Jin-Huan Wei1, Zi-Hao Feng1, Yun Cao2, Hong-Wei Zhao3, Zhen-Hua Chen1, Bing Liao4, Qing Wang5, Hui Han6, Jin Zhang7, Yun-Ze Xu7, Bo Li8, Ji-Tao Wu3, Gui-Mei Qu9, Guo-Ping Wang10, Cong Liu10, Wei Xue7, Qiang Liu11, Jun Lu1, Cai-Xia Li12, Pei-Xing Li12, Zhi-Ling Zhang6, Hao-Hua Yao1, Yi-Hui Pan1, Wen-Fang Chen4, Dan Xie2, Lei Shi3, Zhen-Li Gao3, Yi-Ran Huang7, Fang-Jian Zhou6, Shao-Gang Wang5, Zhi-Ping Liu13, Wei Chen1, Jun-Hang Luo14. 1. Department of Urology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China. 2. Department of Pathology, Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong, China. 3. Department of Urology, Affiliated Yantai Yuhuangding Hospital, Qingdao University Medical College, Shandong, China. 4. Department of Pathology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China. 5. Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, China. 6. Department of Urology, Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong, China. 7. Department of Urology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. 8. Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China. 9. Department of Pathology, Affiliated Yantai Yuhuangding Hospital, Qingdao University Medical College, Shandong, China. 10. Department of Pathology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Hubei, China. 11. Department of Pathology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China. 12. School of Mathematics and Computational Science, Sun Yat-sen University, Guangzhou, Guangdong, China. 13. Department of Internal Medicine and Department of Molecular Biology, University of Texas Southwestern Medical Center at Dallas, TX, USA. 14. Department of Urology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong, China. Electronic address: luojunh@mail.sysu.edu.cn.
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.
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.