The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper
ABSTRACT Background 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 significan...
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Wiley
2025-08-01
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| Series: | Cancer Innovation |
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| Online Access: | https://doi.org/10.1002/cai2.70013 |
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| author | Feng Du Jie Ju Fangchao Zheng Songlin Gao Peng Yuan |
| author_facet | Feng Du Jie Ju Fangchao Zheng Songlin Gao Peng Yuan |
| author_sort | Feng Du |
| collection | DOAJ |
| description | ABSTRACT Background 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. Methods 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. Results In a comprehensive analysis of 6578 breast cancer samples from four data sets, Ecotyper identified 69 cellular states and 10 tumor ecotypes. Of these, 37 cellular states significantly correlated with overall survival. Notably, specific states within epithelial cells, macrophages/monocytes, and fibroblasts were linked to a worse prognosis. CE2 abundance was identified as the most significant marker indicating unfavorable prognosis and was further validated in an additional data set of 116 HER2‐negative patients. These biomarkers also indicated the efficacy of neoadjuvant immunotherapy in breast cancer. CE2‐high cancers were characterized by an abundance of basal‐like epithelial cells, scant lymphocytic infiltration, and activation of hypoxia signaling. Single‐cell analysis showed that CE2‐high areas were rich in SPP1‐positive tumor‐associated macrophages(TAM), basal‐like epithelial cells, and hypoxic cancer‐associated fibroblasts(CAF). Spatially, these regions were often peripheral in triple‐negative breast cancer, adjacent to fibrotic/necrotic zones. Multiplex immunofluorescence confirmed the enrichment of SPP1+CD68+TAM and HIF1A+SMA+CAF in hypoxic triple‐negative breast cancer (TNBC) regions. Conclusions Ecotyper identified novel biomarkers for breast cancer prognosis and treatment prediction. The CE2‐high region may represent a hypoxic immune‐suppressive niche. |
| format | Article |
| id | doaj-art-74f0c5cb21724f2fbdc2ea9ac676d7da |
| institution | Kabale University |
| issn | 2770-9191 2770-9183 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Wiley |
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| series | Cancer Innovation |
| spelling | doaj-art-74f0c5cb21724f2fbdc2ea9ac676d7da2025-08-20T03:28:48ZengWileyCancer Innovation2770-91912770-91832025-08-0144n/an/a10.1002/cai2.70013The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using EcotyperFeng Du0Jie Ju1Fangchao Zheng2Songlin Gao3Peng Yuan4Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), The VIPII Gastrointestinal Cancer Division of Medical Department Peking University Cancer Hospital and Institute Beijing ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Day Care Peking University Cancer Hospital and Institute Beijing ChinaDepartment of Medical Oncology, Cancer Research Center, Shandong Cancer Hospital and Institute Shandong First Medical University and Shandong Academy of Medical Sciences Jinan Shandong Province ChinaKey Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), The VIPII Gastrointestinal Cancer Division of Medical Department Peking University Cancer Hospital and Institute Beijing ChinaDepartment of VIP Medical Services, National Cancer Centre/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing ChinaABSTRACT Background 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. Methods 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. Results In a comprehensive analysis of 6578 breast cancer samples from four data sets, Ecotyper identified 69 cellular states and 10 tumor ecotypes. Of these, 37 cellular states significantly correlated with overall survival. Notably, specific states within epithelial cells, macrophages/monocytes, and fibroblasts were linked to a worse prognosis. CE2 abundance was identified as the most significant marker indicating unfavorable prognosis and was further validated in an additional data set of 116 HER2‐negative patients. These biomarkers also indicated the efficacy of neoadjuvant immunotherapy in breast cancer. CE2‐high cancers were characterized by an abundance of basal‐like epithelial cells, scant lymphocytic infiltration, and activation of hypoxia signaling. Single‐cell analysis showed that CE2‐high areas were rich in SPP1‐positive tumor‐associated macrophages(TAM), basal‐like epithelial cells, and hypoxic cancer‐associated fibroblasts(CAF). Spatially, these regions were often peripheral in triple‐negative breast cancer, adjacent to fibrotic/necrotic zones. Multiplex immunofluorescence confirmed the enrichment of SPP1+CD68+TAM and HIF1A+SMA+CAF in hypoxic triple‐negative breast cancer (TNBC) regions. Conclusions Ecotyper identified novel biomarkers for breast cancer prognosis and treatment prediction. The CE2‐high region may represent a hypoxic immune‐suppressive niche.https://doi.org/10.1002/cai2.70013breast cancerintra‐tumor heterogeneityprognosis |
| spellingShingle | Feng Du Jie Ju Fangchao Zheng Songlin Gao Peng Yuan The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper Cancer Innovation breast cancer intra‐tumor heterogeneity prognosis |
| title | The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper |
| title_full | The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper |
| title_fullStr | The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper |
| title_full_unstemmed | The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper |
| title_short | The Identification of Novel Prognostic and Predictive Biomarkers in Breast Cancer via the Elucidation of Tumor Ecotypes Using Ecotyper |
| title_sort | identification of novel prognostic and predictive biomarkers in breast cancer via the elucidation of tumor ecotypes using ecotyper |
| topic | breast cancer intra‐tumor heterogeneity prognosis |
| url | https://doi.org/10.1002/cai2.70013 |
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