Enroth Stefan, Ivansson Emma, Lindberg Julia Hedlund, Lycke Maria, Bergman Jessica, Reneland Anna, Stålberg Karin, Sundfeldt Karin, Gyllensten Ulf
Department of Immunology, Genetics, and Pathology, Biomedical Center, SciLifeLab Uppsala, Uppsala University, SE-75108 Uppsala, Sweden.
Swedish Collegium for Advanced Study, Thunbergsvägen 2, SE-752 38 Uppsala, Sweden.
Commun Med (Lond). 2022 Oct 1;2:124. doi: 10.1038/s43856-022-00193-6. eCollection 2022.
Ovarian cancer is the eighth most common cancer among women and due to late detection prognosis is poor with an overall 5-year survival of 30-50%. Novel biomarkers are needed to reduce diagnostic surgery and enable detection of early-stage cancer by population screening. We have previously developed a risk score based on an 11-biomarker plasma protein assay to distinguish benign tumors (cysts) from malignant ovarian cancer in women with adnexal ovarian mass.
Protein concentrations of 11 proteins were characterized in plasma from 1120 clinical samples with a custom version of the proximity extension assay. The performance of the assay was evaluated in terms of prediction accuracy based on receiver operating characteristics (ROC) and multiple hypothesis adjusted Fisher's Exact tests on achieved sensitivity and specificity.
The assay's performance is validated in two independent clinical cohorts with a sensitivity of 0.83/0.91 and specificity of 0.88/0.92. We also show that the risk score follows the clinical development and is reduced upon treatment, and increased with relapse and cancer progression. Data-driven modeling of the risk score patterns during a 2-year follow-up after diagnosis identifies four separate risk score trajectories linked to clinical development and survival. A Cox proportional hazard regression analysis of 5-year survival shows that at time of diagnosis the risk score is the second-strongest predictive variable for survival after tumor stage, whereas MUCIN-16 (CA-125) alone is not significantly predictive.
The robust performance of the biomarker assay across clinical cohorts and the correlation with clinical development indicates its usefulness both in the diagnostic work-up of women with adnexal ovarian mass and for predicting their clinical course.
卵巢癌是女性中第八大常见癌症，由于发现较晚，预后较差，总体5年生存率为30%-50%。需要新的生物标志物来减少诊断性手术，并通过人群筛查实现早期癌症的检测。我们之前开发了一种基于11种生物标志物血浆蛋白检测的风险评分，以区分附件卵巢肿块女性的良性肿瘤（囊肿）和恶性卵巢癌。
使用定制版邻近延伸分析对1120份临床样本的血浆中11种蛋白质的浓度进行了表征。根据受试者工作特征（ROC）和对获得的敏感性和特异性进行的多重假设调整费舍尔精确检验，评估了该检测的预测准确性。
该检测在两个独立的临床队列中得到验证，敏感性为0.83/0.91，特异性为0.88/0.92。我们还表明，风险评分随临床进展而变化，治疗后降低，复发和癌症进展时升高。对诊断后2年随访期间风险评分模式进行数据驱动建模，确定了与临床进展和生存相关的四种不同风险评分轨迹。对五年生存率的Cox比例风险回归分析表明，在诊断时，风险评分是肿瘤分期后生存的第二强预测变量，而单独的粘蛋白16（CA-125）没有显著预测性。
生物标志物检测在临床队列中的稳健表现及其与临床进展的相关性表明，它在附件卵巢肿块女性的诊断检查以及预测其临床病程方面均有用处。
