Sutter Health Research Institute

1. OMIC Unravelling of Transplant Rejection Minnie Sarwal Professor, Pediatrics and Surgery, CPMC Research Professor, Biostatistics, UCSF Consulting Professor, Immunology, Stanford University Director, BIOMARC Theranostics Program, Sutter Health San Francisco, USA Sutter Health Research Institute

2. The Omic Tools • RNA scanning: Genomics • Protein Scanning: Proteomics • Antibody Scanning: Antibiomics • Metabolite Scanning: Metabolomics • DNA Scanning: Sequencing

3. The Omic Applications in Organ Transplantation • Understanding Mechanisms of Organ Injury • Finding new drug targets for Organ Transplantation • Harnessing highly selected clinical phenotypes to monitor transplant patients • Biopsy confirmed Acute Rejection • Clinically defined Operational Tolerance • Clinically defined Induced Tolerance (Strober, Sykes)

4. Understanding Mechanisms of Organ Injury Antibodies

6. Incidence and Impact of de novo DSA and MICA-Ab in unsensitized children?Evaluation in the SNSO1 randomizedtrial

7. Low Incidence DSA; mean detection time 11 months 22% anti-HLA AB 6% are donor specific 6% anti-MICA AB

8. CAUSES for RENAL GRAFT LOSS Chapman J et al. J Am Soc Nephrol 2005

9. Rapid Evolution of CAI in the absence of rejection or DSA in children Naesens et al. JASN 2009; Naesens et al, AJT, 2012

10. Clinical Impact of de novo nHLA-Ab Unbiased interrogation of sera by Protein Arrays Sigdel et al, Hum Immunol, 2013 Li et al, PNAS, 2010

11. Additional Humoral Axis of Injury…

12. Understanding Mechanisms of Organ InjuryConclusion 1 Titers of nHLA Ab can to specific graft antigens are highly correlative with progressive graft injury Many of these nHLA Ab after transplant are patient specific and non-pathogenic

13. Understanding Mechanisms of Organ Injury RNA

14. 794 Samples analyzed by Microarray and PCR Kidney, Heart, Lung, Liver Transplant Tissue

15. Public data sets studying AR in different organs using microarrays: 392 bx, 8 datasets • Biological and technical confounding factors • Only biopsy data from human; no blood data

16. Common Immune Response Module (CRM) of 12 genes in All Solid Organ Acute Rejection kidney, lung, heart and liver transplant (n=236 arrays) CRM gene Score geometric mean of the CRM expression in each sample is computed as a CRM score Khatri et al, J Exp Med, Oct 21, 210(11):2205-21; 2013

17. CRM score on 6 mo protocol bximmune burden in pCAI, in the absence of clinical AR CRM score correlates with Banff ct score ( p=1.995E-05) and ci score (p=6.195E-07 ) Each unit increment in the CRM score increased the odds of AR by 4.17 vs.; P. 0.0003 CRM 0.75 ± 0.75 pCAI CRM 3.2 ± 1.8 AR CRM 5.98 ±0.85 120 bx; GSE1563 Naesens et al, KI, 2011 Khatri et al, J ExpMed; 2013

18. Understanding Mechanisms of Organ Injury Conclusion 2 There is a highly conserved set of genes that drive the injury of graft rejection, irrespective of tissue source

19. Finding new drug targets for Organ Transplantation

20. FDA approved drugs that target the common immune response module in all transplanted organs Sulindac Bortezomib Atorvastatin Dasatinib, Imatinib Mycophenolate mofetil Doxycycline Khatri et al, J ExpMed; 2013

21. Over-expression of the CIRM gene-set was validated in FVB hearts transplanted in C57BL/6 WT mice. • NaoyukiKimura,SilkeRoedder * - p-value < 0.05; ** - p-value < 0.005; *** - p-value < 0.001

22. Atorvastatin and Dasatinib treatment significantly reduces infiltrating cells in completely mismatched mouse cardiac allografts

23. Harnessing highly selected clinical phenotypes • to monitor transplant patients • Biopsy confirmed Acute Rejection • Clinically defined Operational Tolerance • Clinically defined Induced Tolerance (Strober, Sykes) Finding a common blood biomarker panel for AR across all solid organs:The Solid Organ Rejection Test (SORT)

24. The hunt for blood biomarkers for acute rejection…..367 unique blood samples with matched biopsies Li et al, AJT, 2012 Multi-Parameter Acute Rejection Biomarker Discovery Affymetrix Whole blood: 44 AR, 46 STA FACS Purified Cell Subsets: 6 AR, 9 STA Agilent Whole blood: 15 AR, 11 STA Lymphochip cDNA Whole blood: 7 AR, 14 STA SAM Analysis (FDR <0.05) Microarray discovery Selection Criteria (at least 2) • Identical fold change direction • AR/STA Classifier (2+ Datasets) • Statistical Deconvolution • Cell Specific Enrichment • Biologic Significance 43 genes Biomarker Discovery Verification Biomarker Selection Gene Selection p < 0.05 Fluidigm 12 Center Clinical Trial n = 198 Biomarker Validation n = 90 Biomarker Definition n = 177 Assess Cross-Validation Performance ABI

25. 10 Gene Panel in Blood Differentiates AR from noAR Independent multicenter validation NIH SNSO1 12 US center randomized prospective trial in peds renal txp AR Probability threshold = Biopsy proven AR = Biopsy proven stable; STA n=198 blood samples, matched with biopsy, central blinded histology, Li et al, AJT, 2012 Sarwal et al, AJT, 2012 Naesens et al, AJT, 2012

26. 10 Gene Panel in Blood Differentiates AR from noAR and CMV in cardiac allograft recipients Data are from 120 cardiac allograft recipients; CARGO subset AR1A AR1B AR2/3 CMV not a confounder cSORTis not dependent on time post-txp -SORT distinguishes CMV from AR cSORThigh scores predict transplant vasculopathy Roedder et al, Plos One, 2013

27. The SORT Blood Gene Assay PREDICTS Histological and Clinical Rejection

28. Harnessing highly selected clinical phenotypes to monitor transplant patientsConclusion 3 Serial Post-Transplant Patient Monitoring for the SORT assay q 3 mo to monitor for AR risk Precision Medicine: Titrating Rx Prospectively to Immune risk, prior to evolution of clinical/histological injury