定量免疫组化和人工智能技术定量测定乳腺癌标本中her2低表达和超低表达

Q2 Medicine
Frederik Aidt , Elad Arbel , Itay Remer , Oded Ben-David , Amir Ben-Dor , Daniela Rabkin , Kirsten Hoff , Karin Salomon , Sarit Aviel-Ronen , Gitte Nielsen , Jens Mollerup , Lars Jacobsen , Anya Tsalenko
{"title":"定量免疫组化和人工智能技术定量测定乳腺癌标本中her2低表达和超低表达","authors":"Frederik Aidt ,&nbsp;Elad Arbel ,&nbsp;Itay Remer ,&nbsp;Oded Ben-David ,&nbsp;Amir Ben-Dor ,&nbsp;Daniela Rabkin ,&nbsp;Kirsten Hoff ,&nbsp;Karin Salomon ,&nbsp;Sarit Aviel-Ronen ,&nbsp;Gitte Nielsen ,&nbsp;Jens Mollerup ,&nbsp;Lars Jacobsen ,&nbsp;Anya Tsalenko","doi":"10.1016/j.jpi.2025.100513","DOIUrl":null,"url":null,"abstract":"<div><div>Recent results of clinical trials in antibody drug conjugate (ADC) therapies have significantly broadened treatment options for the HER2 low and ultra-low breast cancer patients. However, sensitive, accurate and quantitative evaluation of HER2 expression based on current immunohistochemistry (IHC) assays remains challenging, especially in low and ultra-low HER2 expression ranges.</div><div>We developed a novel methodology for quantifying HER2 protein expression, targeting breast cancer cases in the HER2 IHC 0 and 1+ categories. We measured HER2 expression using quantitative IHC (qIHC) that enables precise and tunable HER2 detection across different expression levels as demonstrated in formalin-fixed paraffin-embedded cell lines. Additionally, we developed an AI-based interpretation of HercepTest™ mAb pharmDx (Dako Omnis) (HercepTest™ mAb) using qIHC measurements as the ground truth. Both methodologies allowed spatial resolution and visualization of low and ultra-low levels of HER2 expression across entire tissue sections to demonstrate and enable quantification of heterogeneity of HER2 expression.</div><div>Serial sections of 82 formalin-fixed paraffin-embedded tissue blocks of invasive breast carcinoma with HER2 IHC scores 0 or 1+ were stained with H&amp;E, HercepTest™ (mAb), qIHC and p63, then scanned and digitally aligned. Tumor areas were manually selected and reviewed by expert pathologists. HER2 expression was quantitatively evaluated based on the qIHC assay in each 128x128μm<sup>2</sup> area within tumor regions. We observed statistically significant differences in HER2 expression between IHC 0, 0 &lt; IHC &lt; 1+, and IHC 1+ groups, and a high degree of spatial heterogeneity of the HER2 expression levels within the same tissue, up to five-fold in some cases. We demonstrated high slide-level tumor region agreement of estimates of HER2 expression between the AI-based interpretation of HercepTest™ mAb and the qIHC ground truth with a Pearson correlation of 0.94, and R<sup>2</sup> of 0.87.</div><div>The developed methodologies can be used to stratify HER2 low-expression patient groups, potentially improving the interpretation of IHC assays and maximizing therapeutic benefits. This method can be implemented in histology labs without requiring a specialized workflow.</div></div>","PeriodicalId":37769,"journal":{"name":"Journal of Pathology Informatics","volume":"19 ","pages":"Article 100513"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence\",\"authors\":\"Frederik Aidt ,&nbsp;Elad Arbel ,&nbsp;Itay Remer ,&nbsp;Oded Ben-David ,&nbsp;Amir Ben-Dor ,&nbsp;Daniela Rabkin ,&nbsp;Kirsten Hoff ,&nbsp;Karin Salomon ,&nbsp;Sarit Aviel-Ronen ,&nbsp;Gitte Nielsen ,&nbsp;Jens Mollerup ,&nbsp;Lars Jacobsen ,&nbsp;Anya Tsalenko\",\"doi\":\"10.1016/j.jpi.2025.100513\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Recent results of clinical trials in antibody drug conjugate (ADC) therapies have significantly broadened treatment options for the HER2 low and ultra-low breast cancer patients. However, sensitive, accurate and quantitative evaluation of HER2 expression based on current immunohistochemistry (IHC) assays remains challenging, especially in low and ultra-low HER2 expression ranges.</div><div>We developed a novel methodology for quantifying HER2 protein expression, targeting breast cancer cases in the HER2 IHC 0 and 1+ categories. We measured HER2 expression using quantitative IHC (qIHC) that enables precise and tunable HER2 detection across different expression levels as demonstrated in formalin-fixed paraffin-embedded cell lines. Additionally, we developed an AI-based interpretation of HercepTest™ mAb pharmDx (Dako Omnis) (HercepTest™ mAb) using qIHC measurements as the ground truth. Both methodologies allowed spatial resolution and visualization of low and ultra-low levels of HER2 expression across entire tissue sections to demonstrate and enable quantification of heterogeneity of HER2 expression.</div><div>Serial sections of 82 formalin-fixed paraffin-embedded tissue blocks of invasive breast carcinoma with HER2 IHC scores 0 or 1+ were stained with H&amp;E, HercepTest™ (mAb), qIHC and p63, then scanned and digitally aligned. Tumor areas were manually selected and reviewed by expert pathologists. HER2 expression was quantitatively evaluated based on the qIHC assay in each 128x128μm<sup>2</sup> area within tumor regions. We observed statistically significant differences in HER2 expression between IHC 0, 0 &lt; IHC &lt; 1+, and IHC 1+ groups, and a high degree of spatial heterogeneity of the HER2 expression levels within the same tissue, up to five-fold in some cases. We demonstrated high slide-level tumor region agreement of estimates of HER2 expression between the AI-based interpretation of HercepTest™ mAb and the qIHC ground truth with a Pearson correlation of 0.94, and R<sup>2</sup> of 0.87.</div><div>The developed methodologies can be used to stratify HER2 low-expression patient groups, potentially improving the interpretation of IHC assays and maximizing therapeutic benefits. This method can be implemented in histology labs without requiring a specialized workflow.</div></div>\",\"PeriodicalId\":37769,\"journal\":{\"name\":\"Journal of Pathology Informatics\",\"volume\":\"19 \",\"pages\":\"Article 100513\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Pathology Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2153353925000999\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Pathology Informatics","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2153353925000999","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0

摘要

抗体药物偶联(ADC)疗法的最新临床试验结果显著拓宽了HER2低和超低乳腺癌患者的治疗选择。然而,基于当前免疫组织化学(IHC)检测的HER2表达的敏感、准确和定量评估仍然具有挑战性,特别是在低和超低HER2表达范围内。我们开发了一种新的方法来定量HER2蛋白表达,针对HER2 IHC 0和1+类别的乳腺癌病例。我们使用定量免疫组化(qIHC)测量了HER2的表达,这种方法能够在不同的表达水平上进行精确和可调的HER2检测,正如在福尔马林固定石蜡包埋细胞系中所证明的那样。此外,我们开发了一种基于人工智能的解释HercepTest™mAb pharmDx (Dako Omnis) (HercepTest™mAb),使用qIHC测量作为基础事实。这两种方法都允许在整个组织切片上进行低水平和超低水平HER2表达的空间分辨率和可视化,以证明和量化HER2表达的异质性。采用H&;E、HercepTest™(mAb)、qIHC和p63染色,对82例HER2 IHC评分为0或1+的浸润性乳腺癌用福尔马林固定石蜡包埋组织块的连续切片进行扫描和数字对齐。肿瘤区域由病理学专家手工选择和检查。采用qIHC法定量评价肿瘤区域内每个128x128μm2区域的HER2表达。我们观察到,在IHC 0、0 <; IHC <; 1+和IHC 1+组之间,HER2表达有统计学上的显著差异,并且在同一组织内,HER2表达水平具有高度的空间异质性,在某些情况下高达5倍。我们证明基于人工智能的HercepTest™mAb解释与qIHC基本事实之间的HER2表达估计在肿瘤区域高度一致,Pearson相关系数为0.94,R2为0.87。开发的方法可用于对HER2低表达患者群体进行分层,潜在地改善免疫组化分析的解释,并最大限度地提高治疗效果。这种方法可以在组织学实验室中实施,而不需要专门的工作流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence
Recent results of clinical trials in antibody drug conjugate (ADC) therapies have significantly broadened treatment options for the HER2 low and ultra-low breast cancer patients. However, sensitive, accurate and quantitative evaluation of HER2 expression based on current immunohistochemistry (IHC) assays remains challenging, especially in low and ultra-low HER2 expression ranges.
We developed a novel methodology for quantifying HER2 protein expression, targeting breast cancer cases in the HER2 IHC 0 and 1+ categories. We measured HER2 expression using quantitative IHC (qIHC) that enables precise and tunable HER2 detection across different expression levels as demonstrated in formalin-fixed paraffin-embedded cell lines. Additionally, we developed an AI-based interpretation of HercepTest™ mAb pharmDx (Dako Omnis) (HercepTest™ mAb) using qIHC measurements as the ground truth. Both methodologies allowed spatial resolution and visualization of low and ultra-low levels of HER2 expression across entire tissue sections to demonstrate and enable quantification of heterogeneity of HER2 expression.
Serial sections of 82 formalin-fixed paraffin-embedded tissue blocks of invasive breast carcinoma with HER2 IHC scores 0 or 1+ were stained with H&E, HercepTest™ (mAb), qIHC and p63, then scanned and digitally aligned. Tumor areas were manually selected and reviewed by expert pathologists. HER2 expression was quantitatively evaluated based on the qIHC assay in each 128x128μm2 area within tumor regions. We observed statistically significant differences in HER2 expression between IHC 0, 0 < IHC < 1+, and IHC 1+ groups, and a high degree of spatial heterogeneity of the HER2 expression levels within the same tissue, up to five-fold in some cases. We demonstrated high slide-level tumor region agreement of estimates of HER2 expression between the AI-based interpretation of HercepTest™ mAb and the qIHC ground truth with a Pearson correlation of 0.94, and R2 of 0.87.
The developed methodologies can be used to stratify HER2 low-expression patient groups, potentially improving the interpretation of IHC assays and maximizing therapeutic benefits. This method can be implemented in histology labs without requiring a specialized workflow.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Pathology Informatics
Journal of Pathology Informatics Medicine-Pathology and Forensic Medicine
CiteScore
3.70
自引率
0.00%
发文量
2
审稿时长
18 weeks
期刊介绍: The Journal of Pathology Informatics (JPI) is an open access peer-reviewed journal dedicated to the advancement of pathology informatics. This is the official journal of the Association for Pathology Informatics (API). The journal aims to publish broadly about pathology informatics and freely disseminate all articles worldwide. This journal is of interest to pathologists, informaticians, academics, researchers, health IT specialists, information officers, IT staff, vendors, and anyone with an interest in informatics. We encourage submissions from anyone with an interest in the field of pathology informatics. We publish all types of papers related to pathology informatics including original research articles, technical notes, reviews, viewpoints, commentaries, editorials, symposia, meeting abstracts, book reviews, and correspondence to the editors. All submissions are subject to rigorous peer review by the well-regarded editorial board and by expert referees in appropriate specialties.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信