ArticleJournal of pathology informatics2025
Quantification of HER2-low and ultra-low expression in breast cancer specimens by quantitative IHC and artificial intelligence.
Article in Journal of pathology informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence (AI)-Assisted Human Epidermal Growth Factor Receptor 2 (HER2) Scoring in Breast Cancer: Diagnostic Agreement and Understanding Discordance.Laboratory investigation; a journal of technical methods and pathology · 2026Article
- Repurposing HER2 IHC: Pan-HER2 and Low-HER2.Applied immunohistochemistry & molecular morphology : AIMM · 2026Review
- Elucidating the relationship between affinity and potency in the performance of therapeutic IgE.Scientific reports · 2026Article
- "HER2-Low" and the Challenge of Repurposing Legacy HER2 IHC Biomarker Assays.Current oncology (Toronto, Ont.) · 2026Article
- AI-enhanced precision: boosting pathologist confidence in HER2 ultra-low diagnosis through intelligent re-screening.Breast cancer research : BCR · 2026Article
- Redefining HER2 in Breast Cancer: From Conventional Positivity to Low and Ultralow in the New Era of Antibody-Drug Conjugates.Cancer research and treatment · 2026Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 128x128μm
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