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...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| 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
|
| 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 |
| Summary: | 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 translation at scale. Histology-based imaging remains a predominant means of diagnosis that is widely accessible. To more broadly leverage limited molecular datasets, models have been trained to use histology to infer the expression of individual genes or pathways, with varying levels of accuracy and explainability. |
|---|---|
| Item Description: | Gesehen am 17.12.2025 |
| Physical Description: | Online Resource |
| ISSN: | 1756-994X |
| DOI: | 10.1186/s13073-025-01502-z |