Cancer-immunity cycle-based molecular subtypes in breast cancer predict the response to immune checkpoint inhibitors.

Abstract

Objective: The cancer-immunity cycle (CIC) outlines key steps of anti-tumor immunity from antigen release to T cell effector function. A comprehensive evaluation of the CIC in patients with breast cancer is lacking, which limits the accurate assessment of immune status and selection of patients suitable for immune checkpoint inhibitor (ICI) therapy.

Methods: A signature that describes the six steps of the CIC in the primary tumor was constructed. This signature was used to calculate a CIC score in our previously published breast cancer cohort (n = 752) and classify patients into 3 distinct CIC clusters. The predictive value of the ICI response was validated in pan-cancer ICI-treated cohorts. Clusters with distinct characteristics were further identified through multi-omic analyses, including genomics, metabolomics, and single-cell RNA sequencing.

Results: Breast cancer patients were classified into three CIC clusters: cluster 1 [C1] (immune-cold); cluster 2 [C2] (antigen presentation-deficient); and cluster 3 [C3] (immune-hot). C3 showed abundant immune infiltration that correlated with a better ICI response. In addition to reduced immune infiltration, C1 patients exhibited macrophage phenotypic conversion. The tumor microenvironment of C2 was marked by elevated regulatory T cells and dysfunctional dendritic cells. Genomic analysis of C2 showed a high tumor mutational burden with frequent HLA loss of heterozygosity. C1 was enriched in lipid metabolism pathways and C3 in glycolysis features. PSAT1, a serine-related gene, was identified as a key metabolic regulator in C2, suggesting a role in influencing immunoregulatory molecules.

Conclusions: This study provided a novel framework for classifying tumors based on the CIC characteristics, revealing distinct biological and clinical profiles and suggesting broad clinical significance.