Abstract 7391: Beyond traditional staining: A virtual-IHC digital assay for HER2 score prediction from routine H&E images

Abstract

Abstract Background: In breast cancer diagnostics, the accurate assessment of HER2 status is essential for treatment stratification. Conventionally, HER2 immunohistochemistry (IHC) scoring necessitates specialized staining protocols, which are resource- and time-intensive. This study presents a virtual IHC (vIHC) digital assay to predict HER2 scores directly from hematoxylin and eosin (H&E) stained images, which are routinely acquired for all specimens. This methodology aims to accelerate standard practices by reducing the need for additional IHC staining, thereby decreasing laboratory workload, costs, and turnaround time. It also presents an opportunity to screen a wider array of samples promptly, potentially identifying candidates for HER2-targeted therapy that might otherwise be delayed or overlooked. Methods: Serially sectioned breast core needle biopsy samples were stained with H&E and HER2-IHC following Leica-Bond staining protocols. Stained slides were imaged using the Hologic GeniusTM Digital Diagnostics System with Research Use Only software for Whole Slide Imaging. From this initial image dataset, 240 regions of interest (ROIs) were manually selected in order to create a dataset that uniformly covers: (i) all HER2-IHC scores (0-3), (ii) a wide range of HER2 stain intensities, and (iii) expression patterns. The IHC scores were assigned to each selected ROI by a pathology expert, following the scoring recommendations of ASCO/CAP American Society of Clinical Oncology and College of American Pathologists, with the caveat that ductal carcinoma in situ was also given a score for the purpose of training the model. These ROIs were then extracted from the images of the serially sectioned H&E-stained slides using Reveal Bio’s proprietary co-registration digital assay. A predictive digital assay was developed at Reveal Biosciences to classify the 240 H&E-stained breast tissue ROIs into four HER2 IHC score categories: 0, 1, 2, and 3. For model evaluation, cross-validation was performed 10 times by randomly splitting the dataset of H&E ROIs into 80% for training MIL and 20% for testing the model, followed by averaging the performance metrics across the 10 dataset splits. The performance metrics included classification accuracy, precision, recall, and F1 scores for each HER2 score category. Results: The vIHC digital assay for predicting the HER2 scores directly from H&E stained breast tissue images achieved the average classification accuracy of 93% across the four HER2 score categories on the test set of ROIs. This performance demonstrates that the model is capable of successfully distinguishing between the HER2 score categories directly from H&E images without the need for IHC staining. The results suggest that the vIHC digital assay could be a reliable and cost-effective tool for preliminary breast cancer screening, providing a valuable adjunct to traditional histopathological analysis. Citation Format: Misagh Naderi, Nikola Mitic, Nikola Spasic, Djordje Cikic, Kristen Ruf, Nikolaus Wagner, Igor Mihajlovic, Sinisa Todorovic, Ray Jenoski, Sid Mayer, Michael Quick, Claire Weston. Beyond traditional staining: A virtual-IHC digital assay for HER2 score prediction from routine H&E images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7391.