Classifying Breast Cancer Molecular Subtypes by Using Deep Clustering Approach - Rohani, Narjes; Eslahchi, Changiz (Frontiers in Genetics, 2020)
Cancer is a complex disease with a high rate of mortality. The characteristics of tumor masses are very heterogeneous; thus, the appropriate classification of tumors is a critical point in the correct treatment. A high level of heterogeneity has also been observed in breast cancer. Therefore, detecting the molecular subtypes of this disease is a worthwhile issue for medicine that could be facilitated using bioinformatics. The aim of this study is to discover the molecular subtypes of breast cancer using somatic mutation profiles of tumors. Nonetheless, the somatic mutation profiles are very sparse. To address this issue, a network propagation method is used on the gene interaction network to make the mutation profiles dense. Afterward, we used deep embedded clustering (DEC) method to classify breast tumors into four subtypes. In the next step, gene signatures of each subtype are obtained by using Fisher exact test. Clinical and molecular analyses, besides enrichment of genes in numerous biological databases, verify that the proposed method using mutation profiles can efficiently detect the molecular subtypes of breast cancer. Finally, a supervised classifier is proposed based on discovered subtypes to predict the molecular subtype of a new patient. The code and material of the method are available at https://github.com/nrohani/MolecularSubtypes