Identifying biomarkers as diagnostic tools in kidney transplantation

References

• Hariharan S, Johnson CP, Bresnahan BA, Taranto SE, McIntosh MJ, Stablein D. Improved graft survival after renal transplantation in the United States, 1988 to 1996. N. Engl. J. Med.342(9), 605–612 (2000).
   PubMed Web of Science ®Google Scholar
• Meier-Kriesche HU, Schold JD, Srinivas TR, Kaplan B. Lack of improvement in renal allograft survival despite a marked decrease in acute rejection rates over the most recent era. Am. J. Transplant.4(3), 378–383 (2004).
   PubMed Web of Science ®Google Scholar
• Solez K, Vincenti F, Filo RS. Histopathologic findings from 2-year protocol biopsies from a U.S. multicenter kidney transplant trial comparing tarolimus versus cyclosporine: a report of the FK506 Kidney Transplant Study Group. Transplantation66(12), 1736–1740 (1998).
   PubMed Web of Science ®Google Scholar
• Cunningham MJ. Genomics and proteomics: the new millennium of drug discovery and development. J. Pharmacol. Toxicol. Methods44(1), 291–300 (2000).
   PubMed Web of Science ®Google Scholar
• Ashton-Chess J, Giral M, Soulillou JP, Brouard S. Using biomarkers of tolerance and rejection to identify high- and low-risk patients following kidney transplantation. Transplantation87(9 Suppl.), S95–S99 (2009).
   Google Scholar
• Keown PA, McMaster WR, McManus BM. Tools to identify organ rejection and immune quiescence for biological understanding and personalized medical care. Biomark. Med.4(1), 115–121 (2010).
   PubMedGoogle Scholar
• Sigdel TK, Sarwal MM. The proteogenomic path towards biomarker discovery. Pediatr. Transplant.12(7), 737–747 (2008).
   PubMed Web of Science ®Google Scholar
• Biomarkers Definitions Working Group. Biomarkers and surrogate end points: preferred definitions and conceptual framework. Clin. Pharmacol. Ther.69, 89–95 (2001).
   PubMed Web of Science ®Google Scholar
• Gillespie A, Lee IJ. Biomarkers in renal transplantation. Biomark. Med.2(6), 603–612 (2008).
   PubMedGoogle Scholar
• Fitzsimmons W, Thompson D, Hariharan S, Van Veldhuisen P. Serum creatinine as a surrogate end point for graft loss in kidney transplantation: validation efforts from multicentre trials. Am. J. Transplant.2(Suppl. 3), 272 (2002).
   Google Scholar
• Kaplan B, Schold J, Srinivas T et al. Effect of sirolimus withdrawal in patients with deteriorating renal function. Am. J. Transplant.4(10), 1709–1712 (2004).
   PubMedGoogle Scholar
• Schold JD, Kaplan B. The elephant in the room: failings of current clinical end points in kidney transplantation. Am. J. Transplant.10(5), 1163–1166 (2010).
   PubMed Web of Science ®Google Scholar
• Poggio ED, Batty DS, Flechner SM. Evaluation of renal function in transplantation. Transplantation84(2), 131–136 (2007).
   PubMedGoogle Scholar
• Goerdt PJ, Heim-Duthoy KL, Macres M, Swan SK. Predictive performance of renal function estimate equations in renal allografts. Br. J. Clin. Pharmacol.44, 261–265 (1997).
   PubMedGoogle Scholar
• Stoves J, Lindley EJ, Barnfield MC, Burniston MT, Newstead CG. MDRD equation estimates of glomerular filtration rate in potential living kidney donors and renal transplant recipients with impaired graft function. Nephrol. Dial. Transplant.17, 2036–2037 (2002).
   PubMed Web of Science ®Google Scholar
• Manotham K, Booranalertpaisarn V, Eiam-Ong S, Chusil S, Praditpornsilpa K, Tungsanga K. Accurately simple estimation of glomerular filtration rate in kidney transplant patients. Transplant. Proc.34, 1148–1151 (2002).
   PubMedGoogle Scholar
• Mariat C, Alamartine E, Barthelemy JC et al. Assessing renal graft function in clinical trials: can tests predicting glomerular filtration rate substitute for a reference method? Kidney Int.65(1), 289–297 (2004).
   PubMedGoogle Scholar
• Gaspari F, Ferrari S, Stucchi N et al. Performance of different prediction equations for estimating renal function in kidney transplantation. Am. J. Transplant.4(11), 1826–1835 (2004).
   PubMed Web of Science ®Google Scholar
• Bosma RJ, Doorenbos CR, Stegeman CA, van der Heide JJ, Navis G. Predictive performance of renal function equations in renal transplant recipients: an analysis of patient factors in bias. Am. J. Transplant.5(9), 2193–2203 (2005).
   PubMedGoogle Scholar
• Pascual M, Vallhonrat H, Cosimi AB et al. The clinical usefulness of the renal allograft biopsy in the cyclosporine era: a prospective study. Transplantation67(5), 737–741 (1999).
   PubMedGoogle Scholar
• Furness PN. Predicting allograft survival: abundant data, but insufficient knowledge? Transplantation83(6), 681 (2007).
   PubMedGoogle Scholar
• Rush D. Can protocol biopsy better inform our choices in renal transplantation? Transplant. Proc.41(6 Suppl.), S6–S8 (2009).
   PubMedGoogle Scholar
• Piovesan AC, Lucon AM, David DS, Nahas WC, Antonopoulos IM, Srougi M. Multifocal renal allograft biopsy: impact on therapeutic decisions. Transplant. Proc.40(10), 3397–3400 (2008).
   PubMedGoogle Scholar
• Strimbu K, Tavel JA. What are biomarkers? Curr. Opin. HIV AIDS5(6), 463–466 (2010).
   PubMed Web of Science ®Google Scholar
• De Gruttola VG, Clax P, DeMets DL et al. Considerations in the evaluation of surrogate end points in clinical trials. Summary of a National Institutes of Health workshop. Control Clin. Trials22(5), 485–502 (2001).
   PubMedGoogle Scholar
• Daemen A, Gevaert O, Ojeda F et al. A kernel-based integration of genome-wide data for clinical decision support. Genome Med.1(4), 39 (2009).
   PubMedGoogle Scholar
• Losko S, Heumann K. Semantic data integration and knowledge management to represent biological network associations. Methods Mol. Biol.563, 241–258 (2009).
   PubMedGoogle Scholar
• Furness PN. Predicting allograft survival: abundant data, but insufficient knowledge? Transplantation83(6), 681 (2007).
   PubMedGoogle Scholar
• Kong X, Mas V, Archer KJ. A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy. BMC Genomics26(9), 98 (2008).
   Google Scholar
• Morgan AA, Khatri P, Jones RH, Sarwal MM, Butte AJ. Comparison of multiplex meta analysis techniques for understanding the acute rejection of solid organ transplants. BMC Bioinformatics11(Suppl. 9), S6 (2010).
   PubMedGoogle Scholar
• Hockley SL, Mathijs K, Staal YC et al. Interlaboratory and interplatform comparison of microarray gene expression analysis of HepG2 cells exposed to benzo(a)pyrene. OMICS13(2), 115–125 (2009).
   PubMed Web of Science ®Google Scholar
• Pedotti P, ‘t Hoen PA, Vreugdenhil E et al. Can subtle changes in gene expression be consistently detected with different microarray platforms? BMC Genomics10(9), 124 (2008).
   Google Scholar
• Fan J, Feng Y, Niu YS. Nonparametric estimation of genewise variance for microarray data. Ann. Stat.201038(5), 2723–2750 (2010).
   Google Scholar
• Shi L, Campbell G, Jones WD et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat. Biotechnol.28(8), 827–838 (2010).
   PubMed Web of Science ®Google Scholar
• Sato F, Tsuchiya S, Terasawa K, Tsujimoto G. Intra-platform repeatability and inter-platform comparability of microRNA microarray technology. PLoS One4(5), e5540 (2009).
   PubMed Web of Science ®Google Scholar
• Mao S, Wang C, Dong G. Evaluation of inter-laboratory and cross-platform concordance of DNA microarrays through discriminating genes and classifier transferability. J. Bioinform. Comput. Biol.7(1), 157–173 (2009).
   PubMedGoogle Scholar
• Sarwal MM. Deconvoluting the ‘omics’ for organ transplantation. Curr. Opin. Organ Transplant.14(5), 544–551 (2009).
   PubMedGoogle Scholar
• Irish WD, Ilsley JN, Schnitzler MA, Feng S, Brennan DC. A risk prediction model for delayed graft function in the current era of deceased donor renal transplantation. Am. J. Transplant.10(10), 2279–2286 (2010).
   PubMed Web of Science ®Google Scholar
• Bartlett ST, Schweitzer EJ, Cooper M. Prediction model for delayed kidney transplant function: no need for new regulation. Am. J. Transplant.10(10), 2191–2192 (2010).
   PubMedGoogle Scholar
• Mühlberger I, Perco P, Fechete R, Mayer B, Oberbauer R. Biomarkers in renal transplantation ischemia reperfusion injury. Transplantation88(3 Suppl.), S14–S19 (2009).
   Google Scholar
• Basile DP. Novel approaches in the investigation of acute kidney injury. J. Am. Soc. Nephrol.18(1), 7–9 (2007).
   PubMed Web of Science ®Google Scholar
• Hauser P, Schwarz C, Mitterbauer C et al. Genome-wide gene-expression patterns of donor kidney biopsies distinguish primary allograft function. Lab. Invest.84(3), 353–361 (2004).
   PubMedGoogle Scholar
• Kainz A, Mitterbauer C, Hauser P et al. Alterations in gene expression in cadaveric vs. live donor kidneys suggest impaired tubular counterbalance of oxidative stress at implantation. Am. J. Transplant.4(10), 1595–1604 (2004).
   PubMedGoogle Scholar
• Mueller TF, Reeve J, Jhangri GS et al. The transcriptome of the implant biopsy identifies donor kidneys at increased risk of delayed graft function. Am. J. Transplant.8(1), 78–85 (2008).
   PubMedGoogle Scholar
• Mas VR, Archer KJ, Yanek K et al. Gene expression patterns in deceased donor kidneys developing delayed graft function after kidney transplantation. Transplantation85(4), 626–635 (2008).
   PubMed Web of Science ®Google Scholar
• Naesens M, Sarwal M. Looking into the crystal chip: can microarrays predict graft function? Transplantation85(4), 499–500 (2008).
   PubMedGoogle Scholar
• Korbély R, Wilflingseder J, Perco P et al. Molecular biomarker candidates of acute kidney injury in zero-hour renal transplant needle biopsies. Transpl. Int.24(2), 143–149 (2010).
   PubMedGoogle Scholar
• Kainz A, Wilflingseder J, Mitterbauer C et al. Steroid pretreatment of organ donors to prevent postischemic renal allograft failure: a randomized, controlled trial. Ann. Intern. Med.153(4), 222–230 (2010).
   PubMedGoogle Scholar
• Wilflingseder J, Kainz A, Mühlberger I et al. Impaired metabolism in donor kidney grafts after steroid pretreatment. Transpl. Int.23(8), 796–804 (2010).
   PubMedGoogle Scholar
• Hall IE, Yarlagadda SG, Coca SG et al. IL-18 and urinary NGAL predict dialysis and graft recovery after kidney transplantation. J. Am. Soc. Nephrol.21(1), 189–197 (2010).
   PubMed Web of Science ®Google Scholar
• Mishra J, Ma Q, Kelly C et al. Kidney NGAL is a novel early marker of acute injury following transplantation. Pediatr. Nephrol.21(6), 856–863 (2006).
   PubMed Web of Science ®Google Scholar
• Zhang PL, Rothblum LI, Han WK, Blasick TM, Potdar S, Bonventre JV. Kidney injury molecule-1 expression in transplant biopsies is a sensitive measure of cell injury. Kidney Int.73(5), 608–614 (2008).
   PubMed Web of Science ®Google Scholar
• Nijboer WN, Schuurs TA, Damman J et al. Kidney injury molecule-1 is an early noninvasive indicator for donor brain death-induced injury prior to kidney transplantation. Am. J. Transplant.9(8), 1752–1759 (2009).
   PubMed Web of Science ®Google Scholar
• Coca SG, Yalavarthy R, Concato J, Parikh CR. Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. Kidney Int.73(9), 1008–1016 (2008).
   PubMed Web of Science ®Google Scholar
• Hartono C, Muthukumar T, Suthanthiran M. Noninvasive diagnosis of acute rejection of renal allografts. Curr. Opin. Organ Transplant.15(1), 35–41 (2010).
   PubMed Web of Science ®Google Scholar
• Solez K, Colvin RB, Racusen LC et al. Banff 07 classification of renal allograft pathology: updates and future directions. Am. J. Transplant.8, 753–760 (2008).
   PubMed Web of Science ®Google Scholar
• Sorof JM, Vartanian RK, Olson JL et al. Histopathological concordance of paired renal allograft biopsy cores. Effect on the diagnosis and management of acute rejection. Transplantation60, 1215–1219 (1995).
   PubMed Web of Science ®Google Scholar
• Furness PN, Taub N. International variation in the interpretation of renal transplant biopsies: report of the CERTRAP project. Kidney Int.60, 1998–2012 (2001).
   PubMed Web of Science ®Google Scholar
• Sarwal M, Fernandez-Fresnedo G, Rodrigo E et al. Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N. Engl. J. Med.349(2), 125–138 (2003).
   PubMed Web of Science ®Google Scholar
• Reeve J, Einecke G, Mengel M et al. Diagnosing rejection in renal transplants: a comparison of molecular- and histopathology-based approaches. Am. J. Transplant.9(8), 1802–1810 (2009).
   PubMed Web of Science ®Google Scholar
• Desvaux D, Schwarzinger M, Pastural M et al. Molecular diagnosis of renal-allograft rejection: correlation with histopathologic evaluation and antirejection-therapy resistance. Transplantation78(5), 647–653 (2004).
   PubMed Web of Science ®Google Scholar
• Chua MS, Mansfield E, Sarwal M. Applications of microarrays to renal transplantation: progress and possibilities. Front. Biosci.8, S913–S923 (2003).
   Google Scholar
• Jurcevic S, Sacks S. Gene microarrays in transplantation. Curr. Opin. Nephrol. Hypertens.12(6), 577–579 (2003).
   PubMedGoogle Scholar
• Ying L, Sarwal M. In praise of arrays. Pediatr. Nephrol.24(9), 1643–1659 (2009).
   PubMed Web of Science ®Google Scholar
• Flechner SM, Kurian SM, Head SR et al. Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes. Am. J. Transplant.4(9), 1475–1489 (2004).
   PubMed Web of Science ®Google Scholar
• Li B, Hartono C, Ding R et al. Noninvasive diagnosis of renal-allograft rejection by measurement of messenger RNA for perforin and granzyme B in urine. N. Engl. J. Med.344, 947–954 (2001).
   PubMed Web of Science ®Google Scholar
• Yannaraki M, Rebibou JM, Ducloux D et al. Urinary cytotoxic molecular markers for a noninvasive diagnosis in acute renal transplant rejection. Transpl. Int.19(9), 759–768 (2006).
   PubMed Web of Science ®Google Scholar
• Vasconcellos LM, Schachter AD, Zheng XX et al. Cytotoxic lymphocyte gene expression in peripheral blood leukocytes correlates with rejecting renal allografts. Transplantation66(5), 562–566 (1998).
   PubMed Web of Science ®Google Scholar
• Ting YT, Coates PT, Marti HP et al. Urinary soluble HLA-DR is a potential biomarker for acute renal transplant rejection. Transplantation89(9), 1071–1078 (2010).
   PubMedGoogle Scholar
• Nickerson P. Post-transplant monitoring of renal allografts: are we there yet? Curr. Opin. Immunol.21(5), 563–568 (2009).
   PubMedGoogle Scholar
• Schaub S, Wilkins JA, Nickerson P. Proteomics and renal transplantation: searching for novel biomarkers and therapeutic targets. Contrib. Nephrol.160, 65–75 (2008).
   PubMed Web of Science ®Google Scholar
• Schaub S, Rush D, Wilkins J et al. Proteomic-based detection of urine proteins associated with acute renal allograft rejection. J. Am. Soc. Nephrol.15(1), 219–227 (2004).
   PubMed Web of Science ®Google Scholar
• Sigdel TK, Kaushal A, Gritsenko M et al. Shotgun proteomics identifies proteins specific for acute renal transplant rejection. Proteomics Clin. Appl.4(1), 32–47 (2010).
   PubMed Web of Science ®Google Scholar
• Tian J, Shi WF, Zhang LW et al. HLA class I (ABC) upregulation on peripheral blood CD3+/CD8+ T lymphocyte surface is a potential predictor of acute rejection in renal transplantation. Transplantation88(12), 1393–1397 (2009).
   PubMedGoogle Scholar
• Hoefig KP, Heissmeyer V. MicroRNAs grow up in the immune system. Curr. Opin. Immunol.20, 281–287 (2008).
   PubMed Web of Science ®Google Scholar
• Lodish HF, Zhou B, Liu G et al. Micromanagement of the immune system by microRNAs. Nat. Rev. Immunol.8, 120–130 (2008).
   PubMed Web of Science ®Google Scholar
• Valencia-Sanchez MA, Liu J, Hannon GJ et al. Control of translational and mRNA degradation by miRNAs and siRNAs. Genes Dev.20, 515–524 (2006).
   PubMed Web of Science ®Google Scholar
• Sui W, Dai Y, Huang Y, Lan H, Yan Q, Huang H. Microarray analysis of MicroRNA expression in acute rejection after renal transplantation. Transpl. Immunol.19(1), 81–85 (2008).
   PubMed Web of Science ®Google Scholar
• Anglicheau D, Sharma VK, Ding R et al. MicroRNA expression profiles predictive of human renal allograft status. Proc. Natl Acad. Sci. USA106, 5330–5335 (2009).
   PubMed Web of Science ®Google Scholar
• Anglicheau D, Muthukumar T, Suthanthiran M. MicroRNAs: small RNAs with big effects. Transplantation90(2), 105–112 (2010).
   PubMed Web of Science ®Google Scholar
• Serón D, Moreso F, Ramón JM et al. Protocol renal allograft biopsies and the design of clinical trials aimed to prevent or treat chronic allograft nephropathy. Transplantation69(9), 1849–1855 (2000).
   PubMedGoogle Scholar
• de Matos AC, Câmara NO, de Oliveira AF et al. Functional and morphologic evaluation of kidney proximal tubuli and correlation with renal allograft prognosis. Transpl. Int.23(5), 493–499 (2010).
   PubMedGoogle Scholar
• Vazquez MA, Jeyarajah DR, Kielar ML, Lu CY. Long-term outcomes of renal transplantation: a result of the original endowment of the donor kidney and the inflammatory response to both alloantigens and injury. Curr. Opin. Nephrol. Hypertens.9(6), 643–648 (2000).
   PubMedGoogle Scholar
• Giral M, Nguyen JM, Karam G et al. Impact of graft mass on the clinical outcome of kidney transplants. J. Am. Soc. Nephrol.16(1), 261–268 (2005).
   PubMedGoogle Scholar
• Nankivell BJ, Borrows RJ, Fung CL, O’Connell PJ, Allen RD, Chapman JR. The natural history of chronic allograft nephropathy. N. Engl. J. Med.349(24), 2326–2333 (2003).
   PubMedGoogle Scholar
• 2008 Annual Report of the US Organ Procurement and Transplantation Network and the Scientific Registry of Transplant Recipients: transplant data 1998–2007. US Department of Health and Human Services, Health Resources and Services Administration, Healthcare Systems Bureau, Division of Transplantation, Rockville, MD, USA (2008).
   Google Scholar
• Halloran PF, Langone AJ, Helderman JH, Kaplan B. Assessing long-term nephron loss: is it time to kick the CAN grading system? Am. J. Transplant.4(11), 1729–1730 (2004).
   PubMed Web of Science ®Google Scholar
• Mengel M. The kidney transplant: new horizons. Curr. Opin. Nephrol. Hypertens.19(3), 260–265 (2010).
   PubMedGoogle Scholar
• Bestard O, Cruzado JM, la Franquesa M, Grinyó JM. Biomarkers in renal transplantation. Curr. Opin. Organ Transplant.15(4), 467–473 (2010).
   PubMedGoogle Scholar
• Serón D, Moreso F, Ramón JM et al. Protocol renal allograft biopsies and the design of clinical trials aimed to prevent or treat chronic allograft nephropathy. Transplantation69(9), 1849–1855 (2000).
   PubMedGoogle Scholar
• de Matos AC, Câmara NO, de Oliveira AF et al. Functional and morphologic evaluation of kidney proximal tubuli and correlation with renal allograft prognosis. Transpl. Int.23(5), 493–499 (2010).
   PubMedGoogle Scholar
• Rosen S, Stillman IE. Acute tubular necrosis is a syndrome of physiologic and pathologic dissociation. J. Am. Soc. Nephrol.19(5), 871–875 (2008).
   PubMed Web of Science ®Google Scholar
• Schlondorff DO. Overview of factors contributing to the pathophysiology of progressive renal disease. Kidney Int.74(7), 860–866 (2008).
   PubMed Web of Science ®Google Scholar
• Schröppel B, Heeger PS. Gazing into a crystal ball to predict kidney transplant outcome. J. Clin. Invest.120(6), 1803–1806 (2010).
   PubMedGoogle Scholar
• Mas VR, Archer KJ, Scian M, Maluf DG. Molecular pathways involved in loss of graft function in kidney transplant recipients. Expert Rev. Mol. Diagn.10(3), 269–284 (2010).
   PubMed Web of Science ®Google Scholar
• Scherer A, Gwinner W, Mengel M et al. Transcriptome changes in renal allograft protocol biopsies at 3 months precede the onset of interstitial fibrosis/tubular atrophy (IF/TA) at 6 months. Nephrol. Dial.Transplant.24(8), 2567–2575 (2009).
   PubMedGoogle Scholar
• Scherer A, Krause A, Walker JR, Korn A, Niese D, Raulf F. Early prognosis of the development of renal chronic allograft rejection by gene expression profiling of human protocol biopsies. Transplantation75(8), 1323–1330 (2003).
   PubMedGoogle Scholar
• Hotchkiss H, Chu TT, Hancock WW et al. Differential expression of profibrotic and growth factors in chronic allograft nephropathy. Transplantation81(3), 342–349 (2006).
   PubMedGoogle Scholar
• Mas V, Maluf D, Archer K et al. Establishing the molecular pathways involved in chronic allograft nephropathy for testing new noninvasive diagnostic markers. Transplantation83(4), 448–457 (2007).
   PubMed Web of Science ®Google Scholar
• Maluf DG, Mas VR, Archer KJ et al. Molecular pathways involved in loss of kidney graft function with tubular atrophy and interstitial fibrosis. Mol. Med.14(5–6), 276–285 (2008).
   PubMedGoogle Scholar
• Mengel M, Reeve J, Bunnag S, Einecke G et al. Molecular correlates of scarring in kidney transplants: the emergence of mast cell transcripts. Am. J. Transplant.9(1), 169–178 (2009).
   PubMedGoogle Scholar
• Einecke G, Reeve J, Sis B et al. A molecular classifier for predicting future graft loss in late kidney transplant biopsies. J. Clin. Invest.120(6), 1862–1872 (2010).
   PubMed Web of Science ®Google Scholar
• Park WD, Griffin MD, Cornell LD, Cosio FG, Stegall MD. Fibrosis with inflammation at one year predicts transplant functional decline. J. Am. Soc. Nephrol.21(11), 1987–1997 (2010).
   PubMedGoogle Scholar
• Kurian SM, Heilman R, Mondala TS et al. Biomarkers for early and late stage chronic allograft nephropathy by proteogenomic profiling of peripheral blood. PLoS One4(7), e6212 (2009).
   Web of Science ®Google Scholar
• Mas VR, Mas LA, Archer KJ et al. Evaluation of gene panel mRNAs in urine samples of kidney transplant recipients as a non-invasive tool of graft function. Mol. Med.13(5–6), 315–324 (2007).
   PubMed Web of Science ®Google Scholar
• Schröppel B, Heeger PS. Gazing into a crystal ball to predict kidney transplant outcome. J. Clin. Invest.120(6), 1803–1806 (2010).
   PubMedGoogle Scholar
• Afzali B, Lombardi G, Lechler RI. Pathways of major histocompatibility complex allorecognition. Curr. Opin. Organ Transplant.13, 438–444 (2008).
   PubMed Web of Science ®Google Scholar
• Hernandez-Fuentes MP, Lechler RI. A ‘biomarker signature’ for tolerance in transplantation. Nat. Rev. Nephrol.6(10), 606–613 (2010).
   PubMedGoogle Scholar
• Newell KA, Asare A, Kirk AD et al. Immune Tolerance Network ST507 Study Group. Identification of a B cell signature associated with renal transplant tolerance in humans. J. Clin. Invest.120(6), 1836–1847 (2010).
   PubMed Web of Science ®Google Scholar
• Sagoo P, Perucha E, Sawitzki B. Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans. J. Clin. Invest.120(6), 1848–1861 (2010).
   PubMed Web of Science ®Google Scholar
• Zaza G, Granata S, Sallustio F, Grandaliano G, Schena FP.Pharmacogenomics: a new paradigm to personalize treatments in nephrology patients. Clin. Exp. Immunol.159(3), 268–280 (2010).
   PubMed Web of Science ®Google Scholar
• Goldfarb-Rumyantzev AS, Naiman N. Genetic prediction of renal transplant outcome. Curr. Opin. Nephrol. Hypertens.17(6), 573–579 (2008).
   PubMed Web of Science ®Google Scholar
• McKnight AJ, Currie D, Maxwell AP. Unravelling the genetic basis of renal diseases; from single gene to multifactorial disorders. J. Pathol.220(2), 198–216 (2010).
   PubMedGoogle Scholar
• Little J, Higgins JP, Ioannidis JP et al. Strengthening the Reporting of Genetic Association Studies (STREGA) – an extension of the STROBE statement. Genet. Epidemiol.33, 581–598 (2009).
   PubMed Web of Science ®Google Scholar