Authors' Affiliations: Departments of Community and Family Medicine, Statistical Science, Obstetrics and Gynecology, Division of Gynecologic Oncology, Pathology, and Surgery, Duke Cancer Institute, and Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, North Carolina.
Cancer Epidemiol Biomarkers Prev. 2013 Oct;22(10):1709-21. doi: 10.1158/1055-9965.EPI-13-0192. Epub 2013 Aug 5.
Six gene expression subtypes of invasive epithelial ovarian cancer were recently defined using microarrays by Tothill and colleagues. The Cancer Genome Atlas (TCGA) project subsequently replicated these subtypes and identified a signature predictive of survival in high-grade serous (HGS) cancers. We previously validated these signatures for use in formalin-fixed paraffin-embedded tissues. The aim of the present study was to determine whether these signatures are associated with specific ovarian cancer risk factors, which would add to the evidence that they reflect the heterogeneous etiology of this disease.
We modeled signature-specific tumor characteristics and epidemiologic risk factor relationships using multiple regression and multivariate response multiple regression models in 193 patients from a case-control study of epithelial ovarian cancer.
We observed associations between the Tothill gene expression subtype signatures and both age at diagnosis (P = 0.0008) and race (P = 0.008). Although most established epidemiologic risk factors were not associated with molecular signatures, there was an association between breast feeding (P = 0.024) and first-degree family history of breast or ovarian cancer (P = 0.034) among the 106 HGS cases. Some of the above associations were validated using gene expression microarray data from the TCGA project. Weak associations were seen with age at menarche and duration of oral contraceptive use and the TCGA survival signature.
These data support the potential for genomic characterization to elucidate the etiologic heterogeneity of epithelial ovarian cancer.
This study suggests that molecular signatures may augment the ability to define etiologic subtypes of epithelial ovarian cancer.
最近，托希尔（Tothill）及其同事使用微阵列技术定义了侵袭性上皮性卵巢癌的 6 种基因表达亚型。癌症基因组图谱（TCGA）项目随后复制了这些亚型，并确定了一个可预测高级别浆液性（HGS）癌症生存的特征签名。我们之前已经验证了这些签名在福尔马林固定石蜡包埋组织中的应用。本研究旨在确定这些签名是否与特定的卵巢癌危险因素相关，这将增加证据表明它们反映了该疾病的异质性病因。
我们使用多变量回归和多元响应多变量回归模型，对来自上皮性卵巢癌病例对照研究的 193 名患者的肿瘤特征和流行病学危险因素进行建模。
我们观察到 Tothill 基因表达亚型特征与诊断时的年龄（P=0.0008）和种族（P=0.008）之间存在关联。尽管大多数已确立的流行病学危险因素与分子特征无关，但在 106 例 HGS 病例中，哺乳（P=0.024）和一级亲属乳腺癌或卵巢癌家族史（P=0.034）之间存在关联。上述一些关联使用 TCGA 项目的基因表达微阵列数据进行了验证。月经初潮年龄、口服避孕药使用时间与 TCGA 生存特征之间存在较弱的关联。
这些数据支持基因组特征分析阐明上皮性卵巢癌病因异质性的潜力。
本研究表明，分子特征可能增强定义上皮性卵巢癌病因亚型的能力。
