Urinary cell mRNA profiles and differential diagnosis of acute kidney graft dysfunction

Abstract

Noninvasive tests to differentiate the basis for acute dysfunction of the kidney allograft are preferable to invasive allograft biopsies. We measured absolute levels of 26 prespecified mRNAs in urine samples collected from kidney graft recipients at the time of for-cause biopsy for acute allograft dysfunction and investigated whether differential diagnosis of acute graft dysfunction is feasible using urinary cell mRNA profiles. We profiled 52 urine samples from 52 patients with biopsy specimens indicating acute rejection (26 acute T cell-mediated rejection and 26 acute antibody-mediated rejection) and 32 urine samples from 32 patients with acute tubular injury without acute rejection. A stepwise quadratic discriminant analysis of mRNA measures identified a linear combination of mRNAs for CD3ε, CD105, TLR4, CD14, complement factor B, and vimentin that distinguishes acute rejection from acute tubular injury; 10-fold cross-validation of the six-gene signature yielded an estimate of the area under the curve of 0.92 (95% confidence interval, 0.86 to 0.98). In a decision analysis, the six-gene signature yielded the highest net benefit across a range of reasonable threshold probabilities for biopsy. Next, among patients diagnosed with acute rejection, a similar statistical approach identified a linear combination of mRNAs for CD3ε, CD105, CD14, CD46, and 18S rRNA that distinguishes T cell-mediated rejection from antibody-mediated rejection, with a cross-validated estimate of the area under the curve of 0.81 (95% confidence interval, 0.68 to 0.93). Incorporation of these urinary cell mRNA signatures in clinical decisions may reduce the number of biopsies in patients with acute dysfunction of the kidney allograft.

Keywords: acute allograft rejection; mRNA; renal dysfunction.

Copyright © 2014 by the American Society of Nephrology.

Figures

Figure 1.

Flowchart showing the two-step approach for the discovery and validation of urinary cell diagnostic signatures for the differential diagnosis of acute kidney graft dysfunction. We measured urinary cell transcript levels from 84 kidney transplant recipients with acute allograft dysfunction with the use of preamplification-enhanced real-time quantitative PCR assays using a customized amplicon to construct the standard curve and quantified mRNA abundance as copies per microgram of total RNA obtained from urinary cells. We used individual transcripts as variables to construct statistical models using discriminant analysis. In each model, the linear combination of variables yielded a discriminant score that constituted the diagnostic signature. We used a two-step approach to develop the diagnostic signatures. In the first step, we sought to differentiate AR (both types; n=52) from ATI (n=32). In the second step, with the use of the same PCR assay results, we sought to differentiate ACR (n=26) from AMR (n=26). We used 10-fold cross-validation to validate both the models.

Figure 2.

Predicted probability of AR from the 10-fold cross-validation of the six-gene urinary cell diagnostic signature. We measured absolute levels of 26 mRNAs and 18S rRNA in the urinary cells from 84 kidney graft recipients. We used quadratic discriminant function analysis to derive linear combination of mRNAs to better differentiate 52 AR biopsies (ACR and AMR; n=52 patients) from 32 ATI biopsies (n=32 patients) than any single mRNA measure. A linear combination of six mRNAs (CD3ε, CD105, TLR4, CD14, Complement Factor B, and Vimentin) emerged as the parsimonious model and yielded a discriminant score that constituted the diagnostic signature. We did 10-fold cross-validation to internally validate the six-gene diagnostic signature. The entire study cohort of 84 patients was randomly divided into 10 equal groups. Within each of 10 groups, the proportion of samples (AR versus ATI) was similar to the undivided cohort. At the first run, group 1 (10% of samples) was excluded, and a signature was derived from the remaining nine groups (90% of samples), including both variables selection and model fitting. Next, this newly derived signature was applied to samples of group 1 to predict their diagnostic outcome. In the second run, group 2 was excluded, and a signature was derived from the remaining nine groups (90% of samples), including both variables selection and model fitting. This newly derived signature was applied to samples of group 2 (10% of samples) to predict their diagnostic outcome. This iteration was done for all 10 groups. Thus, all observations were used for both deriving and validating a model, and each observation was used for validation exactly one time. Accordingly, the predicted probability for an individual patient was derived from a model that did not include any data from that patient. We used the predicted probability for each patient from the cross-validation to construct an ROC curve. The left panel shows the box plot of predicted probability of AR from the cross-validation. The horizontal line within each box represents the median, and the plus symbol represents the mean. The bottom and top of each box represent 1.5 times the interquartile range. The values beyond 1.5 times the interquartile range are shown as dots. The discrimination slope is the difference between the means of the predicted probabilities of the two groups. The right panel shows the ROC curve of the predicted probability for each patient from the cross-validation to diagnose AR. The sensitivity (true positive fraction), specificity (false positive fraction), likelihood ratio of a positive test (LR+; sensitivity/1−specificity), and likelihood ratio of a negative test (LR−; 1−sensitivity/specificity) for various cutpoints of predicted risks are shown beneath the x axis. The AUC is the estimate of the expected value in an independent sample not used for deriving the diagnostic signature.

Figure 3.

Decision curve analysis to assess the clinical benefit of the six-gene urinary cell diagnostic signature to differentiate AR from ATI. We used the predicted probability for each patient from the 10-fold cross-validation in decision curve analysis to quantify the clinical benefit of the diagnostic signature in terms of the number of unnecessary biopsies that can be avoided in the diagnosis of AR. In the upper panel, the y axis represents the net benefit ((true positive count/n)−(false positive count/n)×[pt/(1−pt)]), where true positive count is the number of patients with AR, false positive count is the number of patients with ATI, n is the total number of patients, and pt is the threshold probability. Here, pt/(1−pt) is the ratio of the harms of false positive to false negative results. Of 84 patients that we studied, 52 (62%) patients had AR. This proportion of AR is a reasonable approximation of the expected incidence of AR in consecutive for-cause (diagnostic) biopsies done to identify the cause of acute graft dysfunction. The green line is the net benefit of the urinary cell diagnostic signature. This strategy is compared with the biopsy all patients strategy (red line), which is essentially the current approach. The blue line, which represents no net benefit, is the biopsy none strategy. The decision curve plot depicts that, among patients who present with acute graft dysfunction, within a reasonable physician/patient threshold probability for doing a biopsy to diagnose AR, the use of urinary cell diagnostic signature is beneficial compared with the current biopsy all patients strategy. In the lower panel, for each threshold probability on the x axis, the corresponding value on the y axis represents the net reduction in avoidable biopsies per 100 patients when using the diagnostic signature.

Figure 4.

Predicted probability of ACR from the 10-fold cross-validation of the five-gene urinary cell diagnostic signature. After the differentiation of AR from ATI in the first step (Figure 1), in the second step and among patients diagnosed with AR biopsies, we derived (using the same assay results) another urinary cell diagnostic signature to better differentiate ACR biopsies (n=26 patients) from AMR biopsies (n=26 patients) than any single mRNA measure. By quadratic discriminant function analysis, a linear combination of four mRNAs (CD3ε, CD105, CD14, and CD46) and 18S rRNA emerged as the parsimonious model and yielded a discriminant score that constituted the diagnostic signature. We did 10-fold cross-validation to internally validate the five-gene diagnostic signature. The left panel shows the box plot of predicted probability of ACR biopsies from the cross-validation. The right panel shows the ROC curve of the five-gene urinary cell diagnostic signature to diagnose ACR. The AUC is the estimate of the expected value in an independent sample not used for deriving the diagnostic signature.

Figure 5.

Relationship between the urinary cell diagnostic signature score and the time from transplantation to biopsy/urine sample collection. The diagnostic signature score is represented on the y axis (upper panel, six-gene signature; lower panel, five-gene signature), and time from transplantation to biopsy/urine sample collection, in logarithmic scale, is represented on the x axis. Induction immunosuppression therapy with lymphocyte-depleting Thymoglobulin (including one patient with alemtuzumab) is shown as closed symbols, whereas induction with lymphocyte-nondepleting IL-2 receptor antibody or no induction therapy is shown as open symbols. Within each diagnostic category, analysis involving Spearman rank order correlation showed that there was no significant association (P>0.05) between the score of the six- or five-gene diagnostic signatures and the time from transplantation to biopsy in patients with biopsies showing ACR, AMR, or ATI and induced with depleting or nondepleting antibodies. There was also no association between the scores of the signatures and either serum creatinine levels (six-gene signature: ACR: rs=−0.39, P=0.06; AMR: rs=−0.19, P=0.30; ATI: rs=−0.002, P=0.90; five-gene signature: ACR: rs=−0.14, P=0.50; AMR: rs=−0.07, P=0.70) or tacrolimus trough levels (six-gene signature: ACR: rs=0.14, P=0.50; AMR: rs=−0.14, P=0.50; ATI: rs=−0.02, P=0.90; five-gene signature: ACR: rs=−0.12, P=0.60; AMR: rs=−0.02, P=0.90; not shown).