Abstract P5-03-05: AI Based Quantitative Estimation of HER2 Protein Expression in Low and Ultra Low Ranges

Abstract The anti-HER2 monoclonal antibody drug trastuzumab (Herceptin®) was approved for treatment of HER2 positive breast cancer by the FDA in 1998. Concurrently, the HER2 IHC assay HercepTest™ was approved as an aid in the assessment of breast cancer patients where trastuzumab treatment was considered (review by Jørgensen et al. 2021). Recently, FDA approved antibody drug conjugate (ADC) trastuzumab deruxtecan (T-DXd; Enhertu®) for the treatment of metastatic HER2-positive and HER2-low breast cancer. Results from the Destiny Breast-06 clinical trial showed that T-Dxd provides statistically significant and clinically meaningful benefits to HER2-ultralow breast cancer patients as well. However, sensitive, accurate and quantitative evaluation of HER2 expression based on currently approved IHC assays remains challenging, especially in low and ultra-low ranges of HER2. Advanced computational approaches could improve the interpretation of such IHC assays and could be of high benefit for identifying treatment options for current and future HER2 targeted therapies. More importantly, it could provide a basis for objectively assessing the lower level of HER2 protein expression that maximizes therapeutic benefits, while minimizing drug exposure risks for patients. Here we demonstrate a novel AI-based method that quantifies HER2 protein expression following staining using the HercepTest™ mAB pharmDx (Dako Omnis, GE001) assay (HercepTest™ (mAB)) for breast cancer cases characterized as HER2 IHC 0 or 1+. We inferred HER2 expression spatially across the entire breast cancer tissue section and quantified its heterogeneity. We identified low levels of HER2 expression that are challenging to detect by the human eye and provided a graphical overlay of stained tissues for visualization and quantification of the heterogeneity of HER2 expression. We used the quantitative IHC (qIHC) method described by Jensen et al. (2017) to quantitively measure the HER2 expression in invasive breast carcinomas. These measurements were used to train an AI model to predict the expression based on the HercepTest™ (mAb) stain which was previously demonstrated to detect HER2 expression with higher sensitivity in the lower ranges of HER2 compared to PATHWAY 4B5 (Ruschoff et al, 2022). Note that quantitative analysis of the qIHC assay could also be used to directly quantify HER2 expression. 82 formalin fixed, paraffin embedded (FFPE) tissue blocks of invasive breast carcinoma with HER2 IHC scores 0 or 1+ and with areas of solid tumor tissue were selected for this study. Serial sections from each tissue block were stained with H&E, HercepTest™ (mAB), qIHC and p63 (GA662, Dako Omnis). Stained tissues were scanned on the Philips Ultra Fast Scanner and digitally aligned. Tumor areas were manually selected and reviewed by expert pathologists in HercepTest™ (mAB) using aligned H&E and p63 IHC staining information on consecutive tissue sections to help identify tumor areas and separate invasive tumor areas from carcinoma in situ. HER2 expression was evaluated based on the qIHC assay in each 128µm2 area within tumor regions. The differences in average qIHC expressions across tumor areas were found to be statistically significant between IHC 0, IHC>0 and IHC<1+, and IHC 1+ groups. We observed high level of spatial heterogeneity of the HER2 expression levels within the same tissue, up to five-fold in some cases. Using these qIHC based estimates of HER2 expression as ground truth, we trained an AI based interpretation of HercepTest™ (mAB) assay. K-fold cross validation scheme was used to separate train and test slides, with test folds aggregated for model evaluation. We demonstrated high slide-level agreement of the AI-based estimate of HER2 expression in tumor regions and the ground-truth with Pearson correlation of 0.94, and R2 of 0.87. In the future we expect AI-assisted quantification and visualization of HER2 expression to enable fast and safe treatment decisions. Citation Format: Anya Tsalenko, Frederik Aidt, Elad Arbel, Itay Remer, Oded Ben-David, Amir Ben-Dor, Daniela Rabkin, Kirsten Hoff, Karin Salomon, Sarit Aviel-Ronen, Jens Mollerup, Lars Jacobsen. AI Based Quantitative Estimation of HER2 Protein Expression in Low and Ultra Low Ranges [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2024; 2024 Dec 10-13; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(12 Suppl):Abstract nr P5-03-05.