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Immunologic Risk Prediction Model for Kidney Graft Function

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Immunologic Risk Prediction Model for Kidney Graft Function University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 8-2011 Immunologic Risk Prediction Model for Kidney Graft Function Christina Diane Bishop [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Medical Immunology Commons, and the Other Analytical, Diagnostic and Therapeutic Techniques and Equipment Commons Recommended Citation Bishop, Christina Diane, "Immunologic Risk Prediction Model for Kidney Graft Function. " PhD diss., University of Tennessee, 2011. https://trace.tennessee.edu/utk_graddiss/1059 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Christina Diane Bishop entitled "Immunologic Risk Prediction Model for Kidney Graft Function." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Comparative and Experimental Medicine. Oscar H. Grandas, Major Professor We have read this dissertation and recommend its acceptance: Karla Matteson, Jonathan Wall, Melissa Kennedy, Arnold Saxton Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) Immunologic Risk Prediction Model for Kidney Graft Function A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Christina Diane Bishop August 2011 AKNOWLEDGEMENTS I would like to express my gratitude to Dr. Oscar Grandas for his support and guidance during the last four years and for “planting the seed” of thought that encouraged me to go to graduate school in the first place. Special thanks to Dr. Karla Matteson for directing me from the very beginning. Thanks to Dr. Jonathan Wall for opening my eyes to different perspectives, to Dr. Mellissa Kennedy for the encouragement when things went awry, and to Dr. Arnold Saxton for his willingness to join my committee during the most hectic time. I would also like to thank the entire DCI Transplant Lab for their assistance in my research; especially Nichole Barton for the time she spent helping me with the database and countless hours of digging through freezers for samples. I would like to express my appreciation to Dialysis Clinic Inc., Jim Lappin (mortuus ), and Dr. Deborah Crowe for their support of my scholastic pursuits and their flexibility as employers. I would like to acknowledge the generous contribution of reagent kits by Jason Crumpton and One Lambda, Inc., data support from Yulin Cheng at UNOS, and statistical guidance from Eric Heidel with the UT GSM Dean’s Office. I want to express my heartfelt thanks to my family and friends for all their support and encouragement. I would especially like to thank Dr. Emily Rogers and (soon to be Dr.) Catherine Nyinyi for being my GRA emotional and technical support system, for calming me through exams, and for all of the great advice. To my dearest friends Megan and Jason, I would like to express my sincerest thanks and love for the encouragement, for catching me when I stumbled, and for reminding me who I am and what I can accomplish. ii ABSTRACT Clinicians lack appropriate non-invasive methods to be able to predict, diagnose, and reduce the risk of rejection in the years following kidney transplantation. Protocol biopsies and monitoring of serum creatinine levels are the most common methods of monitoring graft function after transplant; however, they have several negative aspects. Use of traditional factors regarding donors and recipients such as Human Leukocyte Antigen (HLA) DNA typing, pre-transplant anti-HLA antibody levels, and basic demographics (age, ethnicity/race, gender), has proved inadequate for post-transplant graft monitoring past the first few years. We propose that by utilizing immunologic factors available to clinicians across the United States, development of a non-invasive model for predicting renal graft outcome will provide a useful tool for post-transplant patient monitoring. We advocate an expanded model which incorporates both the traditional factors, as well as new factors, which have shown promise in predicting kidney outcome and are widely available for testing using commercial kits. These additional factors include major histocompatibility complex class I chain-related gene A (MICA) typing of donor and recipient, degree of matching for killer cell immunoglobulin- like receptors (KIRs) between donor and recipient, detection of MICA antibodies, and soluble CD30 level (sCD30). This proposed graft-function prediction model is the first to include all of these factors. Using multi-center data from adult recipients of standard-criteria deceased-donor (SCD) kidneys, we were able to construct models, containing the traditional factors only, for prediction of outcome at 1 year and 3 years post-transplant. Using single-center data from adult recipients of standard-criteria deceased-donor kidneys, we developed comparison models containing traditional factors only, as well as, expanded models containing the new suggested variables for prediction of outcome post-transplant. These additional variables, when incorporated into the expanded models provided greater positive predictive values, greater negative predictive values, and lower false negative rates for graft outcome at 1 year and at 3 years post-transplant than the iii models utilizing traditional factors only . Our results indicate that evaluation of sCD30, MICA and KIR as part of routine protocol testing, is helpful to clinicians for predicting risk of kidney graft rejection. iv TABLE OF CONTENTS Chapter Page CHAPTER I: INTRODUCTION ....................................................................................... 1 Available Organs/ Deceased Donors ........................................................................... 1 Available Organs/ Living Donors ................................................................................. 3 Immunosuppressant Drugs .......................................................................................... 4 Tolerance Induction ..................................................................................................... 5 Hypotheses and Theoretical Framework ..................................................................... 6 CHAPTER II: REVIEW OF LITERATURE ...................................................................... 9 Conventional Elements of Renal Transplantation ........................................................... 9 Major Histocompatibility Complex (MHC) —Human Leukocyte Antigens (HLA) ...... 9 Panel Reactive Antibodies (PRA) .............................................................................. 12 Affects of Age, Sex, and Ethnicity on Graft Outcome ................................................ 14 Age ......................................................................................................................... 14 Gender ................................................................................................................... 16 Ethnicity ................................................................................................................. 17 Novel Supplementary Elements of Renal Transplantation ............................................ 19 Major Histocompatibility Complex Class I Chain-Related Gene A (MICA) ................. 19 Killer-cell immunoglobulin-like receptor (KIR) ............................................................ 21 Soluble CD30 (sCD30) .............................................................................................. 23 Summary ................................................................................................................... 25 CHAPTER III: SUBJECTS AND METHODS ................................................................ 26 Definitions .................................................................................................................. 26 v Traditional Risk Score / Model ............................................................................... 26 Expanded Risk Score / Model ................................................................................ 26 Study Subjects ........................................................................................................... 27 Statewide Population ............................................................................................. 27 TNUK Population ................................................................................................... 28 Additional testing: MICA, KIR, sCD30 ....................................................................... 29 Statistical Analysis ..................................................................................................... 29 Variables ................................................................................................................ 30 Univariate Analysis and Descriptive Statistics ........................................................ 31 Generation of Models and Appropriateness of Design ..........................................
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