EMBER creates a unified space for independent breast cancer transcriptomic datasets enabling precision oncology.

ABSTRACT: Transcriptomics has revolutionized biomedical research and refined breast cancer subtyping and diagnostics. However, wider use in clinical practice is hampered for a number of reasons including the application of transcriptomic signatures as single sample predictors. Here, we present an embedding approach called EMBER that creates a unified space of 11,000 breast cancer transcriptomes and predicts phenotypes of transcriptomic profiles on a single sample basis. EMBER accurately captures the five molecular subtypes. Key biological pathways, such as estrogen receptor signaling, cell proliferation, DNA repair, and epithelial-mesenchymal transition determine sample position in the space. We validate EMBER in four independent patient cohorts and show with samples from the window trial, POETIC, t

SUBMITTER: Ronchi C 

PROVIDER: S-EPMC11233672 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature