  Differentiating the intrinsic subtypes of breast cancer is crucial for
deciding the best treatment strategy. Deep learning can predict the subtypes
from genetic information more accurately than conventional statistical methods,
but to date, deep learning has not been directly utilized to examine which
genes are associated with which subtypes. To clarify the mechanisms embedded in
the intrinsic subtypes, we developed an explainable deep learning model called
a point-wise linear (PWL) model that generates a custom-made logistic
regression for each patient. Logistic regression, which is familiar to both
physicians and medical informatics researchers, allows us to analyze the
importance of the feature variables, and the PWL model harnesses these
practical abilities of logistic regression. In this study, we show that
analyzing breast cancer subtypes is clinically beneficial for patients and one
of the best ways to validate the capability of the PWL model. First, we trained
the PWL model with RNA-seq data to predict PAM50 intrinsic subtypes and applied
it to the 41/50 genes of PAM50 through the subtype prediction task. Second, we
developed a deep enrichment analysis method to reveal the relationships between
the PAM50 subtypes and the copy numbers of breast cancer. Our findings showed
that the PWL model utilized genes relevant to the cell cycle-related pathways.
These preliminary successes in breast cancer subtype analysis demonstrate the
potential of our analysis strategy to clarify the mechanisms underlying breast
cancer and improve overall clinical outcomes.
