Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Biomarkers for kidney transplant rejection

Nature Reviews Nephrology volume 10, pages 215–225 (2014)Cite this article

Subjects

Key Points

  • Biomarkers can be classified as prognostic or predictive biomarkers, and surrogate end points

  • Sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic curves are important measures used to assess the clinical utility of a biomarker

  • Noninvasive candidate biomarkers of early or late graft rejection found in peripheral blood and urine include mRNA transcripts, lymphocyte phenotype markers, chemokines, microRNAs, and donor specific antibodies

  • Robust validation studies and assay standardization are required before biomarkers can be used in patient care

  • Biomarker validation will be challenging, given the interindividual variation in transplant recipients

Abstract

The immune management of organ transplant recipients is imperfect. Beyond general dosing guidelines for immunosuppressive agents and clinical diagnostic tests for rejection or infection, there are few objective tools to determine the aggregate status of a patient's alloimmune response or protective immune capacity. The lack of prognostic precision significantly contributes to patient morbidity and reduces long-term allograft survival after kidney transplantation. Noninvasive biomarkers that could serve as predictive tools or surrogate end points for rejection might help clinicians individualize immunosuppression and allow for early intervention, ideally prior to clinically evident organ dysfunction. Although the growing understanding of organ rejection has provided numerous candidate biomarkers, none has been confirmed in robust validation studies as sufficiently useful to guide clinical practice independent of traditional clinical methods. In this Review, the general characteristics of biomarkers and surrogate end points; current biomarkers under active clinical investigation; and the prominent barriers to the translation of biomarkers into clinical practice are discussed.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1

Similar content being viewed by others

References

  1. Kaneku, H. K. & Terasaki, P. I. Thirty year trend in kidney transplants: UCLA and UNOS Renal Transplant Registry. Clin. Transpl. 1–27 (2006).

  2. Biomarker definitions workgroup. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin. Pharmacol. Ther. 69, 89–95 (2001).

  3. Lachenbruch, P. A., Rosenberg, A. S., Bonvini, E., Cavaille-Coll, M. W. & Colvin, R. B. Biomarkers and surrogate endpoints in renal transplantation: present status and considerations for clinical trial design. Am. J. Transplant. 4, 451–457 (2004).

    PubMed  Google Scholar 

  4. Singh, N. et al. Pretransplant donor-specific antibodies detected by single-antigen bead flow cytometry are associated with inferior kidney transplant outcomes. Transplantation 90, 1079–1084 (2010).

    CAS  PubMed  Google Scholar 

  5. Racusen, L. C. et al. The Banff 97 working classification of renal allograft pathology. Kidney Int. 55, 713–723 (1999).

    CAS  PubMed  Google Scholar 

  6. Solez, K. et al. Banff 07 classification of renal allograft pathology: updates and future directions. Am. J. Transplant. 8, 753–760 (2008).

    CAS  PubMed  Google Scholar 

  7. Thaunat, O., Legendre, C., Morelon, E., Kreis, H. & Mamzer-Bruneel, M. F. To biopsy or not to biopsy? Should we screen the histology of stable renal grafts? Transplantation 84, 671–676 (2007).

    PubMed  Google Scholar 

  8. Kirk, A. D. et al. Clinically stable human renal allografts contain histological and RNA-based findings that correlate with deteriorating graft function. Transplantation 68, 1578–1582 (1999).

    CAS  PubMed  Google Scholar 

  9. Rush, D. N., Henry, S. F., Jeffery, J. R., Schroeder, T. J. & Gough, J. Histological findings in early routine biopsies of stable renal allograft recipients. Transplantation 57, 208–211 (1994).

    CAS  PubMed  Google Scholar 

  10. Rush, D. et al. Lack of benefit of early protocol biopsies in renal transplant patients receiving TAC and MMF: a randomized study. Am. J. Transplant. 7, 2538–2545 (2007).

    CAS  PubMed  Google Scholar 

  11. Mueller, T. F. & Mas, V. R. Microarray applications in nephrology with special focus on transplantation. J. Nephrol. 25, 589–602 (2012).

    PubMed  Google Scholar 

  12. Mas, V. R., Dumur, C. I., Scian, M. J., Gehrau, R. C. & Maluf, D. G. MicroRNAs as biomarkers in solid organ transplantation. Am. J. Transplant. 13, 11–19 (2013).

    CAS  PubMed  Google Scholar 

  13. Sigdel, T. K. & Sarwal, M. M. Recent advances in biomarker discovery in solid organ transplant by proteomics. Expert Rev. Proteomics 8, 705–715 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Chattopadhyay, P. K. & Roederer, M. Cytometry: today's technology and tomorrow's horizons. Methods 57, 251–258 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Augustine, J. J. & Hricik, D. E. T-cell immune monitoring by the ELISPOT assay for interferon γ. Clin. Chim. Acta 413, 1359–1363 (2012).

    CAS  PubMed  Google Scholar 

  16. Barry, M. & Bleackley, R. C. Cytotoxic T lymphocytes: all roads lead to death. Nat. Rev. Immunol. 2, 401–409 (2002).

    CAS  PubMed  Google Scholar 

  17. Cornell, L. D., Smith, R. N. & Colvin, R. B. Kidney transplantation: mechanisms of rejection and acceptance. Annu. Rev. Pathol. 3, 189–220 (2008).

    CAS  PubMed  Google Scholar 

  18. Vasconcellos, L. M. et al. Cytotoxic lymphocyte gene expression in peripheral blood leukocytes correlates with rejecting renal allografts. Transplantation 66, 562–566 (1998).

    CAS  PubMed  Google Scholar 

  19. Gibson, I. W. et al. Peritubular capillaritis in renal allografts: prevalence, scoring system, reproducibility and clinicopathological correlates. Am. J. Transplant. 8, 819–825 (2008).

    CAS  PubMed  Google Scholar 

  20. Marcussen, N., Olsen, T. S., Benediktsson, H., Racusen, L. & Solez, K. Reproducibility of the Banff classification of renal allograft pathology. Inter- and intraobserver variation. Transplantation 60, 1083–1089 (1995).

    CAS  PubMed  Google Scholar 

  21. Aquino-Dias, E. C. et al. Non-invasive diagnosis of acute rejection in kidney transplants with delayed graft function. Kidney Int. 73, 877–884 (2008).

    CAS  PubMed  Google Scholar 

  22. Netto, M. V. et al. Granzyme, B., Fas-ligand and perforin expression during acute cellular rejection episodes after kidney transplantation: comparison between blood and renal aspirates. Transplant. Proc. 34, 476–478 (2002).

    CAS  PubMed  Google Scholar 

  23. Sabek, O., Dorak, M. T., Kotb, M., Gaber, A. O. & Gaber, L. Quantitative detection of T-cell activation markers by real-time PCR in renal transplant rejection and correlation with histopathologic evaluation. Transplantation 74, 701–707 (2002).

    CAS  PubMed  Google Scholar 

  24. Graziotto, R. et al. Perforin, granzyme B, and Fas ligand for molecular diagnosis of acute renal-allograft rejection: analyses on serial biopsies suggest methodological issues. Transplantation 81, 1125–1132 (2006).

    CAS  PubMed  Google Scholar 

  25. Heidt, S. et al. Peripheral blood sampling for the detection of allograft rejection: biomarker identification and validation. Transplantation 92, 1–9 (2011).

    CAS  PubMed  Google Scholar 

  26. Kirk, A. D., Bollinger, R. R. & Finn, O. J. Rapid, comprehensive analysis of human cytokine mRNA and its application to the study of acute renal allograft rejection. Hum. Immunol. 43, 113–128 (1995).

    CAS  PubMed  Google Scholar 

  27. Strehlau, J. et al. Quantitative detection of immune activation transcripts as a diagnostic tool in kidney transplantation. Proc. Natl Acad. Sci. USA 94, 695–700 (1997).

    CAS  PubMed  Google Scholar 

  28. Reeve, J. et al. Molecular diagnosis of T cell-mediated rejection in human kidney transplant biopsies. Am. J. Transplant. 13, 645–655 (2013).

    CAS  PubMed  Google Scholar 

  29. Famulski, K. S. et al. Kidney transplants with progressing chronic diseases express high levels of acute kidney injury transcripts. Am. J. Transplant. 13, 634–644 (2013).

    CAS  PubMed  Google Scholar 

  30. Sellares, J. et al. Molecular diagnosis of antibody-mediated rejection in human kidney transplants. Am. J. Transplant. 13, 971–983 (2013).

    CAS  PubMed  Google Scholar 

  31. Li, L. et al. A peripheral blood diagnostic test for acute rejection in renal transplantation. Am. J. Transplant. 12, 2710–2718 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Keslar, K. S. et al. Multicenter evaluation of a standardized protocol for noninvasive gene expression profiling. Am. J. Transplant. 13, 1891–1897 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Hartono, C., Muthukumar, T. & Suthanthiran, M. Noninvasive diagnosis of acute rejection of renal allografts. Curr. Opin. Organ Transplant. 15, 35–41 (2010).

    PubMed  PubMed Central  Google Scholar 

  34. Li, B. 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).

    CAS  PubMed  Google Scholar 

  35. Galante, N. Z. et al. Noninvasive immune monitoring assessed by flow cytometry and real time RT-PCR in urine of renal transplantation recipients. Transpl. Immunol. 16, 73–80 (2006).

    CAS  PubMed  Google Scholar 

  36. Muthukumar, T. et al. Serine proteinase inhibitor-9, an endogenous blocker of granzyme B/perforin lytic pathway, is hyperexpressed during acute rejection of renal allografts. Transplantation 75, 1565–1570 (2003).

    CAS  PubMed  Google Scholar 

  37. Yannaraki, M. et al. Urinary cytotoxic molecular markers for a noninvasive diagnosis in acute renal transplant rejection. Transpl. Int. 19, 759–768 (2006).

    CAS  PubMed  Google Scholar 

  38. Muthukumar, T. et al. Messenger RNA for FOXP3 in the urine of renal-allograft recipients. N. Engl. J. Med. 353, 2342–2351 (2005).

    CAS  PubMed  Google Scholar 

  39. Afaneh, C. et al. Urinary cell levels of mRNA for OX40, OX40L, PD-1, PD-L1, or PD-L2 and acute rejection of human renal allografts. Transplantation 90, 1381–1387 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Suthanthiran, M. et al. Urinary-cell mRNA profile and acute cellular rejection in kidney allografts. N. Engl. J. Med. 369, 20–31 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Manfro, R. C. et al. Noninvasive TIM-3 messenger RNA evaluation in renal transplant recipients with graft dysfunction. Transplantation 86, 1869–1874 (2008).

    CAS  PubMed  Google Scholar 

  42. Renesto, P. G., Ponciano, V. C., Cenedeze, M. A., Saraiva Camara, N. O. & Pacheco-Silva, A. High expression of TIM-3 mRNA in urinary cells from kidney transplant recipients with acute rejection. Am. J. Transplant. 7, 1661–1665 (2007).

    CAS  PubMed  Google Scholar 

  43. Luo, Y., Shi, B., Qian, Y., Bai, H. & Chang, J. Sequential monitoring of TIM-3 gene expression in peripheral blood for diagnostic and prognostic evaluation of acute rejection in renal graft recipients. Transplant. Proc. 43, 3669–3674 (2011).

    CAS  PubMed  Google Scholar 

  44. Lo, D. J. et al. Chemokines and their receptors in human renal allotransplantation. Transplantation 91, 70–77 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. el-Sawy, T., Fahmy, N. M. & Fairchild, R. L. Chemokines: directing leukocyte infiltration into allografts. Curr. Opin. Immunol. 14, 562–568 (2002).

    CAS  PubMed  Google Scholar 

  46. Hu, H. et al. Elevation of CXCR3-binding chemokines in urine indicates acute renal-allograft dysfunction. Am. J. Transplant. 4, 432–437 (2004).

    CAS  PubMed  Google Scholar 

  47. Dufour, J. H. et al. IFN-γ-inducible protein 10 (IP-10; CXCL10)-deficient mice reveal a role for IP-10 in effector T cell generation and trafficking. J. Immunol. 168, 3195–3204 (2002).

    CAS  PubMed  Google Scholar 

  48. Miura, M. et al. Monokine induced by IFN-γ is a dominant factor directing T cells into murine cardiac allografts during acute rejection. J. Immunol. 167, 3494–3504 (2001).

    CAS  PubMed  Google Scholar 

  49. Hu, H., Kwun, J., Aizenstein, B. D. & Knechtle, S. J. Noninvasive detection of acute and chronic injuries in human renal transplant by elevation of multiple cytokines/chemokines in urine. Transplantation 87, 1814–1820 (2009).

    CAS  PubMed  Google Scholar 

  50. Schaub, S. et al. Urinary CXCL9 and CXCL10 levels correlate with the extent of subclinical tubulitis. Am. J. Transplant. 9, 1347–1353 (2009).

    CAS  PubMed  Google Scholar 

  51. Jackson, J. A. et al. Urinary chemokines CXCL9 and CXCL10 are noninvasive markers of renal allograft rejection and BK viral infection. Am. J. Transplant. 11, 2228–2234 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Hricik, D. E. et al. Multicenter validation of urinary CXCL9 as a risk-stratifying biomarker for kidney transplant injury. Am. J. Transplant. 13, 2634–2644 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Ho, J. et al. Early urinary CCL2 is associated with the later development of interstitial fibrosis and tubular atrophy in renal allografts. Transplantation 90, 394–400 (2010).

    CAS  PubMed  Google Scholar 

  54. Ho, J. et al. Increased urinary CCL2:Cr ratio at 6 months is associated with late renal allograft loss. Transplantation 95, 595–602 (2013).

    CAS  PubMed  Google Scholar 

  55. Guo, H., Ingolia, N. T., Weissman, J. S. & Bartel, D. P. Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466, 835–840 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Sui, W. et al. Microarray analysis of microRNA expression in acute rejection after renal transplantation. Transpl. Immunol. 19, 81–85 (2008).

    CAS  PubMed  Google Scholar 

  57. Anglicheau, D. et al. MicroRNA expression profiles predictive of human renal allograft status. Proc. Natl Acad. Sci. USA 106, 5330–5335 (2009).

    CAS  PubMed  Google Scholar 

  58. Lorenzen, J. M. et al. Urinary miR-210 as a mediator of acute T-cell mediated rejection in renal allograft recipients. Am. J. Transplant. 11, 2221–2227 (2011).

    CAS  PubMed  Google Scholar 

  59. Valujskikh, A. & Lakkis, F. G. In remembrance of things past: memory T cells and transplant rejection. Immunol. Rev. 196, 65–74 (2003).

    CAS  PubMed  Google Scholar 

  60. Kirk, A. D., Ibrahim, M. A., Bollinger, R. R., Dawson, D. V. & Finn, O. J. Renal allograft-infiltrating lymphocytes. A prospective analysis of in vitro growth characteristics and clinical relevance. Transplantation 53, 329–338 (1992).

    CAS  PubMed  Google Scholar 

  61. Heeger, P. S. et al. Pretransplant frequency of donor-specific, IFN-γ-producing lymphocytes is a manifestation of immunologic memory and correlates with the risk of posttransplant rejection episodes. J. Immunol. 163, 2267–2275 (1999).

    CAS  PubMed  Google Scholar 

  62. Kim, S. H. et al. Pretransplant donor-specific interferon-γ ELISPOT assay predicts acute rejection episodes in renal transplant recipients. Transplant. Proc. 39, 3057–3060 (2007).

    CAS  PubMed  Google Scholar 

  63. Nather, B. J. et al. Modified ELISPOT technique—highly significant inverse correlation of post-Tx donor-reactive IFNγ-producing cell frequencies with 6 and 12 months graft function in kidney transplant recipients. Transpl. Immunol. 16, 232–237 (2006).

    PubMed  Google Scholar 

  64. Nickel, P. et al. Enzyme-linked immunosorbent spot assay for donor-reactive interferon-γ-producing cells identifies T-cell presensitization and correlates with graft function at 6 and 12 months in renal-transplant recipients. Transplantation 78, 1640–1646 (2004).

    CAS  PubMed  Google Scholar 

  65. Bestard, O. et al. Cross-validation of IFN-γ ELISPOT assay for measuring alloreactive memory/effector T cell responses in renal transplant recipients. Am. J. Transplant. 13, 1880–1890 (2013).

    CAS  PubMed  Google Scholar 

  66. Kirk, A. D. et al. Results from a human renal allograft tolerance trial evaluating the humanized CD52-specific monoclonal antibody alemtuzumab (Campath-1H). Transplantation 76, 120–129 (2003).

    CAS  PubMed  Google Scholar 

  67. Kowalski, R. J. et al. Assessing relative risks of infection and rejection: a meta-analysis using an immune function assay. Transplantation 82, 663–668 (2006).

    PubMed  Google Scholar 

  68. Egli, A., Humar, A. & Kumar, D. State-of-the-art monitoring of cytomegalovirus-specific cell-mediated immunity after organ transplant: a primer for the clinician. Clin. Infect. Dis. 55, 1678–1689 (2012).

    PubMed  Google Scholar 

  69. Ginevri, F. et al. Prospective monitoring of polyomavirus BK replication and impact of pre-emptive intervention in pediatric kidney recipients. Am. J. Transplant. 7, 2727–2735 (2007).

    CAS  PubMed  Google Scholar 

  70. Ritta, M. et al. Evaluation of Epstein–Barr virus-specific immunologic response in solid organ transplant recipients with an enzyme-linked ImmunoSpot assay. Transplant. Proc. 45, 2754–2757 (2013).

    CAS  PubMed  Google Scholar 

  71. Abate, D. et al. Comparison of cytomegalovirus (CMV) enzyme-linked immunosorbent spot and CMV QuantiFERON γ interferon-releasing assays in assessing risk of CMV infection in kidney transplant recipients. J. Clin. Microbiol. 51, 2501–2507 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Walker, S. et al. Ex vivo monitoring of human cytomegalovirus-specific CD8+ T-cell responses using QuantiFERON-CMV. Transpl. Infect. Dis. 9, 165–170 (2007).

    CAS  PubMed  Google Scholar 

  73. Newell, K. A. et al. Identification of a B cell signature associated with renal transplant tolerance in humans. J. Clin. Invest. 120, 1836–1847 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Sagoo, P. et al. Development of a cross-platform biomarker signature to detect renal transplant tolerance in humans. J. Clin. Invest. 120, 1848–1861 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Brouard, S. et al. Identification of a gene expression profile associated with operational tolerance among a selected group of stable kidney transplant patients. Transpl. Int. 24, 536–547 (2011).

    CAS  PubMed  Google Scholar 

  76. Patel, R. & Terasaki, P. I. Significance of the positive crossmatch test in kidney transplantation. N. Engl. J. Med. 280, 735–739 (1969).

    CAS  PubMed  Google Scholar 

  77. Gebel, H. M. & Bray, R. A. The evolution and clinical impact of human leukocyte antigen technology. Curr. Opin. Nephrol. Hypertens. 19, 598–602 (2010).

    CAS  PubMed  Google Scholar 

  78. Terasaki, P. I., Ozawa, M. & Castro, R. Four-year follow-up of a prospective trial of HLA and MICA antibodies on kidney graft survival. Am. J. Transplant. 7, 408–415 (2007).

    CAS  PubMed  Google Scholar 

  79. Wiebe, C. et al. Evolution and clinical pathologic correlations of de novo donor-specific HLA antibody post kidney transplant. Am. J. Transplant. 12, 1157–1167 (2012).

    CAS  PubMed  Google Scholar 

  80. Reed, E. F. et al. Comprehensive assessment and standardization of solid phase multiplex-bead arrays for the detection of antibodies to, HLA. Am. J. Transplant. 13, 1859–1870 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Archdeacon, P. et al. Summary of FDA antibody-mediated rejection workshop. Am. J. Transplant. 11, 896–906 (2011).

    CAS  PubMed  Google Scholar 

  82. Kirk, A. D. Meteorology and tolerance. Am. J. Transplant. 6, 645–646 (2006).

    CAS  PubMed  Google Scholar 

  83. Morel, P. A., Ta'asan, S., Morel, B. F., Kirschner, D. E. & Flynn, J. L. New insights into mathematical modeling of the immune system. Immunol. Res. 36, 157–165 (2006).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

A. D. Kirk is supported by NIH grant U01AI077821, FDA grant FD003539 and by the Georgia Research Alliance.

Author information

Authors and Affiliations

  1. Emory Transplant Center, Emory University, 101 Woodruff Circle, #5105-WMB, Atlanta, 30322, GA, USA

    Denise J. Lo & Allan D. Kirk

  2. University of Kansas Medical Center, Center for Transplantation, 3901 Rainbow Boulevard, Kansas City, 66160, KS, USA

    Bruce Kaplan

Authors
  1. Denise J. Lo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bruce Kaplan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Allan D. Kirk
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors researched the data for the article, provided substantial contributions to discussions of its content, wrote the article and undertook review and/or editing of the manuscript before submission.

Corresponding author

Correspondence to Allan D. Kirk.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lo, D., Kaplan, B. & Kirk, A. Biomarkers for kidney transplant rejection. Nat Rev Nephrol 10, 215–225 (2014). https://doi.org/10.1038/nrneph.2013.281

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrneph.2013.281

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing