A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype - Chen, Zihao; Wang, Maoli; De Wilde, Rudy Leon; Feng, Ruifa; Su, Mingqiang; Torres-de la Roche, Luz Angela; Shi, Wenjie (Frontiers in Immunology, 2021)
Abstract Background Immune checkpoint blockade (ICB) has been approved for the treatment of triple-negative breast cancer (TNBC) since the progression-free survival (PFS) was significantly improved by ICB. However, only about 10% of TNBC patients could achieve the complete response (CR) to ICB. Because of the low response rate and potential adverse reactions to ICB. Methods Open-datasets from TCGA and GEO were downloaded to perform an unsupervised clustering analysis to identify the immune subtype according to the expression profiles. The prognosis, enriched pathways, and the ICB indicators were compared between immune subtypes. Afterward, samples from METABRIC dataset were used to validate the correlation of immune subtype with prognosis. Data from patients who received ICB were selected to validate the correlation of the immune subtype with ICB response. Machine learning models were used to build a visual web server to predict the immune subtype of TNBC patients requiring ICB. Results A total of 8 open-datasets including 931 TNBC samples were used for the unsupervised clustering. Two novel immune subtypes (referred to as S1 and S2) were identified among TNBC patients. Compared with S2, S1 was associated with higher immune scores, higher levels of immune cells, and a better prognosis for immunotherapy. In the validation dataset，the Subtype1 had a better prognosis than Subtype2 samples，no matter in overall survival (OS) (p = 0.00036) or relapse-free survival (RFS) (p = 0.0022). Bioinformatics analysis identified 11 hub genes (LCK, IL2RG, CD3G, STAT1, CD247, IL2RB, CD3D, IRF1, OAS2, IRF4, and IFNG) related to the immune subtype. A robust machine learning model based on Random forest algorithm was established by 11 hub genes and it performed reasonably well with AUC values = 0.76. An open and free web server based on the random forest model, named as triple-negative breast cancer immune subtype (TNBCIS), was developed and is available from https://immunotypes.shinyapps.io/TNBCIS/. Conclusion TNBC open-datasets allowed us to stratify samples into distinct immunotherapy response subgroups according to gene expression profiles. Based on two novel subtypes, candidates for ICB with a higher response rate and better prognosis could be selected by using the free visual online webserver we designed.