Immune subtype identification and multi-layer perceptron classifier construction for breast cancer - Yang, Xinbo; Zheng, Yuanjie; Xing, Xianrong; Sui, Xiaodan; Jia, Weikuan; Pan, Huali (Frontiers in Oncology, 2022)
Breast cancer is a heterogeneous tumor. Tumor microenvironment (TME) has an important effect on the proliferation, metastasis, treatment, and prognosis of breast cancer. In this study, we calculated the relative proportion of tumor-infiltrating immune cells (TIICs) in the breast cancer TME, used the consensus clustering algorithm to cluster the breast cancer subtypes, and determined the two subtypes (ICS-A and ICS-B) of the research cohort. The Kaplan-Meier survival estimate analysis showed significant differences in the overall survival (OS) between the two identified subtypes. Estimating the difference in the relative proportion of TIICs showed that the two subtypes had significant differences in multiple immune cells, such as CD8, CD4, and regulatory T cells. Further, the expression level of immune checkpoint molecules (PDL1, CTLA4, LAG3, TIGIT, CD27, IDO1, ICOS) and tumor mutational burden (TMB) also showed significant differences between the two subtypes, indicating the clinical value of the two subtypes. Gene ontology terms and KEGG pathway enrichment analysis showed that the differentially expressed genes (DEGs) of the two subtypes were significantly enriched in T cell activation, external side of plasma membrane, immune receptor activity, and cytokine−cytokine receptor interactions. Finally, we identified a 38-gene signature and developed a multi-layer perceptron (MLP) classifier that combined multi-gene signature to identify breast cancer subtypes. We used 70% of the breast cancer research cohort for training and 30% for validation. The results showed that the classifier had an accuracy rate of 93.56% and can be robustly used for the breast cancer subtype diagnosis.