Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker
Science Translational Medicine • 2 Dec 2015 • Vol 7, Issue 316 • p. 316ra193 • DOI: 10.1126/scitranslmed.aac7071
Urine marker to the rescue
Chronic kidney disease is a common medical problem worldwide, but it is difficult to predict which patients are more likely to progress to end-stage disease and need aggressive management. Ju et al. have now drawn on four independent cohorts totaling hundreds of patients from around the world to identify the expression of epidermal growth factor (EGF) in the kidneys as a marker of kidney disease progression. Moreover, the authors demonstrated that the amount of EGF in the urine is just as useful, providing a biomarker that can be easily tracked over time without requiring invasive biopsies.
Abstract
Chronic kidney disease (CKD) affects 8 to 16% people worldwide, with an increasing incidence and prevalence of end-stage kidney disease (ESKD). The effective management of CKD is confounded by the inability to identify patients at high risk of progression while in early stages of CKD. To address this challenge, a renal biopsy transcriptome-driven approach was applied to develop noninvasive prognostic biomarkers for CKD progression. Expression of intrarenal transcripts was correlated with the baseline estimated glomerular filtration rate (eGFR) in 261 patients. Proteins encoded by eGFR-associated transcripts were tested in urine for association with renal tissue injury and baseline eGFR. The ability to predict CKD progression, defined as the composite of ESKD or 40% reduction of baseline eGFR, was then determined in three independent CKD cohorts. A panel of intrarenal transcripts, including epidermal growth factor (EGF), a tubule-specific protein critical for cell differentiation and regeneration, predicted eGFR. The amount of EGF protein in urine (uEGF) showed significant correlation (P < 0.001) with intrarenal EGF mRNA, interstitial fibrosis/tubular atrophy, eGFR, and rate of eGFR loss. Prediction of the composite renal end point by age, gender, eGFR, and albuminuria was significantly (P < 0.001) improved by addition of uEGF, with an increase of the C-statistic from 0.75 to 0.87. Outcome predictions were replicated in two independent CKD cohorts. Our approach identified uEGF as an independent risk predictor of CKD progression. Addition of uEGF to standard clinical parameters improved the prediction of disease events in diverse CKD populations with a wide spectrum of causes and stages.
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Supplementary Material
Summary
Materials and Methods
Fig. S1. Baseline eGFR prediction by a three-marker panel in C-PROBE.
Fig. S2. EGF and two other candidate mRNAs’ expression patterns in a panel of 84 human organs, tissues, and cell lines.
Fig. S3. Correlation of EGF mRNA and uEGF/Cr with eGFR and eGFR slope in DN patients and CKD patients with diabetes.
Fig. S4. Correlation of uEGF/Cr with ACR.
Fig. S5. EGF as the top upstream regulator of genes correlated with eGFR slope.
Fig. S6. Correlation of uEGF/Cr with eGFR slope.
Fig. S7. ROC curve and corresponding AUC statistics for models with and without uEGF.
Fig. S8. mRNA localization by in situ hybridization: Negative and positive control images.
Table S1. Significantly enriched canonical pathways in intrarenal marker set.
Table S2. qRT-PCR assays used to validate expression of the intrarenal transcripts.
Table S3. Correlations of identified intrarenal transcripts with log2 eGFR of patients from the discovery and two validation cohorts.
Table S4. Demographic characteristics of NEPTUNE patients with intrarenal EGF expression data available.
Table S5. Top 10 upstream regulators of transcripts correlated with eGFR change over time (eGFR slope).
Resources
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Published In
Science Translational Medicine
Volume 7 | Issue 316
December 2015
December 2015
Copyright
Copyright © 2015, American Association for the Advancement of Science.
Submission history
Received: 12 June 2015
Accepted: 21 October 2015
Acknowledgments
We acknowledge all participating centers of the ERCB–Kröner-Fresenius Biopsy Bank (ERCB-KFB), the C-PROBE, the NEPTUNE, the PKU-IgAN study cohort, and their participants for their cooperation. We thank the support of George M. O’Brien Michigan Kidney Translational Core Center and the Michigan Diabetes Research Center at the University of Michigan. Funding: This study was supported by the Else Kröner-Fresenius Foundation (for ERCB); by the European Consortium for High-Throughput Research in Rare Kidney Diseases (EURenOmics; European Union FP 7:305608); by NIH (R01DK079912, P30DK081943, DK083912, P30DK020572, and UL1RR000433); by Office of Rare Diseases Research, National Center for Advancing Translational Sciences, National Institute of Diabetes and Digestive and Kidney Diseases, University of Michigan and NephCure Kidney International (U54DK083912); and by the University of Michigan Health System and Peking University Health Sciences Center Joint Institute for Translational and Clinical Research. Analysis of urine samples of C-PROBE patients was supported by Hoffman–La Roche. Author contributions: M.K., W.J., and V.N. participated in the study design. W.J., S.S., L.Z., A.R., M.T., C.S., and B.S. (for C-PROBE), W.J., S.S., S.M.B., and L.B. (for NEPTUNE), L.Z. (for PKU-IgAN), and C.D.C. (for ERCB) participated in data generation. K.S., V.N., L.Z., W.J., S.S., F.H.E., C.C.B., J.Y.-C.L., Y.Z., P.X.K.S., L.H.M., L.E., I.F., G.C.D.-P., G.D.-N., B.S., M.C.M., M. Bobadilla, H.-Y.W., H.Z., J.L., L.Z., and M.K. participated in data analysis. W.J., V.N., L.Z., H.Z., L.H.M., K.S., M. Bitzer, S.M.B., L.B., M.G.S., F.C.B., and M.K. participated in data interpretation; J.J.H., C.A.G., H.-Y.W., H.Z., and C.D.C. provided study materials; W.J. and C.C.B. participated in figure preparation; and W.J., V.N., L.H.M., P.X.K.S., F.C.B., K.S., and M.K. participated in writing the paper. The contribution of V.N. in this study is, in part, to fulfill the thesis requirement at the Ludwig Maximilian University of Munich. W.J. and V.N. share first authorship. W.J. and M.K. share correspondence. Competing interests: M. Bobadilla, G.C.D.-P., G.D.-N., L.E., M.C.M., M.T., I.F., C.S., M.K., V.N., and W.J. hold a patent PCT/EP2014/073413 “Biomarkers and methods for progression prediction for chronic kidney disease” related to this work. M.K. reports grants from Hoffman–La Roche during the conduct of the study; research support from AbbVie, AstraZeneca, Boehringer Ingelheim, and Eli Lilly outside the submitted work. M.K. is on the Board Advisory Committee of AbbVie, Eli Lilly, and Pfizer (honoraria paid to institution). C.D.C. received speaker honoraria from Hoffman–La Roche. Data and materials availability: The gene expression Cel files are available at Gene Expression Omnibus (www.ncbi.nlm.nih.gov/geo/) under reference nos. GSE32591, GSE37455, GSE35488, GSE47185, and GSE69438.
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