Nicholas P Giangreco1, Guillaume Lebreton2, Susan Restaino3, Mary Jane Farr3, Emmanuel Zorn4, Paolo C Colombo3, Jignesh Patel5, Ryan Levine5, Lauren Truby3, Rajesh Kumar Soni6, Pascal Leprince2, Jon Kobashigawa5, Nicholas P Tatonetti7, Barry M Fine8. 1. Departments of Systems Biology, Biomedical Informatics, and Medicine, Columbia University, New York, New York. 2. Chirurgie Thoracique et Cardiovasculaire, Pitiíe-Salpetriere University Hospital, Paris, France. 3. Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York. 4. Center for Translational Immunology, Columbia University Irving Medical Center, New York, New York. 5. Cedars-Sinai Heart Institute, Cedars Sinai Medical Center, Los Angeles, California. 6. Proteomics and Macromolecular Crystallography Shared Resource, Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, New York. 7. Departments of Systems Biology, Biomedical Informatics, and Medicine, Columbia University, New York, New York; Institute for Genomic Medicine, Columbia University, New York, New York. 8. Department of Medicine, Division of Cardiology, Columbia University Irving Medical Center, New York, New York. Electronic address: barry.fine@columbia.edu.
Abstract
BACKGROUND: Primary graft dysfunction (PGD) is the leading cause of early mortality after heart transplant. Pre-transplant predictors of PGD remain elusive and its etiology remains unclear. METHODS: Microvesicles were isolated from 88 pre-transplant serum samples and underwent proteomic evaluation using TMT mass spectrometry. Monte Carlo cross validation (MCCV) was used to predict the occurrence of severe PGD after transplant using recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA) within the MCCV prediction methodology. RESULTS: Using our MCCV prediction methodology, decreased levels of plasma kallikrein (KLKB1), a critical regulator of the kinin-kallikrein system, was the most predictive factor identified for PGD (AUROC 0.6444 [0.6293, 0.6655]; odds 0.1959 [0.0592, 0.3663]. Furthermore, a predictive panel combining KLKB1 with inotrope therapy achieved peak performance (AUROC 0.7181 [0.7020, 0.7372]) across and within (AUROCs of 0.66-0.78) each cohort. A classifier utilizing KLKB1 and inotrope therapy outperforms existing composite scores by more than 50 percent. The diagnostic utility of the classifier was validated on 65 consecutive transplant patients, resulting in an AUROC of 0.71 and a negative predictive value of 0.92-0.96. Differential expression analysis revealed a enrichment in inflammatory and immune pathways prior to PGD. CONCLUSIONS: Pre-transplant level of KLKB1 is a robust predictor of post-transplant PGD. The combination with pre-transplant inotrope therapy enhances the prediction of PGD compared to pre-transplant KLKB1 levels alone and the resulting classifier equation validates within a prospective validation cohort. Inflammation and immune pathway enrichment characterize the pre-transplant proteomic signature predictive of PGD.
BACKGROUND: Primary graft dysfunction (PGD) is the leading cause of early mortality after heart transplant. Pre-transplant predictors of PGD remain elusive and its etiology remains unclear. METHODS: Microvesicles were isolated from 88 pre-transplant serum samples and underwent proteomic evaluation using TMT mass spectrometry. Monte Carlo cross validation (MCCV) was used to predict the occurrence of severe PGD after transplant using recipient pre-transplant clinical characteristics and serum microvesicle proteomic data. Putative biological functions and pathways were assessed using gene set enrichment analysis (GSEA) within the MCCV prediction methodology. RESULTS: Using our MCCV prediction methodology, decreased levels of plasma kallikrein (KLKB1), a critical regulator of the kinin-kallikrein system, was the most predictive factor identified for PGD (AUROC 0.6444 [0.6293, 0.6655]; odds 0.1959 [0.0592, 0.3663]. Furthermore, a predictive panel combining KLKB1 with inotrope therapy achieved peak performance (AUROC 0.7181 [0.7020, 0.7372]) across and within (AUROCs of 0.66-0.78) each cohort. A classifier utilizing KLKB1 and inotrope therapy outperforms existing composite scores by more than 50 percent. The diagnostic utility of the classifier was validated on 65 consecutive transplant patients, resulting in an AUROC of 0.71 and a negative predictive value of 0.92-0.96. Differential expression analysis revealed a enrichment in inflammatory and immune pathways prior to PGD. CONCLUSIONS: Pre-transplant level of KLKB1 is a robust predictor of post-transplant PGD. The combination with pre-transplant inotrope therapy enhances the prediction of PGD compared to pre-transplant KLKB1 levels alone and the resulting classifier equation validates within a prospective validation cohort. Inflammation and immune pathway enrichment characterize the pre-transplant proteomic signature predictive of PGD.
Authors: Josef Stehlik; Leah B Edwards; Anna Y Kucheryavaya; Paul Aurora; Jason D Christie; Richard Kirk; Fabienne Dobbels; Axel O Rahmel; Marshall I Hertz Journal: J Heart Lung Transplant Date: 2010-10 Impact factor: 10.247
Authors: Elizabeth L Profita; Kimberlee Gauvreau; Peter Rycus; Ravi Thiagarajan; Tajinder P Singh Journal: J Heart Lung Transplant Date: 2019-01-24 Impact factor: 10.247
Authors: Aravind Subramanian; Pablo Tamayo; Vamsi K Mootha; Sayan Mukherjee; Benjamin L Ebert; Michael A Gillette; Amanda Paulovich; Scott L Pomeroy; Todd R Golub; Eric S Lander; Jill P Mesirov Journal: Proc Natl Acad Sci U S A Date: 2005-09-30 Impact factor: 11.205
Authors: Jennifer E Ho; Asya Lyass; Paul Courchesne; George Chen; Chunyu Liu; Xiaoyan Yin; Shih-Jen Hwang; Joseph M Massaro; Martin G Larson; Daniel Levy Journal: J Am Heart Assoc Date: 2018-07-13 Impact factor: 5.501
Authors: Lauren K Truby; Lydia Coulter Kwee; Richa Agarwal; Elizabeth Grass; Adam D DeVore; Chetan B Patel; Dongfeng Chen; Jacob N Schroder; Dawn Bowles; Carmelo A Milano; Svati H Shah; Christopher L Holley Journal: J Heart Lung Transplant Date: 2021-08-11 Impact factor: 10.247
Authors: Nicholas P Giangreco; Guillaume Lebreton; Susan Restaino; Maryjane Farr; Emmanuel Zorn; Paolo C Colombo; Jignesh Patel; Rajesh Kumar Soni; Pascal Leprince; Jon Kobashigawa; Nicholas P Tatonetti; Barry M Fine Journal: Sci Rep Date: 2022-08-19 Impact factor: 4.996