Building digital histology models of transcriptional tumor programs with generative deep learning for pathology-based precision medicine

Precision oncology depends on identifying the biological vulnerabilities of a tumor. Molecular assays, like transcriptomics, provide an information-rich view of the tumor that can be leveraged to inform therapeutic selection. However, the costs of such assays can be prohibitive for clinical translat...

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Main Authors: Hieromnimon, Hanna M. (Author) , Dolezal, James (Author) , Doytcheva, Kristina (Author) , Howard, Frederick M. (Author) , Kochanny, Sara (Author) , Zhang, Zhenyu (Author) , Grossman, Robert L. (Author) , Tanager, Kevin (Author) , Wang, Cindy (Author) , Kather, Jakob Nikolas (Author) , Izumchenko, Evgeny (Author) , Cipriani, Nicole A. (Author) , Fertig, Elana J. (Author) , Pearson, Alexander T. (Author) , Riesenfeld, Samantha J. (Author)
Format: Article (Journal)
Language:English
Published: 07 August 2025
In: Genome medicine
Year: 2025, Volume: 17, Pages: 1-21
ISSN:1756-994X
DOI:10.1186/s13073-025-01502-z
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1186/s13073-025-01502-z
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Author Notes:Hanna M. Hieromnimon, James Dolezal, Kristina Doytcheva, Frederick M. Howard, Sara Kochanny, Zhenyu Zhang, Robert L. Grossman, Kevin Tanager, Cindy Wang, Jakob Nikolas Kather, Evgeny Izumchenko, Nicole A. Cipriani, Elana J. Fertig, Alexander T. Pearson and Samantha J. Riesenfeld

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