Ovarian cancer is a leading cause of death for women worldwide in part due to ineffective screening methods. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker (CA-125 and HE4) analyses to evaluate 591 women with ovarian cancer, benign adnexal masses, or without ovarian lesions. Using a machine learning model with the combined features, we detected ovarian cancer with specificity >99% and sensitivity of 72%, 69%, 87%, and 100% for stages I–IV, respectively. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100% of ovarian cancers for stages I–IV. Our approach differentiated benign masses from ovarian cancers with high accuracy (AUC=0.88, 95% CI=0.83-0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation.

中文翻译：

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使用游离 DNA 片段组和蛋白质生物标志物早期检测卵巢癌


卵巢癌是全世界女性死亡的主要原因，部分原因是筛查方法无效。在这项研究中，我们使用全基因组无细胞 DNA (cfDNA) 片段组和蛋白质生物标志物（CA-125 和 HE4）分析来评估 591 名患有卵巢癌、良性附件肿块或无卵巢病变的女性。使用具有组合特征的机器学习模型，我们检测到卵巢癌，I-IV 期的特异性为 >99%，敏感性分别为 72%、69%、87% 和 100%。在相同的特异性下，单独使用 CA-125 可以检测到 34%、62%、63% 和 100% 的 I-IV 期卵巢癌。我们的方法以高精度区分良性肿块和卵巢癌（AUC=0.88，95% CI=0.83-0.92）。这些结果在独立人群中得到了验证。这些发现表明，整合的 cfDNA 片段组和蛋白质分析可以高性能地检测卵巢癌，为非侵入性卵巢癌筛查和诊断评估提供一种新的可行方法。