Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Journal of Laboratory Medicine

Official Journal of the German Society of Clinical Chemistry and Laboratory Medicine

Editor-in-Chief: Schuff-Werner, Peter

Ed. by Ahmad-Nejad, Parviz / Bidlingmaier, Martin / Bietenbeck, Andreas / Conrad, Karsten / Findeisen, Peter / Fraunberger, Peter / Ghebremedhin, Beniam / Holdenrieder, Stefan / Kiehntopf, Michael / Klein, Hanns-Georg / Kohse, Klaus P. / Kratzsch, Jürgen / Luppa, Peter B. / Meyer, Alexander von / Nebe, Carl Thomas / Orth, Matthias / Röhrig-Herzog, Gabriele / Sack, Ulrich / Steimer, Werner / Weber, Thomas / Wieland, Eberhard / Winter, Christof / Zettl, Uwe K.


IMPACT FACTOR 2018: 0.389

CiteScore 2018: 0.22

SCImago Journal Rank (SJR) 2018: 0.156
Source Normalized Impact per Paper (SNIP) 2018: 0.089

Online
ISSN
2567-9449
See all formats and pricing
More options …
Volume 38, Issue 6

Issues

Biomarker im Blut zur Individualisierung der immunsuppressiven Therapie nach der Transplantation solider Organe

Blood-based biomarkers to individualize immunosuppression after solid organ transplantation

Eberhard Wieland / Maria Shipkova
Published Online: 2014-12-02 | DOI: https://doi.org/10.1515/labmed-2014-0043

Zusammenfassung

Die Erfolge der Transplantationsmedizin hängen eng mit der Entwicklung potenter Immunsuppressiva zusammen. Die Steuerung der Therapie mit den Calineurininhibitoren Ciclosporin und Tacrolimus sowie den mTOR (Nammalian target of rapamycin)-Inhibitoren Sirolimus und Everolimus wird durch ein therapeutisches Drug Monitoring (TDM) unterstützt. Für andere Medikamente wie z.B. die Mycophenolsäure ist ein TDM bisher nicht etabliert. Es hat sich aber gezeigt, dass eine bessere Individualisierung der Therapie dabei helfen könnte eine Über- oder Unterimmunsuppression bei einzelnen Patienten zu vermeiden und das immer noch entäuschende Langzeitüberleben der Organe und Patienten zu verbessern. Daher werden weltweit Biomarker gesucht, die als Ergänzung zum TDM eine bessere Individualisierung der Therapie ermöglichen. Dabei kann es sich um pharmakodynamische Biomarker handeln, die unspezifisch den globalen Effekt der Therapie auf das Immunsystem erfassen oder, um Marker die sehr spezifisch direkt den phramakologischen Effekt messen. Unspezifische Biomarker verfolgen entweder den Grad der Immunaktivierung durch Messung der T- und B-Zellaktivierung, die Toleranzentwicklung z.B. durch Erfassung regulatorischer T-Zellen oder den Transplantschaden z.B. durch die Messung von DNS, die aus dem Spenderorgan in das Blut des Empfänger freigesetzt wird. Spezifische pharmakodynamische Biomarker wurden u.a. für die Bestimmung der pharmakologischen Wirkung der Mycophenolsäure, von Calineurininhibitoren und von mTOR-Inhibitoren publiziert. Das Feld der Biomarker als Ergänzung zum TDM für die Individualisierung der immunsuppressiven Therapie ist noch in den Anfängen und es werden prospektive Studien benötigt, die das Potenzial von Biomarkern oder Biomarkerkombinationen in verschiedenen Patientenkollektiven mit unterschiedlichen Transplantaten beweisen oder verwerfen.

Abstract

The success of transplantation medicine is closely associated with the introduction of potent immunosuppressive drugs. Therapy guidance of the calcineurin inhibitors cyclosporine and tacrolimus as well as the mammalian target of rapamycin (mTOR) inhibitors sirolimus and everolimus is achieved by therapeutic drug monitoring (TDM). For other immunosuppressive drugs such as mycophenolic acid, TDM is not established. It has been suggested that a better individualization of pharmacotherapy could be helpful in avoiding over- and under-immunosuppression, which have been associated with poor long-term outcome of grafts and patients. Therefore, a search for biomarkers that could complement TDM, thereby allowing a better individualization of therapy, is ongoing worldwide. Pharmacodynamic biomarkers in transplantation medicine can be either non-drug- specific, aiming at assessing the global effect on the immune system, or drug-specific, aiming at recording the pharmacologic effect on a drug target. Non-specific pharmacodynamic biomarkers can reflect the degree of immune activation by measuring T- or B-cell activation, the development of operational tolerance by monitoring, e.g., regulatory T cells, or the graft damage by measuring cell-free DNA released by the graft in response to injury. Drug-specific pharmacodynamic biomarkers have been published for mycophenolic acid, calcineurin, and mTOR inhibitors. The field of biomarker research to complement TDM for a better individualization of immunosuppression is still in its infancy, and controlled prospective clinical trials are needed to unravel or dismiss the potential of particular biomarkers or biomarker combinations in various patient cohorts with different grafts.

Reviewed publication

KloucheM.

Schlüsselwörter:: donor-spezifische Antikörper; Pharmakodynamik; regulatorische T-Zellen; T-Zellaktivierung; T-Zellfunktions-Assays; zellfreie DNS

Keywords: cell-free DNA; donor-specific antibodies; pharmacodynamic; regulatory T cells; T-cell activation; T-cell function assays

Literatur

  • 1.

    Koch-Weser J. Drug therapy. Serum drug concentrations as therapeutic guides. N Engl J Med 1972;287:227–31.CrossrefGoogle Scholar

  • 2.

    Wieland E, Olbricht CJ, Süsal C, Gurragchaa P, Böhler T, Israeli M, et al. Biomarkers as a tool for management of immunosuppression in transplant patients. Ther Drug Monit 2010;32: 560–72.CrossrefGoogle Scholar

  • 3.

    Biomarkers Definition Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Therapeutics. 2001;69:89–95.Google Scholar

  • 4.

    Kowalski R, Post D, Schneider MC, Britz J, Thomas J, Deierhoi M, et al. Immune cell function testing: an adjunct to therapeutic drug monitoring in transplant patient management. Clin Transplant 2003;17:77–88.CrossrefGoogle Scholar

  • 5.

    Kowalski RJ, Zeevi A, Mannon RB, Britz JA, Carruth LM. Immunodiagnostics: evaluation of functional T-cell immunocompetence in whole blood independent of circulating cell numbers. J Immunotoxicol 2007;4:225–32.CrossrefGoogle Scholar

  • 6.

    Akhlaghi F, Gohh RY. The level of ATP production in mitogen-stimulated CD4+ lymphocytes is independent of the time of ingestion of immunosuppressive agents. Ther Drug Monit 2010;32:116–7.CrossrefGoogle Scholar

  • 7.

    Kowalski RJ, Post DR, Mannon RB, Sebastian A, Wright HI, Sigle G, et al. Assessing relative risks of infection and rejection: a meta-analysis using an immune function assay. Transplantation 2006;82:663–8.CrossrefGoogle Scholar

  • 8.

    Reinsmoen NL, Cornett KM, Kloehn R, Burnette AD, McHugh L, Flewellen BK, et al. Pretransplant donor-specific and non-specific immune parameters associated with early acute rejection. Transplantation 2008;85:462–70.CrossrefGoogle Scholar

  • 9.

    Huskey J, Gralla J, Wiseman AC. Single time point immune function assay (ImmuKnowTM) testing does not aid in the prediction of future opportunistic infections or acute rejection. Clin J Am Soc Nephrol 2011;6:423–9.CrossrefGoogle Scholar

  • 10.

    Israeli M, Ben-Gal T, Yaari V, Valdman A, Matz I, Medalion B, et al. Individualized immune monitoring of cardiac transplant recipients by noninvasive longitudinal cellular immunity tests. Transplantation 2010;89:968–76.CrossrefGoogle Scholar

  • 11.

    Kobashigawa JA, Kiyosaki KK, Patel JK, Kittleson MM, Kubak BM, Davis SN, et al. Benefit of immune monitoring in heart transplant patients using ATP production in activated lymphocytes. J Heart Lung Transplant 2010;29:504–8.CrossrefGoogle Scholar

  • 12.

    Bhorade SM, Janata K, Vigneswaran WT, Alex CG, Garrity ER. Cylex ImmuKnow assay levels are lower in lung transplant recipients with infection. J Heart Lung Transplant 2008;27:990–4.CrossrefGoogle Scholar

  • 13.

    Husain S, Raza K, Pilewski JM, Zaldonis D, Crespo M, Toyoda Y, et al. Experience with immune monitoring in lung transplant recipients: correlation of low immune function with infection. Transplantation 2009;87:1852–7.CrossrefGoogle Scholar

  • 14.

    Xue F, Zhang J, Han L, Li Q, Xu N, Zhou T, et al. Immune cell functional assay in monitoring of adult liver transplantation recipients with infection. Transplantation 2010;89:620–6.CrossrefGoogle Scholar

  • 15.

    Nankivell B, Alexander SI. Rejection of the kidney allograft. N Engl J Med 2010;363:1451–62.Google Scholar

  • 16.

    Augustine JJ, Hricik DE. T-cell immune monitoring by the ELISPOT assay for interferon gamma. Clin Chim Acta. 2012;413: 1359–63.CrossrefGoogle Scholar

  • 17.

    Augustine JJ, Siu DS, Clemente MJ, Schulak JA, Heeger PS, Hricik DE. Pre-transplant IFN-gamma ELISPOTs are associated with post-transplant renal function in African American renal transplant recipients. Am J Transplant 2005;5:1971–5.CrossrefGoogle Scholar

  • 18.

    Cravedi P, Heeger PS. Immunologic monitoring in transplantation revisited. Curr Opin Organ Transplant 2012;17:26–32.CrossrefGoogle Scholar

  • 19.

    Hricik DE, Rodriguez V, Riley J, Bryan K, Tary-Lehmann M, Greenspan N, et al. Enzyme linked immunosorbent spot (ELISPOT) assay for interferon-gamma independently predicts renal function in kidney transplant recipients. Am J Transplant 2003;3:878–84.CrossrefGoogle Scholar

  • 20.

    Andree H, Nickel P, Nasiadko C, Hammer MH, Schönemann C, Pruss A, et al. Identification of dialysis patients with panel-reactive memory T cells before kidney transplantation using an allogeneic cell bank. J Am Soc Nephrol 2006,17:573–80.CrossrefGoogle Scholar

  • 21.

    Poggio ED, Clemente M, Hricik DE, Heeger PS. Panel of reactive T cells as a measurement of primed cellular alloimmunity in kidney transplant candidates. J Am Soc Nephrol 2006;17:564–72.CrossrefGoogle Scholar

  • 22.

    Kim SH, Oh EJ, Kim MJ, Park YJ, Han K, Yang HJ, et al. Pretransplant donor-specific interferon-gamma ELISPOT assay predicts acute rejection episodes in renal transplant recipients. Transplant Proc 2007;39:3057–60.CrossrefGoogle Scholar

  • 23.

    Bestard O, Nickel P, Cruzado JM, Schoenemann C, Boenisch O, Sefrin A, et al. Circulating alloreactive T cells correlate with graft function in longstanding renal transplant recipients. J Am Soc Nephrol 2008;19:1419–29.CrossrefGoogle Scholar

  • 24.

    Ashoor I, Najafian N, Korin Y, Reed EF, Mohanakumar T, Ikle D, et al. Standardization and cross validation of alloreactive IFNγ ELISPOT assays within the clinical trials in organ transplantation consortium. Am J Transplant 2013;1:1871–9.CrossrefGoogle Scholar

  • 25.

    Sanchez AM, Rountree W, Berrong M, Garcia A, Schuetz A, Cox J, et al. The External Quality Assurance Oversight Laboratory (EQAPOL) proficiency program for IFN-gamma enzyme-linked immunospot (IFN-γ ELISpot) assay. J Immunol Methods 2014;409:31–43.CrossrefGoogle Scholar

  • 26.

    Bestard O, Cruzado JM, Lucia M, Crespo E, Casis L, Sawitzki B, et al. Prospective assessment of antidonor cellular alloreactivity is a tool for guidance of immunosuppression in kidney transplantation. Kidney Int 2013;84:1226–36.CrossrefGoogle Scholar

  • 27.

    Shipkova M, Wieland E. Surface markers of lymphocyte activation and markers of cell proliferation. Clin Chim Acta 2012;413:1338–49.CrossrefGoogle Scholar

  • 28.

    Holling TM, Schooten E, van Den Elsen PJ. Function and regulation of MHC class II molecules in T-lymphocytes: of mice and men. Hum Immunol 2004;65:282–90.CrossrefGoogle Scholar

  • 29.

    Böhler T, Nolting J, Kamar N, Gurragchaa P, Reisener K, Glander P, et al. Validation of immunological biomarkers for the pharmacodynamic monitoring of immunosuppressive drugs in humans. Ther Drug Monit 2007;29:77–86.CrossrefGoogle Scholar

  • 30.

    Stalder M, Bîrsan T, Holm B, Haririfar M, Scandling J, Morris RE. Quantification of immunosuppression by flow cytometry in stable renal transplant recipients. Ther Drug Monit 2003;25:22–7.CrossrefGoogle Scholar

  • 31.

    Böhler T, Canivet C, Nguyen PN, Galvani S, Thomsen M, Durand D, et al. Cytokines correlate with age in healthy volunteers, dialysis patients and kidney-transplant patients. Cytokine 2009,45:169–73.CrossrefGoogle Scholar

  • 32.

    Prémaud A, Rousseau A, Johnson G, Canivet C, Gandia P, Muscari F, et al. Inibition of T-cell activation and proliferation by mycophenolic acid in patients awaiting liver transplantation: PK/PD relationships. Pharmacol Res 2011;63:432–8.CrossrefGoogle Scholar

  • 33.

    Kamar N, Glander P, Nolting J, Böhler T, Hambach P, Liefeldt L, et al. Pharmacodynamic evaluation of the first dose of mycophenolate mofetil before kidney transplantation Clin J Am Soc Nephrol 2009;4:936–42.CrossrefGoogle Scholar

  • 34.

    Weimer R, Zipperle S, Daniel V, Carl S, Staehler G, Opelz G. Pretransplant CD4 helper function and interleukin 10 response predict risk of acute kidney graft rejection. Transplantation 1996;62:1606–14.CrossrefGoogle Scholar

  • 35.

    Deng MC, Erren M, Roeder N, Dreimann V, Günther F, Kerber S, et al. T-cell and monocyte subsets, inflammatory molecules, rejection, and hemodynamics early after cardiac transplantation. Transplantation 1998;65:1255–61.CrossrefGoogle Scholar

  • 36.

    Chang DM, Ding YA, Kuo SY, Chang ML, Wei J. Cytokines and cell surface markers in prediction of cardiac allograft rejection. Immunol Invest 1996;25:13–21.CrossrefGoogle Scholar

  • 37.

    Wieland E, Shipkova M, Martius Y, Hasche G, Klett C, Bolley R, et al. Association between pharmacodynamic biomarkers and clinical events in the early phase after kidney transplantation: a single-center pilot study. Ther Drug Monit 2011;3:341–9.CrossrefGoogle Scholar

  • 38.

    Boleslawski E, BenOthman S, Grabar S, Correia L, Podevin P, Chouzenoux S, et al.CD25, CD28 and CD38 expression in peripheral blood lymphocytes as a tool to predict acute rejection after liver transplantation. Clin Transplant 2008;22:494–501.Google Scholar

  • 39.

    Boleslawski E, Othman SB, Aoudjehane L, Chouzenoux S, Scatton O, Soubrane O, et al. CD28 expression by peripheral blood lymphocytes as a potential predictor of the development of de novo malignancies in long-term survivors after liver transplantation. Liver Transpl 2011;17:299–305.Google Scholar

  • 40.

    Süsal C, Opelz G. Posttransplant sCD30 as a biomarker to predict kidney graft outcome. Clin Chim Acta 2012;413:1350–3.Google Scholar

  • 41.

    Josimovic-Alasevic O, Dürkop H, Schwarting R, Backé E, Stein H, Diamantstein T Ki-1 (CD30) antigen is released by Ki-1-positive tumor cells in vitro and in vivo. I. Partial characterization of soluble Ki-1 antigen and detection of the antigen in cell culture supernatants and in serum by an enzyme-linked immunosorbent assay. Eur J Immunol 1989;19:157–62.Google Scholar

  • 42.

    Pavlov I, Martins TB, Delgado JC. Development and validation of a fluorescent microsphere immunoassay for soluble CD30 testing. Clin Vaccine Immunol 2009;16:1327–31.Google Scholar

  • 43.

    Velásquez SY, García LF, Opelz G, Alvarez CM, Süsal C. Release of soluble CD30 after allogeneic stimulation is mediated by memory T cells and regulated by IFN-γ and IL-2. Transplantation 2013;96:154–61.Google Scholar

  • 44.

    Pelzl S, Opelz G, Daniel V, Wiesel M, Süsal C. Evaluation of posttransplantation soluble CD30 for diagnosis of acute renal allograft rejection. Transplantation 2003;75:421–3.Google Scholar

  • 45.

    Weimer R, Süsal C, Yildiz S, Staak A, Pelzl S, Renner F, et al. Post-transplant sCD30 and neopterin as predictors of chronic allograft nephropathy: impact of different immunosuppressive regimens. Am J Transplant 2006;6:1865–74.Google Scholar

  • 46.

    López-Hoyos M, San Segundo D, Benito MJ, Fernández-Fresnedo G, Ruiz JC, Rodrigo E, et al. Association between serum soluble CD30 and serum creatinine before and after renal transplantation. Transplant Proc 2008;40:2903–5.Google Scholar

  • 47.

    Spiridon C, Nikaein A, Lerman M, Hunt J, Dickerman R, Mack M. CD30, a marker to detect the high-risk kidney transplant recipients. Clin Transplant 2008;22:765–9.Google Scholar

  • 48.

    Süsal C, Pelzl S, Döhler B, Opelz G. Identification of highly responsive kidney transplant recipients using pretransplant soluble CD30. J Am Soc Nephrol 2002;13:1650–6.Google Scholar

  • 49.

    Süsal C, Döhler B, Sadeghi M, Salmela KT, Weimer R, Zeier M, et al. Posttransplant sCD30 as a Predictor of Kidney Graft Outcome. Transplantation 2011;91:1364–9.Google Scholar

  • 50.

    Halim MA, Al-Otaibi T, Al-Muzairai I, Mansour M, Tawab KA, Awadain WH, et al. Serial soluble CD30 measurements as a predictor of kidney graft outcome. Transplant Proc 2010;42:801–3.Google Scholar

  • 51.

    Kovač J, Arnol M, Vidan Jeras B, Bren AF, Kandus A. Pretransplant soluble CD30 serum concentration does not affect kidney graft outcomes 3 years after transplantation. Transplant Proc 2010;42:4043–6.Google Scholar

  • 52.

    Chen Y, Tai Q, Hong S, Kong Y, Shang Y, Liang W, et al. Pretransplantation soluble CD30 level as a predictor of acute rejection in kidney transplantation: a meta-analysis. Transplantation 2012;94:911–8.Google Scholar

  • 53.

    Altermann W, Schlaf G, Rothhoff A, Seliger B. High variation of individual soluble serum CD30 levels of pre-transplantation patients: sCD30 a feasible marker for prediction of kidney allograft rejection? Nephrol Dial Transplant 2007;22:2795–9.Google Scholar

  • 54.

    Shah AS, Leffell MS, Lucas D, Zachary AA. Elevated pretransplantation soluble CD30 is associated with decreased early allograft function after human lung transplantation. Hum Immunol 2009;70:101–3.Google Scholar

  • 55.

    Ypsilantis E, Key T, Bradley JA, Morgan CH, Tsui S, Parameshwar J, et al. Soluble CD30 levels in recipients undergoing heart transplantation do not predict post-transplant outcome. J Heart Lung Transplant 2009;28:1206–10.Google Scholar

  • 56.

    Hire K, Hering B, Bansal-Pakala P. Relative reductions in soluble CD30 levels post-transplant predict acute graft function in islet allograft recipients receiving three different immunosuppression protocols. Transpl Immunol 2010;23:209–14.Google Scholar

  • 57.

    Kim KH, Oh EJ, Jung ES, Park YJ, Choi JY, Kim DG, et al. Evaluation of pre- and posttransplantation serum interferon-gamma and soluble CD30 for predicting liver allograft rejection. Transplant Proc 2006;38:1429–31.Google Scholar

  • 58.

    Wu J, Palladino MA, Figari IS, Morris RE. Comparative immunoregulatory effects of rapamycin, FK 506 and cyclosporine on mitogen-induced cytokine production and lymphoproliferation Transplant Proc 1991;23:238–40.Google Scholar

  • 59.

    Shipkova M, Wieland E, Schütz E, Wiese C, Niedmann PD, Oellerich M, Armstrong VW. The acyl glucuronide metabolite of mycophenolic acid inhibits the proliferation of human mononuclear leukocytes. Transplant Proc 2001;33:1080–1.CrossrefGoogle Scholar

  • 60.

    Lyons AB, Parish CR. Determination of lymphocyte division by flow cytometry. J Immunol Methods 1994;71:131–7.CrossrefGoogle Scholar

  • 61.

    Böhler T, Waiser J, Budde K, Lichter S, Jauho A, Fritsche L, et al. The in vivo effect of rapamycin derivative SDZ RAD on lymphocyte proliferation. Transplant Proc 1998;30:2195–7.CrossrefGoogle Scholar

  • 62.

    Niwa M, Miwa Y, Kuzuya T, Iwasaki K, Haneda M, Ueki T, et al. Stimulation index for PCNA mRNA in peripheral blood as immune function monitoring after renal transplantation. Transplantation 2009;87:1411–4.CrossrefGoogle Scholar

  • 63.

    Millán O, Benitez C, Guillén D, Lopez A, Rimola A, Sánchez-Fueyo A, et al. Biomarkers of immunoregulatory status in stable liver transplant recipients undergoing weaning of immunosuppressive therapy. Clin Immunol 2010;137:337–46.CrossrefGoogle Scholar

  • 64.

    Ashokkumar C, Bentlejewski C, Sun Q, Higgs BW, Snyder S, Mazariegos GV, et al. Allospecific CD154+ B cells associate with intestine allograft rejection in children. Transplantation 2010;90:1226–31.Google Scholar

  • 65.

    Terasaki PI, Ozawa M, Castro R. Four-year follow-up of a prospective trial of HLA and MICA antibodies on kidney graft survival. Four-year follow-up of a prospective trial of HLA and MICA antibodies on kidney graft survival. Am J Transplant 2007;7:408–15.CrossrefGoogle Scholar

  • 66.

    Tait BD, Süsal C, Gebel HM, Nickerson PW, Zachary AA, Claas FH, et al. Consensus guidelines on the testing and clinical management issues associated with HLA and non-HLA antibodies in transplantation. Transplantation 2013;95:19–47.CrossrefGoogle Scholar

  • 67.

    Loupy A, Lefaucheur C, Vernerey D, Prugger C, Duong van Huyen JP, Mooney N, et al. Complement-binding anti-HLA antibodies and kidney-allograft survival. N Engl J Med 2013;369:1215–26.CrossrefGoogle Scholar

  • 68.

    Hoshino J, Kaneku H, Everly MJ, Greenland S, Terasaki PI. Using donor-specific antibodies to monitor the need for immunosuppression. Transplantation 2012;93:1173–8.CrossrefGoogle Scholar

  • 69.

    Liefeldt L, Brakemeier S, Glander P, Waiser J, Lachmann N, Schönemann C, et al. Donor-specific HLA antibodies in a cohort comparing everolimus with cyclosporine after kidney transplantation. Am J Transplant 2012;12:1192–8.CrossrefGoogle Scholar

  • 70.

    Reinsmoen NL, Lai CH, Mirocha J, Cao K, Ong G, Naim M, et al. Increased negative impact of donor HLA-specific together with non-HLA-specific antibodies on graft outcome. Transplantation 2014;97:595–601.CrossrefGoogle Scholar

  • 71.

    O’Leary JG, Demetris AJ, Friedman LS, Gebel HM, Halloran PF, Kirk AD, et al. The role of donor-specific HLA alloantibodies in liver transplantation. Am J Transplant 2014;14:779–87.CrossrefGoogle Scholar

  • 72.

    Sellarés J, de Freitas DG, Mengel M, Reeve J, Einecke G, Sis B, et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and nonadherence. Am J Transplant 2012;12:388–99.CrossrefGoogle Scholar

  • 73.

    Ling XB, Sigdel TK, Lau K, Ying L, Lau I, Schilling J, et al. Integrative urinary peptidomics in renal transplantation identifies biomarkers for acute rejection. J Am Soc Nephrol 2010;21:646–53.CrossrefGoogle Scholar

  • 74.

    Kurian SM, Heilman R, Mondala TS, Nakorchevsky A, Hewel JA, Campbell D, et al. Biomarkers for early and late stage chronic allograft nephropathy by proteogenomic profiling of peripheral blood. PLoS One 2009;4:e6212.CrossrefGoogle Scholar

  • 75.

    García Moreira V, Prieto García B, Baltar Martín JM, Ortega Suárez F, Alvarez FV. Cell-free DNA as a noninvasive acute rejection marker in renal transplantation. Clin Chem 2009;55:1958–66.CrossrefGoogle Scholar

  • 76.

    Snyder TM, Khush KK, Valantine HA, Quake SR. Universal noninvasive detection of solid organ transplant rejection. Proc Natl Acad Sci USA 2011;108:6229–34.CrossrefGoogle Scholar

  • 77.

    Beck J, Bierau S, Balzer S, Andag R, Kanzow P, Schmitz J, et al. Digital droplet PCR for rapid quantification of donor DNA in the circulation of transplant recipients as a potential universal biomarker of graft injury. Clin Chem 2013;59:1732–41.CrossrefGoogle Scholar

  • 78.

    Oellerich M, Schütz E, Kanzow P, Schmitz J, Beck J, Kollmar O, et al. Use of graft-derived cell-free DNA as an organ integrity biomarker to reexamine effective tacrolimus trough concentrations after liver transplantation. Ther Drug Monit 2014;36:136–40.CrossrefGoogle Scholar

  • 79.

    Wieczorek G, Asemissen A, Model F, Turbachova I, Floess S, Liebenberg V, et al. Quantitative DNA methylation analysis of FOXP3 as a new method for counting regulatory T cells in peripheral blood and solid tissue. Cancer Res 2009;69:599–608.CrossrefGoogle Scholar

  • 80.

    San Segundo D, Fernández-Fresnedo G, Gago M, Beares I, Ruiz-Criado J, González M, et al. Number of peripheral blood regulatory T cells and lymphocyte activation at 3 months after conversion to mTOR inhibitor therapy. Transplant Proc 2010;42:2871–3.CrossrefGoogle Scholar

  • 81.

    Bouvy AP, Klepper M, Kho MM, Boer K, Betjes MG, Weimar W, et al. The impact of induction therapy on the homeostasis and function of regulatory T cells in kidney transplant patients. Nephrol Dial Transplant 2014;29:1587–97.Google Scholar

  • 82.

    Dugast E, Chesneau M, Soulillou JP, Brouard S. Biomarkers and possible mechanisms of operational tolerance in kidney transplant patients. Immunol Rev 2014;258:208–17.CrossrefGoogle Scholar

  • 83.

    Newell KA, Asare A, Kirk AD, Gisler TD, Bourcier K, Suthanthiran M, et al.; Immune Tolerance Network ST507 Study Group. Identification of a B cell signature associated with renal transplant tolerance in humans. J Clin Invest 2010;120:1836–47.Google Scholar

  • 84.

    Sagoo P, Perucha E, Sawitzki B, Tomiuk S, Stephens DA, Miqueu P, et al. Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans. J Clin Invest 2010;120:1848–61.CrossrefGoogle Scholar

  • 85.

    Halloran PF, Helms LM, Kung L, Noujaim J. The temporal profile of calcineurin inhibition by cyclosporine in vivo. Transplantation 1999;68:1356–61.CrossrefGoogle Scholar

  • 86.

    Marquet P. Is pharmacokinetic or pharmacodynamic monitoring of calcineurin inhibition therapy necessary? Clin Chem 2010;56:736–9.CrossrefGoogle Scholar

  • 87.

    O’Reilly T, McSheehy PM. Biomarker development for the clinical activity of the mTOR inhibitor everolimus (RAD001): processes, limitations, and further proposals. Transl Oncol 2010;3:65–79.CrossrefGoogle Scholar

  • 88.

    Hartmann B, Schmid G, Graeb C, Bruns CJ, Fischereder M, Jauch K-W, et al. Biochemical monitoring of mTOR inhibitor-based immunosuppression following kidney transplantation: a novel approach for tailored immunosuppressive therapy. Kidney Int 2005;68:2593–8.CrossrefGoogle Scholar

  • 89.

    Hartmann B, He X, Keller F, Fischereder M, Guba M, Schmid H. Development of a sensitive phospho-p70 S6 kinase ELISA to quantify mTOR proliferation signal inhibition. Ther Drug Monit 2013;35:233–9.Google Scholar

  • 90.

    Dieterlen MT, Bittner HB, Klein S, von Salisch S, Mittag A, Tárnok A, et al. Assay validation of phosphorylated S6 ribosomal protein for a pharmacodynamic monitoring of mTOR-inhibitors in peripheral human blood. Cytometry B Clin Cytom 2012;82:151–7.CrossrefGoogle Scholar

  • 91.

    Hoerning A, Wilde B, Wang J, Tebbe B, Jing L, Wang X, et al. Pharmacodynamic monitoring of mammalian target of rapamycin inhibition by phosphoflow cytometric determination of p70S6 kinase activity. Transplantation. 2014 Aug 4. [Epub ahead of print].Google Scholar

  • 92.

    Glander P, Hambach P, Braun KP, Fritsche L, Giessing M, Mai I, et al. Pre-transplant inosine monophosphate dehydrogenase activity is associated with clinical outcome after renal transplantation. Am J Transplant 2004;4:2045–51.CrossrefGoogle Scholar

  • 93.

    Fukuda T, Goebel J, Thøgersen H, Maseck D, Cox S, Logan B, et al. Inosine monophosphate dehydrogenase (IMPDH) activity as a pharmacodynamic biomarker of mycophenolic acid effects in pediatric kidney transplant recipients. J Clin Pharmacol 2011;51:309–20.CrossrefGoogle Scholar

  • 94.

    Raggi MC, Siebert SB, Steimer W, Schuster T, Stangl MJ, Abendroth DK. Customized mycophenolate dosing based on measuring inosine-monophosphate dehydrogenase activity significantly improves patients’ outcomes after renal transplantation. Transplantation 2010;90:1536–41.CrossrefGoogle Scholar

  • 95.

    Gensburger O, Van Schaik RH, Picard N, Le Meur Y, Rousseau A, Woillard JB, et al. Polymorphisms in type I and II inosine monophosphate dehydrogenase genes and association with clinical outcome in patients on mycophenolate mofetil. Pharmacogenet Genomics 2010;20:537–43.CrossrefGoogle Scholar

  • 96.

    Boleslawski E, Conti F, Sanquer S, Podevin P, Chouzenoux S, Batteux F, et al. Defective inhibition of peripheral CD8+ T cell IL-2 production by anti-calcineurin drugs during acute liver allograft rejection. Transplantation 2004;77:1815–20.Google Scholar

  • 97.

    Akoglu B, Kriener S, Martens S, Herrmann E, Hofmann WP, Milovic V, et al. Interleukin-2 in CD8+ T cells correlates with Banff score during organ rejection in liver transplant recipients. Clin Exp Med 2009;9:259–62.CrossrefGoogle Scholar

  • 98.

    Giese T, Zeier M, Schemmer P, Uhl W, Schoels M, Dengler T et al. Monitoring of NFAT-regulated gene expression in the peripheral blood of allograft recipients: a novel perspective toward individually optimized drug doses of cyclosporine A. Transplantation 2004;77:339–44.CrossrefGoogle Scholar

  • 99.

    Sommerer C, Konstandin M, Dengler T, Schmidt J, Meuer S, Zeier M, et al. Pharmacodynamic monitoring of cyclosporine a in renal allograft recipients shows a quantitative relationship between immunosuppression and the occurrence of recurrent infections and malignancies. Transplantation 2006,82:1280–5.CrossrefGoogle Scholar

  • 100.

    Sommerer C, Giese T, Schmidt J, Meuer S, Zeier M. Ciclosporin A tapering monitored by NFAT-regulated gene expression: a new concept of individual immunosuppression. Transplantation 2008;85:15–21.CrossrefGoogle Scholar

  • 101.

    Zahn A, Schott N, Hinz U, Stremmel W, Schmidt J, Ganten T, et al. Immunomonitoring of nuclear factor of activated T cells-regulated gene expression: the first clinical trial in liver allograft recipients. Liver Transpl 2011;17:466–73.CrossrefGoogle Scholar

  • 102.

    Billing H, Giese T, Sommerer C, Zeier M, Feneberg R, Meuer S, et al. Pharmacodynamic monitoring of cyclosporine A by NFAT-regulated gene expression and the relationship with infectious complications in pediatric renal transplant recipients. Pediatr Transplant 2010;14:844–51.CrossrefGoogle Scholar

  • 103.

    Konstandin MH, Sommerer C, Doesch A, Zeier M, Meuer SC, Katus HA, et al. Pharmacodynamic cyclosporine A-monitoring: relation of gene expression in lymphocytes to cyclosporine blood levels in cardiac allograft recipients. Transpl Int 2007;20:1036–43.CrossrefGoogle Scholar

  • 104.

    Sommerer C, Zeier M, Schnitzler P, Meuer S, Giese T. Pharmacodynamic monitoring of ciclosporin A reveals risk of opportunistic infections and malignancies in renal transplant recipients 65 years and older. Ther Drug Monit 2011;33:694–8.CrossrefGoogle Scholar

  • 105.

    Sommerer C, Zeier M, Czock D, Schnitzler P, Meuer S, Giese T. Pharmacodynamic Disparities in Tacrolimus-Treated Patients Developing Cytomegalus Virus Viremia. Ther Drug Monit 2011;33:373–9.CrossrefGoogle Scholar

  • 106.

    Sommerer C, Zeier M, Meuer S, Giese T. Individualized monitoring of nuclear factor of activated T cells-regulated gene expression in FK506-treated kidney transplant recipients. Transplantation 2010;89:1417–23.Google Scholar

About the article

Korrespondenz: Prof. Dr. med. Eberhard Wieland, Zentralinstitut für Klinische Chemie und Laboratoriumsmedizin Klinikum Stuttgart, Stuttgart, Kriegsbergstr. 62, 70174 Stuttgart, Deutschland, E-Mail:


Received: 2014-11-13

Accepted: 2014-11-13

Published Online: 2014-12-02

Published in Print: 2014-12-01


Citation Information: LaboratoriumsMedizin, Volume 38, Issue 6, Pages 333–343, ISSN (Online) 1439-0477, ISSN (Print) 0342-3026, DOI: https://doi.org/10.1515/labmed-2014-0043.

Export Citation

©2014 by De Gruyter.Get Permission

Comments (0)

Please log in or register to comment.
Log in