Transforming Medicine Through Genomics – Geoffrey Ginsburg, MD, PhD; Director, Center for Genomic Medicine at Duke Institute for Genome Sciences and Policy; Executive Director, Duke’s Center for Personalized Medicine

1. Molecular Signatures to Personalize
Medical Decisions
Geoffrey S Ginsburg, MD, PhD
Director, Center for Genomic Medicine
Professor of Medicine and Pathology
Duke University
Ohio State University
October 14, 2010

2. Disclosures
 CardioDx – Advisory Board
 CancerGuideDx – Advisory Board

3. Concept of a Signature
Biological State A Biological State B
Genome-scale gene expression data
State A State B
Prognosis
Drug sensitivity
Biology

4. Signatures in Medical Practice Today
Cancer Diagnosis and Prognosis
 Oncotoype DX®: 21 gene RNA signature from breast tumor; 12 gene RNA
signature from colon tumor
 MammaPrint®: 70 gene RNA signature from breast tumor (FDA approved)
 BluePrint™: 80 gene RNA signature that distinguishes basal, luminal and
ERBB2 subgroups of breast cancer
 Pathwork® Tissue of Origin Test: 2,000 RNAs to classify cancer of
unknown primary (FDA approved)
Cardiovascular Disease Diagnosis and Prognosis
 AlloMap®: 11 blood gene RNA signature for rejection following cardiac
transplant (FDA approved)
 Corus™ CAD: 23 gene blood RNA signature for coronary artery disease
 Triage® Cardiac Panel: 5 blood protein signature for assessment of chest
pain and shortness of breath

5. Molecular Signatures for Class Prediction
 Cardiovascular Disease
 Exposures
 Pharmacogenomics

6. Metabolomic Signatures for Coronary
Artery Disease
 CAD Discrimination
 CATHGEN biorepository of patients undergoing cardiac
catheterization (total N=7500)
 Initial set: 174 early-onset CAD cases, 174 race- and sex-
matched controls
 Replication set: 140 CAD cases, 140 controls
 Cases: CAD defined as CADindex ≥32
 Controls: no CAD, no h/o MI, CABG, PCI, CVD

7. Metabolite Profiles Discriminate CAD
Shah et al., Circ CV Genetics 2010

8. Receiver Operating Characteristic
(ROC) Curves for CAD
1.0
1.0
0.8
0.8
Sensitivity
Sensitivity
0.6
0.6
0.4
0.4
0.2
0.2
0.0
0.0
0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity 1-Specificity
Discovery Set C-statistic Replication Set C-statistic
Clinical Model: 0.756 Clinical Model: 0.745
Clinical Model + Factors 4 & 9: 0.776 Clinical Model + Factors 4 & 9: 0.775
Clinical Model + All Factors: 0.804 Clinical Model + All Factors: 0.872

9. Metabolomic Signatures for Near Term
Cardiac Events
 CV Event Prediction
 CATHGEN biorepository of patients undergoing cardiac
catheterization (total N=7500)
 74 events (death/MI) within 2 years of index
catheterization
 74 race- sex- matched controls with no events > 2
years
 No prior h/o CAD, no h/o MI, CABG, PCI

10. A Dicarboxylacylcarnitines Signature
Predicts Subsequent Death/MI
HR 2.17 (1.23-3.70), p=0.007 HR 1.75 (0.97-3.23), p=0.06
*adjusted for BMI, DM, HTN, FHx, smoking, EJFX, CAD severity, creatinine, age, race, sex
Shah et al., Circ CV Genetics 2010

11. Exposure Signatures:
Motivation and Inspiration
 DARPA BAA – Predicting Health and Disease
 To develop a predictor of incipient viral infection following exposure
 Prevent our troops from being in harms way or spreading infection
 Obvious public health applications
 Robust host response to environmental exposures
 Transcriptional programs to radiation, tobacco, etc
 Specific response to PBMCs and epithelial cells to pathogens
 Pattern recognition receptors / pathogen molecular patterns
(PAMPs)

12. Blood Signatures of Exposure to Ionizing
Radiation
Human Validation in
Transplant Patients vs
Controls
Irradiated
Non-
Irradiated
Healthy
Dressman et al., PLoS Medicine 2007
Dressman et al., PloS One, 2008

13. Can we predict who will develop upper
respiratory viral illness before they get sick?
Viral Challenge Studies (Rhino, RSV, H3N2, H1N1)
disease
disease
disease
disease
Standard disease
Healthy Volunteers Viral 5 day observation
Challenge
R R R R R R
P P P P P P No
No No No
disease
disease
No disease
diseaseNo
M M M M M M No disease
disease
No
disease
disease
R = Blood RNA expression profiling
P = Blood/urine/saliva/breath proteomics profiling
M = Blood/urine/saliva/breath metabolomics profiling

14. Challenge study model New sample
• Experimental data matrix‫ ‏‬of 4 physiologic zones
Subject
Labe
Labe
Label
l l Pre-challenge | post-challenge
Pheno Data BL H00 H005 H012 H021 H029 H036 H045 H053 H060 H069 H077 H084
?
H093 H101 H108
Z02 A 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Z03 A 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Z04 A 15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Pre-challenge (1)‫‏‬
Z09 A 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Z11 A 16 1 1 1 1 1 1 1 Asx post-challenge (2)‫‏‬
1 1 1 1 1 1 1 1 1
Z14 A 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Z16 A 16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Z17 A 15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Z01 S 16 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Z05 S 16 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Z06 S 16 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Z07 S 16 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Z08 S 15 2 2 2 2 Sx early (3)‫‏‬
2 2 2 2 2 2 2 Sx late (4)‫‏‬
2 2 2 2 2
Z10 S 16 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Z12 S 16 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Z13 S 14 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Z15 S 16 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
Overall objective:
Discover clinico-molecular signatures that can classify new sample
Sparsity constraints and dimensionality reduction drive the analysis

15. Host Gene Expression Signatures Detect
and Classify Upper Respiratory Infection
Viral vs Bacterial Infection
Rhinovirus RSV Influenza
95% accuracy for
for viral vs no viral
infection
Zaas et al Cell Host and Microbe, 2009
Ramillo et al Blood, 2009

16. Aspirin: A PGx Challenge Study
Aspirin
Healthy Volunteers 325 mg Repeat @ 3 hours & 14 days
P R P R P R
P = Nine platelet function assays
• PFA 100
• ADP 1 µM, 5 µM, 10 µM
• Epinephrine 0.5 µM, 1 µM, 10 µM
• Collagen 2 µg, 5 µM
R = RNA expression profiling (whole blood and PLTs)
Bayesian Factor Regression Modeling
Voora et al, AHA 2010

17. Surrogate Signatures - A Link with Pathway
Activation
Erolotinib
Cetuximab
Tamoxifen
Dasatinib FTI
Roscovitine
LY294002
Hypothemycin
Bild et al Nature, 2006

18. Signatures from Systems Biology:
New Predictive Models of Outcome
Gene Expression Profiles
Clinical Data Genome Data
Treatments SNPs and CNVs
Signatures
Family history Genome-scale sequence
Demographics
Models Metabolomic Data
Environmental
Proteomic Data
Imaging
???
Predictions:
Risk
Individualized Prognosis and Diagnosis
Drug Response
Environmental Response
Ginsburg GS et al. J Am Coll Cardiol 2005;46:1615−1627.