Introduction to the Medical Community The Measurement to Understand Reclassification of Disease Of Cabarrus/Kannapolis Study The M.U.R.D.O.C.K.
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Transcript Introduction to the Medical Community The Measurement to Understand Reclassification of Disease Of Cabarrus/Kannapolis Study The M.U.R.D.O.C.K.
Introduction to the Medical Community
The Measurement to Understand Reclassification
of Disease Of Cabarrus/Kannapolis Study
The M.U.R.D.O.C.K. Study
22 Oct 2007
Agenda
Welcome and introductory comments
Andrew Conrad PhD - CSO LabCorp and NCRC
Lynne Scott Safrit - President Castle & Cooke
Allan Dobson MD – VP Clinical Practice Development, CFM
Overview of study plan - Rob Califf MD
Overview of -omics study tools – Jessie Tenenbaum PhD
Example of liver project - John McHutchison MD
Community engagement plan - Lloyd Michener MD
Discussion - All
Timeline and next steps - Victoria Christian
NCRC collectively improving human health
Farming Practices
Effective Healthcare
Public Health
Food Sciences
Disease Management
Population Health
Nutrition
Health Maintenance
Productivity
Medical Education
Global Health
In 20 years…
All people in developed nations will have —
An electronic health record
Biological samples
Digitized images
Healthcare will be personalized using an individual’s
images, samples and clinical data.
The health of a community will be monitored using
aggregate records.
Kannapolis — as hub of the Carolinas — could define
this future through a public-private partnership.
The MURDOCK Study will …
Build value using assets already
in our reach.
Sub-classify major diseases into
populations with specific risks
and optimal therapy.
Apply new knowledge to the
study of community health.
Re-define clinical research using
the power of genomics and
biomedical informatics.
Re-write the textbook of medicine.
Improve
human
health.
The MURDOCK Study will ...
Fuel the financial success of the DHMRI Core
Laboratory and Biorepository.
Foster breakthrough collaborations between NCRC
schools with diverse and interconnected
perspectives and expertise
Quickly increase the visibility and scientific impact
of the DHMRI Lab.
Attract the scientific community.
Attract the biotechnology community.
Engage the local population in a high-impact,
internationally recognized project.
The modern equivalent of the Framingham Heart Study
Current data assets & DHMRI tools:
Powerful once-in-a-lifetime value proposition
Aging
Arthritis
Heart
Disease
Obesity
Diabetes
Liver
Disease
Brain
Disorders
Mental Illness
Alzheimers
Genomics, Proteomics, Metabolomics
DHMRI Core Lab
Image Analysis
DHMRI Core Lab
Massive
patient database
to be mined
Cancer
Breast
Prostate
Lung
A new era of biomedical research
Novel research technologies have enabled the
study of thousands of molecules at a time
Referred to as “high throughput” approach
These novel methods enable ‘-Omics’ scale
research
What is ‘-Omics’?
The study of the totality of a type of biological
data
All genes: Genomics
All transcribed genes: Transcriptomics
All proteins: Proteomics
All metabolites: Metabolomics
Omics scale research has enabled patient
profiling at the molecular level
Continuum of -Omics
Genes mRNA Proteins Metabolites
Genomics
Transcriptomics
Proteomics
Metabolomics
Genomics and the media
An example: DNA Microarrays
Cells of
interest
Known DNA
Isolate mRNA, label
Glass slide
Dr. Russ Altman, Stanford Univ.
Reference
sample
DNA Microarray visualization: heatmaps
Expt 1
Experiment 1
(e.g. Patient X)
2 3 4 5 6
Gene 1
Gene 2
Gene 3
Gene 4
Gene 5
Gene 6
Gene 7
Gene 8
Gene 9
An opportunity
For the first time, diseases can be defined by
molecular fingerprints or profiles
Mechanistic pathways of diseases can be
elucidated
Symptomatic descriptors can be replaced by
meaningful tools for stratification
These tools will enable truly personalized
medicine
Medicine today
Drugs are developed to treat all patients with
the same clinical diagnosis - “one size fits all”
Many drugs only work in less than half of the
patients for which they are prescribed
Over 100,000 people die annually from drug
related adverse events - a ‘top 10’ cause of
death
‘-Omics’ technologies can help predict
treatment response.
Cancer
Diabetes
-Omics Technologies
Excercise
Responder+
Diet A
Adverse
Exercise
event
+
Diet B
Non-responder
Exercise +
Diet +
Medication
Combining clinical and molecular data
will redefine disease management.
Quantify risks of developing diseases.
Apply preventive measures more effectively.
Establish diagnosis earlier.
Prevent disability by treating earlier.
Predict death and disability.
Use healthcare resources strategically.
Three horizons of MURDOCK Study
Horizon 2
•Validate hypotheses prospectively
•Apply new knowledge to local
community through partnership
with local medical community
•Improve human health
Horizon 1.5
•Engage community
•Build community registry
Horizon 1
•Use assets on hand to
generate molecular data
•Generate hypotheses by
leveraging bioinformatics
Outcomes of Hepatitis C virus infection
Spontaneous clearance (~25%)
Chronic infection
Treatment
Responders
Hepatic Fibrosis
Steatosis
Non-responders (>50%)
Insulin resistance
Dyslipidemia
Increased risk
of diabetes
Unknown
consequences
Reclassification of HCV disease
Use genomic technologies to subset patients
based on their molecular signature
This signature may become a useful marker of:
Treatment response – therapeutic decision-making
Development of fibrosis or steatosis - non-invasive
diagnostic alternative
Insulin resistance or dyslipidemia – may have
broader relevance for diagnosing and treating nonHCV patients with these conditions
Selection of biomarkers for HCV profiling
Standard available assays: inflammatory, lipid
metabolism, glucose metabolism, etc.
Novel protein
biomarkers –
proteomic discovery
Novel protein
biomarkers –
genetic approaches
Novel biomarker discovery strategies
Proteomic discovery
gt1
gt2
Genetic discovery
gt3
R
NR
Open discovery platform allows for
discovery of novel protein biomarkers
(host or viral-specific) that may be
associated with treatment response
and/or HCV genotype
These biomarkers, along with
others, will be typed in a large HCV
patient cohort
Unbiased discovery platform tests
for association of >500,000 gene
variants with HCV outcomes and/or
quantitative traits (protein markers)
Genes discovered in this way may
become useful protein biomarkers or
remain as DNA diagnostics
Molecular profiling of HCV patients
Tx response
Fibrosis
Steatosis
Diabetes
Dyslipidemia
Type large number of Use statistical
biomarkers in ~1000
modeling to subset
chronic HCV patients
HCV patients based
from the Duke
on common
Hepatology Research
biomarker signatures
Clinic cohort
Correlate molecular
signatures with
outcomes in HCV
patients and similar
traits in non-HCV
patients
Deploy assets for maximum potential
benefit to communities.
Uncover new knowledge in diseases that afflict large
patient populations.
Epidemics — obesity, diabetes, depression
Diseases of aging — arthritis, dementia
Use this new knowledge in clinical practice.
Make decisions based on breakthroughs in the
individualized treatment of breast cancer and depression
Make new discoveries with commercial potential.
Contributory drug-able pathways
Novel biomarkers
Challenge of biomedical informatics:
Turning data into knowledge
Knowledge
DATA
Groundwork for successful community
engagement
Transparency of efforts
Advice from appropriate community groups
Questions to ask: how are citizens best reached? where
do they gather? how do they prefer to receive
information?
Preparation of documents and study plans in
iterative process with feedback from community
Communication strategy based on community
groups’ advice
Formation of Community Advisory Group
Possible modes of engagement
Interactive website
Community surveys
Posters, brochures, other written materials
Educational presence at community events (e.g., health fairs)
Targeted cable television programs
Local physician and patient with 15 min on specific topic (living with
osteoarthritis, managing diabetes, etc.)
Videos for doctors’ offices
Interactive kiosks
Open communication with local media outlets
Meetings with community groups (health related and non-health
related)
Community Registry
Accelerating Discovery:
Finding
Suppose we identify a Biomarker that
distinguishes a sub-population of patients
correlating with new insulin resistance
Application If this is true, we’d treat differently to
achieve better patient outcomes
Confirm
We need to test this to assure improved
patient outcomes
Translation Positive result would drive creation of the
into Practice diagnostic and care guidelines.
Community Registry
Allow patients to declare interest in research
participation
Store information
People interested in research participation
Summary level health information
Permission to contact
Primary physician
Accelerate discovery by having this information
when discoveries are ready to be tested
Summary
We are committed to building transparent, open
partnership with local community.
We will seek to maximize opportunities to have
meaningful impact on local human health and
local economy.
The MURDOCK Study offers an opportunity for
the local community to have global impact by
generating knowledge that improves health and
alleviates disease.