Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China, 211166.
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.
EBioMedicine. 2022 May;79:104007. doi: 10.1016/j.ebiom.2022.104007. Epub 2022 Apr 15.
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.
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.
APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20 percentile of APOLLO risk score, patients with more than the 90 percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC=0·901 (95% CI: 0·879-0·923), AUC=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 (NB=0·166, NR=40·1% and NB=0·258, NR=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness.
APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO).
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).
目前几乎没有准确且稳健的低级别胶质瘤（LGG）生存预测模型，无法帮助医生做出临床决策。我们旨在通过纳入具有主要作用或基因-基因相互作用的人口统计学、临床和转录组生物标志物，开发 LGG 的预后预测模型。
基于来自包含欧洲和亚洲人群的六个独立队列的 1420 名 LGG 患者的基因表达谱，我们提出了一种 3-D 分析策略来开发和验证低级别胶质瘤总生存的准确预测模型（APOLLO）。我们进一步进行了决策曲线分析，以评估识别真正阳性的净获益（NB）和不必要干预的净减少（NR）。最后，我们通过首次系统评价比较了 APOLLO 和现有预测模型的性能。
APOLLO 具有识别高死亡率风险患者的出色鉴别能力。与 APOLLO 风险评分低于 20 百分位的患者相比，APOLLO 风险评分高于 90 百分位的患者的总生存明显更差（HR=54.18，95%CI：34.73-84.52，P=2.66×10）。此外，APOLLO 可以在六个独立队列中准确预测 36 个月和 60 个月的生存，合并 AUC=0.901（95%CI：0.879-0.923）、AUC=0.843（95%CI：0.815-0.871）和 C 指数=0.818（95%CI：0.800-0.835）。此外，APOLLO 为检测易死亡的 LGG 患者提供了一种有效的筛查策略（NB=0.166，NR=40.1%和 NB=0.258，NR=19.2%）。系统比较表明，APOLLO 在准确性和稳健性方面优于现有模型。
APOLLO 已证明具有预测 LGG 生存的可行性和实用性（http://bigdata.njmu.edu.cn/APOLLO）。
国家重点研发计划（2016YFE0204900）；江苏省自然科学基金（BK20191354）；国家自然科学基金（81973142 和 82103946）；中国博士后科学基金（2020M681671）；美国国立卫生研究院（CA209414、CA249096、CA092824 和 ES000002）。
