Abstract B054: Identification of spatial motifs linked to tumor genotype using graph attention networks

作者：Nicholas Ceglia, Maryam Pourmaleki, Alessandro Del Grande, Adam C. Weiner, Jose Meza Llamosas, Andrew McPherson, Sohrab P. Shah · 发表于：Clinical Cancer Research · 年份：2025 · DOI：10.1158/1557-3265.aimachine-b054 · 研究领域：Advanced Computing and Algorithms

Abstract Spatial analysis of cancer requires computational methods capable of addressing the complexity and heterogeneity characteristic of tumor tissue architectures. Unlike well-organized tissues such as developing organs or the brain, cancer tissues lack easily identifiable spatial motifs, necessitating computational models that model a principled definition of a spatial motif tailored to their unique structure. We present GRAFITI, a graph autoencoder specifically designed to identify spatial motifs within cancer tissues using a formal graph-based definition. GRAFITI employs state of the art machine learning methods, including a deep multi-head graph-attention (GAT) encoder with dual decoders that reconstruct both expression profiles and spatial relationships among cells. The latent representation produced by GRAFITI is clustering using an integrated clustering head, that refines the spatial motif annotations during training. Using this output, GRAFITI minimizes an objective function designed around intra-motif similarity, inter-motif dissimilarity, and explicit spatial coherence constraints. GRAFITI employs additional spatial regularization techniques—such as continuity and separation losses—to effectively manage the spatial noise typical of disorganized cancer tissues. Its attention mechanism enhances interpretability by dynamically highlighting significant cell-cell interactions, such as tumor-immune infiltration events, identifying meaningful relationships within chaot...