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Updated: Jan 6, 2026

Monitoring the Cancer-Immunity Cycle and Exploring Tumor Microenvironment Dynamics
Published on: June 7, 2024
Emerging AI approaches for cancer spatial omics
Javad Noorbakhsh1, Ali Foroughi Pour2, Jeffrey Chuang1,3
1The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA.
Abstract:
Technological breakthroughs in spatial omics and artificial intelligence (AI) have the potential to transform the understanding of cancer cells and the tumor microenvironment. Here we review the role of AI in spatial omics, discussing the current state-of-the-art and further needs to decipher cancer biology from large-scale spatial tissue data. An overarching challenge is the development of interpretable spatial AI models, an activity that demands not only improved data integration but also new conceptual frameworks. We discuss emerging paradigms-in particular, data-driven spatial AI, constraint-based spatial AI, and mechanistic spatial modeling-as well as the importance of integrating AI with hypothesis-driven strategies and model systems to realize the value of cancer spatial information.
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