Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study

Jin Huan Wei, Zi Hao Feng, Yun Cao, Hong Wei Zhao, Zhen Hua Chen, Bing Liao, Qing Wang, Hui Han, Jin Zhang, Yun Ze Xu, Bo Li, Ji Tao Wu, Gui Mei Qu, Guo Ping Wang, Cong Liu, Wei Xue, Qiang Liu, Jun Lu, Cai Xia Li, Pei Xing LiZhi Ling Zhang, Hao Hua Yao, Yi Hui Pan, Wen Fang Chen, Dan Xie, Lei Shi, Zhen Li Gao, Yi Ran Huang, Fang Jian Zhou, Shao Gang Wang, Zhi-Ping Liu, Wei Chen, Jun Hang Luo

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3 Citations (Scopus)

Abstract

Background: Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier. Methods: In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival. Findings: Although intratumour heterogeneity was found in 48 (23%) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95% CI 0·660–0·826] in region 1, 0·734 [0·651–0·814] in region 2, and 0·736 [0·649–0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95% CI 2·81–10·07] in the internal testing set, 5·39 [3·38–8·59] in the independent validation set, and 4·62 [2·48–8·61] in the TCGA set; all p<0·0001), independently of patient age or sex and tumour stage, grade, or necrosis. The classifier and the clinicopathological risk factors (tumour stage, grade, and necrosis) were combined to construct a nomogram, which had a predictive accuracy significantly higher than that of each variable alone (AUC at 5 years 0·811 [95% CI 0·756–0·861]). Interpretation: Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence. Funding: National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.

Original languageEnglish (US)
Pages (from-to)591-600
Number of pages10
JournalThe Lancet Oncology
Volume20
Issue number4
DOIs
StatePublished - Apr 1 2019

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Validation Studies
Renal Cell Carcinoma
Multicenter Studies
Single Nucleotide Polymorphism
Recurrence
China
Atlases
Neoplasms
Survival
Genome
Area Under Curve
Necrosis
Technology
Natural Science Disciplines
Nomograms
Genome-Wide Association Study
Solar System
Therapeutics
Computational Biology
Paraffin

ASJC Scopus subject areas

  • Oncology

Cite this

Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma : a retrospective analysis and multicentre validation study. / Wei, Jin Huan; Feng, Zi Hao; Cao, Yun; Zhao, Hong Wei; Chen, Zhen Hua; Liao, Bing; Wang, Qing; Han, Hui; Zhang, Jin; Xu, Yun Ze; Li, Bo; Wu, Ji Tao; Qu, Gui Mei; Wang, Guo Ping; Liu, Cong; Xue, Wei; Liu, Qiang; Lu, Jun; Li, Cai Xia; Li, Pei Xing; Zhang, Zhi Ling; Yao, Hao Hua; Pan, Yi Hui; Chen, Wen Fang; Xie, Dan; Shi, Lei; Gao, Zhen Li; Huang, Yi Ran; Zhou, Fang Jian; Wang, Shao Gang; Liu, Zhi-Ping; Chen, Wei; Luo, Jun Hang.

In: The Lancet Oncology, Vol. 20, No. 4, 01.04.2019, p. 591-600.

Research output: Contribution to journalArticle

Wei, JH, Feng, ZH, Cao, Y, Zhao, HW, Chen, ZH, Liao, B, Wang, Q, Han, H, Zhang, J, Xu, YZ, Li, B, Wu, JT, Qu, GM, Wang, GP, Liu, C, Xue, W, Liu, Q, Lu, J, Li, CX, Li, PX, Zhang, ZL, Yao, HH, Pan, YH, Chen, WF, Xie, D, Shi, L, Gao, ZL, Huang, YR, Zhou, FJ, Wang, SG, Liu, Z-P, Chen, W & Luo, JH 2019, 'Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study', The Lancet Oncology, vol. 20, no. 4, pp. 591-600. https://doi.org/10.1016/S1470-2045(18)30932-X
Wei, Jin Huan ; Feng, Zi Hao ; Cao, Yun ; Zhao, Hong Wei ; Chen, Zhen Hua ; Liao, Bing ; Wang, Qing ; Han, Hui ; Zhang, Jin ; Xu, Yun Ze ; Li, Bo ; Wu, Ji Tao ; Qu, Gui Mei ; Wang, Guo Ping ; Liu, Cong ; Xue, Wei ; Liu, Qiang ; Lu, Jun ; Li, Cai Xia ; Li, Pei Xing ; Zhang, Zhi Ling ; Yao, Hao Hua ; Pan, Yi Hui ; Chen, Wen Fang ; Xie, Dan ; Shi, Lei ; Gao, Zhen Li ; Huang, Yi Ran ; Zhou, Fang Jian ; Wang, Shao Gang ; Liu, Zhi-Ping ; Chen, Wei ; Luo, Jun Hang. / Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma : a retrospective analysis and multicentre validation study. In: The Lancet Oncology. 2019 ; Vol. 20, No. 4. pp. 591-600.
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title = "Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study",
abstract = "Background: Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier. Methods: In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival. Findings: Although intratumour heterogeneity was found in 48 (23{\%}) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95{\%} CI 0·660–0·826] in region 1, 0·734 [0·651–0·814] in region 2, and 0·736 [0·649–0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95{\%} CI 2·81–10·07] in the internal testing set, 5·39 [3·38–8·59] in the independent validation set, and 4·62 [2·48–8·61] in the TCGA set; all p<0·0001), independently of patient age or sex and tumour stage, grade, or necrosis. The classifier and the clinicopathological risk factors (tumour stage, grade, and necrosis) were combined to construct a nomogram, which had a predictive accuracy significantly higher than that of each variable alone (AUC at 5 years 0·811 [95{\%} CI 0·756–0·861]). Interpretation: Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence. Funding: National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.",
author = "Wei, {Jin Huan} and Feng, {Zi Hao} and Yun Cao and Zhao, {Hong Wei} and Chen, {Zhen Hua} and Bing Liao and Qing Wang and Hui Han and Jin Zhang and Xu, {Yun Ze} and Bo Li and Wu, {Ji Tao} and Qu, {Gui Mei} and Wang, {Guo Ping} and Cong Liu and Wei Xue and Qiang Liu and Jun Lu and Li, {Cai Xia} and Li, {Pei Xing} and Zhang, {Zhi Ling} and Yao, {Hao Hua} and Pan, {Yi Hui} and Chen, {Wen Fang} and Dan Xie and Lei Shi and Gao, {Zhen Li} and Huang, {Yi Ran} and Zhou, {Fang Jian} and Wang, {Shao Gang} and Zhi-Ping Liu and Wei Chen and Luo, {Jun Hang}",
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TY - JOUR

T1 - Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma

T2 - a retrospective analysis and multicentre validation study

AU - Wei, Jin Huan

AU - Feng, Zi Hao

AU - Cao, Yun

AU - Zhao, Hong Wei

AU - Chen, Zhen Hua

AU - Liao, Bing

AU - Wang, Qing

AU - Han, Hui

AU - Zhang, Jin

AU - Xu, Yun Ze

AU - Li, Bo

AU - Wu, Ji Tao

AU - Qu, Gui Mei

AU - Wang, Guo Ping

AU - Liu, Cong

AU - Xue, Wei

AU - Liu, Qiang

AU - Lu, Jun

AU - Li, Cai Xia

AU - Li, Pei Xing

AU - Zhang, Zhi Ling

AU - Yao, Hao Hua

AU - Pan, Yi Hui

AU - Chen, Wen Fang

AU - Xie, Dan

AU - Shi, Lei

AU - Gao, Zhen Li

AU - Huang, Yi Ran

AU - Zhou, Fang Jian

AU - Wang, Shao Gang

AU - Liu, Zhi-Ping

AU - Chen, Wei

AU - Luo, Jun Hang

PY - 2019/4/1

Y1 - 2019/4/1

N2 - Background: Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier. Methods: In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival. Findings: Although intratumour heterogeneity was found in 48 (23%) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95% CI 0·660–0·826] in region 1, 0·734 [0·651–0·814] in region 2, and 0·736 [0·649–0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95% CI 2·81–10·07] in the internal testing set, 5·39 [3·38–8·59] in the independent validation set, and 4·62 [2·48–8·61] in the TCGA set; all p<0·0001), independently of patient age or sex and tumour stage, grade, or necrosis. The classifier and the clinicopathological risk factors (tumour stage, grade, and necrosis) were combined to construct a nomogram, which had a predictive accuracy significantly higher than that of each variable alone (AUC at 5 years 0·811 [95% CI 0·756–0·861]). Interpretation: Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence. Funding: National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.

AB - Background: Identification of high-risk localised renal cell carcinoma is key for the selection of patients for adjuvant treatment who are at truly higher risk of reccurrence. We developed a classifier based on single-nucleotide polymorphisms (SNPs) to improve the predictive accuracy for renal cell carcinoma recurrence and investigated whether intratumour heterogeneity affected the precision of the classifier. Methods: In this retrospective analysis and multicentre validation study, we used paraffin-embedded specimens from the training set of 227 patients from Sun Yat-sen University (Guangzhou, Guangdong, China) with localised clear cell renal cell carcinoma to examine 44 potential recurrence-associated SNPs, which were identified by exploratory bioinformatics analyses of a genome-wide association study from The Cancer Genome Atlas (TCGA) Kidney Renal Clear Cell Carcinoma (KIRC) dataset (n=114, 906 600 SNPs). We developed a six-SNP-based classifier by use of LASSO Cox regression, based on the association between SNP status and patients' recurrence-free survival. Intratumour heterogeneity was investigated from two other regions within the same tumours in the training set. The six-SNP-based classifier was validated in the internal testing set (n=226), the independent validation set (Chinese multicentre study; 428 patients treated between Jan 1, 2004 and Dec 31, 2012, at three hospitals in China), and TCGA set (441 retrospectively identified patients who underwent resection between 1998 and 2010 for localised clear cell renal cell carcinoma in the USA). The main outcome was recurrence-free survival; the secondary outcome was overall survival. Findings: Although intratumour heterogeneity was found in 48 (23%) of 206 cases in the internal testing set with complete SNP information, the predictive accuracy of the six-SNP-based classifier was similar in the three different regions of the training set (areas under the curve [AUC] at 5 years: 0·749 [95% CI 0·660–0·826] in region 1, 0·734 [0·651–0·814] in region 2, and 0·736 [0·649–0·824] in region 3). The six-SNP-based classifier precisely predicted recurrence-free survival of patients in three validation sets (hazard ratio [HR] 5·32 [95% CI 2·81–10·07] in the internal testing set, 5·39 [3·38–8·59] in the independent validation set, and 4·62 [2·48–8·61] in the TCGA set; all p<0·0001), independently of patient age or sex and tumour stage, grade, or necrosis. The classifier and the clinicopathological risk factors (tumour stage, grade, and necrosis) were combined to construct a nomogram, which had a predictive accuracy significantly higher than that of each variable alone (AUC at 5 years 0·811 [95% CI 0·756–0·861]). Interpretation: Our six-SNP-based classifier could be a practical and reliable predictor that can complement the existing staging system for prediction of localised renal cell carcinoma recurrence after surgery, which might enable physicians to make more informed treatment decisions about adjuvant therapy. Intratumour heterogeneity does not seem to hamper the accuracy of the six-SNP-based classifier as a reliable predictor of recurrence. The classifier has the potential to guide treatment decisions for patients at differing risks of recurrence. Funding: National Key Research and Development Program of China, National Natural Science Foundation of China, Guangdong Provincial Science and Technology Foundation of China, and Guangzhou Science and Technology Foundation of China.

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