{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ronchi C"],"funding":["Breast Cancer Now","EC | Horizon 2020 Framework Programme","EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020)"],"pagination":["56"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11233672"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["10(1)"],"pubmed_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"],"journal":["NPJ breast cancer"],"pubmed_title":["EMBER creates a unified space for independent breast cancer transcriptomic datasets enabling precision oncology."],"pmcid":["PMC11233672"],"funding_grant_id":["859860"],"pubmed_authors":["Brisken C","Haider S","Ronchi C"],"additional_accession":[]},"is_claimable":false,"name":"EMBER creates a unified space for independent breast cancer transcriptomic datasets enabling precision oncology.","description":"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","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2026-07-15T05:15:41.603Z","creation":"2024-11-09T15:16:06.682Z"},"accession":"S-EPMC11233672","cross_references":{"pubmed":["38982086"],"doi":["10.1038/s41523-024-00665-z"]}}