By Jeff Otto

1. The promise, current state, and
future of personalized medicine
Jeffrey M. Otto, PhD MBA
National Director, CHI’s Center for Translational Research
April 17, 2013

2. Overview
§ Intro to personalized medicine
§ Short look at the early days, circa
2000-2001
§ Review of current state
§ Discussion of the gap between the initial
promise and the current state
§ The CTR’s approach
§ Summary and conclusion

3. Definitions
§ Personalized medicine: the tailoring of medical treatment to
the individual characteristics of each patient in order to classify
individuals into subpopulations that differ in their susceptibility
to a particular disease or their response to a specific treatment.
Preventative or therapeutic interventions can then be
concentrated on those who will benefit, sparing expense and side
effects for those who will not.
§ Biomarkers: An indicator or pattern in a patient that reflects
normal biologic processes, disease processes, or the effect of
medical treatment.
§ Translational Research: Translational research transforms
scientific discoveries arising from laboratory, clinical, or
population studies into clinical applications to reduce disease
incidence, morbidity, and mortality.

4. Personalized Medicine 101
The Promise
§ Better diagnoses and earlier interventions
§ More efficient drug development
§ More effective therapies
The Challenges
§ Intellectual property
§ Regulatory oversight
§ Reimbursement
Retrieved 03/28/2013 from
http://www.personalizedmedicinecoalition.org/about/about-personalized-medicine

5. The Classic
Personalized Medicine Paradigm
Toxic
Not
Toxic
Effec%ve
Not
Effec%ve
The standard approach to medicine does not distinguish between individuals….
…although individuals within a population are often very different.
Biomarkers can be used to stratify patients…
…and to select a safer, more efficacious treatment for the individual.

6. Personalized medicine is akin to shoe
manufacturing:
Like shoes at a department store, many
different drugs are available. Although
several drugs may be available to treat
a particular disease, all drugs are not
safe or effective for all people.
Similar to sizing for a shoe,
molecular diagnostic tests
inform the selection of the
appropriate drug.
Although the selected drug
was not created
specifically for you, it is
more likely to work for you.

7. Biomarkers currently used
in clinical medicine
§ Electrocardiogram
§ PET brain image
§ Bone densitometric
measurement
§ Serum chemistries
§ Auto-antigens in blood
§ Pulmonary function test
§ X-ray
§ MRI

8. Examples of -Omic Biomarkers
§ DNA variation
q SNPs, rearrangements,
CNVs
§ DNA methylation
§ Chromosomal
rearrangements
§ microRNA
§ RNA expression
§ Protein panels

9. The beginning of “irrational
exuberance” in personalized medicine

10. June
11,
2001
"We
strongly
believe
that
pharmacogenomics
will
shortly
transform
the
way
drugs
are
developed,
marketed,
and
prescribed.
I
think
you're
going
to
see
the
benefits
of
this
appearing
within
a
five-­‐year
%meframe,"
Gerald
F.
Vovis
SVP
&
Chief
Technology
Officer
of
Genaissance
Pharmaceu%cals

11. The Challenge of “chasing the tail”
§ Statistically significant
results are easier to
achieve between
populations at the left
and right ends of the
diagram, but are not
necessarily meaningful
from a health economics
perspective
Treatment efficacy
Frequencyinpopulation

12. Genomic medicine milestones
1953:
Structure
of
DNA
elucidated
by
Watson
&
Crick
1950 1960
1956:
1st
discovery
of
a
gene%c
basis
for
selec%ve
toxicity
(primaquine
–
an%malarial
drug)
1970
1977:
DNA
sequencing
technology
developed
by
Fred
Sanger
1977:
Discovery
of
CYP450
metabolic
enzymes
-­‐
varia%on
in
these
enzymes
significantly
influence
the
effec%ve
dose
of
a
drug
1980
1994:
EGFR
TKI
cla
1990
1990:
The
Human
Genome
Project
is
launched
2000
1998:
H
HER2+
m
1998:
1s
Hercep-­‐
Milestones
Research
Drugs
Diagnos?c
Drug
+
CDx
Regulatory

13. Overview of Targeted Cancer Therapies
Manchana, T., Ittiwut, C., Mutirangura, A., & Kavanagh, J. J. (2010). Targeted therapies for rare gynaecological cancers. Lancet
Oncol, 11(7), 685-693. doi: http://dx.doi.org/10.1016/S1470-2045(09)70368-7

14. Why so few success stories?
§ Genomic era of medicine is
less than 15 yrs old
§ Technology is not
sufficient on its own
§ Biomarkers are not
necessarily “fit for
purpose”
§ Test needs to work within
the existing healthcare
workflow
§ Stakeholder alignment
§ Is the patient the customer?
Cartoon: Agres, Ted. (2009) The hunt for personalization. Retrieved 03/08/2013 from
http://www.dddmag.com/articles/2009/06/hunt-personalization

15. Catholic Health Initiatives &
The Center for Translational Research

16. CHI: 5th Largest Hospital Network in US
Strength in Numbers
§ 5th largest US network
§ 81 acute care hospitals in 17 states
§ 40 LTC facilities
§ 86,000 employees
§ 2,900 physicians and midlevel providers
§ Diverse markets with 90% ranked #1 or #2
§ $15B in assets, $9.8B in annual revenue
§ FY 2012 – provided $715M+ in charity care
16

17. CIRI Overview
Center
for
Transla%onal
Research
(CTR)
• Discovery
Research
Network
na%onal
biospecimen
collec%on
&
repository
with
EHR
connec%vity
• Biomarker
discovery,
molecular
diagnos%c
development
&
valida%on
Center
for
Clinical
Research
(CCR)
• Ownership
and
management
of
• Research/clinical
trial
opera%ons:
single
site,
mul%-­‐
site,
mul%-­‐therapeu%c
• Research
data
warehouse
connected
to
EHR
and
de-­‐
iden%fied
pa%ent
data/
outcomes
Center
for
Healthcare
Innova%on
(CHCI)
• Design
and
test
innova%ons
in
care
delivery
• Co-­‐develop
new
technology
and
methods
to
manage
popula%on
health
17
Personalized
Medicine
Clinical
Opera%ons
+
EHR
Research
Environment
Popula%on
Health
Management

18. Executive Summary:
The CHI/CIRI Research “Onion”
18
CCR
CTR
CHCI
CIRI
Hospitals
Government
Academia
Industry
Lab
Pharmacy
Radiology
Tumor
Registry
Pathology

19. Cloud-based Informatics:
Network Strength
Research
Datamart
CCR
CTR
Research
Data
Analysis
19

20. Cloud-based informatics:
Patient Data & Sample Annotation
Hospital
Network
Digital
Slide
Images
(QC)
Staging
–
pTNM,
cTNM
Images
Radiology
Reports
Genomic
Data
Blood
Report
Demographics
Clinical
History
Epidemiology
Drugs
Interac%ons/Adverse
Events
Consent
&
IRB
Approval
Pre-­‐Sampling
Ischemic
dura%on
Chain
of
Custody
Anesthesia
outcomes
Lab
Radiology
ADT*
Tumor
Registry
Pharmacy
Pathology
Post-­‐Sampling
Time
to
freeze
samples
Type
and
%me
of
fixa%ve
Tissue
QC
ELECTRONIC
HEALTH
RECORD
BIOREPOSITORY
NETWORK
Drug
and
Biomarker
Discovery
Protocol
De-Identified Data Warehouse
Biospecimen
Variables
Tissue Report
Anesthesia
Ischemic Time
Time to Freeze
Tumor Staging
Diagnosis
% Tumor
% Necrosis
Image Data Demographics
MRI
PET/CT Scan
Image Report
Age
Race
Gene Expression
Profiling
Genotype/
Sequence Data
Affy Human Genome
Expression Array
EGFR
KRAS
Follow Up Data Treatment
Treatment Outcome
Recurrence
Disease Status
Drugs
Radiation
Response
Pre-­‐
Acquisi%on
Variables
Post-­‐
Acquisi%on
Variables
*Admission/Discharge/Transfer
TRANSLATIONAL
INFORMATICS

21. Bench to Bedside Translational Research
CTR
CCR
Benefits
to
CHI
Hospitals
Clinical
Trials
Personalized
Medicine
Benefits
to
Pa%ents
Bener
Drugs
Bener
Diagnos%cs
Research
Ques?ons
Hypothesis
Generated
Hypothesis
Tested
Results:
IP
&
Publica?ons
Generated
Benefits
Research
Data
21
Key
Opinion
Leaders
Physicians
Academic
Researchers
CIRI
Staff
Others
21

22. Study Design: An Integrated Approach
FFPE
Sample
EHR
data
Biosta%s%cal
Analysis
Predic%ve
Signature
Gene%c
Epigene%c
Environmental
22

23. Thank You