Immune checkpoints expression patterns in early-stage triple-negative breast cancer predict prognosis and remodel the tumor immune microenvironment

BackgroundCurrently, targeting immune checkpoint molecules holds great promise for triple-negative breast cancer (TNBC). However, the expression landscape of immune checkpoint genes (ICGs) in TNBC remains largely unknown. MethodHerein, we systematically investigated the ICGs expression patterns in 422 TNBC samples. We evaluated the ICGs molecular typing based on the ICGs expression profile and explored the associations between ICGs molecular subtypes and tumor immune characteristics, clinical significance, and response to immune checkpoint inhibitors (ICIs). ResultsTwo ICGs clusters and two ICGs-related gene clusters were determined, which were involved in different survival outcomes, biological roles and infiltration levels of immune cells. We established a quantification system ICGs riskscore (named IRS) to assess the ICGs expression patterns for individuals. TNBC patients with lower IRS were characterized by increased immune cell infiltration, favorable clinical outcomes and high sensitivity to ICIs therapy. We also developed a nomogram model combining clinicopathological variables to predict overall survival in TNBC. Genomic feature analysis revealed that high IRS group presented an increased tumor mutation burden compared with the low IRS group. ConclusionCollectively, dissecting the ICGs expression patterns not only provides a new insight into TNBC subtypes but also deepens the understanding of ICGs in the tumor immune microenvironment.

Automated summary

By investigating 422 TNBC samples, we identified two ICGs clusters and two ICGs-related gene clusters that were associated with different survival outcomes, biological roles, and immune cell infiltration levels. We established a quantification system, called IRS, to assess ICGs expression patterns and found that TNBC patients with lower IRS had increased immune cell infiltration, favorable clinical outcomes, and high sensitivity to ICIs therapy. Additionally, we developed a nomogram model combining clinicopathological variables to predict overall survival in TNBC. Genomic feature analysis showed that the high IRS group had an increased tumor mutation burden compared with the low IRS group.