Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer
Abstract Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging...
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BMC
2025-01-01
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| Series: | Breast Cancer Research |
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| Online Access: | https://doi.org/10.1186/s13058-025-01959-1 |
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| author | Kaimin Hu Yinan Wu Yajing Huang Meiqi Zhou Yanyan Wang Xingru Huang |
| author_facet | Kaimin Hu Yinan Wu Yajing Huang Meiqi Zhou Yanyan Wang Xingru Huang |
| author_sort | Kaimin Hu |
| collection | DOAJ |
| description | Abstract Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging in clinical practice. In this study, we validated the correlation between EMT status and resistance to endocrine therapy in ER+ breast cancer from a transcriptomic perspective. To confirm the presence of morphological discrepancies in tumor tissues of ER+ breast cancer classified as epithelial- and mesenchymal-phenotypes according to EMT-related transcriptional features, we trained deep learning algorithms based on EfficientNetV2 architecture to assign the phenotypic status for each patient utilizing hematoxylin & eosin (H&E)-stained slides from The Cancer Genome Atlas database. Our classifier model accurately identified the precise phenotypic status, achieving an area under the curve (AUC) of 0.886 at the tile-level and an AUC of 0.910 at the slide-level. Furthermore, we evaluated the efficacy of the classifier in predicting endocrine response using data from an independent ER+ breast cancer patient cohort. Our classifier achieved a predicting accuracy of 81.25%, and 88.7% slides labeled as endocrine resistant were predicted as the mesenchymal-phenotype, while 75.6% slides labeled as sensitive were predicted as the epithelial-phenotype. Our work introduces an H&E-based framework capable of accurately predicting EMT phenotype and endocrine response for ER+ breast cancer, demonstrating its potential for clinical application and benefit. |
| format | Article |
| id | doaj-art-a098d8c1c13d48cc9bac724641b6f44f |
| institution | Kabale University |
| issn | 1465-542X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Breast Cancer Research |
| spelling | doaj-art-a098d8c1c13d48cc9bac724641b6f44f2025-01-12T12:45:28ZengBMCBreast Cancer Research1465-542X2025-01-0127111210.1186/s13058-025-01959-1Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancerKaimin Hu0Yinan Wu1Yajing Huang2Meiqi Zhou3Yanyan Wang4Xingru Huang5Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of MedicineHangzhou Institute of Medicine (HIM), Zhejiang Cancer Hospital, Chinese Academy of SciencesDepartment of Pathology, the Second Affiliated Hospital, Zhejiang University School of MedicineDepartment of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of MedicineDepartment of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of MedicineSchool of Electronic Engineering and Computer Science, Queen Mary University of LondonAbstract Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging in clinical practice. In this study, we validated the correlation between EMT status and resistance to endocrine therapy in ER+ breast cancer from a transcriptomic perspective. To confirm the presence of morphological discrepancies in tumor tissues of ER+ breast cancer classified as epithelial- and mesenchymal-phenotypes according to EMT-related transcriptional features, we trained deep learning algorithms based on EfficientNetV2 architecture to assign the phenotypic status for each patient utilizing hematoxylin & eosin (H&E)-stained slides from The Cancer Genome Atlas database. Our classifier model accurately identified the precise phenotypic status, achieving an area under the curve (AUC) of 0.886 at the tile-level and an AUC of 0.910 at the slide-level. Furthermore, we evaluated the efficacy of the classifier in predicting endocrine response using data from an independent ER+ breast cancer patient cohort. Our classifier achieved a predicting accuracy of 81.25%, and 88.7% slides labeled as endocrine resistant were predicted as the mesenchymal-phenotype, while 75.6% slides labeled as sensitive were predicted as the epithelial-phenotype. Our work introduces an H&E-based framework capable of accurately predicting EMT phenotype and endocrine response for ER+ breast cancer, demonstrating its potential for clinical application and benefit.https://doi.org/10.1186/s13058-025-01959-1Deep learningEstrogen receptor-positive breast cancerEpithelial-to-mesenchymal transitionDigital pathologyPhenotypic classifierEndocrine therapy response |
| spellingShingle | Kaimin Hu Yinan Wu Yajing Huang Meiqi Zhou Yanyan Wang Xingru Huang Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer Breast Cancer Research Deep learning Estrogen receptor-positive breast cancer Epithelial-to-mesenchymal transition Digital pathology Phenotypic classifier Endocrine therapy response |
| title | Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer |
| title_full | Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer |
| title_fullStr | Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer |
| title_full_unstemmed | Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer |
| title_short | Annotation-free deep learning algorithm trained on hematoxylin & eosin images predicts epithelial-to-mesenchymal transition phenotype and endocrine response in estrogen receptor-positive breast cancer |
| title_sort | annotation free deep learning algorithm trained on hematoxylin eosin images predicts epithelial to mesenchymal transition phenotype and endocrine response in estrogen receptor positive breast cancer |
| topic | Deep learning Estrogen receptor-positive breast cancer Epithelial-to-mesenchymal transition Digital pathology Phenotypic classifier Endocrine therapy response |
| url | https://doi.org/10.1186/s13058-025-01959-1 |
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