Document Type
Article
Publication Date
1-6-2025
Original Citation
Noorbakhsh J,
Foroughi Pour A,
Chuang J.
Emerging AI approaches for cancer spatial omics. Gigascience. 2025;14:giaf128.
Keywords
JGM, Humans, Neoplasms, Artificial Intelligence, Tumor Microenvironment, Genomics, Computational Biology
JAX Source
Gigascience. 2025;14:giaf128.
ISSN
2047-217X
PMID
41100170
DOI
https://doi.org/10.1093/gigascience/giaf128
Grant
The authors acknowledge support from The Jackson Laboratory Cancer Center’s Cancer Advanced Technology (CATch) program, as well as National Institute of Health (NIH) grants R01 CA230031, U54 AG075941, and P30 CA034196.
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.