Abstract 5630: Robust spatial biomarker discovery through multi-platform multiplex image analysis of breast cancer clinical cohorts

Abstract Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and enable development of spatial biomarkers to predict response to immunotherapy and other therapeutics. However, spatial biomarker discovery is often limited to small patient cohorts and single technologies, limiting statistical power and increasing the likelihood of technical artifacts. We utilized cyclic immunofluorescence, (CyCIF), to profile patient tissues from 102 breast cancer patients, 63 with clinical follow up. We then developed methods for comparative analysis of data from three disparate imaging technologies including our CyCIF images, as well as publicly available imaging mass cytometry and multiplex ion-beam imaging breast cancer data sets. We demonstrate similar single-cell phenotyping results across breast cancer patient cohorts imaged with the three methods, and furthermore, show agreement in the prognostic value of cellular and spatial biomarkers across platforms. We identified T cell infiltration as independently associated with longer survival in high-proliferation breast cancer, which was enriched for activated and spatially clustered T cells. A comparison of six spatial analysis methods revealed robust spatial biomarkers, including tumor-macrophage and tumor-fibroblast proximity associated with poor prognosis in estrogen receptor positive and triple negative tumors, respectively. Our methods enable assembly of larger clinical cohorts from diverse platforms to aid in predictive and prognostic spatial biomarker identification and validation. Citation Format: Jennifer R. Eng, Elmar Bucher, Zhi Hu, Melinda Sanders, Bapsi Chakravarthy, Jennifer Pietenpol, Rosalie C. Sears, Summer Gibbs, Joe W. Gray, Koei Chin. Robust spatial biomarker discovery through multi-platform multiplex image analysis of breast cancer clinical cohorts. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5630.