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A clinically applicable molecular classification for high-grade serous ovarian cancer based on hormone receptor expression

机译:基于激素受体表达的高级别浆液性卵巢癌的临床适用分子分类

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To establish an effective hormone receptor-based molecular classification of high-grade serous ovarian cancer (HGSC), we retrospectively examined 875 consecutive HGSC patients who underwent primary surgery at our hospital and constructed tissue microarrays from these specimens. The expression levels of the hormone receptors were as follows: ER 64.4%, PR 12.6%, AR 35.6%, FSHR 54.5%, LHR 34.8%, and GnRHR 88.3%. Based on clustering of their expression patterns, we classified patients into five subgroups with distinctive clinical features (PR+, PR?-?ER?+?AR+, PR?-?ER?+?AR-, PR?-?ER?-?AR+, and PR?-?ER?-?AR-). Patients in the PR?+?group were younger compared to those in the other groups (p??0.001). More patients were of advanced stage in the PR?-?ER?+?AR- group than the other groups (p?=?0.020). A greater proportion of patients were sensitive to platinum-based chemotherapy in the PR?-?ER?-?AR?+?group compared with the other groups (p?=?0.034). A trend of increasing risk of death was observed among these subgroups (p??0.001). In the multivariate analysis, patients also had orderly increased hazard ratios for death in the PR?+?(HR?=?2.256, 95% CI, 0.983-5.175), PR?-?ER?+?AR?+?(HR?=?2.188, 95% CI, 1.004-4.796), PR?-?ER?-?AR- (HR?=?2.316, 95% CI, 1.097-5.082) and PR?-?ER?+?AR- (HR?=?2.928, 95% CI, 1.366-6.276) subgroups compared to the PR?-?ER?-?AR+ subgroup. Our classification could help predict patient clinical outcomes, guide individual treatments and stratify patients in future clinical trials.
机译:为了建立有效的基于激素受体的高级别浆液性卵巢癌(HGSC)的分子分类,我们回顾性检查了875名在我院接受了初次手术的HGSC患者,并从这些标本中构建了组织芯片。激素受体的表达水平如下:ER 64.4%,PR 12.6%,AR 35.6%,FSHR 54.5%,LHR 34.8%和GnRHR 88.3%。基于他们的表达方式的聚类,我们将患者分为具有不同临床特征的五个亚组(PR +,PRα-αERα+αAR+,PRα-αERα+αAR-,PRα-αERα-β)。 AR +和PR?-?ER?-?AR-)。 PR?+?组的患者比其他组的患者年轻(p?<?0.001)。 PR?-?ER?+?AR-组的晚期患者多于其他组(p?=?0.020)。与其他组相比,PRα-βERα-αARβ+β组中有较大比例的患者对铂类化疗敏感(pα=β0.034)。在这些亚组中观察到死亡风险增加的趋势(p <0.001)。在多变量分析中,患者的PRα+β死亡风险比也有序增加(HRβ= β2.256,95%CI,0.983-5.175),PRβ-βERβ+αARβ+β(HR α= 2.188,95%CI,1.004-4.796),PRα-αERα-ΔAR-(HRα= α2.316,95%CI,1.097-5.082)和PRα-αERα+αAR-。 (HR?=?2.928,95%CI,1.366-6.276)子组与PR?-?ER?-?AR +子组相比。我们的分类可以帮助预测患者的临床结局,指导个体治疗以及在未来的临床试验中对患者进行分层。

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