Serum Glycomics on Postoperative Day 7 Are Associated With Graft Loss Within 3 Months After Liver Transplantation Regardless of Early Allograft Dysfunction.
Journal
Transplantation
ISSN: 1534-6080
Titre abrégé: Transplantation
Pays: United States
ID NLM: 0132144
Informations de publication
Date de publication:
01 11 2021
01 11 2021
Historique:
pubmed:
5
12
2020
medline:
1
4
2022
entrez:
4
12
2020
Statut:
ppublish
Résumé
Prediction of outcome after liver transplantation (LT) is limited by the lack of robust predictors of graft failure. In this prospective study, we aimed to define a serum glycomic signature in the first week after LT that is associated with graft loss at 3 mo after LT. Patients were included between January 1, 2011, and February 28, 2017. Glycomic analysis was performed using DNA sequencer-associated fluorophore-associated capillary electrophoresis on a serum sample 1 wk after LT. Making use of Lasso regression, an optimal glycomic signature was identified associated with 3-mo graft survival. In this cohort of 131 patients, graft loss at 3 mo occurred in 14 patients (11.9%). The optimal mode, called the GlycoTransplantTest, yielded an area under the curve of 0.95 for association with graft loss at 3 mo. Using an optimized cutoff for this biomarker, sensitivity was 86% and specificity 89%. Negative predictive value was 98%. Odds ratio for graft loss at 3 mo was 70.211 (P < 0.001; 95% confidence interval, 10.876-453.231). A serum glycomic signature is highly associated with graft loss at 3 mo. It could support decision making in early retransplantation.
Sections du résumé
BACKGROUND
Prediction of outcome after liver transplantation (LT) is limited by the lack of robust predictors of graft failure. In this prospective study, we aimed to define a serum glycomic signature in the first week after LT that is associated with graft loss at 3 mo after LT.
METHODS
Patients were included between January 1, 2011, and February 28, 2017. Glycomic analysis was performed using DNA sequencer-associated fluorophore-associated capillary electrophoresis on a serum sample 1 wk after LT. Making use of Lasso regression, an optimal glycomic signature was identified associated with 3-mo graft survival.
RESULTS
In this cohort of 131 patients, graft loss at 3 mo occurred in 14 patients (11.9%). The optimal mode, called the GlycoTransplantTest, yielded an area under the curve of 0.95 for association with graft loss at 3 mo. Using an optimized cutoff for this biomarker, sensitivity was 86% and specificity 89%. Negative predictive value was 98%. Odds ratio for graft loss at 3 mo was 70.211 (P < 0.001; 95% confidence interval, 10.876-453.231).
CONCLUSIONS
A serum glycomic signature is highly associated with graft loss at 3 mo. It could support decision making in early retransplantation.
Identifiants
pubmed: 33273318
pii: 00007890-202111000-00024
doi: 10.1097/TP.0000000000003567
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2404-2410Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
X.V. and H.V.V. are coinventors on a patent owned by Ghent University (Belgium) for a glycomics-based biomarker for the prediction of primary nonfunction after LT. N.C. is a coinventor on a patent on GlycoCirrhoTest that is owned by VIB vzw and has been licensed to Helena Biosciences. The other authors declare no conflicts of interest. X.V. and H.V.V. participated in research design. X.V. and R.C. participated in the writing of the article. X.V., A.G., A.V., L.A.C., F.B., and X.R. participated in the performance of the research. X.V., L.M., and N.C. contributed new reagents or analytic tools. X.V., R.C., and H.V.V. participated in data analysis. X.V., A.G., A.V., H.D., L.A.C., L.M., F.B., N.C., and H.V.V. participated in reviewing the article.
Références
StarzL TE, Marchioro TL, Vonkaulla KN, et al. Homotransplantation of the liver in humans. Surg Gynecol Obstet. 1963;117:659–676.
EASL Clinical Practice Guidelines: liver transplantation. J Hepatol. 2016;64:433–485.
Adam R, Karam V, Cailliez V, et al.; all the other 126 contributing centers ( www.eltr.org ) and the European Liver and Intestine Transplant Association (ELITA). 2018 Annual Report of the European Liver Transplant Registry (ELTR)—50-year evolution of liver transplantation. Transpl Int. 2018;31:1293–1317.
Yoo PS, Umman V, Rodriguez-Davalos MI, et al. Retransplantation of the liver: review of current literature for decision making and technical considerations. Transplant Proc. 2013;45:854–859.
Pfitzmann R, Benscheidt B, Langrehr JM, et al. Trends and experiences in liver retransplantation over 15 years. Liver Transpl. 2007;13:248–257.
Nemes B, Gámán G, Polak WG, et al. Extended criteria donors in liver transplantation Part I: reviewing the impact of determining factors. Expert Rev Gastroenterol Hepatol. 2016;10:827–839.
Feng S, Goodrich NP, Bragg-Gresham JL, et al. Characteristics associated with liver graft failure: the concept of a donor risk index. Am J Transplant. 2006;6:783–790.
Schrem H, Reichert B, Frühauf N, et al. The Donor-Risk-Index, ECD-Score and D-MELD-Score all fail to predict short-term outcome after liver transplantation with acceptable sensitivity and specificity. Ann Transplant. 2012;17:5–13.
Braat AE, Blok JJ, Putter H, et al.; European Liver and Intestine Transplant Association (ELITA) and Eurotransplant Liver Intestine Advisory Committee (ELIAC). The Eurotransplant donor risk index in liver transplantation: ET-DRI. Am J Transplant. 2012;12:2789–2796.
Jacob M, Copley LP, Lewsey JD, et al.; UK and Ireland Liver Transplant Audit. Pretransplant MELD score and post liver transplantation survival in the UK and Ireland. Liver Transpl. 2004;10:903–907.
Hayashi PH, Forman L, Steinberg T, et al. Model for end-stage liver disease score does not predict patient or graft survival in living donor liver transplant recipients. Liver Transpl. 2003;9:737–740.
Wagener G, Raffel B, Young AT, et al. Predicting early allograft failure and mortality after liver transplantation: the role of the postoperative model for end-stage liver disease score. Liver Transpl. 2013;19:534–542.
Bolondi G, Mocchegiani F, Montalti R, et al. Predictive factors of short term outcome after liver transplantation: a review. World J Gastroenterol. 2016;22:5936–5949.
Olthoff KM, Kulik L, Samstein B, et al. Validation of a current definition of early allograft dysfunction in liver transplant recipients and analysis of risk factors. Liver Transpl. 2010;16:943–949.
Robertson FP, Bessell PR, Diaz-Nieto R, et al. High serum aspartate transaminase levels on day 3 postliver transplantation correlates with graft and patient survival and would be a valid surrogate for outcome in liver transplantation clinical trials. Transpl Int. 2016;29:323–330.
Wu JF, Wu RY, Chen J, et al. Early lactate clearance as a reliable predictor of initial poor graft function after orthotopic liver transplantation. Hepatobiliary Pancreat Dis Int. 2011;10:587–592.
Zulian MC, Chedid MF, Chedid AD, et al. Low serum factor V level: early predictor of allograft failure and death following liver transplantation. Langenbecks Arch Surg. 2015;400:589–597.
Lesurtel M, Raptis DA, Melloul E, et al. Low platelet counts after liver transplantation predict early posttransplant survival: the 60-5 criterion. Liver Transpl. 2014;20:147–155.
Olmedilla L, Lisbona CJ, Pérez-Peña JM, et al. Early Measurement of indocyanine green clearance accurately predicts short-term outcomes after liver transplantation. Transplantation. 2016;100:613–620.
Schneider L, Spiegel M, Latanowicz S, et al. Noninvasive indocyanine green plasma disappearance rate predicts early complications, graft failure or death after liver transplantation. Hepatobiliary Pancreat Dis Int. 2011;10:362–368.
Lock JF, Schwabauer E, Martus P, et al. Early diagnosis of primary nonfunction and indication for reoperation after liver transplantation. Liver Transpl. 2010;16:172–180.
Verhelst X, Dias AM, Colombel JF, et al. Protein glycosylation as a diagnostic and prognostic marker of chronic inflammatory gastrointestinal and liver diseases. Gastroenterology. 2020;158:95–110.
Verhelst X, Geerts A, Callewaert N, et al. The potential of glycomics as prognostic biomarkers in liver disease and liver transplantation. Acta Gastroenterol Belg. 2019;82:309–313.
Laroy W, Contreras R, Callewaert N. Glycome mapping on DNA sequencing equipment. Nat Protoc. 2006;1:397–405.
Callewaert N, Van Vlierberghe H, Van Hecke A, et al. Noninvasive diagnosis of liver cirrhosis using DNA sequencer-based total serum protein glycomics. Nat Med. 2004;10:429–434.
Vanderschaeghe D, Laroy W, Sablon E, et al. GlycoFibroTest is a highly performant liver fibrosis biomarker derived from DNA sequencer-based serum protein glycomics. Mol Cell Proteomics. 2009;8:986–994.
Liu XE, Desmyter L, Gao CF, et al. N-glycomic changes in hepatocellular carcinoma patients with liver cirrhosis induced by hepatitis B virus. Hepatology. 2007;46:1426–1435.
Debruyne EN, Vanderschaeghe D, Van Vlierberghe H, et al. Diagnostic value of the hemopexin N-glycan profile in hepatocellular carcinoma patients. Clin Chem. 2010;56:823–831.
Blomme B, Francque S, Trépo E, et al. N-glycan based biomarker distinguishing non-alcoholic steatohepatitis from steatosis independently of fibrosis. Dig Liver Dis. 2012;44:315–322.
Blomme B, Fitzpatrick E, Quaglia A, et al. Serum protein N-glycosylation in paediatric non-alcoholic fatty liver disease. Pediatr Obes. 2012;7:165–173.
Vanderschaeghe D, Meuris L, Raes T, et al. Endoglycosidase S enables a highly simplified clinical chemistry procedure for direct assessment of serum IgG undergalactosylation in chronic inflammatory disease. Mol Cell Proteomics. 2018;17:2508–2517.
Verhelst X, Vanderschaeghe D, Castéra L, et al. A glycomics-based test predicts the development of hepatocellular carcinoma in cirrhosis. Clin Cancer Res. 2017;23:2750–2758.
Verhelst X, Geerts A, Jochmans I, et al. Glycome patterns of perfusate in livers before transplantation associate with primary nonfunction. Gastroenterology. 2018;154:1361–1368.
Vanderschaeghe D, Guttman A, Callewaert N. High-throughput profiling of the serum N-glycome on capillary electrophoresis microfluidics systems. Methods Mol Biol. 2013;919:87–96.
Blomme B, Van Steenkiste C, Callewaert N, et al. Alteration of protein glycosylation in liver diseases. J Hepatol. 2009;50:592–603.
Mehta AS, Long RE, Comunale MA, et al. Increased levels of galactose-deficient anti-Gal immunoglobulin G in the sera of hepatitis C virus-infected individuals with fibrosis and cirrhosis. J Virol. 2008;82:1259–1270.
Friedman BH, Wolf JH, Wang L, et al. Serum cytokine profiles associated with early allograft dysfunction in patients undergoing liver transplantation. Liver Transpl. 2012;18:166–176.
Oweira H, Lahdou I, Daniel V, et al. Early post-operative acute phase response in patients with early graft dysfunction is predictive of 6-month and 12-month mortality in liver transplant recipients. Hum Immunol. 2016;77:952–960.
Santiago F, Bueno P, Olmedo C, et al. Time course of intraoperative cytokine levels in liver transplant recipients. Transplant Proc. 2006;38:2492–2494.
Yao M, Zhou DP, Jiang SM, et al. Elevated activity of N-acetylglucosaminyltransferase V in human hepatocellular carcinoma. J Cancer Res Clin Oncol. 1998;124:27–30.
Miyoshi E, Ihara Y, Nishikawa A, et al. Gene expression of N-acetylglucosaminyltransferases III and V: a possible implication for liver regeneration. Hepatology. 1995;22:1847–1855.
Pareja E, Cortes M, Hervás D, et al. A score model for the continuous grading of early allograft dysfunction severity. Liver Transpl. 2015;21:38–46.