Internet Medical and System Applications of National Engineering Laboratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Jiangsu Simcere Diagnostics Co., Ltd., Nanjing Simcere Medical Laboratory Science Co., Ltd., The State Key Laboratory of Neurology and Oncology Drug Development, Nanjing, China; Cancer Center, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China.
Cancer Lett. 2024 Nov 1;604:217216. doi: 10.1016/j.canlet.2024.217216. Epub 2024 Sep 2.
Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95 % CI: 0.915-0.981), with a sensitivity of 79.3 % (95 % CI: 71.5-87.0 %) across all stages at a specificity of 96.0 % (95 % CI: 90.6-100.0 %). Specifically, the sensitivity of the ESim-seq model was 76.5 % (95 % CI: 67.3-85.8 %) in stage I patients, 100 % (95 % CI: 100.0-100.0 %) in stage II patients, 100 % (95 % CI: 100.0-100.0 %) in stage III patients and 87.5 % (95 % CI: 64.6%-100.0 %) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95 % CI: 0.949-0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.
无细胞 DNA (cfDNA) 分析已显示出基于非遗传特征检测早期肺癌的潜力。为了将肺癌患者与健康个体区分开来，从 926 名肺癌患者和 611 名健康个体中采集外周血，进行 cfDNA 提取。进行低深度全基因组测序和靶向甲基化测序，并评估 cfDNA 的各种特征。使用我们的定制算法和最优特征，构建了集成模型的组合堆叠模型，称为 ESim-seq（早期综合模型筛查技术）。在独立验证队列中，ESim-seq 模型的曲线下面积 (AUC) 为 0.948（95%CI：0.915-0.981），在所有阶段的灵敏度为 79.3%（95%CI：71.5-87.0%），特异性为 96.0%（95%CI：90.6-100.0%）。具体而言，ESim-seq 模型在 I 期患者中的灵敏度为 76.5%（95%CI：67.3-85.8%），在 II 期患者中的灵敏度为 100.0%（95%CI：100.0-100.0%），在 III 期患者中的灵敏度为 100.0%（95%CI：100.0-100.0%），在 IV 期患者中的灵敏度为 87.5%（95%CI：64.6%-100.0%）。此外，我们构建了 LCSC 模型（肺癌亚型多重分类），能够准确区分小细胞肺癌患者和非小细胞肺癌患者，AUC 为 0.961（95%CI：0.949-0.957）。本研究建立了评估 cfDNA 特征的框架，并证明了整合多种特征进行肺癌早期检测的优势。
