1. Immunity & Infection Research Centre Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Finding Biomarkers for Transplantation Raymond Ng (Computer Science & iCapture, UBC rng@cs.ubc.ca)

2. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Overview of Application Focus on the Genome Canada project entitled “Better Biomarkers Of Acute and Chronic Allograft Rejection” (www.allomark.ubc.ca) Led by Drs. Paul Keown, Bruce McManus and Rob McMaster 3-year project starting January 2005 $9.1 million over 3 years including contributions from Novartis and IBM

3. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Definitions Acute Rejection –Injury that typically occurs within weeks or a few months after a solid organ is transplanted Chronic Rejection –Injury that occurs over time to a transplanted organ –This injury occurs mostly in the blood vessels of the organ Accommodation –Absence of either form of rejection ( current means of detecting rejection can be very invasive, e.g., frequent biopsies)

4. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health The Overall Goal To establish effective, minimally-invasive and affordable markers that reliably predict rejection of heart, liver, and kidney allografts

5. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health How do we accomplish this goal? Determine patterns of gene expression in white blood cells that react specifically to the transplanted organ Identify protein biomarkers in the plasma Put the identified gene and protein markers together, and use new mathematical tools to determine the best predictors of and diagnostics for rejection

6. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Milestones Year 1 To find possible biomarkers in blood that predict rejection Year 2 To evaluate how well the biomarkers found in Year 1 predict rejection in a separate set of patients Years 3-5 To use the biomarkers in clinical trials to further test their ability to predict rejection To also use these biomarkers to personalize existing immunosuppressive treatment To identify novel targets for new drug development

7. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Discovery Strategy Acute Rejection DE NOVO Patients <=1 year post- transplant Chronic Rejection CURRENT Patients 1-5 years post- transplant Accommodation Blood, Urine, Tissue Immunology Laboratory BioLibrary Dr. Paul Keown Anonymized Data Biomarker Database Dr. Raymond Ng RNA Extraction from Blood, Alloreactive T Cells and Biopsy Tissue Jack Bell Research Centre Dr. Alice Mui Biopsy Tissue Alloreactive T cells Plasma Depletion Jack Bell Research Centre Dr. Robert McMaster ITRAQ Analysis UVic Genome BC Proteomics Platform Victoria, BC Dr. Christoph Borcher RNA Amplification and Affymetrix GeneChip Analysis Microarray Core Laboratory, Children’s Hospital, LA Dr. Tim Triche Pax- gene Blood

8. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Proteomics Team Rob McMaster Lead Ross MacGillivray Co-Lead Janet Wilson-McManus & Martha Casey-Knight Jack Bell Axel Bergman UVic Christoph Borcher Derek Smith Novartis Andreas Scherer, Georges Imbert, Nelson Guerreiro, Stephan Gatzek

9. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Plasma protein level variation Plasma protein levels may vary from patient to patient when compared to pooled normal plasma Highlights of experimental design Analysis of the same patient’s plasma over multiple time points will reduce variation Use of a large number of patients >100 Plasma Biomarker Discovery

10. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health

11. An Example Protein Biomarker

12. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health The Major Plasma Proteins Dynamic range of plasma proteins: 1 pg/ml to 50 mg/ml (10 10 range) Leigh Anderson

13. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health BioLibrary Plasma Jack Bell Research Centre, Dr. Robert McMaster Depleted Plasma Column 1: albumin, fibrinogen, IgG, IgA, IgM, a1-antitrypsin, transferrin, haptoglobin, a1-acid glycoprotein, HDL Apolipoprotein A-I, HDL Apolipoprotein A-II, a2-macroglobulin Column 2: Apolipoprotein B, Complement C3 ITRAQ Analysis UVic Genome BC Proteomics Platform Victoria, BC Biomarkers in Transplantation Discovery Strategy: Proteomics Analysis

14. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health iTRAQ Labeling (Applied Biosystems) + Peptide 14 12 16 14 13 16 14 15 13 17 15 13 18 114 115 116 117

15. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Each depleted plasma sample is digested with trypsin Quantitative 2DLC / MS/MS analysis Protein identification and differential expression analysis Each digested sample is labeled with an iTRAQ reagent 1Pooled plasma control sampleiTRAQ reagent  114 2Baseline (Plasma just before transplant) iTRAQ reagent  115 3Week 1 after the transplantiTRAQ reagent  116 4Week 2 – acute rejection identifiediTRAQ reagent  117 All 4 samples are pooled Plasma Biomarker Discovery iTRAQ Experimental Design

16. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Plasma Biomarker Identification by iTRAQ Technology Heart acute rejection plasma : normal plasma iTRAQ ratio (117:114) increasedecrease number of proteins 123455432

17. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Informatics Team Raymond Ng Robert Balshaw Co-Leads Janet Wilson-McManus & Martha Casey- Knight Data ManagementData Analysis iCAPTURE Mark Wilkinson Nina Opushneva, Wendy Alexander, Joe Comeau, Andrew Ferris IBM Paul Moody, Tony Li Mahendran Maliapen, Agata Szewczyk Novartis Andreas Scherer, Mischa Reinhardt iCAPTURE Bruce McManus, Mark Wilkinson, Zsuzsanna Hollander, Andrew Ferris IBM Jeff Betts, O.K. Baek, Kareem Saad, Usha Reddy, Prasanna Athma Novartis Andreas Scherer, Peter Grass Epicenter Tim Triche, Jonathan Buckley Trainees: Benjamin Good, Gabriella Cohen Freue, Jon Carthy UBC Wyeth Wasserman

18. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Analytical Strategy 50,000 Genes, ESTs, Proteins Preliminary gene / protein selection Biological Knowledge Clinical Data 10,000 Genes, Proteins Secondary gene / protein selection Biological Knowledge Clinical Data 60-120 Genes / Proteins Statistical model building <10 Predictive Markers

19. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Filtering Methods Time not includedTime included 1. T-test 2. Wilcoxon test 3. Permutation test 4. Linear regression 5. GEE 6. Mixed effects model 7. MANOVA 8. Multi-variate Bayesian (MB)

20. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Questions & Methods MethodsQuestions T-test, Wilcoxon, Permutation test 1. Are there proteins with DE from the moment of transplantation? 2. At which one of the TPs does the DE start? 3. Are there proteins with DE between AR & CR? MANOVA, T-test, Wilcoxon, Permutation test, MB 4. Which proteins have DE between last and one before last TPs? 5. During rejection are there non-expressed proteins? Mixed effects model, MB 6. If there are proteins that have DE, do they have similar expression pattern across patients? Linear regression, GEE, MANOVA 7. Are there proteins with DE between liver, kidney, & heart? 8. Does gender, race, age play a role in rejection? 9. Is the concentration of the biomarker detected correlated with the severity of the rejection? All 10. Are there proteins with DE between control & rejection? 11. What are the differences between organs?

21. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Out of range values Data inconsistencies –performed within and across visits for an individual patient, as well as across all patients in the study Warnings (flags) generated when manual verification is required More to discuss later in the “Frontiers” session about QC issues Data Validation Checks

22. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Concluding Remarks Consistency of our sample handling is vital for this project Depletion of plasma is key to our workflow. The more depleted the plasma is, the more sensitive our method becomes in order to identify a potential Biomarker in pg/mL range A time-course analysis adequately supported by the four channels of iTRAQ

23. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health Plasma Biomarker Discovery Using iTRAQ Technology Protein identification Plasminogen peptide FVTWIEGVMR MS/MS spectra Peptide quantitation reporter tags 114 115 116 117

24. Better Biomarkers in Transplantation. A Genome Canada Initiative for Human Health C4A C3A Apo E Plasma Protein Identification by iTRAQ Dynamic Range Common to both methodsLeak from column Specific to MALDI Specific to ESI HGF pg per mLmg per mL That’s 100pg/mL Range with MALDI