TY - JOUR
T1 - Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker
AU - Ju, Wenjun
AU - Nair, Viji
AU - Smith, Shahaan
AU - Zhu, Li
AU - Shedden, Kerby
AU - Song, Peter X.K.
AU - Mariani, Laura H.
AU - Eichinger, Felix H.
AU - Berthier, Celine C.
AU - Randolph, Ann
AU - Lai, Jennifer Yi Chun
AU - Zhou, Yan
AU - Hawkins, Jennifer J.
AU - Bitzer, Markus
AU - Sampson, Matthew G.
AU - Their, Martina
AU - Solier, Corinne
AU - Duran-Pacheco, Gonzalo C.
AU - Duchateau-Nguyen, Guillemette
AU - Essioux, Laurent
AU - Schott, Brigitte
AU - Formentini, Ivan
AU - Magnone, Maria C.
AU - Bobadilla, Maria
AU - Cohen, Clemens D.
AU - Bagnasco, Serena M.
AU - Barisoni, Laura
AU - Lv, Jicheng
AU - Zhang, Hong
AU - Wang, Hai Yan
AU - Brosius, Frank C.
AU - Gadegbeku, Crystal A.
AU - Kretzler, Matthias
N1 - Funding Information:
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 ofMichigan. 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. 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.
PY - 2015/12/2
Y1 - 2015/12/2
N2 - 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 transcriptomedriven 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.
AB - 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 transcriptomedriven 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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U2 - 10.1126/scitranslmed.aac7071
DO - 10.1126/scitranslmed.aac7071
M3 - Article
C2 - 26631632
AN - SCOPUS:84954288448
SN - 1946-6234
VL - 7
JO - Science translational medicine
JF - Science translational medicine
IS - 316
M1 - 7071
ER -