Background/Objectives:Early detection of pancreatic cancer can improve patient survival, and blood-based biomarkers to aid in this are a significant need. The goal of this study was to develop and evaluate the performance of a 4- to 6-plex biomarker signature for detection of early-stage pancreatic ductal adenocarcinoma (PDAC) that performs well in high-risk controls.Methods: Enzyme-linked immunosorbent assays were used to measure 10 previously identified serum protein biomarker candidates in Stage I and II PDAC cases (n= 128), high-risk controls (n= 465), and normal-risk controls (n= 30). Various combinations of biomarker candidates (models) were trained using machine learning and tested for robustness in differentiating cases from controls on the full cohort and in clinically relevant sub-types including those with diabetes, those ≥65 years of age, and low producers of carbohydrate antigen 19-9 (CA 19-9).Results: At 98% specificity, the top performing model, which was comprised of tissue inhibitor of metalloproteinase 1 (TIMP1), intracellular adhesion molecule 1 (ICAM1), thrombospondin 1 (THBS1), cathepsin D (CTSD), and CA 19-9, achieved 85% sensitivity in the full cohort and sensitivities of 91% in diabetics, 90% in ≥65 years of age, and 60% in low CA 19-9 producers. This model demonstrated significantly higher sensitivity in detecting PDAC in the full cohort and all sub-populations compared to CA 19-9 alone (p< 0.001).Conclusions: Our findings demonstrate the feasibility of a blood-based assay for detecting early-stage PDAC in high-risk individuals and key sub-populations, representing an important step towards improving diagnostic success for early-stage disease.
背景/目的：胰腺癌的早期检测可提高患者生存率，亟需基于血液的生物标志物辅助诊断。本研究旨在开发并评估一种4至6标志物组合在检测早期胰腺导管腺癌（PDAC）中的性能，尤其关注其在高风险对照人群中的有效性。 方法：采用酶联免疫吸附法检测128例Ⅰ-Ⅱ期PDAC患者、465例高风险对照者及30例正常风险对照者血清中10种既往已识别的候选蛋白标志物。通过机器学习对多种标志物组合（模型）进行训练，并在全队列及临床相关亚型（包括糖尿病患者、≥65岁人群及碳水化合物抗原19-9低表达者）中测试其区分病例与对照的稳健性。 结果：在特异性为98%的条件下，由金属蛋白酶组织抑制剂1（TIMP1）、细胞间黏附分子1（ICAM1）、血小板反应蛋白1（THBS1）、组织蛋白酶D（CTSD）及CA 19-9组成的最优模型在全队列中达到85%的灵敏度，在糖尿病患者中灵敏度为91%，在≥65岁人群中为90%，在CA 19-9低表达者中为60%。与单独使用CA 19-9相比，该模型在全队列及所有亚群中检测PDAC的灵敏度均显著提高（p<0.001）。 结论：本研究证实了基于血液检测在高风险人群及关键亚群中实现早期PDAC诊断的可行性，为提升早期疾病诊断成功率迈出重要一步。
肿瘤(癌症)患者之家