SCOIGET: Predicting Spatial Tumor Evolution Pattern by Inferring Spatial Copy Number Variation Distributions

Abstract

Constructing a comprehensive spatiotemporal map of tumor heterogeneity is essential for understanding tumor evolution, with copy number variation (CNV) as a significant feature. Existing studies often rely on tools originally developed for single-cell data, which fail to utilize spatial information. Our study aims to develop a model that fully leverages spatial omics data to elucidate spatio-temporal changes in tumor evolution. Here, we introduce SCOIGET (Spatial COpy number Inference by Graph on Evolution of Tumor), a novel framework using graph neural networks with graph attention layers to learn spatial neighborhood features of gene expression and infer copy number variations. This approach integrates spatial multi-omics features to create a comprehensive spatial map of tumor heterogeneity. Notably, SCOIGET achieves a substantial reduction in error metrics (e.g., mean squared error, cosine similarity, and distance measures) and produces superior clustering performance as indicated by higher Silhouette Scores compared to existing methods. Our model significantly enhances the efficiency and accuracy of tumor evolution depiction, capturing detailed spatial and temporal changes within the tumor microenvironment. It is versatile and applicable to various downstream tasks, demonstrating strong generalizability across different spatial omics technologies and cancer types. This robust performance improves research efficiency and provides valuable insights into tumor progression. In conclusion, SCOIGET offers an innovative solution by integrating multiple features and advanced algorithms, providing a detailed and accurate representation of tumor heterogeneity and evolution, aiding in the development of personalized cancer treatment strategies. Key PointsO_ST_ABS1. Innovative Framework for Spatially Contextualized CNV InferenceC_ST_ABSIntroduces SCOIGET, a graph-based model that integrates spatial omics data with graph neural networks and attention mechanisms to accurately infer spatial copy number features. 2. Detailed Tumor Heterogeneity MappingConstructs representations of tumor evolution, capturing heterogeneity and clonal dynamics at both cellular and subcellular resolutions. 3. Versatile and Robust PerformanceOutperforms existing methods across multiple datasets, demonstrating reliability and adaptability to diverse spatial omics platforms and cancer types. 4. Broad Downstream ApplicationsEnables tasks such as tumor subclone identification, clustering, and evolutionary trajectory analysis, facilitating precision oncology strategies.