Simplifying clinical use of TCGA molecular subtypes through machine learning models.

Cancer Cell

Computational Oncology Service, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Halvorsen Center for Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. Electronic address:

Published: January 2025

In this issue of Cancer Cell, Ellrott et al. present machine learning models to classify samples into The Cancer Genome Atlas molecular subtypes using compact sets of genomic features. These validated, ready-to-use models are publicly available, although some clinical hurdles need to be cleared before they are fully implemented.

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http://dx.doi.org/10.1016/j.ccell.2024.12.009DOI Listing

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