LAUSR

Introduction: A limited number of drugs are available for use in breast cancer patients, and several are not in practical use due to the lack of adequate biomarkers. We have recently demonstrated the feasibility of using machine learning on molecular data from bulk tumor analysis to create a nine-protein signature named VEGF-inhibition Response Predictor (ViRP) for selecting BC patients for treatment with chemotherapy and bevacizumab. The ViRP score is currently being validated in the NAPEER+ clinical trial (EudraCT 2021-005850-27). Increasing evidence suggests that spatial organization of cells within the tumor microenvironment influences survival and response to therapy in numerous cancer types. In methods based on bulk tumor analysis all tumor cells are profiled simultaneously with both colocalized and distant stroma and immune cells. We are thus pursuing information on spatial organization of cellular phenotypes expressing selected cancer related proteins including our nine ViRP proteins. Methods: From the NeoAva (NCT00773695) clinical trial evaluating the effect of bevacizumab in combination with neoadjuvant chemotherapy (n=132 pts), FFPE tissue sections from patients before, during, and after treatment were made. Cyclic immunofluorescence (cyCIF) was used to profile the spatial expression of 32 cancer-signaling and 32 immune-related proteins, comprising our nine ViRP proteins, on FFPE tissue sections from selected patients (n = 20). The Galaxy-ME platform was used for image processing and downstream analysis of spatial protein profiling. Results: Use of cyCIF for spatial analysis enabled for evaluation of malignant cells in the context of surrounding microenvironmental cells, including immune cells. We found that cell type-specific protein abundance and subcellular localization formed a highly heterogenous pattern in the tissue. This was particularly evident for the nine ViRP proteins, and differences in expression between tumor cell populations will be further elucidated. Among the patients selected for cyCIF analysis, 4 were chosen based on misclassification by the ViRP signature. Ongoing studies focus on revealing spatial expression patterns to optimize the ViRP biomarker and explore why misclassification occurs. Furthermore, the observed molecular biology of the evolving tissues under treatment in responding and non-responding patients may reveal new biomarkers indicative of treatment response or resistance. Conclusion: We observe that the expression of proteins in tumor tissues is highly heterogeneous, and thus include numerous features not captured by bulk tumor analysis. Future development of new predictive tools and biomarkers that integrate molecular data which is multiparametric and spatial will set the stage for a new class of biomarkers in cancer diagnostics. Citation Format: Mads Haugland Haugen, David Kilburn, Hongli Ma, Cameron Watson, Allison Creason, Dong Zhang, Maria Aa Dahle, Ole Christian Lingjaerde, Marianne L. Smebye, Oeystein Garred, Mette S. Foersund, Mai T. Nguyen, Gunhild M. Maelandsmo, Gordon Mills, Olav Engebraaten. Spatial protein profiling by cyclic immunofluorescence to interpret and improve bulk tumor-based predictor of response to chemotherapy with bevacizumab in neoadjuvant breast cancer treatment. [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 5627.