CLINICAL
AND
TRANSLATIONAL RESEARCH
Validation Study of Peripheral Blood Diagnostic Test for Acute Rejection in Kidney Transplantation Arah Lee,1 Jong-Cheol Jeong,2 Young-Wook Choi,1 Hwa-Young Seok,1 Yang-Gyun Kim,1 Kyung-Hwan Jeong,1 Ju-Young Moon,1 Tae-Won Lee,1 Chun-Gyoo Ihm,1 Hee Jung Jeon,2 Tai-Yeon Koo,2 Curie Ahn,2,3,4 Sung-Jig Lim,5 Jaeseok Yang,2,3,7 and Sang-Ho Lee1,6,7 Background. Diagnosing acute rejection (AR) in kidney transplant recipients typically requires an invasive kidney biopsy. A previous study has suggested that expression of five genes in peripheral blood can indicate the presence of AR in American pediatric kidney transplant recipients. This study aims to validate if these five genes are also useful to diagnose AR in Korean adult kidney transplant patients. Methods. Blood samples were collected from 143 patients (39 biopsy-proven AR, 84 stable patients, and 20 other graft injuries) at an average of 9 months posttransplantation and performed real-time PCR for five-gene biomarkers (DUSP1, NKTR, MAPK9, PSEN1, and PBEF1). Results. Patients with acute cellular rejection (ACR) had a significantly decreased level of MAPK9 and a significantly increased level of PSEN1 when compared with controls and also with patients with other graft injury (OGI). In multivariate logistic regression analysis, for discrimination between ACR and OGI, an excellent diagnostic accuracy was observed in the gene sets but five-gene set generated a higher AUC than two-gene set. With clinical variables combined to these gene sets, the diagnostic accuracy increased in both five-gene set and two-gene set. Conclusions. These results support the validity of 5 gene-set for the prediction of AR in Asian adult kidney transplant recipients and suggest the promising role of the peripheral blood gene test in the diagnosis of AR in kidney transplantation. Keywords: Kidney transplantation, Acute rejection, Biomarker. (Transplantation 2014;98: 760Y765)
idney transplantation is a treatment of choice for patients with end-stage kidney disease. Although the incidence of acute rejection (AR) episodes has decreased significantly with the development of new immunosuppressive agents, it is still an important risk factor for poor graft outcomes (1). AR has been associated with delayed graft function (2), human leukocyte antigen and ABO mismatches (3), and prolonged ischemic time (4).
K
This study was supported by a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI13C1232). The authors declare no conflicts of interest. 1 Division of Nephrology, Department of Internal Medicine, College of Medicine, Kyung Hee University, Seoul, South Korea. 2 Transplantation Center, Seoul National University Hospital, Seoul, South Korea. 3 Transplantation Research Institute, Seoul National University College of Medicine, Seoul, South Korea. 4 Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea. 5 Department of Pathology, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea. 6 Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea. 7 Address correspondence to: Jaeseok Yang, M.D., Ph.D., or Sang-Ho Lee, M.D., Ph.D., Department of Nephrology, Kyung Hee University, 149 Sangil-Dong, Gangdong-Gu, Seoul, 134-702, South Korea. E-mail: [email protected]
760
www.transplantjournal.com
To overcome the alloimmune response and avoid the clinical and subclinical AR, nonspecific immunosuppressive therapy has been the main strategy in the current era; however, it could be an obstacle to morbidity-free long-term graft survival. Therefore, personalized immunosuppressive therapy is requested for improving long-term outcomes (5). The timely and accurate diagnosis of AR as well as the recognition of individual immune response is mandatory to tailor the immunosuppressive therapy and maintain long-term graft function in kidney transplant recipients. Post-transplant kidney damages exist in a variety of clinical and pathological manifestations such as acute rejection, acute tubular necrosis, BK virus nephropathy, and
S.H.L. and J.Y. participated in designing the research. A.L. and J.C.J. participated in performing the research. S.J.L. contributed in the pathologic classification. H.Y.S., Y.G.K., K.H.J., T.W.L., C.G.I., H.J.J., T.Y.K. and C.A. participated in data recruitment. Y.W.C. and A.L. participated in analyzing the data. A.L., J.C.J., S.H.L., and J.S.Y. participated in writing the article. A.L. and J.C.J. equally contributed to this work as co-first authors. Received 8 January 2014. Revision requested 27 January 2014. Accepted 20 February 2014. Copyright * 2014 by Lippincott Williams & Wilkins ISSN: 0041-1337/14/9807-760 DOI: 10.1097/TP.0000000000000138
Transplantation
& Volume 98, Number 7, October 15, 2014
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
* 2014 Lippincott Williams & Wilkins
calcineurin inhibitor toxicity. In clinical transplantation, an unmet need exists for specific and sensitive, noninvasive biomarkers that can track graft injury and graft survival, and potentially replace the allograft biopsy as the gold standard for early detection of acute rejection (5). Transcriptomics analysis, with its ability to evaluate expression levels for thousands of genes at once, has been favorably and conveniently used by many researchers to identify the mechanisms of kidney allograft failure and develop biomarkers for its early diagnosis. The application of molecular diagnostics may uncover these novel biomarkers. Importantly, the discovery of such biomarkers would allow the tailored immunosuppressive therapy for individual patients and to identify the particular threshold between rejection and side effects of the current immunosupressive regimens. Some biomarkers, such as soluble serum CD30 (6), and circulating and urine donor-derived nucleic acids (7) have been suggested to be a predictor for future AR events, but no simple blood-based biomarker has been extensively validated for diagnosing AR in kidney transplant recipients (8). Recently, Li et al. identified differential gene expression levels in peripheral blood cells of pediatric patients with AR (9). These data suggest that AR detection may be possible by simply performing Q-PCR assay for five genes from peripheral blood samples. Clinical application of peripheral gene expression analysis for AR has also been suggested in cardiac transplant recipients (10). This novel, noninvasive strategy using peripheral gene expression in AR is promising and can lead to customization of immune suppression in kidney transplant recipients. However, the differences in mRNA expression levels could be caused by natural genetic variation between individuals and population as well as physiologic or pathologic stresses (11). This study aims to validate if these five genes are also useful to detect AR in Korean adult kidney transplant patients who have different genetic and demographic backgrounds from American pediatric kidney transplant recipients.
RESULTS Characteristics of Study Population Three groups of kidney transplant patients were enrolled in this study: healthy controls (CON, n=84); those with acute rejection (AR, n=39), either cellular- or antibodymediated rejection (n=30 and n=9, respectively); and those with other graft injuries (OGI, n=20). OGI group composed of acute tubular necrosis (n=3), calcineurin inhibitor toxicity (n=6), IgA nephropathy (n=3), focal segmental glomerulosclerosis (n=1), acute phosphate nephropathy (n=1), BK virus nephropathy (n=5), and nonspecific graft injury (n=1). Clinical characteristics and biopsy findings of study population are summarized in Table 1. There was no significant difference in the age or gender of recipients and donors among three groups. However, patients with other graft injuries showed a longer period of time from transplantation and had more HLA mismatches than control groups (PG0.05). Not surprisingly, serum creatinine levels in both AR and OGI groups were significantly higher than that in control group (PG0.01).
Lee et al.
761
Gene Expressions of Five Gene Set Biomarkers Quantitative PCR was performed to measure the expression levels of five genes that were identified as potential diagnostic biomarkers for AR in the previous study (9). Fold change values from quantitative analysis for NKTR, MAPK9, PSEN1, PBEF1, and DUSP1 were normalized to GAPDH and universal reference RNA. Patients with acute cellular rejection (ACR) had lower levels of NKTR and MAPK9 when compared with healthy controls; however, the difference was statistically significant only in MAPK9 (PG0.01; Fig. 1). On the other hand, PSEN1 expression level was significantly higher in ACR than the controls (PG0.05). Patients who have acute antibody-mediated rejection did not show any significant differences from other groups in any of the five genes. Patients with other graft injury showed considerably higher expression level of MAPK9 (PG0.01) and lower expression level of PSEN1 (PG0.05) compared with those who have ACR. PBEF1 expression level displayed a significant increase in OGI group than that in the healthy controls. Next, we assessed the effects of confounding clinical variables on expression of five-gene set. Positive correlations were observed between the age of donor and DUSP1 (r=0.249, PG0.01) and the time from transplant to biopsy and PBEF1 (r=0.332, PG0.01), whereas a negative correlation was seen between the age of recipient and NKTR (r=j0.305, PG0.01). No other clinical variables had correlation with any of the five genes. Comparison of Diagnostic Accuracy Between Different Models Logistic regression models were constructed by using all five genes and two significant genes for ACR (MAPK9 and PSEN1) in gene expression level (Fig. 2). To examine the effect of diagnostic accuracy of these genes for AR, ACR to be more specific, the regression models for ACR group were compared to other three combinations of groups (CON; CON and OGI; CON, OGI, and AMR). Reasonably, area under the curve (AUC) from the receiver operating characteristic (ROC) curves of five genes were greater than that of two genes in all combinations. A comparison between CON and ACR showed the greatest AUC value of 0.83 (95% CI 0.75Y0.91) in five genes and the smallest AUC value of 0.76 (95% CI 0.66Y0.86) in two genes. The greatest AUC value of two genes was generated when CON and OGI were compared with ACR (AUC=0.77, 95% CI 0.68Y0.86). The smallest AUC value in five gene set was yielded when ACR is compared with CON, AMR, and OGI group (AUC=0.81 95% CI 0.73Y0.89). Figure 3 showed ROC curves comparing ACR patients with OGI based on clinical variables alone, biomarkers alone, and the combination of biomarkers and clinical variables. Because graft rejection can be differentiated from the control group by the level of serum creatinine, CON group was eliminated to focus on discriminating ACR from OGI. Six clinical variables, which are age and gender of recipient, time after transplant to biopsy, HLA mismatch, age of donor, and level of serum creatinine, were also included to assess the validity of the model. An excellent diagnostic accuracy was observed in the biomarkers alone, but fivegene set generated higher AUC of 0.89 (95% CI 0.79Y0.99)
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
762
www.transplantjournal.com
TABLE 1.
Transplantation
& Volume 98, Number 7, October 15, 2014
Clinical characteristics of study population
Clinical characteristics Recipients Gender, % male Mean age at transplantation, yr Time from transplant to biopsy, mo HLA mismatch Serum creatinine, mg/dL Immunosuppression, % SF Donors Donor source, % LD Gender, % male Mean age at transplantation, yr Pathologic diagnosis
Control (n=84) 65.48% 46.82T1.43 4.88T3.36 3.28T0.18 1.18T0.05 1.23% 32.14% 57.14% 44.63T1.37
Acute rejection (n=39) 58.97% 45.62T2.02 7.81T3.04 2.74T0.30 1.91T0.21** 10.20% 38.46% 74.36% 46.95T1.75 Acute T-cell rejection Antibody-mediated rejection
Other graft injuries (n=20) 55.00% 43.15T2.71 23.40T9.17* 4.06T0.35* 1.93T0.29** 10.00% 35.00% 55.00% 48.00T2.45 30 Acute tubular necrosis 9 Calcineurin inhibitor toxicity IgA nephropathy Focal segmental glomerulosclerosis Acute phosphate nephropathy BK virus nephropathy Nonspecific
3 6 3 1 1 5 1
* vs. CON, PG0.05; ** vs. CON, PG0.01. Values are expressed as meansTSE. SF, steroid free; LD, living donor.
compared with two genes (AUC=0.84, 95% CI 0.73Y0.95). Clinical data alone was less accurate in discriminating
between ACR and OGI (AUC=0.71, 95% CI 0.55Y0.87). When clinical data were combined with these gene sets, the
FIGURE 1. Box plots of QPCR gene expression values for NKTR, MAPK9, PSEN1, PBEF1, and DUSP1.Cont indicates controls; ACR, acute cellular rejection; AMR, acute antibody-mediated rejection; OGI, other graft injury. * vs. CON, PG0.05. ** vs. CON, PG0.01. # vs. ACR, PG0.05. ## vs. ACR, PG0.01.
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
* 2014 Lippincott Williams & Wilkins
Lee et al.
763
FIGURE 2. Receiver operating characteristic (ROC) curve analyses for five-gene set (DUSP1, NKTR, MAPK9, PSEN1, and PBEF1) and two significant gene set, MAPK9 and PSEN1, comparing ACR to CON (A), to CON and OGI (B), or to CON, OGI, and AMR (C). Cont indicates controls; ACR, acute cellular rejection; AMR, acute antibody-mediated rejection; OGI, other graft injury.
diagnostic accuracy increased from 0.71 to 0.96 (95% CI 0.91Y1.00) in five gene set and from 0.84 to 0.94 (95% CI 0.89Y1.00) in two genes set.
DISCUSSION We carried out a validation study of the five-gene set highlighted in American pediatric kidney transplant recipients to assess the diagnostic accuracy of a peripheral blood gene expression test and discriminate ACR in Korean adult kidney transplant recipients. This multicenter, graft biopsyYbased cohort study showed three major findings. First, blood gene expression of only two genes (MAPK9 and PSEN1) out of five-gene set showed a good association with ACR in the Korean adult kidney transplant recipients. Second, these two genes showed a strong discrimination (AUC=0.84, 95% CI 0.73Y0.95, PG0.001) for patients with ACR from OGI, who had a similar level of serum creatinine. Third, when clinical data were combined with genes, the diagnostic accuracy increased from 0.71 to 0.94 (95% CI 0.89Y1.00, PG0.001). Acute rejection in kidney transplantation is an important factor in a sense that it inhibits long-term graft survival, and therefore easy and early diagnostic tools of acute rejection are necessary to perform early treatment for graft rejection and to prevent immunosuppressive toxicity, and thereby to achieve long-term graft survival (1, 5). Although biopsy is requisite for the diagnosis of acute and chronic graft rejection, it is still invasive, painful, and may induce serious complications such as hematuria, fistula, and hematoma. Host responses can be detected earlier at
molecular level before immune response develops and causes significant damages to the graft, which is detected by biopsy. These changes can be found in peripheral blood and urine as well as in biopsy tissues, and the noninvasive methods could be more practical tools for serial monitoring of patients. Because high-throughput technology for RNA identification and quantification are consistently developing, these technologies can be used to analyze multiple genes in a single sample with reliability and cost efficiency. During the last few decades, various novel immune monitoring tools such as ELISPOT and Immunknow assay have been developed for individualized immunotherapy (12, 13). As the bioinformatic techniques have been advanced, it became possible to combine information from gene expression tests, proteomics, and immune monitoring techniques (5). Combining blood gene test and other immune monitoring test, that is multiplatform approach, can be efficiently used to monitor graft rejection response and immune response of kidney transplant patients. The clinical feasibility of peripheral blood-based gene biomarkers has been demonstrated in the field of heart transplantation. Allomap, a commercially available gene expression profiling test, was developed using high-throughput microarray technology and was extensively studied for comparing with the standard, invasive endomyocardial biopsy (10). These studies suggest that blood gene test is promising in monitoring as a noninvasive biomarker to guide management after organ transplantation as well as in diagnosing acute rejection episodes. Li et al. recently developed and validated a peripheral blood gene expression test to assess AR likelihood
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
764
www.transplantjournal.com
Transplantation
& Volume 98, Number 7, October 15, 2014
FIGURE 3. Receiver operating characteristic (ROC) curves comparing ACR patients with OGI by means of by means of clinical variables alone, biomarkers alone, and the combination of biomarkers and clinical variables. AUC indicates area under the curve; Sens., sensitivity; C.I., confidence interval; Spec., specificity; PPV, positive predictive value; NPV, negative predictive value.
in American pediatric kidney transplant recipients (9). They carried out RNA microarray studies with 198 samples of peripheral blood from pediatric and young adult recipients with acute rejection, stable function, or other abnormalities at the time of biopsy in 12 American transplant centers. A set of five genes were selected from verification and validation tests using subsequent quantitative PCR, and they were able to discriminate patients with acute rejection from stable patients with great sensitivity and specificity. To the best of our knowledge, our study is the first validation study for this peripheral blood gene test in the independent, Asian, biopsybased adult kidney transplant cohort. This novel strategy using peripheral gene expression in AR is promising and can lead noninvasive, tailored immune suppression in kidney transplant recipients. However, differences in mRNA expression levels could be caused by natural genetic variation between individuals and population as well as physiologic or pathologic stresses (11, 14). Our study validated only two genes, MAPK9 and PSEN1, of the five-gene biomarker set found in peripheral blood of Caucasian patients. This difference is presumed to be a result of ethnic differences in the study population, and further study is required on differences in polymorphism in regulatory region of the biomarker gene set. Age difference in the study population might also have had an effect on the significance of individual genes. Because this validation cohort comprises older adult than original training dataset
(9), age may have played as a confounder for age-associated transcripts. Overall, our study suggests that gene expression levels of MAPK9 and PSEN1 can be sufficiently used to diagnose ACR on Korean adult kidney transplant recipients, especially combined with clinical risk factors. MAPK9, also known as JNK2, is a protein coding gene of serine/threonine-protein kinase that regulates a wide variety of cellular function. PSEN1 is a protein-coding gene of presenillin-1 (PS-1), which medicates the regulated proteolytic events of several proteins. It has been suggested to be involved in the pathogenesis of Alzheimer disease and plays a role in Notch and Wnt signaling pathways (15, 16). The exact role of MAPK9 and PSEN1 on immune activation is unveiled yet. However, the recent papers showed that genetic deletion of JNK2 worsens the disease outcome in an experimental model of inflammatory bowel disease (17, 18). The possible role of these genes on immune activation should be investigated to understand the mechanism of the activation during ACR. In summary, in this first independent validation study of a peripheral blood test for ACR in adult kidney transplant recipients, peripheral blood gene test showed a strong discrimination for patients with ACR from OGI. These results suggest a novel diagnostic and monitoring strategy using peripheral gene expression is promising in kidney transplantation. Transcriptome biomarker sets should be more identified and validated in large cohorts to support
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Lee et al.
* 2014 Lippincott Williams & Wilkins
clinical trials and therefore ultimately provide individualized, tailored immunosuppressive treatment for kidney transplantation.
MATERIALS AND METHODS Patients and Study Samples
models were fit with disease status as the binary dependent variable. Correlation of clinical and demographic confounders with the expression level of five genes was analyzed using Pearson correlation to investigate the effects of confounders on five-gene set’s prediction score for AR. Moreover, univariate and multivariate logistic regression models for all nine clinical covariates were constructed with the expression values of five genes to examine the risk for AR. The covariates used in these models were recipient age, recipient gender, posttransplant time, degree of HLA mismatch, type of immunosuppressive drugs, donor age, donor gender, donor source (living vs. deceased), and serum creatinine level at the time of biopsy. All analyses were performed with SPSS statistical software (version 15; SPSS Inc., Chicago, IL, USA).
The study population consisted of Korean adult (recipient 918 years old) kidney recipients who had taken the allograft biopsy at two Kyung Hee University affiliated hospitals and Seoul National University Hospital from January 2010 to December 2012. All recipients universally received nondepleting induction therapy with interleukin 2 receptor antagonist. Maintenance immunosuppression included a calcineurin inhibitor, mycophenolate mofetil, and prednisone. The firstline calcineurin inhibitor was tacrolimus with trough level monitoring. For the patients who were at low immunological risk, prednisone was discontinued within the first 6 months after transplantation. All recipients universally received prophylaxis with daily trimethoprimsulfamethoxazole for 6 months after transplantation. Pre-emptive antiviral therapy based on viral load monitoring for cytomegalovirus was done except for high-risk patients who were D+/Rj on donor and recipient serology. For high-risk patients, chemoprophylaxis with oral valganciclovir was done for 3 months after transplantation. A total of 143 peripheral blood samples from 143 adult kidney transplant recipients were analyzed in this study. Among 143 studied patients, 49 received a renal graft from a living donor (34.3%) and 94 from a deceased donor (95.7%). This study was approved by the institutional review boards of all participating centers, and informed consent was obtained from all participants. The samples were obtained at the time of graft biopsy and paired with the biopsies, which was proven and scored by the pathologists in each medical center. Protocol biopsy for each patient was performed at within 3 weeks and 6 months after transplantation; for cause biopsy was performed if any sign of graft dysfunction was suspected. These samples were divided into three categories: ‘‘acute rejection’’ (AR; n=39); ‘‘control’’ (CON; n=84), if they showed no evidence of AR or any other pathological findings; and ‘‘other graft injuries’’ (OGI; n=20), if any other pathological findings were present other than AR. Patients with mixed finding of AR and OGI in allograft biopsy were excluded. Patients with borderline rejection on Banff classification were also excluded.
10.
Sample Preparation and Quantitative Polymerase Chain Reaction (QPCR)
11.
Peripheral blood was collected in 2.5 mL PAXgene Blood RNA Tubes (PreAnalytiX; Qiagen, Hilden, Germany), and total RNA was extracted using a PAXgene Blood RNA Kit (PreAnalytiX; Qiagen) according to the manufacturer’s instructions. Total RNA was reverse-transcribed into cDNA using the SuperScript Choice System (Invitrogen, Carlsbad, CA, USA). Quantitative polymerase chain reaction was conducted on a Chromo 4 Real-Time PCR system (Bio-Rad, Hercules, CA, USA) under standard cycle conditions (10 min at 95-C, 40 cycles of 15 sec at 95-C, 30 sec at 60-C) by using Taqman gene expression assays for NKTR (Hs00234637_m1), MAPK9 (Hs00177102_m1), PSEN1 (Hs00997789_m1), PBEF1 (Hs00237184_m1), and DUSP1 (Hs00610256_q1) and Taqman master mix (Applied Biosystems, Foster City, CA, USA). Each PCR assay plate included a human universal cDNA, synthesized from Universal Human Reference RNA (Agilent Inc., Santa Clara, CA, USA). For each reaction plate, a housekeeping gene, GAPDH, was used as an endogenous control to normalize the gene expression of each sample. The relative quantification of mRNA expression was calculated using a comparative CT method. Expression values of the samples were averaged and normalized to that of universal human reference RNA.
Statistics and Adjustment for Confounders Unless otherwise specified, ANOVA was used for univariate comparison for continuous variables and chi-square tests for categorical variables. Standard methods (19) were used to estimate ROC curves. Z test was used to test AUCs versus random (AUC=0.50). A series of logistic regression
765
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9.
12.
13. 14. 15. 16. 17. 18.
19.
Matas AJ, Gillingham KJ, Payne WD, et al. The impact of an acute rejection episode on long-term renal allograft survival. Transplantation 1994; 57: 857. Yarlagadda SG, Coca SG, Formica RN Jr, et al. Association between delayed graft function and allograft and patient survival: a systematic review and meta-analysis. Nephrol Dial Transplant 2009; 24: 1039. Lefaucheur C, Loupy A, Vernerey D, et al. Antibody-mediated vascular rejection of kidney allografts: a population-based study. Lancet 2013; 381: 313. Mikhalski D, Wissing KM, Ghisdal L, et al. Cold ischemia is a major determinant of acute rejection and renal graft survival in the modern era of immunosuppression. Transplantation 2008; 85: S3. Chowdhury P, Hernandez-Fuentes MP. Non-invasive biomarkers to guide management following renal transplantation: the need for a multiplatform approach. Curr Opin Organ Transplant 2013; 18: 1. Su¨sal C, Do¨hler B, Sadeghi M, et al. Posttransplant sCD30 as a predictor of kidney graft outcome. Transplantation 2011; 91: 1364. Garcı´a Moreira V, Prieto Garcı´a B, Baltar Martı´n JM, et al. Cell-free DNA as a noninvasive acute rejection marker in renal transplantation. Clin Chem 2009; 55: 1958. Warady B, Davis R, Butte AJ, et al. A peripheral blood diagnostic test for acute rejection in renal transplantation. Am J Transplant 2012; 12: 2710. Li L, Khatri P, Sigdel TK, et al. A peripheral blood diagnostic test for acute rejection in renal transplantation. Am J Transplant 2012; 12: 2710. Pham MX, Teuteberg JJ, Kfoury AG, et al. Gene-expression profiling for rejection surveillance after cardiac transplantation. N Engl J Med 2010; 362: 1890. Dimas AS, Deutsch S, Stranger BE, et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 2009; 325: 1246. Nickel P, Presber F, Bold G, et al. Enzyme-linked immunosorbent spot assay for donor-reactive interferon-gamma-producing cells identifies T-cell presensitization and correlates with graft function at 6 and 12 months in renal-transplant recipients. Transplantation 2004; 78: 1640. Kowalski RJ, Post DR, Mannon RB, et al. Assessing relative risks of infection and rejection: a meta-analysis using an immune function assay. Transplantation 2006; 82: 663. Bushel PR, McGovern R, Liu L, et al. Population differences in transcript-regulator expression quantitative trait loci. PLoS One 2012; 7: e34286. Logeat F, Bessia C, Brou C, et al. The Notch1 receptor is cleaved constitutively by a furin-like convertase. Proc Natl Acad Sci U S A 1998; 95: 8108. Zhang C, Wu B, Beglopoulos V, et al. Presenilins are essential for regulating neurotransmitter release. Nature 2009; 460: 632. Chromik AM, Mu¨ller AM, Ko¨rner J, et al. Genetic deletion of JNK1 and JNK2 aggravates the DSS-induced colitis in mice. J Invest Surg 2007; 20: 23. Kersting S, Reinecke K, Hilgert C, et al. Knockout of the c-Jun Nterminal Kinase 2 aggravates the development of mild chronic dextran sulfate sodium colitis independently of expression of intestinal cytokines TNF>, TGFB1, and IL-6. J Inflamm Res 2013; 6: 13. Vickers AJ. Decision analysis for the evaluation of diagnostic tests, prediction models and molecular markers. Am Stat 2008; 62: 314.
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
AND
TRANSLATIONAL RESEARCH
Validation Study of Peripheral Blood Diagnostic Test for Acute Rejection in Kidney Transplantation Arah Lee,1 Jong-Cheol Jeong,2 Young-Wook Choi,1 Hwa-Young Seok,1 Yang-Gyun Kim,1 Kyung-Hwan Jeong,1 Ju-Young Moon,1 Tae-Won Lee,1 Chun-Gyoo Ihm,1 Hee Jung Jeon,2 Tai-Yeon Koo,2 Curie Ahn,2,3,4 Sung-Jig Lim,5 Jaeseok Yang,2,3,7 and Sang-Ho Lee1,6,7 Background. Diagnosing acute rejection (AR) in kidney transplant recipients typically requires an invasive kidney biopsy. A previous study has suggested that expression of five genes in peripheral blood can indicate the presence of AR in American pediatric kidney transplant recipients. This study aims to validate if these five genes are also useful to diagnose AR in Korean adult kidney transplant patients. Methods. Blood samples were collected from 143 patients (39 biopsy-proven AR, 84 stable patients, and 20 other graft injuries) at an average of 9 months posttransplantation and performed real-time PCR for five-gene biomarkers (DUSP1, NKTR, MAPK9, PSEN1, and PBEF1). Results. Patients with acute cellular rejection (ACR) had a significantly decreased level of MAPK9 and a significantly increased level of PSEN1 when compared with controls and also with patients with other graft injury (OGI). In multivariate logistic regression analysis, for discrimination between ACR and OGI, an excellent diagnostic accuracy was observed in the gene sets but five-gene set generated a higher AUC than two-gene set. With clinical variables combined to these gene sets, the diagnostic accuracy increased in both five-gene set and two-gene set. Conclusions. These results support the validity of 5 gene-set for the prediction of AR in Asian adult kidney transplant recipients and suggest the promising role of the peripheral blood gene test in the diagnosis of AR in kidney transplantation. Keywords: Kidney transplantation, Acute rejection, Biomarker. (Transplantation 2014;98: 760Y765)
idney transplantation is a treatment of choice for patients with end-stage kidney disease. Although the incidence of acute rejection (AR) episodes has decreased significantly with the development of new immunosuppressive agents, it is still an important risk factor for poor graft outcomes (1). AR has been associated with delayed graft function (2), human leukocyte antigen and ABO mismatches (3), and prolonged ischemic time (4).
K
This study was supported by a grant of the Korean Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HI13C1232). The authors declare no conflicts of interest. 1 Division of Nephrology, Department of Internal Medicine, College of Medicine, Kyung Hee University, Seoul, South Korea. 2 Transplantation Center, Seoul National University Hospital, Seoul, South Korea. 3 Transplantation Research Institute, Seoul National University College of Medicine, Seoul, South Korea. 4 Department of Internal Medicine, Seoul National University College of Medicine, Seoul, South Korea. 5 Department of Pathology, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea. 6 Department of Internal Medicine, Kyung Hee University Hospital at Gangdong, Kyung Hee University, Seoul, South Korea. 7 Address correspondence to: Jaeseok Yang, M.D., Ph.D., or Sang-Ho Lee, M.D., Ph.D., Department of Nephrology, Kyung Hee University, 149 Sangil-Dong, Gangdong-Gu, Seoul, 134-702, South Korea. E-mail: [email protected]
760
www.transplantjournal.com
To overcome the alloimmune response and avoid the clinical and subclinical AR, nonspecific immunosuppressive therapy has been the main strategy in the current era; however, it could be an obstacle to morbidity-free long-term graft survival. Therefore, personalized immunosuppressive therapy is requested for improving long-term outcomes (5). The timely and accurate diagnosis of AR as well as the recognition of individual immune response is mandatory to tailor the immunosuppressive therapy and maintain long-term graft function in kidney transplant recipients. Post-transplant kidney damages exist in a variety of clinical and pathological manifestations such as acute rejection, acute tubular necrosis, BK virus nephropathy, and
S.H.L. and J.Y. participated in designing the research. A.L. and J.C.J. participated in performing the research. S.J.L. contributed in the pathologic classification. H.Y.S., Y.G.K., K.H.J., T.W.L., C.G.I., H.J.J., T.Y.K. and C.A. participated in data recruitment. Y.W.C. and A.L. participated in analyzing the data. A.L., J.C.J., S.H.L., and J.S.Y. participated in writing the article. A.L. and J.C.J. equally contributed to this work as co-first authors. Received 8 January 2014. Revision requested 27 January 2014. Accepted 20 February 2014. Copyright * 2014 by Lippincott Williams & Wilkins ISSN: 0041-1337/14/9807-760 DOI: 10.1097/TP.0000000000000138
Transplantation
& Volume 98, Number 7, October 15, 2014
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
* 2014 Lippincott Williams & Wilkins
calcineurin inhibitor toxicity. In clinical transplantation, an unmet need exists for specific and sensitive, noninvasive biomarkers that can track graft injury and graft survival, and potentially replace the allograft biopsy as the gold standard for early detection of acute rejection (5). Transcriptomics analysis, with its ability to evaluate expression levels for thousands of genes at once, has been favorably and conveniently used by many researchers to identify the mechanisms of kidney allograft failure and develop biomarkers for its early diagnosis. The application of molecular diagnostics may uncover these novel biomarkers. Importantly, the discovery of such biomarkers would allow the tailored immunosuppressive therapy for individual patients and to identify the particular threshold between rejection and side effects of the current immunosupressive regimens. Some biomarkers, such as soluble serum CD30 (6), and circulating and urine donor-derived nucleic acids (7) have been suggested to be a predictor for future AR events, but no simple blood-based biomarker has been extensively validated for diagnosing AR in kidney transplant recipients (8). Recently, Li et al. identified differential gene expression levels in peripheral blood cells of pediatric patients with AR (9). These data suggest that AR detection may be possible by simply performing Q-PCR assay for five genes from peripheral blood samples. Clinical application of peripheral gene expression analysis for AR has also been suggested in cardiac transplant recipients (10). This novel, noninvasive strategy using peripheral gene expression in AR is promising and can lead to customization of immune suppression in kidney transplant recipients. However, the differences in mRNA expression levels could be caused by natural genetic variation between individuals and population as well as physiologic or pathologic stresses (11). This study aims to validate if these five genes are also useful to detect AR in Korean adult kidney transplant patients who have different genetic and demographic backgrounds from American pediatric kidney transplant recipients.
RESULTS Characteristics of Study Population Three groups of kidney transplant patients were enrolled in this study: healthy controls (CON, n=84); those with acute rejection (AR, n=39), either cellular- or antibodymediated rejection (n=30 and n=9, respectively); and those with other graft injuries (OGI, n=20). OGI group composed of acute tubular necrosis (n=3), calcineurin inhibitor toxicity (n=6), IgA nephropathy (n=3), focal segmental glomerulosclerosis (n=1), acute phosphate nephropathy (n=1), BK virus nephropathy (n=5), and nonspecific graft injury (n=1). Clinical characteristics and biopsy findings of study population are summarized in Table 1. There was no significant difference in the age or gender of recipients and donors among three groups. However, patients with other graft injuries showed a longer period of time from transplantation and had more HLA mismatches than control groups (PG0.05). Not surprisingly, serum creatinine levels in both AR and OGI groups were significantly higher than that in control group (PG0.01).
Lee et al.
761
Gene Expressions of Five Gene Set Biomarkers Quantitative PCR was performed to measure the expression levels of five genes that were identified as potential diagnostic biomarkers for AR in the previous study (9). Fold change values from quantitative analysis for NKTR, MAPK9, PSEN1, PBEF1, and DUSP1 were normalized to GAPDH and universal reference RNA. Patients with acute cellular rejection (ACR) had lower levels of NKTR and MAPK9 when compared with healthy controls; however, the difference was statistically significant only in MAPK9 (PG0.01; Fig. 1). On the other hand, PSEN1 expression level was significantly higher in ACR than the controls (PG0.05). Patients who have acute antibody-mediated rejection did not show any significant differences from other groups in any of the five genes. Patients with other graft injury showed considerably higher expression level of MAPK9 (PG0.01) and lower expression level of PSEN1 (PG0.05) compared with those who have ACR. PBEF1 expression level displayed a significant increase in OGI group than that in the healthy controls. Next, we assessed the effects of confounding clinical variables on expression of five-gene set. Positive correlations were observed between the age of donor and DUSP1 (r=0.249, PG0.01) and the time from transplant to biopsy and PBEF1 (r=0.332, PG0.01), whereas a negative correlation was seen between the age of recipient and NKTR (r=j0.305, PG0.01). No other clinical variables had correlation with any of the five genes. Comparison of Diagnostic Accuracy Between Different Models Logistic regression models were constructed by using all five genes and two significant genes for ACR (MAPK9 and PSEN1) in gene expression level (Fig. 2). To examine the effect of diagnostic accuracy of these genes for AR, ACR to be more specific, the regression models for ACR group were compared to other three combinations of groups (CON; CON and OGI; CON, OGI, and AMR). Reasonably, area under the curve (AUC) from the receiver operating characteristic (ROC) curves of five genes were greater than that of two genes in all combinations. A comparison between CON and ACR showed the greatest AUC value of 0.83 (95% CI 0.75Y0.91) in five genes and the smallest AUC value of 0.76 (95% CI 0.66Y0.86) in two genes. The greatest AUC value of two genes was generated when CON and OGI were compared with ACR (AUC=0.77, 95% CI 0.68Y0.86). The smallest AUC value in five gene set was yielded when ACR is compared with CON, AMR, and OGI group (AUC=0.81 95% CI 0.73Y0.89). Figure 3 showed ROC curves comparing ACR patients with OGI based on clinical variables alone, biomarkers alone, and the combination of biomarkers and clinical variables. Because graft rejection can be differentiated from the control group by the level of serum creatinine, CON group was eliminated to focus on discriminating ACR from OGI. Six clinical variables, which are age and gender of recipient, time after transplant to biopsy, HLA mismatch, age of donor, and level of serum creatinine, were also included to assess the validity of the model. An excellent diagnostic accuracy was observed in the biomarkers alone, but fivegene set generated higher AUC of 0.89 (95% CI 0.79Y0.99)
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
762
www.transplantjournal.com
TABLE 1.
Transplantation
& Volume 98, Number 7, October 15, 2014
Clinical characteristics of study population
Clinical characteristics Recipients Gender, % male Mean age at transplantation, yr Time from transplant to biopsy, mo HLA mismatch Serum creatinine, mg/dL Immunosuppression, % SF Donors Donor source, % LD Gender, % male Mean age at transplantation, yr Pathologic diagnosis
Control (n=84) 65.48% 46.82T1.43 4.88T3.36 3.28T0.18 1.18T0.05 1.23% 32.14% 57.14% 44.63T1.37
Acute rejection (n=39) 58.97% 45.62T2.02 7.81T3.04 2.74T0.30 1.91T0.21** 10.20% 38.46% 74.36% 46.95T1.75 Acute T-cell rejection Antibody-mediated rejection
Other graft injuries (n=20) 55.00% 43.15T2.71 23.40T9.17* 4.06T0.35* 1.93T0.29** 10.00% 35.00% 55.00% 48.00T2.45 30 Acute tubular necrosis 9 Calcineurin inhibitor toxicity IgA nephropathy Focal segmental glomerulosclerosis Acute phosphate nephropathy BK virus nephropathy Nonspecific
3 6 3 1 1 5 1
* vs. CON, PG0.05; ** vs. CON, PG0.01. Values are expressed as meansTSE. SF, steroid free; LD, living donor.
compared with two genes (AUC=0.84, 95% CI 0.73Y0.95). Clinical data alone was less accurate in discriminating
between ACR and OGI (AUC=0.71, 95% CI 0.55Y0.87). When clinical data were combined with these gene sets, the
FIGURE 1. Box plots of QPCR gene expression values for NKTR, MAPK9, PSEN1, PBEF1, and DUSP1.Cont indicates controls; ACR, acute cellular rejection; AMR, acute antibody-mediated rejection; OGI, other graft injury. * vs. CON, PG0.05. ** vs. CON, PG0.01. # vs. ACR, PG0.05. ## vs. ACR, PG0.01.
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
* 2014 Lippincott Williams & Wilkins
Lee et al.
763
FIGURE 2. Receiver operating characteristic (ROC) curve analyses for five-gene set (DUSP1, NKTR, MAPK9, PSEN1, and PBEF1) and two significant gene set, MAPK9 and PSEN1, comparing ACR to CON (A), to CON and OGI (B), or to CON, OGI, and AMR (C). Cont indicates controls; ACR, acute cellular rejection; AMR, acute antibody-mediated rejection; OGI, other graft injury.
diagnostic accuracy increased from 0.71 to 0.96 (95% CI 0.91Y1.00) in five gene set and from 0.84 to 0.94 (95% CI 0.89Y1.00) in two genes set.
DISCUSSION We carried out a validation study of the five-gene set highlighted in American pediatric kidney transplant recipients to assess the diagnostic accuracy of a peripheral blood gene expression test and discriminate ACR in Korean adult kidney transplant recipients. This multicenter, graft biopsyYbased cohort study showed three major findings. First, blood gene expression of only two genes (MAPK9 and PSEN1) out of five-gene set showed a good association with ACR in the Korean adult kidney transplant recipients. Second, these two genes showed a strong discrimination (AUC=0.84, 95% CI 0.73Y0.95, PG0.001) for patients with ACR from OGI, who had a similar level of serum creatinine. Third, when clinical data were combined with genes, the diagnostic accuracy increased from 0.71 to 0.94 (95% CI 0.89Y1.00, PG0.001). Acute rejection in kidney transplantation is an important factor in a sense that it inhibits long-term graft survival, and therefore easy and early diagnostic tools of acute rejection are necessary to perform early treatment for graft rejection and to prevent immunosuppressive toxicity, and thereby to achieve long-term graft survival (1, 5). Although biopsy is requisite for the diagnosis of acute and chronic graft rejection, it is still invasive, painful, and may induce serious complications such as hematuria, fistula, and hematoma. Host responses can be detected earlier at
molecular level before immune response develops and causes significant damages to the graft, which is detected by biopsy. These changes can be found in peripheral blood and urine as well as in biopsy tissues, and the noninvasive methods could be more practical tools for serial monitoring of patients. Because high-throughput technology for RNA identification and quantification are consistently developing, these technologies can be used to analyze multiple genes in a single sample with reliability and cost efficiency. During the last few decades, various novel immune monitoring tools such as ELISPOT and Immunknow assay have been developed for individualized immunotherapy (12, 13). As the bioinformatic techniques have been advanced, it became possible to combine information from gene expression tests, proteomics, and immune monitoring techniques (5). Combining blood gene test and other immune monitoring test, that is multiplatform approach, can be efficiently used to monitor graft rejection response and immune response of kidney transplant patients. The clinical feasibility of peripheral blood-based gene biomarkers has been demonstrated in the field of heart transplantation. Allomap, a commercially available gene expression profiling test, was developed using high-throughput microarray technology and was extensively studied for comparing with the standard, invasive endomyocardial biopsy (10). These studies suggest that blood gene test is promising in monitoring as a noninvasive biomarker to guide management after organ transplantation as well as in diagnosing acute rejection episodes. Li et al. recently developed and validated a peripheral blood gene expression test to assess AR likelihood
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
764
www.transplantjournal.com
Transplantation
& Volume 98, Number 7, October 15, 2014
FIGURE 3. Receiver operating characteristic (ROC) curves comparing ACR patients with OGI by means of by means of clinical variables alone, biomarkers alone, and the combination of biomarkers and clinical variables. AUC indicates area under the curve; Sens., sensitivity; C.I., confidence interval; Spec., specificity; PPV, positive predictive value; NPV, negative predictive value.
in American pediatric kidney transplant recipients (9). They carried out RNA microarray studies with 198 samples of peripheral blood from pediatric and young adult recipients with acute rejection, stable function, or other abnormalities at the time of biopsy in 12 American transplant centers. A set of five genes were selected from verification and validation tests using subsequent quantitative PCR, and they were able to discriminate patients with acute rejection from stable patients with great sensitivity and specificity. To the best of our knowledge, our study is the first validation study for this peripheral blood gene test in the independent, Asian, biopsybased adult kidney transplant cohort. This novel strategy using peripheral gene expression in AR is promising and can lead noninvasive, tailored immune suppression in kidney transplant recipients. However, differences in mRNA expression levels could be caused by natural genetic variation between individuals and population as well as physiologic or pathologic stresses (11, 14). Our study validated only two genes, MAPK9 and PSEN1, of the five-gene biomarker set found in peripheral blood of Caucasian patients. This difference is presumed to be a result of ethnic differences in the study population, and further study is required on differences in polymorphism in regulatory region of the biomarker gene set. Age difference in the study population might also have had an effect on the significance of individual genes. Because this validation cohort comprises older adult than original training dataset
(9), age may have played as a confounder for age-associated transcripts. Overall, our study suggests that gene expression levels of MAPK9 and PSEN1 can be sufficiently used to diagnose ACR on Korean adult kidney transplant recipients, especially combined with clinical risk factors. MAPK9, also known as JNK2, is a protein coding gene of serine/threonine-protein kinase that regulates a wide variety of cellular function. PSEN1 is a protein-coding gene of presenillin-1 (PS-1), which medicates the regulated proteolytic events of several proteins. It has been suggested to be involved in the pathogenesis of Alzheimer disease and plays a role in Notch and Wnt signaling pathways (15, 16). The exact role of MAPK9 and PSEN1 on immune activation is unveiled yet. However, the recent papers showed that genetic deletion of JNK2 worsens the disease outcome in an experimental model of inflammatory bowel disease (17, 18). The possible role of these genes on immune activation should be investigated to understand the mechanism of the activation during ACR. In summary, in this first independent validation study of a peripheral blood test for ACR in adult kidney transplant recipients, peripheral blood gene test showed a strong discrimination for patients with ACR from OGI. These results suggest a novel diagnostic and monitoring strategy using peripheral gene expression is promising in kidney transplantation. Transcriptome biomarker sets should be more identified and validated in large cohorts to support
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
Lee et al.
* 2014 Lippincott Williams & Wilkins
clinical trials and therefore ultimately provide individualized, tailored immunosuppressive treatment for kidney transplantation.
MATERIALS AND METHODS Patients and Study Samples
models were fit with disease status as the binary dependent variable. Correlation of clinical and demographic confounders with the expression level of five genes was analyzed using Pearson correlation to investigate the effects of confounders on five-gene set’s prediction score for AR. Moreover, univariate and multivariate logistic regression models for all nine clinical covariates were constructed with the expression values of five genes to examine the risk for AR. The covariates used in these models were recipient age, recipient gender, posttransplant time, degree of HLA mismatch, type of immunosuppressive drugs, donor age, donor gender, donor source (living vs. deceased), and serum creatinine level at the time of biopsy. All analyses were performed with SPSS statistical software (version 15; SPSS Inc., Chicago, IL, USA).
The study population consisted of Korean adult (recipient 918 years old) kidney recipients who had taken the allograft biopsy at two Kyung Hee University affiliated hospitals and Seoul National University Hospital from January 2010 to December 2012. All recipients universally received nondepleting induction therapy with interleukin 2 receptor antagonist. Maintenance immunosuppression included a calcineurin inhibitor, mycophenolate mofetil, and prednisone. The firstline calcineurin inhibitor was tacrolimus with trough level monitoring. For the patients who were at low immunological risk, prednisone was discontinued within the first 6 months after transplantation. All recipients universally received prophylaxis with daily trimethoprimsulfamethoxazole for 6 months after transplantation. Pre-emptive antiviral therapy based on viral load monitoring for cytomegalovirus was done except for high-risk patients who were D+/Rj on donor and recipient serology. For high-risk patients, chemoprophylaxis with oral valganciclovir was done for 3 months after transplantation. A total of 143 peripheral blood samples from 143 adult kidney transplant recipients were analyzed in this study. Among 143 studied patients, 49 received a renal graft from a living donor (34.3%) and 94 from a deceased donor (95.7%). This study was approved by the institutional review boards of all participating centers, and informed consent was obtained from all participants. The samples were obtained at the time of graft biopsy and paired with the biopsies, which was proven and scored by the pathologists in each medical center. Protocol biopsy for each patient was performed at within 3 weeks and 6 months after transplantation; for cause biopsy was performed if any sign of graft dysfunction was suspected. These samples were divided into three categories: ‘‘acute rejection’’ (AR; n=39); ‘‘control’’ (CON; n=84), if they showed no evidence of AR or any other pathological findings; and ‘‘other graft injuries’’ (OGI; n=20), if any other pathological findings were present other than AR. Patients with mixed finding of AR and OGI in allograft biopsy were excluded. Patients with borderline rejection on Banff classification were also excluded.
10.
Sample Preparation and Quantitative Polymerase Chain Reaction (QPCR)
11.
Peripheral blood was collected in 2.5 mL PAXgene Blood RNA Tubes (PreAnalytiX; Qiagen, Hilden, Germany), and total RNA was extracted using a PAXgene Blood RNA Kit (PreAnalytiX; Qiagen) according to the manufacturer’s instructions. Total RNA was reverse-transcribed into cDNA using the SuperScript Choice System (Invitrogen, Carlsbad, CA, USA). Quantitative polymerase chain reaction was conducted on a Chromo 4 Real-Time PCR system (Bio-Rad, Hercules, CA, USA) under standard cycle conditions (10 min at 95-C, 40 cycles of 15 sec at 95-C, 30 sec at 60-C) by using Taqman gene expression assays for NKTR (Hs00234637_m1), MAPK9 (Hs00177102_m1), PSEN1 (Hs00997789_m1), PBEF1 (Hs00237184_m1), and DUSP1 (Hs00610256_q1) and Taqman master mix (Applied Biosystems, Foster City, CA, USA). Each PCR assay plate included a human universal cDNA, synthesized from Universal Human Reference RNA (Agilent Inc., Santa Clara, CA, USA). For each reaction plate, a housekeeping gene, GAPDH, was used as an endogenous control to normalize the gene expression of each sample. The relative quantification of mRNA expression was calculated using a comparative CT method. Expression values of the samples were averaged and normalized to that of universal human reference RNA.
Statistics and Adjustment for Confounders Unless otherwise specified, ANOVA was used for univariate comparison for continuous variables and chi-square tests for categorical variables. Standard methods (19) were used to estimate ROC curves. Z test was used to test AUCs versus random (AUC=0.50). A series of logistic regression
765
REFERENCES 1. 2. 3. 4. 5. 6. 7. 8. 9.
12.
13. 14. 15. 16. 17. 18.
19.
Matas AJ, Gillingham KJ, Payne WD, et al. The impact of an acute rejection episode on long-term renal allograft survival. Transplantation 1994; 57: 857. Yarlagadda SG, Coca SG, Formica RN Jr, et al. Association between delayed graft function and allograft and patient survival: a systematic review and meta-analysis. Nephrol Dial Transplant 2009; 24: 1039. Lefaucheur C, Loupy A, Vernerey D, et al. Antibody-mediated vascular rejection of kidney allografts: a population-based study. Lancet 2013; 381: 313. Mikhalski D, Wissing KM, Ghisdal L, et al. Cold ischemia is a major determinant of acute rejection and renal graft survival in the modern era of immunosuppression. Transplantation 2008; 85: S3. Chowdhury P, Hernandez-Fuentes MP. Non-invasive biomarkers to guide management following renal transplantation: the need for a multiplatform approach. Curr Opin Organ Transplant 2013; 18: 1. Su¨sal C, Do¨hler B, Sadeghi M, et al. Posttransplant sCD30 as a predictor of kidney graft outcome. Transplantation 2011; 91: 1364. Garcı´a Moreira V, Prieto Garcı´a B, Baltar Martı´n JM, et al. Cell-free DNA as a noninvasive acute rejection marker in renal transplantation. Clin Chem 2009; 55: 1958. Warady B, Davis R, Butte AJ, et al. A peripheral blood diagnostic test for acute rejection in renal transplantation. Am J Transplant 2012; 12: 2710. Li L, Khatri P, Sigdel TK, et al. A peripheral blood diagnostic test for acute rejection in renal transplantation. Am J Transplant 2012; 12: 2710. Pham MX, Teuteberg JJ, Kfoury AG, et al. Gene-expression profiling for rejection surveillance after cardiac transplantation. N Engl J Med 2010; 362: 1890. Dimas AS, Deutsch S, Stranger BE, et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 2009; 325: 1246. Nickel P, Presber F, Bold G, et al. Enzyme-linked immunosorbent spot assay for donor-reactive interferon-gamma-producing cells identifies T-cell presensitization and correlates with graft function at 6 and 12 months in renal-transplant recipients. Transplantation 2004; 78: 1640. Kowalski RJ, Post DR, Mannon RB, et al. Assessing relative risks of infection and rejection: a meta-analysis using an immune function assay. Transplantation 2006; 82: 663. Bushel PR, McGovern R, Liu L, et al. Population differences in transcript-regulator expression quantitative trait loci. PLoS One 2012; 7: e34286. Logeat F, Bessia C, Brou C, et al. The Notch1 receptor is cleaved constitutively by a furin-like convertase. Proc Natl Acad Sci U S A 1998; 95: 8108. Zhang C, Wu B, Beglopoulos V, et al. Presenilins are essential for regulating neurotransmitter release. Nature 2009; 460: 632. Chromik AM, Mu¨ller AM, Ko¨rner J, et al. Genetic deletion of JNK1 and JNK2 aggravates the DSS-induced colitis in mice. J Invest Surg 2007; 20: 23. Kersting S, Reinecke K, Hilgert C, et al. Knockout of the c-Jun Nterminal Kinase 2 aggravates the development of mild chronic dextran sulfate sodium colitis independently of expression of intestinal cytokines TNF>, TGFB1, and IL-6. J Inflamm Res 2013; 6: 13. Vickers AJ. Decision analysis for the evaluation of diagnostic tests, prediction models and molecular markers. Am Stat 2008; 62: 314.
Copyright © 2014 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
