Abstract 5353: Graph representation learning of tumor topology from spatial imaging data

Abstract

Abstract Recently, novel technologies have enabled spatially resolved multiplexed protein profiling of tumors; however, there is an unmet need for unbiased detection of spatial structures within the tissue. By encoding multiplexed images as a graph, we produce an efficient and well-studied graph representation that allows for the application of sophisticated machine learning methods, particularly graph neural networks (GNNs) and graph autoencoders (GAE). While GNNs have recently become popular tools in the analysis of spatial data, current GNN methods are limited by production of cell-level representations rather than graph-level representations. To understand patterns of local tissue structure and protein expression, we implemented a generalizable GAE framework that summarizes marker intensity and morphological values with the local topology of neighboring cells. In this way, each local cellular graph is represented as a single point in the lower dimensional space. We show that our framework generates low-dimensional features that reflect both cellular attributes and tissue structure. We demonstrate that clustering local structure in 318 fields of view from 22 tumor lesions across 7 patients with in-transit melanoma captured using multiplexed immunofluorescence (mpIF) can produce spatial motifs predictive of response to intralesional interleukin-2 (IL-2) immunotherapy. Additionally, we applied our framework to mpIF data of 146 high grade serous ovarian cancer tumors across 50 patients. We identify clusters with higher CD8+ T cell infiltration that associate with homologous recombination deficiency. Our method can be readily applied to various spatially resolved multiplexed imaging platforms, and the resulting embeddings provide a compressed representation of subgraphs that can be used in further classification or regression analysis of patient- and/or image-level histological, clinical, or genomic labels. Citation Format: Nicholas Ceglia, Samuel S. Freeman, Maryam Pourmaleki, Andrew McPherson, Sohrab P. Shah. Graph representation learning of tumor topology from spatial imaging data. [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 5353.