Xue Maojie, Xu Ziang, Wang Xiang, Chen Jiajin, Kong Xinxin, Zhou Shenxuan, Wu Jiamin, Zhang Yuhao, Li Yi, Christiani David C, Chen Feng, Zhao Yang, Zhang Ruyang
Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases, Nanjing Medical University, Nanjing, Jiangsu 210029, China; Department of Oral Special Consultation, Affiliated Stomatological Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, China.
J Adv Res. 2025 Jul;73:561-573. doi: 10.1016/j.jare.2024.08.015. Epub 2024 Aug 11.
Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis.
Using DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models.
ARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR = 15.43, 95% CI: 9.57-24.88, P = 3.06 × 10). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC = 0.844 (95% CI: 0.805-0.883), AUC = 0.816 (95% CI: 0.775-0.857), and C-index = 0.803 (95% CI: 0.776-0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95% CI: 1.038-1.083) and 1.090 (95% CI: 1.057-1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (P = 0.4: NB = 19‰, NB = 62‰; NR = 69.21%, NR = 56.01%). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/.
ARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.
乳腺癌是一种异质性疾病，受多种遗传和表观遗传因素影响。大多数乳腺癌预后模型仅关注预测因子的主要效应，而忽视了基因-基因相互作用对预后的关键影响。
利用来自9个独立乳腺癌队列的DNA甲基化数据，我们采用创新的三维建模策略，开发了一种纳入具有主要效应的表观遗传生物标志物和基因-基因相互作用的乳腺癌独立验证预后预测模型（ARTEMIS）。使用受试者工作特征曲线下面积（AUC）评估ARTEMIS的区分能力，使用预期与观察（E/O）比率评估校准情况。此外，我们进行决策曲线分析以通过净效益（NB）和净减少（NR）评估其临床疗效。此外，我们进行了系统评价以将其性能与现有模型进行比较。
ARTEMIS在识别高死亡风险患者方面表现出出色的风险分层能力。与ARTEMIS评分低于第25百分位数的患者相比，评分高于第90百分位数的患者总生存时间显著缩短（HR = 15.43，95% CI：9.57 - 24.88，P = 3.06 × 10）。ARTEMIS在四个独立人群中表现出令人满意的区分能力，合并AUC = 0.844（95% CI：0.805 - 0.883）、AUC = 0.816（95% CI：0.775 - 0.857）和C指数 = 0.803（95% CI：0.776 - 0.830）。同时，ARTEMIS在3年和5年生存预测中的校准性能良好，合并E/O比率分别为1.060（95% CI：1.038 - 1.083）和1.090（95% CI：1.057 - 1.122）。此外，ARTEMIS是一种具有可接受成本效益的临床工具，用于检测高死亡风险的乳腺癌患者（P = 0.4：NB = 19‰，NB = 62‰；NR = 69.21%，NR = 56.01%）。系统评价表明，ARTEMIS在准确性、外推性和样本量方面比现有模型具有更优的性能。ARTEMIS作为一个交互式在线工具可在http://bigdata.njmu.edu.cn/ARTEMIS/获取。
ARTEMIS是一种用于乳腺癌预后预测的高效实用工具。
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