Interdisciplinary Program in Bioengineering, Seoul National University, College of Engineering, Seoul, Republic of South Korea.
Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University College of Medicine, Seoul, Republic of South Korea.
Clin Cancer Res. 2021 Apr 15;27(8):2236-2245. doi: 10.1158/1078-0432.CCR-20-3929. Epub 2021 Jan 27.
To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance.
A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls.
A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, < 0.001; validation set AUC = 0.953 vs. 0.832, < 0.01; independent validation set AUC = 0.928 vs. 0.771, < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets.
This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.
开发和验证一种基于蛋白质的多标志物组合，以提供卓越的胰腺导管腺癌（PDAC）检测能力，并具有足够的诊断性能。
共使用了来自多个医疗中心的 959 份血浆样本。为了构建最佳的诊断多标志物组合，我们应用了生物标志物候选物的数据预处理程序。使用由 261 例 PDAC 病例和 290 例对照组成的训练集来开发多标志物组合。随后在由 65 例 PDAC 病例和 72 例对照组成的验证集中进行了评估。进一步在由 75 例 PDAC 病例和 47 例对照组成的独立集中进行了验证。
开发了包含 14 种蛋白质的多标志物组合。多标志物组合在训练集和验证集的 AUC 分别为 0.977 和 0.953。在独立验证集中，多标志物组合的 AUC 为 0.928。与单独的碳水化合物抗原（CA）19-9 相比，多标志物组合的诊断性能有显著提高（训练集 AUC=0.977 比 0.872，<0.001；验证集 AUC=0.953 比 0.832，<0.01；独立验证集 AUC=0.928 比 0.771，<0.001）。当多标志物组合和 CA 19-9 联合使用时，联合组合在所有组中的诊断性能都得到了提高。
与单独的 CA 19-9 相比，该多标志物组合和联合组合在诊断性能方面均有统计学意义的提高，有可能作为临床实践中的一种诊断标志物来补充 CA 19-9。
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