约旦妇女乳腺癌亚型的分布及其与组织病理分级的相关性:分子亚分类研究

JRSM short reports Pub Date : 2013-09-13 eCollection Date: 2013-01-01 DOI:10.1177/2042533313490516
Maha Shomaf, Jamal Masad, Saleh Najjar, Dana Faydi
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引用次数: 21

摘要

目的:探讨约旦女性乳腺癌患者的激素受体状态及人表皮生长因子受体2 (HER2)/neu基因表达。将我们的患者分为分子亚型,并将结果与患者的年龄和肿瘤分级相关联。设计:采用标准免疫组织化学技术评估雌激素受体(ER)、PR和HER2/neu,并将其分类为分子亚型。地点:约旦大学医院,安曼,约旦。参与者:在约旦大学医院诊断的193例乳腺癌病例。主要结局指标:乳腺癌分子亚型、年龄和肿瘤分级。结果:所有病例均分为两组:小于等于50岁的青年组和大于50岁的老年组。病例分为管腔A型、管腔B型、基底细胞样(BCL)型和Her2/neu+型。在老年组中,最常见的亚型是腔内A型(72%)。在这个年龄组中,大多数病例(48%)为II级。在年轻年龄组中,47%的病例为腔A亚类。结论:乳腺癌分子分型是肿瘤对激素治疗反应的重要预测因素。这一事实更加强调迫切需要发展可靠和可重复的分类制度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Distribution of breast cancer subtypes among Jordanian women and correlation with histopathological grade: molecular subclassification study.

Objective: To evaluate the hormone receptor status and human epidermal growth factor receptor 2 (HER2)/neu gene expression among Jordanian women with breast cancer. To classify our patients into molecular subtypes and to correlate the results with age of the patients and tumour grade.

Design: Evaluation of estrogen receptor (ER), PR and HER2/neu was done by standard immunohistochemical technique and subclassification into molecular subtypes.

Setting: Jordan University Hospital, Amman, Jordan.

Participants: One hundred and ninety-three cases of breast cancer diagnosed at Jordan University Hospital.

Main outcome measures: Molecular subtypes of breast cancer, age and tumour grade.

Results: ALL THE CASES WERE DIVIDED INTO TWO GROUPS: the young age group less or equal 50 years of age and the older age group more than 50 years of age. The cases were subclassified into luminal A, luminal B, basal cell like (BCL) and Her2/neu+. In older age group, the most common subtype was luminal A (72%). In this age group, most of the cases (48%) were of grade II. In younger age group, 47% of the cases were of luminal A subclass. In this age group, 42% were of grade I.

Conclusions: Molecular subtyping of breast cancer is an essential predicting factor of tumour response to hormonal therapy. This fact puts increased stress on the urgent need for the development of reliable and reproducible classification systems.

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