Changes in HER2low and HER2-ultralow status in 47 advanced breast carcinoma core biopsies, matching surgical specimens, and their distant metastases assessed by conventional light microscopy, digital pathology, and artificial intelligence.

IF 3 3区 医学 Q2 ONCOLOGY
Breast Cancer Research and Treatment Pub Date : 2025-10-01 Epub Date: 2025-07-22 DOI:10.1007/s10549-025-07776-6
Anikó Kovács, Leif Klint, Barbro Linderholm, Toshima Z Parris
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引用次数: 0

Abstract

Background: HER2-targeted therapies have improved survival in HER2-positive breast cancer, and recent data suggest potential benefits for patients with HER2-low tumors (defined as immunohistochemistry (IHC) 1 + or 2 + and, in situ hybridization (ISH)-negative). HER2-low tumors are heterogenous, spanning the hormone receptor-positive and triple-negative subtypes. Assessing HER2-low and HER2-ultralow status remains challenging, especially across specimen types.

Aims: This study aims to (1) compare HER2 assessment using conventional microscopy, digital pathology, and an artificial intelligence (AI) model, and (2) investigate changes in HER2-low status between core biopsies, surgical specimens, and metastases.

Materials and methods: IHC slides from 47 HER2-low advanced breast carcinomas were analyzed using conventional microscopy, digital pathology, and an AI model developed on Aiforia® Create. HER2 statuses were categorized as low, ultralow (score 1 + in 1-10%), and null (score 0 or 1 + in < 1% with difficult-to-interpret minimal membranous-like staining). Changes in HER2 expression across specimen types were evaluated using agreement measures and visualization tools.

Results: The AI model identified more HER2-low and HER2-ultralow cases compared to conventional methods, improving detection accuracy. HER2 expression differed between specimen types, with metastases exhibiting increased HER2 expression compared to surgical specimens and core biopsies. Digital pathology also showed stronger membranous staining and identified more HER2 expressor tumor cells with any kind of membranous staining than microscopy.

Conclusions: AI evaluation is a more sensitive method for HER2-low assessment and reveals expression changes across disease progression. These findings emphasize the need for standardized HER2 assessment to ensure accurate therapy eligibility, particularly for novel treatments like Trastuzumab-Deruxtecan.

通过常规光镜、数字病理学和人工智能评估47例晚期乳腺癌核心活检、匹配手术标本中HER2low和her2 -超低状态的变化及其远处转移。
背景:her2靶向治疗提高了her2阳性乳腺癌的生存率,最近的数据表明her2低肿瘤(定义为免疫组织化学(IHC) 1 +或2 +和原位杂交(ISH)阴性)患者的潜在益处。her2低肿瘤是异质性的,包括激素受体阳性和三阴性亚型。评估her2低和her2超低状态仍然具有挑战性,特别是在不同的标本类型中。目的:本研究旨在(1)比较传统显微镜、数字病理学和人工智能(AI)模型对HER2的评估,以及(2)研究核心活检、手术标本和转移灶之间HER2低水平状态的变化。材料和方法:使用常规显微镜、数字病理学和Aiforia®Create上开发的人工智能模型分析47例her2低晚期乳腺癌的免疫组化切片。HER2状态分为低、超低(1-10%得分为1 +)和零(结果得分为0或1 +)。与传统方法相比,AI模型识别出更多的HER2-低和HER2-超低病例,提高了检测精度。HER2表达在不同标本类型之间存在差异,与手术标本和核心活检相比,转移灶的HER2表达增加。数字病理学也显示出更强的膜染色,任何一种膜染色都比显微镜下发现更多的HER2表达肿瘤细胞。结论:AI评估是一种更敏感的her2低水平评估方法,可以揭示疾病进展过程中的表达变化。这些发现强调了标准化HER2评估的必要性,以确保准确的治疗资格,特别是对于曲妥珠单抗-德鲁西替康等新型治疗。
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来源期刊
CiteScore
6.80
自引率
2.60%
发文量
342
审稿时长
1 months
期刊介绍: Breast Cancer Research and Treatment provides the surgeon, radiotherapist, medical oncologist, endocrinologist, epidemiologist, immunologist or cell biologist investigating problems in breast cancer a single forum for communication. The journal creates a "market place" for breast cancer topics which cuts across all the usual lines of disciplines, providing a site for presenting pertinent investigations, and for discussing critical questions relevant to the entire field. It seeks to develop a new focus and new perspectives for all those concerned with breast cancer.
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