#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

The kSORT Assay to Detect Renal Transplant Patients at High Risk for Acute Rejection: Results of the Multicenter AART Study


Background:
Development of noninvasive molecular assays to improve disease diagnosis and patient monitoring is a critical need. In renal transplantation, acute rejection (AR) increases the risk for chronic graft injury and failure. Noninvasive diagnostic assays to improve current late and nonspecific diagnosis of rejection are needed. We sought to develop a test using a simple blood gene expression assay to detect patients at high risk for AR.

Methods and Findings:
We developed a novel correlation-based algorithm by step-wise analysis of gene expression data in 558 blood samples from 436 renal transplant patients collected across eight transplant centers in the US, Mexico, and Spain between 5 February 2005 and 15 December 2012 in the Assessment of Acute Rejection in Renal Transplantation (AART) study. Gene expression was assessed by quantitative real-time PCR (QPCR) in one center. A 17-gene set—the Kidney Solid Organ Response Test (kSORT)—was selected in 143 samples for AR classification using discriminant analysis (area under the receiver operating characteristic curve [AUC] = 0.94; 95% CI 0.91–0.98), validated in 124 independent samples (AUC = 0.95; 95% CI 0.88–1.0) and evaluated for AR prediction in 191 serial samples, where it predicted AR up to 3 mo prior to detection by the current gold standard (biopsy). A novel reference-based algorithm (using 13 12-gene models) was developed in 100 independent samples to provide a numerical AR risk score, to classify patients as high risk versus low risk for AR. kSORT was able to detect AR in blood independent of age, time post-transplantation, and sample source without additional data normalization; AUC = 0.93 (95% CI 0.86–0.99). Further validation of kSORT is planned in prospective clinical observational and interventional trials.

Conclusions:
The kSORT blood QPCR assay is a noninvasive tool to detect high risk of AR of renal transplants.

Please see later in the article for the Editors' Summary


Vyšlo v časopise: The kSORT Assay to Detect Renal Transplant Patients at High Risk for Acute Rejection: Results of the Multicenter AART Study. PLoS Med 11(11): e32767. doi:10.1371/journal.pmed.1001759
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1001759

Souhrn

Background:
Development of noninvasive molecular assays to improve disease diagnosis and patient monitoring is a critical need. In renal transplantation, acute rejection (AR) increases the risk for chronic graft injury and failure. Noninvasive diagnostic assays to improve current late and nonspecific diagnosis of rejection are needed. We sought to develop a test using a simple blood gene expression assay to detect patients at high risk for AR.

Methods and Findings:
We developed a novel correlation-based algorithm by step-wise analysis of gene expression data in 558 blood samples from 436 renal transplant patients collected across eight transplant centers in the US, Mexico, and Spain between 5 February 2005 and 15 December 2012 in the Assessment of Acute Rejection in Renal Transplantation (AART) study. Gene expression was assessed by quantitative real-time PCR (QPCR) in one center. A 17-gene set—the Kidney Solid Organ Response Test (kSORT)—was selected in 143 samples for AR classification using discriminant analysis (area under the receiver operating characteristic curve [AUC] = 0.94; 95% CI 0.91–0.98), validated in 124 independent samples (AUC = 0.95; 95% CI 0.88–1.0) and evaluated for AR prediction in 191 serial samples, where it predicted AR up to 3 mo prior to detection by the current gold standard (biopsy). A novel reference-based algorithm (using 13 12-gene models) was developed in 100 independent samples to provide a numerical AR risk score, to classify patients as high risk versus low risk for AR. kSORT was able to detect AR in blood independent of age, time post-transplantation, and sample source without additional data normalization; AUC = 0.93 (95% CI 0.86–0.99). Further validation of kSORT is planned in prospective clinical observational and interventional trials.

Conclusions:
The kSORT blood QPCR assay is a noninvasive tool to detect high risk of AR of renal transplants.

Please see later in the article for the Editors' Summary


Zdroje

1. SarwalM, ChuaMS, KambhamN, HsiehSC, SatterwhiteT, et al. (2003) Molecular heterogeneity in acute renal allograft rejection identified by DNA microarray profiling. N Engl J Med 349: 125–138.

2. LiL, KhatriP, SigdelTK, TranT, YingL, et al. (2012) A peripheral blood diagnostic test for acute rejection in renal transplantation. Am J Transplant 12: 2710–2718.

3. KhatriP, RoedderS, KimuraN, De VusserK, MorganAA, et al. (2013) A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation. J Exp Med 210: 2205–2221.

4. LiL, KhushK, HsiehSC, YingL, LuikartH, et al. (2013) Identification of common blood gene signatures for the diagnosis of renal and cardiac acute allograft rejection. PLoS ONE 8: e82153.

5. NaesensM, SalvatierraO, BenfieldM, EttengerRB, DharnidharkaV, et al. (2012) Subclinical inflammation and chronic renal allograft injury in a randomized trial on steroid avoidance in pediatric kidney transplantation. Am J Transplant 12: 2730–2743.

6. MoresoF, IbernonM, GomaM, CarreraM, FulladosaX, et al. (2006) Subclinical rejection associated with chronic allograft nephropathy in protocol biopsies as a risk factor for late graft loss. Am J Transplant 6: 747–752.

7. FurnessPN, PhilpottCM, ChorbadjianMT, NicholsonML, BosmansJL, et al. (2003) Protocol biopsy of the stable renal transplant: a multicenter study of methods and complication rates. Transplantation 76: 969–973.

8. FurnessPN (2001) Histopathology of chronic renal allograft dysfunction. Transplantation 71(Suppl 11): SS31–SS36.

9. NaesensM, KhatriP, LiL, SigdelTK, VitaloneMJ, et al. (2011) Progressive histological damage in renal allografts is associated with expression of innate and adaptive immunity genes. Kidney Int 80: 1364–1376.

10. RoedderS, VitaloneM, KhatriP, SarwalMM (2011) Biomarkers in solid organ transplantation: establishing personalized transplantation medicine. Genome Med 3: 37.

11. World Medical Association (2013) World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310: 2191–2194.

12. LivakKJ, SchmittgenTD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25: 402–408.

13. SarwalMM, EttengerRB, DharnidharkaV, BenfieldM, MathiasR, et al. (2012) Complete steroid avoidance is effective and safe in children with renal transplants: a multicenter randomized trial with three-year follow-up. Am J Transplant 12: 2719–2729.

14. SisB, MengelM, HaasM, ColvinRB, HalloranPF, et al. (2010) Banff '09 meeting report: antibody mediated graft deterioration and implementation of Banff working groups. Am J Transplant 10: 464–471.

15. FleigeS, PfafflMW (2006) RNA integrity and the effect on the real-time qRT-PCR performance. Mol Aspects Med 27: 126–139.

16. SchroederA, MuellerO, StockerS, SalowskyR, LeiberM, et al. (2006) The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol Biol 7: 3.

17. MartinR, TokdarST (2012) A nonparametric empirical Bayes framework for large-scale multiple testing. Biostatistics 13: 427–439.

18. FontanaA, CopettiM, MazzoccoliG, KypraiosT, PellegriniF (2013) A linear mixed model approach to compare the evolution of multiple biological rhythms. Stat Med 32: 1125–1135.

19. Shen-OrrSS, TibshiraniR, KhatriP, BodianDL, StaedtlerF, et al. (2010) Cell type-specific gene expression differences in complex tissues. Nat Methods 7: 287–289.

20. FriedmanJ, HastieT, TibshiraniR (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33: 1–22.

21. ZhuJ, HastieT (2004) Classification of gene microarrays by penalized logistic regression. Biostatistics 5: 427–443.

22. ChoHW, KimSB, JeongMK, ParkY, MillerNG, et al. (2008) Discovery of metabolite features for the modelling and analysis of high-resolution NMR spectra. Int J Data Min Bioinform 2: 176–192.

23. HanleyJA, McNeilBJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29–36.

24. ThomasD, LangholzB, ClaytonD, PitkaniemiJ, Tuomilehto-WolfE, et al. (1992) Empirical Bayes methods for testing associations with large numbers of candidate genes in the presence of environmental risk factors, with applications to HLA associations in IDDM. Ann Med 24: 387–392.

25. WuC, OrozcoC, BoyerJ, LegliseM, GoodaleJ, et al. (2009) BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol 10: R130.

26. SigdelTK, GaoX, SarwalMM (2012) Protein and peptide biomarkers in organ transplantation. Biomark Med 6: 259–271.

27. NankivellBJ, BorrowsRJ, FungCL, O'ConnellPJ, AllenRD, et al. (2003) The natural history of chronic allograft nephropathy. N Engl J Med 349: 2326–2333.

28. TaylorSC, MrkusichEM (2014) The state of RT-quantitative PCR: firsthand observations of implementation of minimum information for the publication of quantitative real-time PCR experiments (MIQE). J Mol Microbiol Biotechnol 24: 46–52.

29. MenkeA, Rex-HaffnerM, KlengelT, BinderEB, MehtaD (2012) Peripheral blood gene expression: it all boils down to the RNA collection tubes. BMC Res Notes 5: 1.

30. MathesonLA, DuongTT, RosenbergAM, YeungRS (2008) Assessment of sample collection and storage methods for multicenter immunologic research in children. J Immunol Methods 339: 82–89.

31. LiL, YingL, NaesensM, XiaoW, SigdelT, et al. (2008) Interference of globin genes with biomarker discovery for allograft rejection in peripheral blood samples. Physiol Genomics 32: 190–197.

32. DengMC, EisenHJ, MehraMR, BillinghamM, MarboeCC, et al. (2006) Noninvasive discrimination of rejection in cardiac allograft recipients using gene expression profiling. Am J Transplant 6: 150–160.

33. RosenbergS, ElashoffMR, BeinekeP, DanielsSE, WingroveJA, et al. (2010) Multicenter validation of the diagnostic accuracy of a blood-based gene expression test for assessing obstructive coronary artery disease in nondiabetic patients. Ann Intern Med 153: 425–434.

34. LoupyA, LefaucheurC, VernereyD, PruggerC, van HuyenJP, et al. (2013) Complement-binding anti-HLA antibodies and kidney-allograft survival. N Engl J Med 369: 1215–1226.

35. BestardO, CruzadoJM, LuciaM, CrespoE, CasisL, et al. (2013) Prospective assessment of antidonor cellular alloreactivity is a tool for guidance of immunosuppression in kidney transplantation. Kidney Int 84: 1226–1236.

36. NaemiFM, CarterV, KirbyJA, AliS (2013) Anti-donor HLA class I antibodies: pathways to endothelial cell activation and cell-mediated allograft rejection. Transplantation 96: 258–266.

37. HaynesNM (2008) Follicular associated T cells and their B-cell helper qualities. Tissue Antigens 71: 97–104.

38. SellaresJ, ReeveJ, LoupyA, MengelM, SisB, et al. (2013) Molecular diagnosis of antibody-mediated rejection in human kidney transplants. Am J Transplant 13: 971–983.

39. BrouardS, MansfieldE, BraudC, LiL, GiralM, et al. (2007) Identification of a peripheral blood transcriptional biomarker panel associated with operational renal allograft tolerance. Proc Natl Acad Sci U S A 104: 15448–15453.

40. Martinez-LlordellaM, LozanoJJ, Puig-PeyI, OrlandoG, TisoneG, et al. (2008) Using transcriptional profiling to develop a diagnostic test of operational tolerance in liver transplant recipients. J Clin Invest 118: 2845–2857.

41. LozanoJJ, PallierA, Martinez-LlordellaM, DangerR, LopezM, et al. (2011) Comparison of transcriptional and blood cell-phenotypic markers between operationally tolerant liver and kidney recipients. Am J Transplant 11: 1916–1926.

42. DangerR, PallierA, GiralM, Martinez-LlordellaM, LozanoJJ, et al. (2012) Upregulation of miR-142-3p in peripheral blood mononuclear cells of operationally tolerant patients with a renal transplant. J Am Soc Nephrol 23: 597–606.

Štítky
Interné lekárstvo

Článok vyšiel v časopise

PLOS Medicine


2014 Číslo 11
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Získaná hemofilie - Povědomí o nemoci a její diagnostika
nový kurz

Eozinofilní granulomatóza s polyangiitidou
Autori: doc. MUDr. Martina Doubková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#