PO.PR02.02 · 预防研究
采用多组学液体活检技术更早期地检测卵巢癌
A multi-omic liquid biopsy for the earlier detection of ovarian cancer
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
背景：卵巢癌是妇科癌症中死亡率最高的癌症之一，主要原因是超过70%的病例在较晚期才被诊断。相比之下，早期卵巢癌的五年生存率超过90%。目前，由于检测方法效果不佳，尚无经批准的针对卵巢癌的全人群筛查检测，这凸显了对更好的早期检测工具的迫切需求。新型液体活检技术为早期识别卵巢癌提供了一条有前景的途径，此时治疗效果要好得多。
方法：本研究探讨了多组学液体活检作为卵巢癌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，产生对癌症信号敏感的独特图谱。在这项概念验证研究中，将125例卵巢癌患者与260例诊断为非癌症的女性有症状患者进行了分类。血液样本在手术切除或开始其他抗癌治疗之前采集。血清样本通过Dxcover® 液体活检平台进行分析，并采用机器学习算法进行分类。这些经过训练的算法随后在另一个独立的临床数据集上进行测试。
结果：受试者工作特征（ROC）曲线报告曲线下面积（AUC）值为0.86。灵敏度优化算法报告灵敏度为92%，特异度为54%；特异度优化模型报告灵敏度为58%，特异度为90%。重要的是，诊断算法不受癌症分期的影响。高灵敏度模型的检出率为：I期97%、II期86%、III期92%、IV期100%。验证测试为该方法在目标使用人群中的诊断能力提供了进一步确认。
结论：更早期地检测卵巢癌可改善受累患者的预后和生存率。由于该技术使用简单、所需样本量极小且可快速提供结果，将该血液检测整合到现有诊断路径中的障碍很低。这种液体活检代表了一种替代策略，尤其适用于高危人群，可能弥补卵巢癌诊断中的空白。
查看英文原文 English abstract
Background : Ovarian cancer has one of the highest mortality rates among gynecologic cancers, largely because more than 70% of cases are diagnosed at more advanced stages. Early-stage ovarian cancer, by contrast, has a five-year survival rate above 90%. Currently, there are no approved population-wide screening tests for ovarian cancer, due to ineffective testing options highlighting the urgent need for better early detection tools. Novel liquid biopsy technologies offer a promising path to identify ovarian cancer early, when treatment is far more effective.
Methods : This study explored the ability of a multi-omic liquid biopsy as an alternative strategy for ovarian cancer detection. The technology utilizes infrared (IR) spectroscopy and interrogates a blood sample with IR light to produce a distinctive signature that is sensitive to the signals of cancer. In this proof-of-concept study, 125 ovarian cancer patients were classified against 260 female symptomatic patients with a non-cancer diagnosis. Blood was obtained from patients before surgical resection or the start of other anti-cancer therapies. Blood serum samples were analyzed by the Dxcover® Liquid Biopsy Platform and classified with machine learning algorithms. These trained algorithms are then independently tested on an additional clinical dataset.
Results : The receiver operating characteristic (ROC) curve reported an area under the curve (AUC) value of 0.86. The sensitivity-tuned algorithm reported 92% sensitivity with 54% specificity, and the specificity-tuned model reported 58% sensitivity with 90% specificity. Importantly, the diagnostic algorithm was unaffected by cancer stage. The detection rates were 97% stage I, 86% stage II, 92% stage III and 100% stage IV, for the high sensitivity model. Validation testing provided additional confirmation of diagnostic ability in the intended use population.
Conclusions : Detecting ovarian cancer earlier improves prognosis and survival rates of affected patients. There is a low barrier to integrating the blood test into existing diagnostic pathways since the technology is simple to use, minute sample volumes are required, and results can be provided rapidly. This liquid biopsy represents an alternative strategy, particularly for high-risk populations, that may address the gap in ovarian cancer diagnostics.
利益披露 Disclosure
J. M. Cameron,
Dxcover Ltd. Employment.
H. Butler,
Dxcover Ltd. Employment, g., Board of Directors, non-salaried role).
D. Palmer,
Dxcover Ltd. Employment, g., Board of Directors, non-salaried role).
R. McHardy,
Dxcover Ltd. Employment.
M. Baker,
Dxcover Ltd. Employment, g., Board of Directors, non-salaried role).