APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance
Jiajin Chen,
Sipeng Shen,
Yi Li,
Juanjuan Fan,
Shiyu Xiong,
Jingtong Xu,
Chenxu Zhu,
Lijuan Lin,
Xuesi Dong,
Weiwei Duan,
Yang Zhao,
Xu Qian,
Zhonghua Liu,
Yongyue Wei,
David C. Christiani,
Ruyang Zhang,
Feng Chen
Affiliations
Jiajin Chen
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
Sipeng Shen
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China, 211166
Yi Li
Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA, 48109
Juanjuan Fan
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
Shiyu Xiong
Department of Clinical Medicine, The First Clinical Medical College, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
Jingtong Xu
Department of Clinical Medicine, The First Clinical Medical College, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
Chenxu Zhu
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
Lijuan Lin
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
Xuesi Dong
Office of Cancer Screening, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, 100021
Weiwei Duan
Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, Jiangsu, China 211166
Yang Zhao
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166
Xu Qian
Department of Nutrition and Food Hygiene, Institute for Brain Tumors, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China, 211166
Zhonghua Liu
Department of Statistics and Actuarial Science, the University of Hong Kong, Hong Kong, China, 999077
Yongyue Wei
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China, 211166
David C. Christiani
Pulmonary and Critical Care Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA, 02114; Corresponding author: David C. Christiani Harvard School of Public Health and Harvard Medical School, Building I Room 1401, 665 Huntington Avenue, Boston, MA 02115, USA.
Ruyang Zhang
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China, 211166; Corresponding author: Ruyang Zhang, School of Public Health, Nanjing Medical University, SPH Building Room 406, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China.
Feng Chen
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166; China International Cooperation Center for Environment and Human Health, Nanjing Medical University, Nanjing, China, 211166; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Cancer Center, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, Jiangsu, China, 211166; Corresponding author: Feng Chen, School of Public Health, Nanjing Medical University, SPH Building Room 412, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China.
Summary: 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 × 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), AUC60-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).
