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 - Kaimin Hu; Yinan Wu; Yajing Huang; Meiqi Zhou; Yanyan Wang; Xingru Huang (Breast Cancer Research)
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.