Abstract 6844: Functional data analysis of spatial protein imaging data using spatial trajectories with application to ovarian cancer.

Abstract Background: Researchers can study both the abundance and spatial architecture of cell types within the tumor microenvironment (TME) using spatial technologies. Often, Ripley’s K or nearest-neighbor G are used to measure spatial clustering of cells. These measures can be computed at various radii to assess clustering at different spatial ranges. We propose the use of functional principal component analysis (FPCAs) to model the association of the spatial clustering of T cell populations in the TME with survival from high grade serous ovarian cancer (HGSOC). Methods: We applied FPCA to study the clustering of CD3+ and CD3+CD8+ cells in the ovarian TME with survival. Five ovarian cancer studies were included in the analysis: Nurses’ Health Study (N=239), Nurses’ Health Study II (N=68), New England Case Control Study of Ovarian Cancer (N=175), African American Cancer Epidemiology Study (N=155), and the North Carolina Ovarian Cancer Study (N=136). Protein imaging data was collected using AKOYA Biosciences OPALTM IHC Kit with image analysis completed using Vectra®3 Automated Quantitative Pathology Imaging System. Spatial trajectories using G statistic were computed for samples with at least 8 positive cells for a cell type. FPCA was applied to the spatial curves with the top two components (FPC1, FPC2) associated with survival, adjusting for stage, age of diagnosis, and abundance of the cell population (high vs low using 1% threshold). A second model was fit to assess interaction between the abundance and spatial clustering. Analyses were completed for each study with results combined using a random-effect meta-analysis. Results: From the model without spatial information, we observed that high abundance of CD3+ (hazard ratio (HR): 0.81, 95% confidence interval (0.66, 0.98)) and CD3+CD8+ cells (HR: 0.64 (0.52, 0.79)) were associated with improved survival. The model with both abundance and spatial clustering detected a significant effect for CD3+CD8+ clustering (FPC1 HR: 1.17 (1.04, 1.33)) and a borderline association for CD3+ cells (FPC1 HR: 1.06 (0.99, 1.14)). When fitting a model with interactions for abundance and spatial clustering, a significant interaction for CD3+ cells (HR: 1.23 (1.07, 1.42)) and a borderline interaction for CD3+CD8+ cells (HR: 1.19 (0.98, 1.43)) was observed. Hence, we estimated the HRs for 4 tumor types (high/low abundance and high/low spatial clustering). We observed that patients with high abundance but low spatial clustering of CD3+ and CD3+CD8+ cells had the improved survival, with HRs for the high abundance / low spatial clustering group being 0.74 and 0.41, respectively. Discussion: In studying the HGSOC TME using spatial proteomics and FPCA, we found that not only is the abundance of T cell populations related to survival, but also the spatial clustering of these cell populations, with improved survival for women with tumors with diffuse T cell infiltration. Citation Format: Brooke L. Fridley, Alex C. Soupir, Daisy Liao, Chase Sakitis, Joellen Schildkraut, Andrew B. Lawson, Mary K. Townsend, Shelley Tworoger, Kathryn L. Terry, Julia Wrobel, Lauren Cole Peres. Functional data analysis of spatial protein imaging data using spatial trajectories with application to ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6844.