Suppr超能文献

宫颈癌的临床病理及分子标志物:一项前瞻性队列研究。

Clinicopathologic and molecular markers in cervical carcinoma: a prospective cohort study.

作者信息

Halle Mari Kyllesø, Ojesina Akinyemi I, Engerud Hilde, Woie Kathrine, Tangen Ingvild Løberg, Holst Frederik, Høivik Erling, Kusonmano Kanthida, Haldorsen Ingfrid S, Vintermyr Olav K, Trovik Jone, Bertelsen Bjørn I, Salvesen Helga B, Krakstad Camilla

机构信息

Department of Obstetrics and Gynaecology, Haukeland University Hospital, Bergen, Norway; Centre for Cancer Biomarkers, Department of Clinical Science, University of Bergen, Norway.

Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL; HudsonAlpha Institute for Biotechnology, Huntsville, AL.

出版信息

Am J Obstet Gynecol. 2017 Oct;217(4):432.e1-432.e17. doi: 10.1016/j.ajog.2017.05.068. Epub 2017 Jun 24.

Abstract

BACKGROUND

Cervical cancer is a major health problem worldwide. Identification of effective clinicopathologic and molecular markers is vital to improve treatment stratification.

OBJECTIVES

The purpose of this study was to validate a set of well-defined clinicopathologic features in a large population-based, prospectively collected cervical cancer cohort to support their use in the clinic. Further, we explored p53 and human epidermal growth factor receptor 2 as potential prognostic markers in cervical cancer.

STUDY DESIGN

Tissue was collected from 401 patients with cervical cancer. Clinical data that included follow-up evaluations were collected from patient journals. Histopathologic data were evaluated and revised by an expert pathologist. The prognostic impact of selected clinicopathologic variables was analyzed in the whole cohort. Tissue microarrays were prepared from 292 carcinomas, and p53 and human epidermal growth factor receptor 2 protein levels were evaluated by immunohistochemistry. Fresh frozen samples from overlapping cervical carcinomas previously were subjected to human papilloma virus typing (n=94), whole exome (n=100) and RNA (n=79) sequencing; the results were available for our analyses.

RESULTS

Among the clinicopathologic variables, vascular space invasion, histologic type, and tumor size were verified as strong independent prognostic markers. High p53 protein levels were associated significantly with markers for aggressive phenotype and survival, also in multivariate survival analysis, but did not reflect TP53 mutational status. High human epidermal growth factor receptor 2 protein levels were identified in 21% of all tumors. ERBB2 amplification was associated with poor outcome (P=.003); human epidermal growth factor receptor 2 protein level was not.

CONCLUSIONS

Our findings support that the Féderation Internationale de Gynécologie et d'Obstétrique s guidelines should include vascular space invasion and tumor size 2-4 cm and that careful selection of histologic type is essential for stratification of patient risk groups. High p53 levels independently predict poor survival yet do not reflect mutational status in cervical cancer. Amplified ERBB2 significantly links to poor survival, while HercepTest does not. With optimal stratification, human epidermal growth factor receptor 2-based therapy may improve cervical cancer treatment.

摘要

背景

宫颈癌是全球主要的健康问题。识别有效的临床病理和分子标志物对于改善治疗分层至关重要。

目的

本研究的目的是在一个基于人群的大型前瞻性收集的宫颈癌队列中验证一组明确的临床病理特征,以支持其在临床中的应用。此外,我们探索了p53和人表皮生长因子受体2作为宫颈癌潜在的预后标志物。

研究设计

收集了401例宫颈癌患者的组织。从患者病历中收集包括随访评估在内的临床数据。组织病理学数据由一名专家病理学家进行评估和修订。在整个队列中分析选定临床病理变量的预后影响。从292例癌组织中制备组织芯片,通过免疫组织化学评估p53和人表皮生长因子受体2蛋白水平。先前对部分重叠的宫颈癌新鲜冷冻样本进行了人乳头瘤病毒分型(n = 94)、全外显子测序(n = 100)和RNA测序(n = 79);其结果可供我们分析。

结果

在临床病理变量中,血管间隙浸润、组织学类型和肿瘤大小被确认为强有力的独立预后标志物。在多因素生存分析中,高p53蛋白水平也与侵袭性表型和生存的标志物显著相关,但不能反映TP53突变状态。在所有肿瘤中,21%检测到人表皮生长因子受体2蛋白水平升高。ERBB2扩增与不良预后相关(P = 0.003);人表皮生长因子受体2蛋白水平则不然。

结论

我们的研究结果支持国际妇产科联合会(FIGO)指南应纳入血管间隙浸润和2 - 4 cm的肿瘤大小,并且仔细选择组织学类型对于患者风险组的分层至关重要。高p53水平独立预测宫颈癌患者生存不良,但不能反映其突变状态。ERBB2扩增与不良生存显著相关,而HercepTest检测则不然。通过优化分层,基于人表皮生长因子受体2的治疗可能改善宫颈癌治疗。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验