Computational Cancer Ecosystems: Artificial Intelligence for Modeling the Tumor Microenvironment and Treatment Response

Abstract

Cancer is increasingly recognized as a dynamic ecosystem comprising malignant cells, immune populations, stromal fibroblasts, endothelial cells, extracellular matrix components, vascular networks, microbiota, metabolic pathways, and systemic host responses that collectively in fluence tumor evolution and therapeutic outcomes. Traditional reductionist approaches often investigate these components independently, limiting comprehensive understanding of tumor biology and treatment response. Recent advances in artificial intelligence (AI), particularly foundation models, multimodal transformers, graph neural networks, self-supervised learning, spatial computing, and generative AI, have enabled the integration of heterogeneous biomedical information into computational representations of the tumor ecosystem. These intelligent systems combine digital pathology, radiological imaging, genomics, transcriptomics, proteomics, metabolomics, spatial multi-omics, single-cell sequencing, laboratory biomarkers, electronic health records, liquid biopsy, and longitudinal clinical data to characterize complex cellular interactions and predict therapeutic response. Computational cancer ecosystems facilitate biomarker discovery, immunotherapy optimization, adaptive treatment planning, digital twin development, and intelligent clinical decision support while advancing systems oncology and precision medicine. Emerging technologies including large language models, retrieval-augmented generation, federated learning, reinforcement learning, explainable artificial intelligence, and agentic AI are further expanding the capabilities of ecosystem-based computational oncology. Despite remarkable progress, important challenges remain regarding multimodal data integration, computational scalability, interpretability, interoperability, regulatory validation, ethical governance, and clinical implementation. This review provides a comprehensive overview of computational cancer ecosystems, emphasizing artificial intelligence methodologies for modeling the tumor microenvironment and predicting treatment response in precision oncology.[1]