BCtypeFinder: A Semi-Supervised Model with Domain Adaptation for Breast Cancer Subtyping Using DNA Methylation Profiles

Abstract

Accurate breast cancer subtype prediction is critical for precise diagnosis, treatment planning, and prognosis evaluation. Recent studies highlight the important role of epigenetic modifications in breast tumor, especially the potential of abnormal DNA methylation patterns as markers for distinct subtypes. However, developing a reliable model for subtype prediction based on DNA methylation profiles is challenging due to the scarcity of annotated dataset. This work proposes BCtypeFinder, a breast cancer subtype prediction framework that utilizes a domain adaptation network combined with semi-supervised learning to address batch effects. Our model leverages both labeled and unlabeled DNA methylation data to extract domain-invariant features while aligning subtype distributions across various datasets. BCtypeFinder outperforms current methods, showcasing superior classification performance across multiple test cases. Furthermore, we explored the effects of batch correction in BCtypeFinder, demonstrating its ability to remove batch-specific variations among patients of the same subtype, thus improving the robustness of the classifier. BCtypeFinder is publicly available at https://github.com/joungmin-choi/BCtypeFinder.