A Machine Learning Approach to Differentiate Two Specific Breast Cancer Subtypes Using Androgen Receptor Pathway Genes

Triple-negative breast cancer is a heterogeneous disease with different molecular and histological subtypes. The Androgen receptor is expressed in a portion of triple-negative breast cancer cases and the activation of the androgen receptor pathway is thought to be a molecular subtyping signature as well as a therapeutic target for triple-negative breast cancer. Thus, identification of the androgen receptor pathway status is important for both molecular characterization andclinical management. In this study, we investigate the expression of the androgen receptor pathway in metaplastic breast cancer and luminal androgen receptor subtypes of triple-negative breast cancer and found that the androgen receptor pathway was downregulated in metaplastic breast cancer compared to luminal androgen receptor subtype. Using random forest, we found that the two subtypes of breast cancer can be molecularly classified with the gene expression of the androgen receptor pathway.

Automated summary

The expression of the androgen receptor pathway was found to be downregulated in metaplastic breast cancer compared to the luminal androgen receptor subtype of triple-negative breast cancer in this study. Molecular classification of these two breast cancer subtypes can be achieved using gene expression of the androgen receptor pathway with random forest.