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Whole blood RNA signatures accurately classify agonist specific platelet function and highlight common biologic pathways

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

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Whole blood RNA signatures accurately classify agonist specific platelet function and highlight common biologic pathways

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  1. 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

  2. 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

  3. Variability in Response to Aspirin ATC, Lancet 2002 Becker et al, JAMA 2006 Frelinger et al, Circulation 2009 • Variability in the clinical response to aspirin • 20-30% will experience an event on aspirin • Variability in the laboratory response to aspirin • Variability in platelet function assays associated with increased risk of events on aspirin

  4. COX-1 dependent platelet function Non COX-1 dependent platelet function vs. • 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 AA  Thromboxane AA  Thromboxane Gurbel et al, Circulation 2008, Faraday et al, Circulation 2007, Mathias et al, BMC Medical Genomics 2010 • Sensitive to COX-1 inhibition by ASA • Agonist: Arachidonic acid • Minimal variability on ASA • Not heritable • no known genetic variants associated with function

  5. 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.

  6. Methods – Aspirin challenge study in healthy volunteers Visit #1 t = 0 Visit #2 t = 14d 325mg/day aspirin for 14 days • Platelet function (pre-aspirin): • Platelet function (post-aspirin) • Peripheral blood RNA preserved in PAXgene tubes • Adherence: • Medication log • Telephone reminder • Witnessed dose

  7. Methods – Measuring NCDPF • Light transmittance aggregometry • Agonists • ADP 10uM • Epinephrine 10uM • Collagen 5 ug/ul • Area under the aggregometry curve (AUC) • Measured in: % min

  8. Baseline characteristics (n = 40)

  9. Aspirin reduces NCDPF ADP COLLAGEN EPINEPHRINE Pre-aspirin AUC (% min) AUC (% min) AUC (% min)

  10. Aspirin reduces NCDPF ADP COLLAGEN EPINEPHRINE Pre-aspirin Post-aspirin AUC (% min) AUC (% min) AUC (% min)

  11. Methods – RNA analysis overview Hypothesis: A whole blood RNA signature can be identified that correlates with NCDPF on aspirin 54,000 probes 20 factors 6 factors 6 pathways • Affymetrix U133 Plus 2.0 microarray • Bayesian factor analysis • Linear regression followed by variable selection to identify factors that correlate with each agonist on aspirin • Leave one out cross validation • Ingenuity Pathway Analysis of selected factors

  12. Factors correlate with NCDPF COLLAGEN EPINEPHRINE ADP Predicted AUC r = 0.84 r = 0.87 r = 0.84 AUC (% min) AUC (% min) AUC (% min) P < 0.0001 for all correlations

  13. Leave one out cross validation COLLAGEN EPINEPHRINE ADP r = 0.84 r = 0.87 r = 0.84 Predicted AUC r = 0.56 r = 0.58 r = 0.40 AUC (% min) AUC (% min) AUC (% min) P < 0.0001 for all correlations

  14. Top pathways across 3 agonists IFN VEGF IGF-1 Collagen ADP Epinephrine

  15. Top pathways across 2 agonists N-GLYCAN SYNTHESIS Collagen ADP TLR P2YR Epinephrine

  16. 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

  17. 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

  18. 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.

  19. 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

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