinformatics data
Ø Centres for doctoral training - three multi-disciplinary Centres for
Doctoral Training (CDTs), which align with biomedical informatics.
Ø ELIXIR partnership (EC infrastructure)

37. Other UK initiatives
MRC-DH - New £150m initiative in
Clinical Research Infrastructure
to bring into practice novel
technologies to address major
scientific challenges relating to the
stratification of diseases

38. CTR in the US – 60 CTSA in 30 states
The mission of the CTSA program includes providing infrastructure
support to facilitate translational research, promoting training and career
development for translational researchers, and developing innovative
methods and technologies to strengthen translational research.
Biomedical informatics core was mandatory

41. USA Clinical Informatics – Medical subspecialty

42. NLM's University-based Biomedical Informatics
Research Training Programs
1. University of California San Diego
2. Stanford University
3. University of Colorado Anschutz
Medical Campus
4. Yale University
5. Harvard University (Medical
School)
6. Columbia University Medical
Center
7. Ohio State University
8. Oregon Health & Science
University
9. University of Pittsburgh at
Pittsburgh
10. Vanderbilt University
11. Rice University
12. University of Utah
13. University of Washington
14. University of Wisconsin Madison

43. Relevant
research

44. TCGA Genome Pan-Cancer Atlas
• The Cancer Genome Atlas Research Network, John N Weinstein, Eric A Collisson, Gordon B Mills, Kenna R Mills
Shaw, Brad A Ozenberger, Kyle Ellrott, Ilya Shmulevich, Chris Sander & Joshua M Stuart
Nature Genetics 45, 1113–1120 (2013)

45. eMERGE
• High-throughput phenomics
• Disease specific algorithms scanning across
electronic medical records - generate
structured, standardized, anonymized, clinical
data sets for research

46. Exposome related projects around the world
• USA - Funded by the NIEHS
– HERCULES. It is a joint centre between Emory University and
Georgia Institute of Technology
• Europe - European Commission funded
– HELIX – Coordinated by the Centre for Research in
Environmental Epidemiology (Barcelona, Spain)
– EXPOSOMICS Coordinated by Imperial College of London
– HEALS - Coordinated by the University Pierre and Marie Curie
(Paris, France) Health Environment Association based on Large
population Surveys

47. 100 Person Wellness Project

48. Are we
ready?

49. Building blocks
Health Informatics
genomics, genomic epidemiology,
bioinformatics and computational
biology, molecular biology,
biochemistry, stem cells, pharmacology,
animal model testing, clinical trials,
clinical epidemiology & biostatistics,
clinical genetics, biomedical
engineering, imaging, health
economics, health services research.

51. Bioinformatics is different from Health informatics
HEALTH & BIOMEDICAL INFORMATICSBIOINFORMATICS &
COMPUTATIONAL SYSTEMS BIOLOGY

52. Health informatics – clinical data
• Data about humans that
arises from a growing
number of sources and
contexts:
– Clinical research
– Clinical practice - EHRs
– Patient and disease
registries
– mHealth apps
– Smart devices and
sensors
– Environmental data
– Social media data
• Distributed (EMR, clinical depts)
• Different formats (text, images,
numeric, videos,..)
• Same data exists in different
systems
• Patient generated data
• Data is structured and unstructured
• Inconsistent/variable definitions
• New data coming out every day
• Complexity of data (the human
body)
• Changing regulatory requirements
• Privacy issues
Adapted from Dan LeSueur (Health Catalyst)
Why healthcare data is different…

53. Health
Informatics
Bioinformatics
Proteomics
and
Metabolomics
Data
Gene
expression
Data
Genomic
Data
Patient
generated
Data
Population
Data
Clinical
Data
HaBIC
@UoM

54. HaBIC
• The University has
recently established a
collaborative Health and
Biomedical Informatics
Centre (HaBIC), with
support from the Faculty
of Medicine, Dentistry
and Health Sciences,
the School of
Engineering and IBES.

55. Research

56. HaBIC and UoM education strategy in HBI

58. Available platforms
• Infrastructure:
– UoM High-end computing. Alliance with ITSr – Research
Cloud, storage facilities, supercomputing
– VLSCI
• Platforms for data integration
– Biogrid (43 hospitals, clinical data, genomics)
– GRHANITE (GPs, labs, rural, )
• Research support tools
– REDCap
– SAS Visual Analitics
– Galaxy - GVL
– I2B2

59. Researcher
Hospital data
GP, labs, pharmacies data
Researcher-entered
data

60. Problems
• Lack of understanding
(differences with IT, bioinfo)
• Few training programs
• HI is not recognised as an
occupation, Field of Research or
Field of Education
• No certification or accreditation
• Research grants are difficult
(NHMRC, ARC)
• Electronic medical records
• Sense of failure in Health IT
• Unclear academic home
Solutions?
• This talk, publications, education
• University courses
• Actions through HISA, ACHI
• CHIA program
• Specific Panel and experts
appointed as assessors
• Participation in the Ministerial
Health Sector IT Advisory
Council (DoH Victoria)
• Ascribe to a Department
Current situation of Health Informatics in Australia

61. Conclusions

62. Current meaning
• Precision medicine enables a safer, more
efficient, preventive and proactive
medicine, but needs to tackle the
complexity and diversity of personal health
information, beyond the genome
sequence.
Topol E. Cell 2014

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

65. • Both research into and clinical
application of stratified medicine, will
require comprehensive and robust
biomedical and health informatics
systems – a key rate-limiting step.
Stratified Medicine: Principles,
Promise and Progress
UK Academy of Medical Sciences
2013

66. Biomedical Informatics
Martin-Sanchez F et al. J Am Med Inform Assoc. 2014
Biomedical
Informatics

67. © Copyright The University of Melbourne 2014
Thank you for your attention!