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

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/)