Medical Oncology Branch, Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, USA.
Gynecol Oncol. 2011 Jan;120(1):5-10. doi: 10.1016/j.ygyno.2010.09.006. Epub 2010 Oct 15.
There are few validated relapse prediction biomarkers for epithelial ovarian cancer (EOC). We have shown progranulin (PGRN) and secretory leukocyte protease inhibitor (SLPI) are up regulated, overexpressed survival factors in EOC. We hypothesized they would predict presence of occult EOC.
PGRN, SLPI, and the known biomarker HE4 were measured in EOC patient plasma samples, prospectively collected every 3 months from initial remission until relapse. Clinical data and CA125 results were incorporated into statistical analyses. Exploratory Kaplan-Meier estimates, dividing markers at median values, evaluated association with progression-free survival (PFS) and overall survival (OS). Area-under-the-curve (AUC) statistics were computed from receiver operating characteristic (ROC) curves to evaluate discrimination ability. A Cox proportional hazards model assessed the association between PFS, OS, and biomarkers, adjusting for clinical prognostic factors.
Samples from 23 advanced stage EOC patients were evaluated. PGRN at 3 months was the only biomarker independently associated with PFS (P<0.0001) and OS (P<0.003). When used to predict progression by 18 months, sensitivity and specificity were 93% and 100%, respectively, with AUC=0.944. The Cox model hazard ratio for PFS, divided at 59 ng/ml by ROC analysis and adjusted for clinical factors, was 23.5 (95% CI: 2.49-220). Combinations with SLPI, HE4, and/or CA125 did not improve the model.
We report pilot data indicating a potential independent association of PGRN on EOC patient PFS and OS. A validation study will be required to confirm this finding and to inform whether PGRN warrants evaluation as a potential screening biomarker.
上皮性卵巢癌(EOC)缺乏经过验证的复发预测生物标志物。我们已经证明,颗粒蛋白前体(PGRN)和分泌白细胞蛋白酶抑制剂(SLPI)在上皮性卵巢癌中上调和过表达,是生存的关键因素。我们假设它们可以预测隐匿性上皮性卵巢癌的存在。
我们在上皮性卵巢癌患者的血浆样本中测量了 PGRN、SLPI 和已知的生物标志物 HE4,这些样本是从初始缓解期开始,每 3 个月前瞻性采集的,直到复发。将临床数据和 CA125 结果纳入统计分析。通过探索性 Kaplan-Meier 估计值,将标志物中位数划分,评估与无进展生存期(PFS)和总生存期(OS)的相关性。从接受者操作特征(ROC)曲线计算曲线下面积(AUC)统计数据,以评估区分能力。Cox 比例风险模型评估了 PFS、OS 与生物标志物之间的相关性,同时调整了临床预后因素。
评估了 23 例晚期上皮性卵巢癌患者的样本。PGRN 在 3 个月时是唯一与 PFS(P<0.0001)和 OS(P<0.003)独立相关的生物标志物。当用于预测 18 个月时的进展时,敏感性和特异性分别为 93%和 100%,AUC=0.944。ROC 分析将 PGRN 分为 59ng/ml 时,Cox 模型的 PFS 风险比为 23.5(95%CI:2.49-220),并调整了临床因素。与 SLPI、HE4 和/或 CA125 的组合并未改善模型。
我们报告了初步数据,表明 PGRN 与上皮性卵巢癌患者的 PFS 和 OS 存在潜在的独立关联。需要进行验证研究以确认这一发现,并确定 PGRN 是否值得作为潜在的筛查生物标志物进行评估。