Blood and Biopsy mrna Expression Signatures Can Distinguish Major Causes of Graft Injury in Liver Transplant Recipients

Transcription

1 Northwestern University Feinberg School of Medicine Blood and Biopsy mrna Expression Signatures Can Distinguish Major Causes of Graft Injury in Liver Transplant Recipients Josh Levitsky, Dan Salomon, Sunil Kurian, John Friedewald, Simbasiva Rao, Anthony Demetris, Stephanie Rindt, Jane Charette, Michael Abecassis Northwestern University, Comprehensive Transplant Center The Scripps Research Institute The University of Pittsburgh Transplant Center

2 Disclosures Levitsky, Friedewald, Kurian Consultant, Transplant Genomics Incorporated Salomon, Abecassis Founder and Consultant, Transplant Genomics Incorporated

3 Background Biomarker profiles diagnostic of specific types of graft injury post-liver transplantation (LT), such as acute rejection (AR), hepatitis C virus recurrence (HCV-R), and alternative causes could enhance the diagnosis and management of recipients 3

4 Aim To identify diagnostic gene expression (mrna) profiles specific for clinical/histological phenotypes in the peripheral blood and allograft tissue in liver transplant recipients: Acute Rejection (AR) HCV-R AR + HCV-R Acute Dysfunction No Rejection or Recurrence (ADNRR) Normal function (Tx) 4

5 Methods Patients/Samples Patient Population: Adult liver transplant recipients undergoing liver biopsy for cause or by protocol (HCV) from at Northwestern Medicine Sample Collection at Liver Biopsy: Blood: PaxGene collection tubes; Sera and PBMC stored also 0.5 cm of graft tissue placed in RNA later 5

6 Methods: Histological and Clinical Phenotyping All biopsies reviewed blindly by two independent hepatologists (Rao, Demetris) and scored according to standard Banff criteria: Rejection Activity Index (0-9) Hepatitis Activity index (0-18) for HCV+ subjects Other pathology verified between the pathologists Clinical Phenotyping: Review of laboratory values and patient course for confirmation 6

7 Methods: Final Groups 1. Pure AR (non-viral) with RAI scoring ( 3); laboratory resolution following rejection therapy 2. Pure HCV-R (HCV+) with HAI scoring ( 3) 3. Mixed AR/HCV-R (HCV+), RAI and HAI both 3 4. ADNRR (non-viral): no rejection, mixture of other etiologies (steatohepatitis, bile duct obstruction, drug toxicity, etc) 5. Normal (non-viral): no biopsy done but normal function [LFTs normal 3 months around laboratory collection; no graft complications] 7

8 Methods: Genomics Samples were profiled using Affymetrix HG-U133 PM Peg microarrays. 3-way ANOVA analysis was performed with a FDR cut-off of <5%. Three different predictive algorithms - Diagonal Linear Discriminant Analysis (DLDA), Nearest Centroid (NC) and Support Vector Machines (SVM) - to build the predictive models. Biological pathways were mapped in Ingenuity Pathway Analysis 8

9 Results 114 biopsy-documented Liver PAXgene whole blood samples were processed on the Affymetrix HG-U133 PM only PEG microarrays: 1. AR (n=25) 2. HCV (n=36) 3. HCV+AR (n=13) 4. ADNRR (n=16) 5. Normal TX (n=24) 9

10 Comparison of Group Characteristics Normal LTx (n=24) ADNR (n=16) AR (n=25) HCV- R (n=36) AR+HCV- R (n=13) p value Age in Years < Caucasian Race % < Years from LT Female Sex % < Tacrolimus % < Mycophenolate % < Prednisone % < ALT (U/L) < Alkaline phosphatase (U/L) < Total Bilirubin (mg/dl)

11 Biomarker Data Blood mrna AR vs. ADNRR vs. TX Predictors One- Level Cross AUC's Corrected Bootstrapping (1000 data sets) AUC's DLDA SVM NC

12 Blood mrna: AR vs. ADNRR vs. Normal Tx 12

13 Top 15 Biological Pathways of AR vs. ADNRR vs. Normal (Whole Blood)

14 Top 15 Biological of AR vs. ADNRR vs. Normal (Whole Blood)

15 Biomarker Data Blood mrna AR vs. HCV vs. HCV+AR Predictors One- Level Cross AUC's Corrected Bootstrapping (1000 data sets) AUC's DLDA SVM NC

16 Blood mrna: AR vs. HCV-R vs. Mixed AR/HCV-R 16

17 Results We also processed 57 Liver biopsy samples on the same Affymetrix HG-U133 PM platform. The samples that were analyzed consisted of 3 different phenotypes: AR (n=16) HCV (n=30) HCV+AR (n=11) 17

18 Biomarker Results Liver Biopsy mrna AR vs. HCV vs. HCV+AR Predictors One- Level Cross AUC's Corrected Bootstrapping (1000 data sets) AUC's DLDA SVM NC

19 Biopsy mrna: AR vs. HCV-R vs. Mixed AR/HCV-R 19

20 Summary In both the peripheral blood and graft tissue from LT recipients, we have identified gene expression classifier sets that can distinguish between AR, HCV and HCV+AR with AUCs between (blood) and (biopsies). We have also discovered profiles in whole blood that can distinguish between AR, ADNRR and normal TX with AUCs ranging from

21 Conclusion These signatures have the potential to enhance Decision-making in the need to perform liver biopsy The specificity of diagnosis, particularly in managing patients with contrasting etiologies (e.g., AR vs. HCV-R vs. ADNRR) The understanding of mechanisms of graft injury in LT recipients The ability to minimize immunosuppression or need to augment Prospective multicenter studies, including our NIAID funded CTOT-14 are underway to validate these diagnostic signatures, test their predictive value before graft injury occurs, and determine how they respond to therapy 21

22 Northwestern University Feinberg School of Medicine