Department of Pathology, University of Turku, Turku University Hospital, PL 52, 20520 Turku, Finland.
Department of Mathematics and Statistics, University of Turku, PL20, 00014 Helsinki, Finland; Institute for Molecular Medicine Finland, FIMM, University of Helsinki, PL20, 00014 Helsinki, Finland.
Gynecol Oncol. 2018 Apr;149(1):173-180. doi: 10.1016/j.ygyno.2018.02.016. Epub 2018 Mar 2.
In clinical practise, prognostication of endometrial cancer is based on clinicopathological risk factors. The use of immunohistochemistry-based markers as prognostic tools is generally not recommended and a systematic analysis of their utility as a panel is lacking. We evaluated whether an immunohistochemical marker panel could reliably assess endometrioid endometrial cancer (EEC) outcome independent of clinicopathological information.
A cohort of 306 EEC specimens was profiled using tissue microarray (TMA). Cost- and time-efficient immunohistochemical analysis of well-established tissue biomarkers (ER, PR, HER2, Ki-67, MLH1 and p53) and two new biomarkers (L1CAM and ASRGL1) was carried out. Statistical modelling with embedded variable selection was applied on the staining results to identify minimal prognostic panels with maximal prognostic accuracy without compromising generalizability.
A panel including p53 and ASRGL1 immunohistochemistry was identified as the most accurate predictor of relapse-free and disease-specific survival. Within this panel, patients were allocated into high- (5.9%), intermediate- (29.5%) and low- (64.6%) risk groups where high-risk patients had a 30-fold risk (P<0.001) of dying of EEC compared to the low-risk group.
P53 and ASRGL1 immunoprofiling stratifies EEC patients into three risk groups with significantly different outcomes. This simple and easily applicable panel could provide a useful tool in EEC risk stratification and guiding the allocation of treatment modalities.
在临床实践中，子宫内膜癌的预后基于临床病理危险因素。使用基于免疫组织化学的标志物作为预后工具通常不被推荐，并且缺乏对其作为一个标志物组合的效用的系统分析。我们评估了免疫组织化学标志物组合是否能够独立于临床病理信息可靠地评估子宫内膜样型子宫内膜癌（EEC）的结局。
使用组织微阵列（TMA）对 306 例 EEC 标本进行了分析。对经过验证的组织生物标志物（ER、PR、HER2、Ki-67、MLH1 和 p53）和两个新标志物（L1CAM 和 ASRGL1）进行了成本效益高、耗时少的免疫组织化学分析。对染色结果进行了带有嵌入式变量选择的统计建模，以确定具有最大预测准确性的最小预后标志物组合，同时不影响通用性。
确定包括 p53 和 ASRGL1 免疫组织化学的标志物组合为无复发生存和疾病特异性生存的最准确预测因子。在这个组合中，患者被分配到高风险（5.9%）、中风险（29.5%）和低风险（64.6%）风险组，高风险患者死于 EEC 的风险是低风险患者的 30 倍（P<0.001）。
p53 和 ASRGL1 免疫分析将 EEC 患者分为三个具有显著不同结局的风险组。这个简单且易于应用的组合可以为 EEC 风险分层和指导治疗方式的分配提供有用的工具。
