title: Predicting acute cardiac rejection from donor heart and pre-transplant recipient blood gene expression.
abstract: BACKGROUND
  Acute rejection in cardiac transplant patients remains a contributory factor to limited survival of implanted hearts. Currently, there are no biomarkers in clinical use that can predict, at the time of transplantation, the likelihood of post-transplant acute cellular rejection. Such a development would be of great value in personalizing immunosuppressive treatment.
  
  
  METHODS
  Recipient age, donor age, cold ischemic time, warm ischemic time, panel-reactive antibody, gender mismatch, blood type mismatch and human leukocyte antigens (HLA-A, -B and -DR) mismatch between recipients and donors were tested in 53 heart transplant patients for their power to predict post-transplant acute cellular rejection. Donor transplant biopsy and recipient pre-transplant blood were also examined for the presence of genomic biomarkers in 7 rejection and 11 non-rejection patients, using non-targeted data mining techniques.
  
  
  RESULTS
  The biomarker based on the 8 clinical variables had an area under the receiver operating characteristic curve (AUC) of 0.53. The pre-transplant recipient blood gene-based panel did not yield better performance, but the donor heart tissue gene-based panel had an AUC = 0.78. A combination of 25 probe sets from the transplant donor biopsy and 18 probe sets from the pre-transplant recipient whole blood had an AUC = 0.90. Biologic pathways implicated include VEGF- and EGFR-signaling, and MAPK.
  
  
  CONCLUSIONS
  Based on this study, the best predictive biomarker panel contains genes from recipient whole blood and donor myocardial tissue. This panel provides clinically relevant prediction power and, if validated, may personalize immunosuppressive treatment and rejection monitoring.
authors.affiliation: NCE CECR Prevention of Organ Failure Centre of Excellence, Vancouver, Canada.
authors.forename: Zsuzsanna
authors.initials: Z
authors.lastname: Hollander
authors.forename: Virginia
authors.initials: V
authors.lastname: Chen
authors.forename: Keerat
authors.initials: K
authors.lastname: Sidhu
authors.forename: David
authors.initials: D
authors.lastname: Lin
authors.forename: Raymond T
authors.initials: RT
authors.lastname: Ng
authors.forename: Robert
authors.initials: R
authors.lastname: Balshaw
authors.forename: Gabriela V
authors.initials: GV
authors.lastname: Cohen-Freue
authors.forename: Andrew
authors.initials: A
authors.lastname: Ignaszewski
authors.forename: Carol
authors.initials: C
authors.lastname: Imai
authors.forename: Annemarie
authors.initials: A
authors.lastname: Kaan
authors.forename: Scott J
authors.initials: SJ
authors.lastname: Tebbutt
authors.forename: Janet E
authors.initials: JE
authors.lastname: Wilson-McManus
authors.forename: Robert W
authors.initials: RW
authors.lastname: McMaster
authors.forename: Paul A
authors.initials: PA
authors.lastname: Keown
authors.forename: Bruce M
authors.initials: BM
authors.lastname: McManus
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000032:analysis; D005260:Female; D015870:Gene Expression; D006084:Graft Rejection / Q000453:epidemiology*; D016027:Heart Transplantation / Q000276:immunology*; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012372:ROC Curve; D018570:Risk Assessment; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers