Abstract 2086: Multiplex spatial bioimaging and AI-based image analysis for precision cancer therapeutics

Abstract Breast cancer (BC) is a heterogeneous disease both at the molecular and clinical level. Worldwide, BC is the leading cause of cancer-related deaths in women. Besides conventional chemo/radio therapy, immunotherapy is one of the most promising choices for a small subset of responding individuals. However, these treatments often fail to prevent recurrence and metastasis. Disease heterogeneity in BC poses challenges for survival prediction, as patients with similar diagnoses often respond differently to treatment. In this study, we apply multiplexed spatial proteomics analysis on breast cancer tissue paired with normal tissue and observe multiple immune cell types and biomarkers that could provide insight into features of the tumor microenvironment (TME) that influence response to immunotherapy. Our findings show that systematic mapping of the TME by multiplexed spatial proteomics, could empower precision immuno-oncology. The Cell DIVE Multiplex Imaging Solution allows probing and imaging of dozens of biomarkers on a whole single tissue section with an iterative staining and dye inactivation workflow. At its core, Cell DIVE is a precise and adaptable open multiplexing solution that enables flexibility in antibody selection of biomarker panels used in a multiplexed imaging study. Cell Signaling Technology (CST) has a broad portfolio of IHC-validated antibodies to detect key proteins in the TME, enabling immune cell detection and phenotyping in tissue. CST offers off the shelf (OTS), ready-to-ship antibody conjugates that have been verified to work on Cell DIVE. CST employs a rigorous approach to IHC validation, followed by verification on the Cell DIVE platform to ensure successful detection of proteins. Here, we demonstrate multiplexed Cell DIVE imaging using a novel panel of dozens of CST biomarkers across human breast cancer tissue samples. In summary, multiplexed whole slide imaging using validated antibodies and Aivia, AI-based image processing software enables spatially resolved tissue analysis of the tumor microenvironment. Citation Format: Arindam Bose, Emily Quann Alonzo, Sophie Struble, Jeremy Fisher, Richard A. Heil-Chapdelaine, Natasha F. Diaz Granados, Vasundhara Agrawal. Multiplex spatial bioimaging and AI-based image analysis for precision cancer therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2086.