Patients with triple-negative breast cancer (TNBC) reportedly benefit from immune checkpoint blockade (ICB) therapy. However, the subtype-specific vulnerabilities of ICB in TNBC remain unclear. As the complex interplay between cellular senescence and anti-tumor immunity has been previously discussed, we aimed to identify markers related to cellular senescence that may serve as potential predictors of response to ICB in TNBC. We used three transcriptomic datasets derived from ICB-treated breast cancer samples at both scRNA-seq and bulk-RNA-seq levels to define the subtype-specific vulnerabilities of ICB in TNBC. Differences in the molecular features and immune cell infiltration among the different TNBC subtypes were further explored using two scRNA-seq, three bulk-RNA-seq, and two proteomic datasets. 18 TNBC samples were collected and utilized to verify the association between gene expression and immune cell infiltration by multiplex immunohistochemistry (mIHC). A specific type of cellular senescence was found to be significantly associated with response to ICB in TNBC. We employed the expression of four senescence-related genes, namely CDKN2A, CXCL10, CCND1, and IGF1R, to define a distinct senescence-related classifier using the non-negative matrix factorization approach. Two clusters were identified, namely the senescence-enriching cluster (C1; CDKN2A high CXCL10 high CCND1 low IGF1R low) and proliferating-enriching cluster (C2; CDKN2A low CXCL10 low CCND1 high IGF1R high). Our results indicated that the C1 cluster responds better to ICB and behaves with higher CD8+ T cell infiltration than the C2 cluster. Altogether, in this study, we developed a robust cellular senescence-related classifier of TNBC based on the expression of CDKN2A, CXCL10, CCND1, and IGF1R. This classifier act as a potential predictor of clinical outcomes and response to ICB.
Keywords: Immune checkpoint blockade; bioinformatics; cellular senescence; scRNA-seq; triple-negative breast cancer.
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