Department of Thoracic Surgery, Yan'an Hospital of Kunming Medical University, Kunming, 650051, China.
Genecast Biotechnology Co., Ltd., Wuxi, Jiangsu, 214105, China.
BMC Cancer. 2024 Jul 15;24(1):840. doi: 10.1186/s12885-024-12626-7.
BACKGROUND: Detection of cancer and identification of tumor origin at an early stage improve the survival and prognosis of patients. Herein, we proposed a plasma cfDNA-based approach called TOTEM to detect and trace the cancer signal origin (CSO) through methylation markers. METHODS: We performed enzymatic conversion-based targeted methylation sequencing on plasma cfDNA samples collected from a clinical cohort of 500 healthy controls and 733 cancer patients with seven types of cancer (breast, colorectum, esophagus, stomach, liver, lung, and pancreas) and randomly divided these samples into a training cohort and a testing cohort. An independent validation cohort of 143 healthy controls, 79 liver cancer patients and 100 stomach cancer patients were recruited to validate the generalizability of our approach. RESULTS: A total of 57 multi-cancer diagnostic markers and 873 CSO markers were selected for model development. The binary diagnostic model achieved an area under the curve (AUC) of 0.907, 0.908 and 0.868 in the training, testing and independent validation cohorts, respectively. With a training specificity of 98%, the specificities in the testing and independent validation cohorts were 100% and 98.6%, respectively. Overall sensitivity across all cancer stages was 65.5%, 67.3% and 55.9% in the training, testing and independent validation cohorts, respectively. Early-stage (I and II) sensitivity was 50.3% and 45.7% in the training and testing cohorts, respectively. For cancer patients correctly identified by the binary classifier, the top 1 and top 2 CSO accuracies were 77.7% and 86.5% in the testing cohort (n = 148) and 76.0% and 84.0% in the independent validation cohort (n = 100). Notably, performance was maintained with only 21 diagnostic and 214 CSO markers, achieving a training AUC of 0.865, a testing AUC of 0.866, and an integrated top 2 accuracy of 83.1% in the testing cohort. CONCLUSIONS: TOTEM demonstrates promising potential for accurate multi-cancer detection and localization by profiling plasma methylation markers. The real-world clinical performance of our approach needs to be investigated in a much larger prospective cohort.
背景：早期发现癌症并确定肿瘤起源可提高患者的生存率和预后。在此，我们提出了一种基于血浆 cfDNA 的方法 TOTEM，通过甲基化标记物来检测和追踪癌症信号起源 (CSO)。
方法：我们对来自 500 名健康对照者和 733 例 7 种癌症（乳腺癌、结直肠癌、食管癌、胃癌、肝癌、肺癌和胰腺癌）癌症患者的血浆 cfDNA 样本进行基于酶促转化的靶向甲基化测序，并将这些样本随机分为训练队列和测试队列。招募了 143 名健康对照者、79 名肝癌患者和 100 名胃癌患者的独立验证队列，以验证我们方法的通用性。
结果：共选择了 57 个多癌诊断标志物和 873 个 CSO 标志物用于模型开发。二元诊断模型在训练、测试和独立验证队列中的曲线下面积（AUC）分别为 0.907、0.908 和 0.868。训练特异性为 98%时，测试和独立验证队列的特异性分别为 100%和 98.6%。所有癌症阶段的总体敏感性在训练、测试和独立验证队列中分别为 65.5%、67.3%和 55.9%。在训练和测试队列中，早期（I 期和 II 期）的敏感性分别为 50.3%和 45.7%。对于被二元分类器正确识别的癌症患者，在测试队列（n=148）中前 1 和前 2 CSO 准确率分别为 77.7%和 86.5%，在独立验证队列（n=100）中分别为 76.0%和 84.0%。值得注意的是，仅使用 21 个诊断标志物和 214 个 CSO 标志物，在测试队列中获得训练 AUC 为 0.865、测试 AUC 为 0.866 和整合前 2 准确率为 83.1%。
结论：TOTEM 通过分析血浆甲基化标志物显示出准确检测和定位多癌的潜力。我们的方法的实际临床性能需要在更大的前瞻性队列中进行研究。
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Nature. 2019-5-29
Zotero 插件
