PO.PR02.02 · 预防研究
采用多组学液体活检技术早期检测胰腺癌
Early detection of pancreatic cancer with a multi-omic liquid biopsy
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：早期诊断是胰腺导管腺癌（PDAC）的核心挑战，成功开发一种早期检测生物标志物检测方法将彻底改变这一领域。PDAC是美国癌症相关死亡的第3大原因，2024年新诊断病例达66,400例。五年生存率仅为12.8%，因为80%的患者在晚期才被诊断，从而限制了根治性手术切除的可能性。目前迫切需要将高危人群分流纳入诊断路径，尤其是那些患有胰腺囊肿、慢性胰腺炎以及50岁以上新发糖尿病的人群。然而，在这些高危人群中，PDAC在未来3年内的发病率约为1%。诸如CT/MRI和内镜超声结合细针穿刺等影像学手段成本过高且具侵入性，无法用作主要筛查工具。
方法：本研究评估了光谱血液检测作为PDAC检测替代策略的能力。该技术利用红外（IR）光谱，用红外光照射血液样本，产生对癌症标志物敏感的独特图谱。当与机器学习结合时，该检测通过监测样本的所有生物分子成分来检测PDAC。在这项概念验证研究中，将166例PDAC患者与459例诊断为非癌症的有症状患者进行了分类。
结果：受试者工作特征（ROC）曲线报告曲线下面积（AUC）值为0.84。诊断算法报告灵敏度为92%，特异度为52%。重要的是，该模型似乎不受癌症分期的影响。使用灵敏度优化模型的检出率为：I期88%、II期94%、III期99%、IV期95%。
结论：在早期阶段检测PDAC将改善受累患者的预后和生存率。基因测序技术的进步为肿瘤来源的生物标志物开辟了机会，利用基因组学、表观基因组学和转录组学来分离循环肿瘤DNA、外泌体和/或microRNA。然而，由于早期PDAC释放量低、信号几乎无法检测，这些生物标志物在早期阶段受到限制。基于光谱的多组学血液检测代表了一种替代策略，尤其适用于高危人群，可能弥补PDAC诊断中的空白。
查看英文原文 English abstract
Background: Early diagnosis is the central challenge in pancreatic ductal adenocarcinoma (PDAC) and the successful development of an early detection biomarker test would revolutionize the field. PDAC is the 3rd leading cause of cancer-related deaths in the United States with 66,400 new diagnoses in 2024. The five-year survival is only 12.8% because 80% of patients are diagnosed in advanced stages limiting the potential for curative surgical resection. There is an urgent need to facilitate the triage of high-risk groups into the diagnostic pathway, particularly those with pancreatic cysts, chronic pancreatitis and new-onset diabetes over the age of 50. However, the incidence of PDAC in these at-risk populations is ~1% over the next 3 years. Imaging modalities, such as CT/MRI and endoscopic ultrasound with fine-needle aspiration, are too expensive and invasive to serve as primary screening tools.
Methods: Here the ability of a spectroscopic blood test as an alternative strategy for PDAC detection is assessed. The technology utilizes infrared (IR) spectroscopy, and interrogates a blood sample with IR light to produce a distinctive signature that is sensitive to the hallmarks of cancer. When combined with machine learning, the test detects PDAC by monitoring of all biomolecular components of the sample. In this proof-of-concept study, 166 PDAC patients were classified against 459 symptomatic patients with a non-cancer diagnosis.
Results: The receiver operating characteristic (ROC) curve reported an area under the curve (AUC) value of 0.84. The diagnostic algorithm reported 92% sensitivity with 52% specificity. Importantly, the model did not seem to be affected by cancer stage. The detection rates with the sensitivity-tuned model were 88% stage I, 94% stage II, 99% stage III and 95% stage IV.
Conclusions: The detection of PDAC in early stages would improve prognosis and survival rates of affected patients. Advancements in genetic sequencing have opened opportunities for tumor-derived biomarkers, using genomics, epigenomics, and transcriptomics to isolate circulating tumor DNA, exosomes, and/or microRNA. However, these biomarkers are limited in PDAC at early stages due low release and near-undetectable signals. A spectroscopy-based multi-omic blood test represents an alternative strategy, particularly for high-risk populations, that may address the gap in PDAC diagnostics.
利益披露 Disclosure
J. M. Cameron,
Dxcover Ltd Employment.
H. J. Butler,
Dxcover Ltd Employment, g., Board of Directors, non-salaried role).
D. S. Palmer,
Dxcover Ltd. Employment, g., Board of Directors, non-salaried role).
R. G. McHardy,
Dxcover Ltd. Employment.
M. J. Baker,
Dxcover Ltd. Employment, g., Board of Directors, non-salaried role).