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

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 re... Full description

Journal Title: The lancet oncology 2019, Vol.20 (4), p.591-600
Main Author: Wei, Jin-Huan
Other Authors: 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
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: England: Elsevier Ltd
ID: ISSN: 1470-2045
Link: https://www.ncbi.nlm.nih.gov/pubmed/30880070
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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
format: Article
creator:
  • 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
subjects:
  • Accuracy
  • Analysis
  • Bioinformatics
  • Biomarkers
  • Carcinoma, Renal cell
  • Cell survival
  • Clear cell-type renal cell carcinoma
  • Clinical medicine
  • Diseases
  • Genetic aspects
  • Genome-wide association studies
  • Genomes
  • Hospitals
  • Kidney cancer
  • Mass spectrometry
  • Necrosis
  • Paraffin
  • Patients
  • Relapse
  • Risk factors
  • Scientific imaging
  • Single nucleotide polymorphisms
  • Single-nucleotide polymorphism
  • Surgery
  • Tumors
ispartof: The lancet oncology, 2019, Vol.20 (4), p.591-600
description: 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. 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. 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
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 1470-2045
fulltext: fulltext
issn:
  • 1470-2045
  • 1474-5488
url: Link


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titlePredictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study
sourceAlma/SFX Local Collection
creatorWei, 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
creatorcontribWei, 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
descriptionIdentification 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. 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. 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]). 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. 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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languageeng
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subjectAccuracy ; Analysis ; Bioinformatics ; Biomarkers ; Carcinoma, Renal cell ; Cell survival ; Clear cell-type renal cell carcinoma ; Clinical medicine ; Diseases ; Genetic aspects ; Genome-wide association studies ; Genomes ; Hospitals ; Kidney cancer ; Mass spectrometry ; Necrosis ; Paraffin ; Patients ; Relapse ; Risk factors ; Scientific imaging ; Single nucleotide polymorphisms ; Single-nucleotide polymorphism ; Surgery ; Tumors
ispartofThe lancet oncology, 2019, Vol.20 (4), p.591-600
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3Zhao, Hong-Wei
4Chen, Zhen-Hua
5Liao, Bing
6Wang, Qing
7Han, Hui
8Zhang, Jin
9Xu, Yun-Ze
10Li, Bo
11Wu, Ji-Tao
12Qu, Gui-Mei
13Wang, Guo-Ping
14Liu, Cong
15Xue, Wei
16Liu, Qiang
17Lu, Jun
18Li, Cai-Xia
19Li, Pei-Xing
20Zhang, Zhi-Ling
21Yao, Hao-Hua
22Pan, Yi-Hui
23Chen, Wen-Fang
24Xie, Dan
25Shi, Lei
26Gao, Zhen-Li
27Huang, Yi-Ran
28Zhou, Fang-Jian
29Wang, Shao-Gang
30Liu, Zhi-Ping
31Chen, Wei
32Luo, Jun-Hang
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0Predictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study
1The lancet oncology
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descriptionIdentification 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. 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. 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]). 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. 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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1Analysis
2Bioinformatics
3Biomarkers
4Carcinoma, Renal cell
5Cell survival
6Clear cell-type renal cell carcinoma
7Clinical medicine
8Diseases
9Genetic aspects
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12Hospitals
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14Mass spectrometry
15Necrosis
16Paraffin
17Patients
18Relapse
19Risk factors
20Scientific imaging
21Single nucleotide polymorphisms
22Single-nucleotide polymorphism
23Surgery
24Tumors
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titlePredictive value of single-nucleotide polymorphism signature for recurrence in localised renal cell carcinoma: a retrospective analysis and multicentre validation study
authorWei, 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
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issn1470-2045
eissn1474-5488
abstractIdentification 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. 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. 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]). 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. 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.
copEngland
pubElsevier Ltd
pmid30880070
doi10.1016/S1470-2045(18)30932-X