Discovery and validation of a molecular signature for the noninvasive diagnosis of human renal allograft fibrosis

Abstract

Background: Tubulointerstitial fibrosis (fibrosis), a histologic feature associated with a failing kidney allograft, is diagnosed using the invasive allograft biopsy. A noninvasive diagnostic test for fibrosis may help improve allograft outcome.

Methods: We obtained 114 urine specimens from 114 renal allograft recipients: 48 from 48 recipients with fibrosis in their biopsy results and 66 from 66 recipients with normal biopsy results. Levels of messenger RNAs (mRNAs) in urinary cells were measured using kinetic, quantitative polymerase chain reaction assays, and the levels were related to allograft diagnosis. A discovery set of 76 recipients (32 with allograft fibrosis and 44 with normal biopsy results) was used to develop a diagnostic signature, and an independent validation set of 38 recipients (16 with allograft fibrosis and 22 with normal biopsy results) was used to validate the signature.

Results: In the discovery set, urinary cell levels of the following mRNAs were significantly associated with the presence of allograft fibrosis: vimentin (P<0.0001, logistic regression model), hepatocyte growth factor (P<0.0001), α-smooth muscle actin (P<0.0001), fibronectin 1 (P<0.0001), perforin (P=0.0002), plasminogen activator inhibitor 1 (P=0.0002), transforming growth factor β1 (P=0.0004), tissue inhibitor of metalloproteinase 1 (P=0.0009), granzyme B (P=0.0009), fibroblast-specific protein 1 (P=0.006), CD103 (P=0.02), and collagen 1A1 (P=0.04). A four-gene model composed of the levels of mRNA for vimentin, NKCC2, and E-cadherin and of 18S ribosomal RNA provided the most accurate, parsimonious diagnostic model of allograft fibrosis with a sensitivity of 93.8% and a specificity of 84.1% (P<0.0001). In the independent validation set, this same model predicted the presence of allograft fibrosis with a sensitivity of 77.3% and a specificity of 87.5% (P<0.0001).

Conclusions: Measurement of mRNAs in urinary cells may offer a noninvasive means of diagnosing fibrosis in human renal allografts.

Figures

Figure 1. Flow chart for the discovery and validation of urinary cell mRNA profiles

The 114 renal allograft recipients (48 with biopsies showing fibrosis and 66 with normal biopsy results) were rank ordered within group (Fibrosis group or Normal Biopsy group) by the copy number of 18S rRNA and partitioned into triplets. Within each triplet, the first and third patients were assigned to the Discovery set and the second patient was assigned to the Validation set, resulting in the two sets being exactly matched on fibrosis status and very closely matched on 18S rRNA copy number. Twice as many patients were assigned to the Discovery set in order to enhance statistical power for the exploratory analyses which included a procedure to protect against the risk of a Type I error.

Figure 2. Predicted probability of fibrosis as a function of urinary cell mRNA copy number in the Discovery set, for LOESS model and piece-wise linear logistic regression model, after controlling for 18S rRNA copy number

Urine samples were collected from 32 renal transplant recipients with graft dysfunction and biopsy-confirmed fibrosis and 44 recipients with stable allograft function and normal allograft biopsy, and levels of mRNA in urinary cells were measured with the use of pre-amplification enhanced kinetic quantitative PCR assays. Figure shows the predicted probability of fibrosis (Y-axis), controlling for 18S rRNA, as a function of individual log10-transformed mRNA copy numbers (X-axis). Each plot shows the LOESS model’s predicted probabilities (dotted line), their 95% confidence interval (shaded area) and the logistic regression model’s predicted probabilities (solid line). According to the logistic models, the levels of twelve of the twenty-two mRNAs (vimentin, HGF, α-SMA, fibronectin 1, perforin, PAI1, TGFβ1, TIMP1, granzyme B, FSP1, CD103, and collagen 1A1) were significantly (P-values <0.05 with modified Bonferroni correction) associated with the diagnosis of fibrosis. Adjusted P-value for each parametric model is shown. The number of stable patients, number of fibrosis patients, and percentage of fibrosis patients within categories of the mRNA measure appear in each plot.

Figure 3. Final Model Derived from the Discovery Set for the Diagnosis of Fibrosis

The predicted probability of fibrosis (Y-axis) as a function of individual log10-transformed mRNA copy numbers (X-axis) for vimentin (A), NKCC2 (B) and E-cadherin (C) after controlling for the copy numbers for the other two mRNAs and 18S rRNA is shown. Each plot shows the LOESS model’s predicted probabilities (dotted line), their 95% confidence interval (shaded area) and the logistic regression model’s predicted probabilities (solid line). The parameter estimates for the 4-gene model including terms accounting for the relationships, including non-linear relationships, between the mRNAs and diagnosis are provided in Panel D.

Figure 4. Relationship of composite score to fibrosis in the Discovery set (A), ROC curve analysis of the composite score in the Discovery set (B) and the Validation set (C) and the predicted and observed number of transplant recipients with fibrosis for each sextile of the composite score within the Discovery and Validation sets (D)

To predict fibrosis in the Discovery set, a composite score was calculated based on a logistic model, from vimentin mRNA, NKCC2 mRNA and E cadherin mRNA as well as the 18S rRNA in urine samples obtained from the 32 subjects with biopsy-confirmed fibrosis and 44 subjects with stable graft function and normal allograft biopsy. The composite score predicted fibrosis with high accuracy. (A) Figure shows the predicted probability of fibrosis (Y-axis) as a logistic function of the composite score (X-axis). The blue band represents the 95% confidence interval of the model. (B) Figure shows the receiver-operating-characteristic curve for the diagnosis of fibrosis using the composite score. The model had an area under the curve of 0.95 (95%CI: 0.90 to 0.99, P<0.0001). At a cut-point or 4.5, fibrosis was diagnosed with a specificity of 84.1% (95%CI: 73.3 to 94.9%) and a sensitivity of 93.8% (95%CI: 85.4 to 99.9%). The final prediction equation derived from the Discovery set was used to calculate the predicted probability of fibrosis in the Validation set of 38 kidney transplant recipients; 16 with biopsy-confirmed fibrosis and 22 with stable graft function and normal allograft biopsy. (C) Figure shows the receiver-operating characteristic curve of the composite score (applying the equation from Figure 3, Panel D to the urinary cell mRNA levels of vimentin, NKCC2 and E-cadherin and 18S rRNA level of those in the Validation set) for the diagnosis of fibrosis. The area under the curve for the diagnosis of fibrosis in the Validation set was 0.89 (95%CI: 0.78 to 0.99, P<0.0001). At the composite score cut-point of 4.5 derived from the Discovery set, fibrosis was diagnosed in the Validation set with a specificity of 77.3% (95%CI: 59.8 to 94.8%) and a sensitivity of 87.5% (95% CI: 71.3 to 99.9%). (D) Figure shows the predicted and observed number of transplant recipients with fibrosis for each sextile of the composite score within the Discovery and Validation sets.