Feature Selection and Machine Learning Strategies Optimize a Minimal Molecular Assay for Cholangiocarcinoma Subtype
Background: Cholangiocarcinoma is a molecularly heterogenous cancer arising from the biliary epithelium. Individual omic techniques have identified clinically relevant, targetable subtypes but most tumors have no targetable alterations based on exome or transcriptomic evaluation. Integrated multiomics approaches incorporating transcriptomic, proteomic, and phosphoproteomic characterization provide deeper understanding and identify non-mutated, activated pathways that can be targeted.
Authors
- Dong‐Gi Mun (ORCID: https://orcid.org/0000-0003-3588-3440)
- Rory L. Smoot
- Ellen L. Larson (ORCID: https://orcid.org/0000-0002-9420-5941)
- Danielle M. Carlson (ORCID: https://orcid.org/0000-0003-1823-6380)
- Hojjat Salehinejad (ORCID: https://orcid.org/0000-0002-9636-863X)
- Erik Jessen (ORCID: https://orcid.org/0000-0001-9851-6897)
- Jennifer L. Tomlinson (ORCID: https://orcid.org/0000-0002-0909-3165)
- Amro M. Abdelrahman (ORCID: https://orcid.org/0000-0001-9865-0551)
Institutions
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.mcpdig.2025.100283
- Primary Topic
- Cholangiocarcinoma and Gallbladder Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00