PDF(Pubmed)
Abstract：
Using next generation sequencing (NGS), The Cancer Genome Atlas (TCGA) found that endometrial carcinomas (ECs) fall under one of four molecular subtypes, and a POLE mutation status, mismatch repair (MMR) and p53 immunohistochemistry (IHC)-based surrogate has been developed. We sought to retrospectively classify and characterize a large series of unselected ECs that were prospectively subjected to clinical sequencing by utilizing clinical molecular and IHC data.
All patients with EC with clinical tumor-normal MSK-IMPACT NGS from 2014 to 2020 (n = 2115) were classified by integrating molecular data (i.e., POLE mutation, TP53 mutation, MSIsensor score) and MMR and p53 IHC results. Survival analysis was performed for primary EC patients with upfront surgery at our institution.
Utilizing our integrated approach, significantly more ECs were molecularly classified (1834/2115, 87%) as compared to the surrogate (1387/2115, 66%, p < 0.001), with an almost perfect agreement for classifiable cases (Kappa 0.962, 95% CI 0.949-0.975). Discrepancies were primarily due to TP53 mutations in p53-IHC-normal ECs. Of the 1834 ECs, most were of copy number (CN)-high molecular subtype (40%), followed by CN-low (32%), MSI-high (23%) and POLE (5%). Histologic and genomic variability was present amongst all molecular subtypes. Molecular classification was prognostic in early- and advanced-stage disease, including early-stage endometrioid EC.
The integration of clinical NGS and IHC data allows for an algorithmic approach to molecularly classifying newly diagnosed EC, while overcoming issues of IHC-based genetic alteration detection. Such integrated approach will be important moving forward given the prognostic and potentially predictive information afforded by this classification.
All patients with EC with clinical tumor-normal MSK-IMPACT NGS from 2014 to 2020 (n = 2115) were classified by integrating molecular data (i.e., POLE mutation, TP53 mutation, MSIsensor score) and MMR and p53 IHC results. Survival analysis was performed for primary EC patients with upfront surgery at our institution.
Utilizing our integrated approach, significantly more ECs were molecularly classified (1834/2115, 87%) as compared to the surrogate (1387/2115, 66%, p < 0.001), with an almost perfect agreement for classifiable cases (Kappa 0.962, 95% CI 0.949-0.975). Discrepancies were primarily due to TP53 mutations in p53-IHC-normal ECs. Of the 1834 ECs, most were of copy number (CN)-high molecular subtype (40%), followed by CN-low (32%), MSI-high (23%) and POLE (5%). Histologic and genomic variability was present amongst all molecular subtypes. Molecular classification was prognostic in early- and advanced-stage disease, including early-stage endometrioid EC.
The integration of clinical NGS and IHC data allows for an algorithmic approach to molecularly classifying newly diagnosed EC, while overcoming issues of IHC-based genetic alteration detection. Such integrated approach will be important moving forward given the prognostic and potentially predictive information afforded by this classification.
摘要：
目的：使用下一代测序（NGS），癌症基因组图谱（TCGA）发现子宫内膜癌（EC）属于四种分子亚型之一，和POLE突变状态，已经开发了基于错配修复（MMR）和p53免疫组织化学（IHC）的替代方法。我们试图通过利用临床分子和IHC数据对大量未选择的ECs进行回顾性分类和表征，这些ECs前瞻性地进行临床测序。
方法：2014年至2020年所有患有临床肿瘤正常MSK-IMPACTNGS的EC患者（n=2115）通过整合分子数据进行分类（即，POLE突变,TP53突变,MSISensor评分）和MMR和p53IHC结果。在我们机构对接受前期手术的原发性EC患者进行了生存分析。
结果：利用我们的综合方法，与替代（1387/2115，66%，p<0.001)，与可分类病例几乎完美一致（Kappa0.962，95%CI0.949-0.975）。差异主要是由于p53-IHC-正常ECs中的TP53突变。在1834年的EC中，大多数是拷贝数（CN）-高分子亚型（40％），其次是CN-低(32%)，MSI高（23%）和POLE（5%）。在所有分子亚型中都存在组织学和基因组变异性。分子分类是早期和晚期疾病的预后，包括早期子宫内膜样EC。
结论：临床NGS和IHC数据的整合允许对新诊断的EC进行分子分类的算法方法，同时克服了基于IHC的遗传改变检测的问题。鉴于此分类提供的预后和潜在的预测性信息，这种综合方法将很重要。
方法：2014年至2020年所有患有临床肿瘤正常MSK-IMPACTNGS的EC患者（n=2115）通过整合分子数据进行分类（即，POLE突变,TP53突变,MSISensor评分）和MMR和p53IHC结果。在我们机构对接受前期手术的原发性EC患者进行了生存分析。
结果：利用我们的综合方法，与替代（1387/2115，66%，p<0.001)，与可分类病例几乎完美一致（Kappa0.962，95%CI0.949-0.975）。差异主要是由于p53-IHC-正常ECs中的TP53突变。在1834年的EC中，大多数是拷贝数（CN）-高分子亚型（40％），其次是CN-低(32%)，MSI高（23%）和POLE（5%）。在所有分子亚型中都存在组织学和基因组变异性。分子分类是早期和晚期疾病的预后，包括早期子宫内膜样EC。
结论：临床NGS和IHC数据的整合允许对新诊断的EC进行分子分类的算法方法，同时克服了基于IHC的遗传改变检测的问题。鉴于此分类提供的预后和潜在的预测性信息，这种综合方法将很重要。
