Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study

Philip J. O'Connell; Weijia Zhang; Madhav C. Menon; Zhengzi Yi; Bernd Schröppel; Lorenzo Gallon; Yi Luan; Ivy A. Rosales; Yongchao Ge; Bojan Losic; Caixia Xi; Christopher Woytovich; Karen L. Keung; Chengguo Wei; Ilana Greene; Jessica Overbey; Emilia Bagiella; Nader Najafian; Milagros Samaniego; Arjang Djamali; Stephen I. Alexander; Brian J. Nankivell; Jeremy R. Chapman; Rex Neal Smith; Robert Colvin; Barbara Murphy

doi:10.1016/S0140-6736(16)30826-1

Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study

Philip J. O'Connell, Weijia Zhang, Madhav C. Menon, Zhengzi Yi, Bernd Schröppel, Lorenzo Gallon, Yi Luan, Ivy A. Rosales, Yongchao Ge, Bojan Losic, Caixia Xi, Christopher Woytovich, Karen L. Keung, Chengguo Wei, Ilana Greene, Jessica Overbey, Emilia Bagiella, Nader Najafian, Milagros Samaniego, Arjang DjamaliStephen I. Alexander, Brian J. Nankivell, Jeremy R. Chapman, Rex Neal Smith, Robert Colvin, Barbara Murphy^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

149 Scopus citations

Abstract

Background Chronic injury in kidney transplants remains a major cause of allograft loss. The aim of this study was to identify a gene set capable of predicting renal allografts at risk of progressive injury due to fibrosis. Methods This Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicentre study. We prospectively collected biopsies from renal allograft recipients (n=204) with stable renal function 3 months after transplantation. We used microarray analysis to investigate gene expression in 159 of these tissue samples. We aimed to identify genes that correlated with the Chronic Allograft Damage Index (CADI) score at 12 months, but not fibrosis at the time of the biopsy. We applied a penalised regression model in combination with permutation-based approach to derive an optimal gene set to predict allograft fibrosis. The GoCAR study is registered with ClinicalTrials.gov, number NCT00611702. Findings We identified a set of 13 genes that was independently predictive for the development of fibrosis at 1 year (ie, CADI-12 ≥2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fibrosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Interpretation Our results suggest that this set of 13 genes could be used to identify kidney transplant recipients at risk of allograft loss before the development of irreversible damage, thus allowing therapy to be modified to prevent progression to fibrosis. Funding National Institutes of Health.

Original language	English (US)
Pages (from-to)	983-993
Number of pages	11
Journal	The Lancet
Volume	388
Issue number	10048
DOIs	https://doi.org/10.1016/S0140-6736(16)30826-1
State	Published - Sep 3 2016

Funding

This work is a substudy of the GoCAR study sponsored by NIH 5U01AI070107-03. The cost of clinical and genomic experiments and the effort of all the co-authors involved in patient enrolment, data management and analysis, and manuscript preparation were paid through this grant. MCM acknowledges support from the American Heart Association Scientist Development award 15SDG25870018, and past support from a research fellowship from the American Society of Nephrology. IAR would like to acknowledge the ISN Fellowship Program for her participation in this project. PJO'C was a recipient of a Senior Practitioner Fellowship from the National Health and Medical Research Council of Australia.

ASJC Scopus subject areas

General Medicine

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O'Connell, P. J., Zhang, W., Menon, M. C., Yi, Z., Schröppel, B., Gallon, L., Luan, Y., Rosales, I. A., Ge, Y., Losic, B., Xi, C., Woytovich, C., Keung, K. L., Wei, C., Greene, I., Overbey, J., Bagiella, E., Najafian, N., Samaniego, M., ... Murphy, B. (2016). Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study. The Lancet, 388(10048), 983-993. https://doi.org/10.1016/S0140-6736(16)30826-1

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title = "Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study",

abstract = "Background Chronic injury in kidney transplants remains a major cause of allograft loss. The aim of this study was to identify a gene set capable of predicting renal allografts at risk of progressive injury due to fibrosis. Methods This Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicentre study. We prospectively collected biopsies from renal allograft recipients (n=204) with stable renal function 3 months after transplantation. We used microarray analysis to investigate gene expression in 159 of these tissue samples. We aimed to identify genes that correlated with the Chronic Allograft Damage Index (CADI) score at 12 months, but not fibrosis at the time of the biopsy. We applied a penalised regression model in combination with permutation-based approach to derive an optimal gene set to predict allograft fibrosis. The GoCAR study is registered with ClinicalTrials.gov, number NCT00611702. Findings We identified a set of 13 genes that was independently predictive for the development of fibrosis at 1 year (ie, CADI-12 ≥2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fibrosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Interpretation Our results suggest that this set of 13 genes could be used to identify kidney transplant recipients at risk of allograft loss before the development of irreversible damage, thus allowing therapy to be modified to prevent progression to fibrosis. Funding National Institutes of Health.",

author = "O'Connell, {Philip J.} and Weijia Zhang and Menon, {Madhav C.} and Zhengzi Yi and Bernd Schr{\"o}ppel and Lorenzo Gallon and Yi Luan and Rosales, {Ivy A.} and Yongchao Ge and Bojan Losic and Caixia Xi and Christopher Woytovich and Keung, {Karen L.} and Chengguo Wei and Ilana Greene and Jessica Overbey and Emilia Bagiella and Nader Najafian and Milagros Samaniego and Arjang Djamali and Alexander, {Stephen I.} and Nankivell, {Brian J.} and Chapman, {Jeremy R.} and Smith, {Rex Neal} and Robert Colvin and Barbara Murphy",

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year = "2016",

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doi = "10.1016/S0140-6736(16)30826-1",

language = "English (US)",

volume = "388",

pages = "983--993",

journal = "The Lancet",

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O'Connell, PJ, Zhang, W, Menon, MC, Yi, Z, Schröppel, B, Gallon, L, Luan, Y, Rosales, IA, Ge, Y, Losic, B, Xi, C, Woytovich, C, Keung, KL, Wei, C, Greene, I, Overbey, J, Bagiella, E, Najafian, N, Samaniego, M, Djamali, A, Alexander, SI, Nankivell, BJ, Chapman, JR, Smith, RN, Colvin, R & Murphy, B 2016, 'Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study', The Lancet, vol. 388, no. 10048, pp. 983-993. https://doi.org/10.1016/S0140-6736(16)30826-1

TY - JOUR

T1 - Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury

T2 - a multicentre, prospective study

AU - O'Connell, Philip J.

AU - Zhang, Weijia

AU - Menon, Madhav C.

AU - Yi, Zhengzi

AU - Schröppel, Bernd

AU - Gallon, Lorenzo

AU - Luan, Yi

AU - Rosales, Ivy A.

AU - Ge, Yongchao

AU - Losic, Bojan

AU - Xi, Caixia

AU - Woytovich, Christopher

AU - Keung, Karen L.

AU - Wei, Chengguo

AU - Greene, Ilana

AU - Overbey, Jessica

AU - Bagiella, Emilia

AU - Najafian, Nader

AU - Samaniego, Milagros

AU - Djamali, Arjang

AU - Alexander, Stephen I.

AU - Nankivell, Brian J.

AU - Chapman, Jeremy R.

AU - Smith, Rex Neal

AU - Colvin, Robert

AU - Murphy, Barbara

PY - 2016/9/3

Y1 - 2016/9/3

N2 - Background Chronic injury in kidney transplants remains a major cause of allograft loss. The aim of this study was to identify a gene set capable of predicting renal allografts at risk of progressive injury due to fibrosis. Methods This Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicentre study. We prospectively collected biopsies from renal allograft recipients (n=204) with stable renal function 3 months after transplantation. We used microarray analysis to investigate gene expression in 159 of these tissue samples. We aimed to identify genes that correlated with the Chronic Allograft Damage Index (CADI) score at 12 months, but not fibrosis at the time of the biopsy. We applied a penalised regression model in combination with permutation-based approach to derive an optimal gene set to predict allograft fibrosis. The GoCAR study is registered with ClinicalTrials.gov, number NCT00611702. Findings We identified a set of 13 genes that was independently predictive for the development of fibrosis at 1 year (ie, CADI-12 ≥2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fibrosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Interpretation Our results suggest that this set of 13 genes could be used to identify kidney transplant recipients at risk of allograft loss before the development of irreversible damage, thus allowing therapy to be modified to prevent progression to fibrosis. Funding National Institutes of Health.

AB - Background Chronic injury in kidney transplants remains a major cause of allograft loss. The aim of this study was to identify a gene set capable of predicting renal allografts at risk of progressive injury due to fibrosis. Methods This Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicentre study. We prospectively collected biopsies from renal allograft recipients (n=204) with stable renal function 3 months after transplantation. We used microarray analysis to investigate gene expression in 159 of these tissue samples. We aimed to identify genes that correlated with the Chronic Allograft Damage Index (CADI) score at 12 months, but not fibrosis at the time of the biopsy. We applied a penalised regression model in combination with permutation-based approach to derive an optimal gene set to predict allograft fibrosis. The GoCAR study is registered with ClinicalTrials.gov, number NCT00611702. Findings We identified a set of 13 genes that was independently predictive for the development of fibrosis at 1 year (ie, CADI-12 ≥2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fibrosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972). Interpretation Our results suggest that this set of 13 genes could be used to identify kidney transplant recipients at risk of allograft loss before the development of irreversible damage, thus allowing therapy to be modified to prevent progression to fibrosis. Funding National Institutes of Health.

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ER -

Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study

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