Transcript Molecular Signatures of the Response to Aspirin

Whole blood RNA signatures accurately classify
agonist specific platelet function and highlight
common biologic pathways.
Deepak Voora, MD, Thomas L. Ortel MD, PhD, Joseph
Lucas PhD, Jen-Tsan Chi, MD PhD, Richard C. Becker
MD, Geoffrey S. Ginsburg, MD PhD
Duke University
Divisions of Cardiology and Hematology
Institute for Genome Sciences & Policy
Durham, NC
USA
November 15, 2010
Disclosure Information
• Deepak Voora, MD
• Whole blood RNA signatures accurately classify agonist
specific platelet function and highlight common biologic
pathways.
FINANCIAL DISCLOSURE:
None
UNLABELED/UNAPPROVED USES DISCLOSURE:
None
Variability in Response to Aspirin
• Variability in the clinical response to aspirin
– 20-30% will experience an event on aspirin
ATC, Lancet 2002
• Variability in the laboratory response to aspirin
Becker et al, JAMA 2006
• Variability in platelet function assays associated
with increased risk of events on aspirin
Frelinger et al, Circulation 2009
COX-1 dependent
platelet function
AA  Thromboxane
• Sensitive to COX-1
inhibition by ASA
• Agonist: Arachidonic acid
• Minimal variability on ASA
• Not heritable
– no known genetic variants
associated with function
vs.
Non COX-1 dependent
platelet function
AA  Thromboxane
• Can be robust despite
inhibition of COX-1with ASA
• Agonists: ADP, Collagen, Epi
• Highly variable on ASA
• Highly heritable
• GWAS identified genomic
regions associated with
function
Gurbel et al, Circulation 2008, Faraday et al, Circulation 2007, Mathias et al,
BMC Medical Genomics 2010
Rationale
• To use peripheral blood gene expression as a
tool to identify novel pathways that underlie
Non COX-1 dependent platelet function
(NCDPF) on aspirin.
Methods – Aspirin challenge study
in healthy volunteers
Visit #1
t=0
•
Platelet function
(pre-aspirin):
325mg/day aspirin
for 14 days
• Adherence:
• Medication log
• Telephone reminder
• Witnessed dose
Visit #2
t = 14d
• Platelet function
(post-aspirin)
• Peripheral blood RNA
preserved in PAXgene
tubes
Methods – Measuring NCDPF
• Light transmittance
aggregometry
– Agonists
• ADP 10uM
• Epinephrine 10uM
• Collagen 5 ug/ul
• Area under the
aggregometry curve (AUC)
– Measured in: % min
Baseline characteristics (n = 40)
Age
Female
BMI
Medications
Race
Median
IQR
N
(%)
Median
IQR
None/OC/other (N)
(%)
White/Black/Other
(N)
(%)
26
[24,31]
21
(53)
24.6
[26.7, 23.0]
35/4/1
87/10/3
29/6/5
73/15/13
BMI = body mass index; IQR = interquartile range; N = Number; OCP = oral contraceptives
Aspirin reduces NCDPF
COLLAGEN
EPINEPHRINE
Pre-aspirin
ADP
AUC (% min)
AUC (% min)
AUC (% min)
Aspirin reduces NCDPF
COLLAGEN
EPINEPHRINE
AUC (% min)
AUC (% min)
Post-aspirin
Pre-aspirin
ADP
AUC (% min)
Methods – RNA analysis overview
Hypothesis: A whole blood RNA signature can be
identified that correlates with NCDPF on aspirin
• Affymetrix U133 Plus 2.0 microarray
54,000 probes
• Bayesian factor analysis
20 factors
• Linear regression followed by variable selection to
identify factors that correlate with each agonist
on aspirin
6 factors
– Leave one out cross validation
• Ingenuity Pathway Analysis of selected factors
6 pathways
Factors correlate with NCDPF
COLLAGEN
Predicted AUC
ADP
r = 0.87
r = 0.84
AUC (% min)
AUC (% min)
P < 0.0001 for all correlations
EPINEPHRINE
r = 0.84
AUC (% min)
Leave one out cross validation
COLLAGEN
Predicted AUC
ADP
EPINEPHRINE
r = 0.87
r = 0.84
r = 0.84
r = 0.58
r = 0.40
r = 0.56
AUC (% min)
AUC (% min)
P < 0.0001 for all correlations
AUC (% min)
Top pathways across 3 agonists
ADP
IFN
VEGF
IGF-1
Collagen
Epinephrine
Top pathways across 2 agonists
N-GLYCAN SYNTHESIS
Collagen
ADP
TLR
P2YR
Epinephrine
Summary
• NCDPF on 325mg/day aspirin is highly variable
in healthy volunteers
• RNA signatures can be used to develop a
model that classifies the response to multiple
platelet agonists
• Analysis of the underlying genes from the
derived factors identifies known and novel
biology in the platelet response to ADP,
Collagen, and Epinephrine
Conclusions
• Common biological pathways
contributing to NCDPF is a consistent
finding:
– Correlation between assays
– Prior GWAS of platelet function
demonstrate genomic regions
contributing to multiple agonists
Conclusions
• Pathways analysis suggest that
inflammatory pathways contribute to
platelet function on aspirin
• RNA profiling – a testing platform used in
commercial labs – may be used to
identify those with heightened platelet
function on aspirin.
Acknowledgments
• Collaborators
– Geoffrey Ginsburg, MD, PhD (Cardiology, IGSP)
– Richard Becker, MD (Cardiology, Hematology)
– Thomas Ortel, MD, PhD (Hematology)
– Jen-Tsan Chi, MD, PhD (IGSP)
– Joseph Lucas PhD (IGSP)
• Funding:
– IGSP, T32HL007101, UL1RR024128