Gynecologic Oncology Unit, Gynecology Department. Vall d'Hebron University Hospital, Vall d'Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.
Gynecologic Oncology Unit, Gynecology Department. Vall d'Hebron University Hospital, Vall d'Hebron Barcelona Hospital Campus, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.
Gynecol Oncol. 2024 Oct;189:56-63. doi: 10.1016/j.ygyno.2024.07.003. Epub 2024 Jul 16.
We aimed to evaluate the performance of endometrial cancer (EC) molecular classification in predicting extrauterine disease after primary surgery alone and in combination with other clinical data available in preoperative setting.
Retrospective single-center observational study including patients with endometrial adenocarcinoma treated with primary surgery between December 1994 and May 2022. Molecular profiling was performed using immunohistochemistry of p53, MLH1, PMS2, MSH2 and MSH6; and KASP genotyping of the 6 most common mutations of POLE gene. Clinical, pathological and imaging information was reviewed. Logistic regression, regression trees and random forest classification techniques (CART) were performed.
We enrolled 658 patients, 47 with POLEmut (7.1%), 234 with MMRd (35.6%), 95 with p53abn (14.4%) and 282 with NSMP (42.8%) tumors. Advanced stage after primary surgery (III-IV FIGO 2009) was diagnosed in 11.7% of patients, p53abn tumors showed increased extrauterine spread (34.1%) and nodal involvement (30.1%) (p < .001). In multivariate analysis, only p53abn subgroup (aOR = 16.0, CI95% = 1.5-165.1) and radiological suspicion of extrauterine disease (aOR = 24.2, CI95% = 12.2-48.2) independently predicted the finding of extrauterine disease after primary surgery. In patients with preoperative uterine-confined disease, deep myometrial and cervical involvement in radiological assessment and p53abn molecular subtype were the best variables to identify patients at-risk of occult extrauterine disease after the staging surgery.
EC molecular classification is more accurate than histotype or grade in preoperative biopsy to predict advanced disease, and together with imaging tests are the most reliable preoperative information. This work provides an initial framework for using molecular information preoperatively to tailor surgical treatment.
我们旨在评估子宫内膜癌（EC）分子分类在预测单独行初次手术后和结合术前可用的其他临床数据时的预测宫外疾病的性能。
回顾性单中心观察性研究，纳入 1994 年 12 月至 2022 年 5 月期间接受初次手术治疗的子宫内膜腺癌患者。使用 p53、MLH1、PMS2、MSH2 和 MSH6 的免疫组织化学以及 POLE 基因的 6 个最常见突变的 KASP 基因分型进行分子分析。回顾了临床、病理和影像学信息。进行了逻辑回归、回归树和随机森林分类技术（CART）。
我们纳入了 658 名患者，其中 47 名患者为 POLEmut（7.1%），234 名患者为 MMRd（35.6%），95 名患者为 p53abn（14.4%），282 名患者为 NSMP（42.8%）肿瘤。初次手术后诊断为晚期疾病（III-IV FIGO 2009 期）的患者占 11.7%，p53abn 肿瘤显示出增加的宫外扩散（34.1%）和淋巴结受累（30.1%）（p<0.001）。多变量分析显示，只有 p53abn 亚组（aOR=16.0，95%CI=1.5-165.1）和对宫外疾病的影像学怀疑（aOR=24.2，95%CI=12.2-48.2）独立预测了初次手术后宫外疾病的发现。在术前子宫局限性疾病的患者中，放射学评估中的深层肌层和宫颈受累以及 p53abn 分子亚型是识别接受分期手术后隐匿性宫外疾病风险患者的最佳变量。
EC 分子分类在术前活检中比组织类型或分级更准确，可以预测晚期疾病，并且与影像学检查一起是最可靠的术前信息。这项工作为术前使用分子信息来定制手术治疗提供了初步框架。
