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