{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["4(4)"],"submitter":["Du F"],"funding":["Natural Science Foundation of Shandong Province"],"pubmed_abstract":["<h4>Background</h4>Breast cancer is a highly heterogeneous disease, characterized by tumor and nontumor cells at various cell states. Ecotyper is an innovative machine learning framework that quantifies the tumor microenvironment and delineates the tumor ecosystem, demonstrating clinical significance. However, further validation is needed in breast cancer.<h4>Methods</h4>Ecotyper was applied to identify multiple cellular states and tumor ecotypes using large-scale breast cancer bulk sequencing data, followed by a detailed analysis of their associations with clinical classification, molecular subtypes, survival prognosis, and immunotherapy response. Identified subtypes were further characterized using single-cell and spatial data sets to reveal molecular profiles.<h4>Results</h4>In a compre"],"journal":["Cancer innovation"],"pagination":["e70013"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12107130"],"repository":["biostudies-literature"],"pubmed_title":["The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper."],"pmcid":["PMC12107130"],"pubmed_authors":["Yuan P","Ju J","Du F","Zheng F","Gao S"],"additional_accession":[]},"is_claimable":false,"name":"The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper.","description":"<h4>Background</h4>Breast cancer is a highly heterogeneous disease, characterized by tumor and nontumor cells at various cell states. Ecotyper is an innovative machine learning framework that quantifies the tumor microenvironment and delineates the tumor ecosystem, demonstrating clinical significance. However, further validation is needed in breast cancer.<h4>Methods</h4>Ecotyper was applied to identify multiple cellular states and tumor ecotypes using large-scale breast cancer bulk sequencing data, followed by a detailed analysis of their associations with clinical classification, molecular subtypes, survival prognosis, and immunotherapy response. Identified subtypes were further characterized using single-cell and spatial data sets to reveal molecular profiles.<h4>Results</h4>In a compre","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-06-03T02:13:43.145Z","creation":"2026-05-28T03:05:43.058Z"},"accession":"S-EPMC12107130","cross_references":{"pubmed":["40432877"],"doi":["10.1002/cai2.70013"]}}