APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance

被引:13
作者
Chen, Jiajin [1 ]
Shen, Sipeng [1 ,2 ]
Li, Yi [3 ]
Fan, Juanjuan [1 ]
Xiong, Shiyu [4 ]
Xu, Jingtong [4 ]
Zhu, Chenxu [1 ]
Lin, Lijuan [1 ]
Dong, Xuesi [5 ]
Duan, Weiwei [6 ]
Zhao, Yang [1 ]
Qian, Xu [7 ]
Liu, Zhonghua [8 ]
Wei, Yongyue [1 ,2 ]
Christiani, David C. [9 ,10 ]
Zhang, Ruyang [1 ,2 ]
Chen, Feng [1 ,2 ,11 ]
机构
[1] Nanjing Med Univ, Ctr Global Hlth, Sch Publ Hlth, Dept Biostat, Nanjing 211166, Jiangsu, Peoples R China
[2] Nanjing Med Univ, China Int Cooperat Ctr Environm & Human Hlth, Nanjing 211166, Jiangsu, Peoples R China
[3] Univ Michigan, Dept Biostat, Ann Arbor, MI 48109 USA
[4] Nanjing Med Univ, Clin Med Coll 1, Dept Clin Med, Nanjing 211166, Jiangsu, Peoples R China
[5] Chinese Acad Med Sci & Peking Union Med Coll, Natl Canc Ctr, Off Canc Screening, Natl Clin Res Ctr Canc,Canc Hosp, Beijing 100021, Peoples R China
[6] Nanjing Med Univ, Sch Biomed Engn & Informat, Dept Bioinformat, Nanjing 211166, Jiangsu, Peoples R China
[7] Nanjing Med Univ, Ctr Global Hlth, Sch Publ Hlth, Dept Nutr & Food Hyg,Inst Brain Tumors, Nanjing 211166, Peoples R China
[8] Univ Hong Kong, Dept Stat & Actuarial Sci, Hong Kong 999077, Peoples R China
[9] Massachusetts Gen Hosp, Dept Med, Pulm & Crit Care Div, Boston, MA 02114 USA
[10] Harvard Med Sch, Boston, MA 02114 USA
[11] Nanjing Med Univ, Canc Ctr, Collaborat Innovat Ctr Canc Personalized Med, Jiangsu Key Lab Canc Biomarkers Prevent & Treatme, Nanjing 211166, Jiangsu, Peoples R China
来源
EBIOMEDICINE | 2022年 / 79卷
基金
中国国家自然科学基金; 中国博士后科学基金; 美国国家卫生研究院;
关键词
Lower-grade gliomas; Survival; Prognostic prediction; Nomogram; Online tool; Systematic review; MOLECULAR CLASSIFICATION; EXPRESSION; TUMORS;
D O I
10.1016/j.ebiom.2022.104007
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions. Methods Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review. Findings APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54.18, 95% CI: 34.73-84.52, P=2.66 x 10(-69)). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36.month=0.901 (95% CI: 0.879-0.923), AUC(60-month)=0.843 (95% CI: 0.815-0.871) and C-index=0.818 (95% CI: 0.800-0.835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0.166, NR36-month=40.1% and NB60-month=0.258, NR60-month=19.2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness. Interpretation APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO). Funding National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002). Copyright (C) 2022 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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页数:15
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