OBJECTIVE: To develop a diagnostic method with no morbidity or mortality for the detection of acute renal transplant rejection. SUMMARY BACKGROUND DATA: Rejection constitutes the major impediment to the success of transplantation. Currently available methods, including clinical presentation and biochemical organ function parameters, often fail to detect rejection until late stages of progression. Renal biopsies have associated morbidity and mortality and provide only a limited sample of the organ. METHODS: Thirty-four urine samples were collected from 32 renal transplant patients at various stages posttransplantation. Samples were collected from 17 transplant recipients with acute rejection and 15 patients with no rejection. Samples from patients less than 4 days posttransplant were omitted from data analysis due to the presence of excessive inflammatory response proteins. Rejection status was confirmed by kidney biopsy. Specimens were analyzed in triplicate using SELDI mass spectrometry. The obtained spectra were subjected to bioinformatic analysis using ProPeak as well as CART (Classification and Regression Tree) algorithms to identify rejection biomarker candidates. These candidates were identified by their molecular weight and ranked by their ability to distinguish between nonrejection and rejection based on receiver operating characteristic (ROC) analysis. The candidates with the highest area under the ROC curve (AUC) exhibited the best diagnostic performance. RESULTS: The best candidate biomarkers demonstrated highly successful diagnostic performance: 6.5 kd (AUC = 0.839, P <.0001), 6.7 kd (AUC = 0.839, P <.0001), 6.6 kd (AUC = 0.807, P <.0001), 7.1 kd (AUC = 0.807, P <.0001), and 13.4 kd (AUC = 0.804, P <.0001). A separate analysis using the CART algorithm in the Ciphergen Biomarker Pattern Software correctly classified 91% of the 34 specimens in the training set, giving a sensitivity of 83% and specificity of 100% using two separate biomarker candidates at 10.0 kd and 3.4 kd. CONCLUSIONS: Biomarker candidates exist in urine that have the ability to distinguish between renal transplant patients with no rejection and those with acute rejection. These biomarker candidates are the basis for development of a noninvasive method of diagnosing acute rejection without the morbidity and mortality associated with needle biopsy. The combination of biomarkers into a panel for diagnosis leads to the possibility of enhanced diagnostic performance.
OBJECTIVE: To develop a diagnostic method with no morbidity or mortality for the detection of acute renal transplant rejection. SUMMARY BACKGROUND DATA: Rejection constitutes the major impediment to the success of transplantation. Currently available methods, including clinical presentation and biochemical organ function parameters, often fail to detect rejection until late stages of progression. Renal biopsies have associated morbidity and mortality and provide only a limited sample of the organ. METHODS: Thirty-four urine samples were collected from 32 renal transplant patients at various stages posttransplantation. Samples were collected from 17 transplant recipients with acute rejection and 15 patients with no rejection. Samples from patients less than 4 days posttransplant were omitted from data analysis due to the presence of excessive inflammatory response proteins. Rejection status was confirmed by kidney biopsy. Specimens were analyzed in triplicate using SELDI mass spectrometry. The obtained spectra were subjected to bioinformatic analysis using ProPeak as well as CART (Classification and Regression Tree) algorithms to identify rejection biomarker candidates. These candidates were identified by their molecular weight and ranked by their ability to distinguish between nonrejection and rejection based on receiver operating characteristic (ROC) analysis. The candidates with the highest area under the ROC curve (AUC) exhibited the best diagnostic performance. RESULTS: The best candidate biomarkers demonstrated highly successful diagnostic performance: 6.5 kd (AUC = 0.839, P <.0001), 6.7 kd (AUC = 0.839, P <.0001), 6.6 kd (AUC = 0.807, P <.0001), 7.1 kd (AUC = 0.807, P <.0001), and 13.4 kd (AUC = 0.804, P <.0001). A separate analysis using the CART algorithm in the Ciphergen Biomarker Pattern Software correctly classified 91% of the 34 specimens in the training set, giving a sensitivity of 83% and specificity of 100% using two separate biomarker candidates at 10.0 kd and 3.4 kd. CONCLUSIONS: Biomarker candidates exist in urine that have the ability to distinguish between renal transplant patients with no rejection and those with acute rejection. These biomarker candidates are the basis for development of a noninvasive method of diagnosing acute rejection without the morbidity and mortality associated with needle biopsy. The combination of biomarkers into a panel for diagnosis leads to the possibility of enhanced diagnostic performance.
Authors: Alex J Rai; Zhen Zhang; Jason Rosenzweig; Ie-Ming Shih; Thang Pham; Eric T Fung; Lori J Sokoll; Daniel W Chan Journal: Arch Pathol Lab Med Date: 2002-12 Impact factor: 5.534
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