Identification of HER2 ultra-low based on an artificial intelligence (AI)-powered HER2 subcellular quantification from HER2 immunohistochemistry images.

person Wonkyung Jung Oncology, Lunit Inc., Seoul, South Korea info_outline Wonkyung Jung, Taebum Lee, Sangwon SHIN, Biagio Brattoli, Mohammad Mostafavi, Gahee Park, Sukjun Kim, Hyunwoo Lee, Yoon Ah Cho, Eun Yoon Cho, So-Woon Kim, Sergio Pereira, Chang Ho Ahn, Siraj Mahamed Ali, Douglas E Mirsky, Chan-Young Ock

Authors person Wonkyung Jung Oncology, Lunit Inc., Seoul, South Korea info_outline Wonkyung Jung, Taebum Lee, Sangwon SHIN, Biagio Brattoli, Mohammad Mostafavi, Gahee Park, Sukjun Kim, Hyunwoo Lee, Yoon Ah Cho, Eun Yoon Cho, So-Woon Kim, Sergio Pereira, Chang Ho Ahn, Siraj Mahamed Ali, Douglas E Mirsky, Chan-Young Ock Organizations Oncology, Lunit Inc., Seoul, South Korea, Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea, Department of Pathology, Kyung Hee University Hospital, Kyung Hee University College of Medicine, Seoul, South Korea, Digital Medicine Society (DiMe), Boston, MA Abstract Disclosures Research Funding No funding sources reported Background: HER2-targeted antibody-drug conjugates (ADCs) can effectively target tumor cells even in HER2-low breast cancers (1+ and 2+/no amp). We developed an AI-based whole-slide image (WSI) analyzer for immunohistochemistry (IHC)-stained slides, exploring a cutoff differentiating between true HER2 negative versus HER2 ultra-low. Methods: An AI-based IHC WSI analyzer was developed using 6,188 WSIs stained by 19 types of IHC including HER2 and from various cancers, annotated by 153 pathologists. The model predicts cells as tumor cells or non-tumor cells and generates HER2 expression continuous score (HER2ecs, 0-100%), quantifying IHC staining intensity of membrane, nucleus, cytoplasm, and membrane+cytoplasm separately for each cell. A total of 401 HER2 IHC WSIs were acquired from Samsung Medical Center (n=275), Kyung Hee University Hospital (n=78), and Commercial Biobank (n=48), scored as 0 (n=347) and 1+ (n=54) by pathologists. Results: In total, 67,169,114 tumor cells and 119,113,886 non-tumor cells from 401 WSIs were successfully evaluated. Comparing HER2 score 0 versus 1+ by pathologists, mean HER2ecs of nucleus (1.7% vs 4.1%), cytoplasm (6.5% vs 12.9%), and membrane (3.6% vs 8.2%) were comparable. In HER2 score 0 WSIs, membrane HER2ecs of tumor cell was 3.6 ± 7.8% (mean ± standard deviation), while that of non-tumor cell was 0.9 ± 2.9% (p <0.001 vs. tumor cell), suggesting, even in HER2 score 0 cases, tumor cell exhibits HER2 expression beyond non-specific HER2 staining intensity. Based on our internal validation dataset, membrane-specific HER2ecs of 6.0% was the optimal threshold discriminating HER2 no-staining cells versus HER2 incomplete and faint/barely perceptible cells (AI-HER2-weak) per ASCO/CAP guideline. Among HER2 score 0 by pathologists, when applying this cutoff, the proportion of WSIs showing AI-HER2-weak or higher membrane-specific HER2ecs in over 10% of all tumor cells was 23.6% (82/347). Interestingly, that of WSIs among HER2 score 1+ by pathologists was 51.9% (28/54), which is comparable to 52.3%, objective response rate of trastuzumab deruxtecan in HER2-low patients in DESTINY-Breast04. Conclusions: AI-powered subcellular-level analysis enables identification of 23.6% HER2 ultra-low WSIs among HER2 score 0 by pathologist, which is defined by AI-HER2-weak or higher membrane-specific HER2 expression ≥ 10%. Clinical validation of the AI-powered HER2 ultra-low definition is warranted.