Integrated multi-cohorts for characterizing the immunogenomic landscape and predicting drug response in triple-negative breast cancer. (26th March 2022)

• Record Type: Journal Article
• Authors: Su, Dongqing; Chi, Meng; Wang, Shiyuan; Di, Shuhong; Zhang, Haoxing; Lu, Qianzi; Yu, Yao; Xiong, Yuqiang; Wei, Haodong; Lv, Yingli; Zuo, Yongchun; Yang, Lei
• Is Part Of: Briefings in functional genomics. Volume 21:Number 3(2022)
• Page Start: 188
• Page End: 201
• Publication Date: 2022-03-26
• Subjects: triple-negative breast cancer -- immune infiltration -- immunotherapy -- chemotherapy; Genomics -- Methodology -- Periodicals; Genomics -- Technological innovations -- Periodicals; 572.86072
• DOI: 10.1093/bfgp/elac005
• Languages: English
• ISSNs: 2041-2649

Abstract

Abstract: Triple-negative breast cancer (TNBC) is the breast cancer subtype with the highest fatality rate, and it seriously threatens women's health. Recent studies found that the level of immune cell infiltration in TNBC was associated with tumor progression and prognosis. However, due to practical constraints, most of these TNBC immune infiltration studies only used a small number of patient samples and a few immune cell types. Therefore, it is necessary to integrate more TNBC patient samples and immune cell types to comprehensively study immune infiltration in TNBC to contribute to the prognosis and treatment of patients. In this study, 12 TNBC datasets were integrated and an extensive collection of 182 gene sets with immune-related signatures were included to comprehensively investigate tumor immune microenvironment of TNBC. A single sample gene set enrichment analysis was performed to calculate the infiltration score of each immune-related signature in each patient, and an immune-related risk scoring model for TNBC was constructed to accurately assess patient prognosis. Significant differences were found in immunogenomic landscape between different immune risk subtypes. In addition, the immunotherapy response and chemotherapy drug sensitivity of patients with different immune risk subtypes were also analyzed. The results showed that there were significant differences in these characteristics. Finally, a prediction model for immune risk subtypes of TNBC patients was constructed to accurately predict patients with unknown subtypes. Based on the aforementioned findings, we believed that the immune-related risk score constructed in this study can assist in providing personalized medicine to TNBC patients.