Identifying prognostic subgroups of luminal-A breast cancer using a deep autoencoder

Abstract

Luminal-A breast cancer is the most frequently occurring breast cancer subtype. However, it shows high variability in prognosis, and more precise stratification is required for personalized medicine. In this paper, we identify two prognostic subgroups of luminal-A breast cancer. We train a deep autoencoder with gene expression profiles of luminal-A breast cancer, and it automatically generates informative latent features that represent essential properties of gene expressions. We find that two subgroups (BPS-LumA and WPS-LumA) clustered using the latent features are significantly different in prognosis (p-value=1.23e-6; log-rank test). This prognostic difference is validated with other luminal-A breast cancer cohort. The results in our method suggest that the deep autoencoder is able to extract and compress complex properties of gene expressions patterns, and that it is usefully applicable to patient stratification for precision medicine of luminal-A breast cancer.