中国癌症杂志 ›› 2021, Vol. 31 ›› Issue (11): 1126-1144.doi: 10.19401/j.cnki.1007-3639.2021.11.012

• 指南与共识 • 上一篇    

子宫内膜癌分子检测中国专家共识(2021年版)

中国抗癌协会妇科肿瘤专业委员会,中华医学会病理学分会,国家病理质控中心   

  • 出版日期:2021-11-30 发布日期:2021-12-03

The Chinese Expert Consensus Recommendations on Molecular Testing in Endometrial Cancer (2021 edition)

The Society of Gynecological Cancer of China Anti-Cancer Association, Chinese Society of Pathology of the Chinese Medical Association, National Pathology Quality Control Center   

  • Published:2021-11-30 Online:2021-12-03

摘要: 近年来,高通量测序技术的发展加深了我们对子宫内膜癌发病机制和分子遗传特征的理解,基于分子遗传特征的个体化精准治疗,革新了子宫内膜癌的治疗模式。但国内对子宫内膜癌的遗传风险筛查流程、分子分型检测策略及其对患者预后评估和治疗选择的临床价值、晚期子宫内膜癌患者分子检测的选择及临床价值探讨和认识尚有不足。根据子宫内膜癌分子病理学检测与精准治疗领域的最新研究进展,中国抗癌协会妇科肿瘤专业委员会、中华医学会病理学分会及国家病理质控中心针对子宫内膜癌组织标本的分子病理学检测制订了《子宫内膜癌分子检测中国专家共识》(以下简称本共识),希望通过本共识,提高中国临床工作者对于子宫内膜癌分子检测的认识,以指导与规范子宫内膜癌分子检测在国内的临床应用。

关键词: 子宫内膜癌, 林奇综合征, 分子分型, 生物标志物

Abstract: Recently, our understanding of the pathogenesis and molecular genetic characteristics of endometrial cancer has been improved along with the development of high-throughput sequencing technology. Precision medicine based on genetic characteristics has already transformed the care of endometrial cancer. However, there is no specific consensus in China on the methods and strategies of genetic screening for hereditary syndrome, molecular classification and other biomarkers testing in endometrial cancer. Based on the most recent advances in molecular classification and precision medicine in endometrial cancer, The Society of Gynecological Cancer of China Anti-Cancer Association, Chinese Society of Pathology of the Chinese Medical Association and National Pathology Quality Control Center have formulated “The Chinese Expert Consensus Recommendations on Molecular Testing in Endometrial Cancer”, in order to improve the understanding of molecular testing among gynecologic oncologists, pathologists, and other specialists, and further standardize the application of molecular testing in endometrial cancer in China.

Key words: Endometrial cancer, Lynch syndrome, Molecular classification, Biomarkers