子宫内膜癌(EC)是全球女性中第六大最常见的癌症,也是发达国家中最常见的癌症。EC在历史上被分为两个主要类别,I型和II型,主要基于组织病理学特征。尽管这种分类在诊所很有用,到目前为止,它未能在术前将患者充分分层为低危组或高危组.一些证据表明,分子特征也可以作为更好的患者风险分层和治疗决策的基础。癌症基因组图谱(TCGA)早在2013年,将EC重新定义为四个主要的分子亚群。尽管人们寄予厚望,欢迎将分子特征纳入实践的可能性,目前,它们尚未在诊所中系统地应用。这里,我们概述了新兴的分子模式如何与肿瘤组织病理学和分级一起用作预后因素,以及他们如何帮助识别高风险EC亚群,以更好地进行风险分层和改善治疗策略。考虑到在转化研究中使用临床前模型的重要性,我们还讨论了新的患者衍生模型如何有助于识别新的潜在目标并有助于治疗决策.
Endometrial carcinomas (EC) are the sixth most common cancer in women worldwide and the most prevalent in the developed world. ECs have been historically sub-classified in two major groups, type I and type II, based primarily on histopathological characteristics. Notwithstanding the usefulness of such classification in the clinics, until now it failed to adequately stratify patients preoperatively into low- or high-risk groups. Pieces of evidence point to the fact that molecular features could also serve as a base for better patients\' risk stratification and treatment decision-making. The Cancer Genome Atlas (TCGA), back in 2013, redefined EC into four main molecular subgroups. Despite the high hopes that welcomed the possibility to incorporate molecular features into practice, currently they have not been systematically applied in the clinics. Here, we outline how the emerging molecular patterns can be used as prognostic factors together with tumor histopathology and grade, and how they can help to identify high-risk EC subpopulations for better risk stratification and treatment strategy improvement. Considering the importance of the use of preclinical models in translational research, we also discuss how the new patient-derived models can help in identifying novel potential targets and help in treatment decisions.