The luminal androgen receptor (LAR) subtype of triple-negative breast cancer (TNBC) differentiation displays low proliferation yet strong metastatic potential and a poor chemotherapy response. This study aimed to define the molecular basis of the LAR subtype and identify actionable therapeutic targets.

Comprehensive multi-omic analyses were performed on the FUSCC-TNBC cohort, integrating whole-exome sequencing, RNA sequencing, and functional validation in vitro and in vivo. Somatic mutation profiling, gene set enrichment analysis (GSEA), and weighted gene co-expression network analysis (WGCNA) were used to define genomic and transcriptomic signatures. A machine learning model using the Mime1 package was applied to derive a senescence-associated prognostic signature (LAR-S) and validation in external cohorts. Immune deconvolution was performed to decipher the tumor microenvironment. Functional assays, patient-derived organoids (PDOs), and TS/V mouse models were used to evaluate therapeutic responses to senescence-modulating agent and immunotherapy combinations.

The LAR subtype was enriched for PIK3CA, PTEN, and ERBB2 kinase domain mutations. Functional studies confirmed ERBB2 variants (e.g., V777L and E698_P699delinsA) as oncogenic drivers conferring sensitivity to neratinib. Transcriptomic analyses revealed a dominant cellular senescence program associated with immune suppression. The LAR-S signature stratified survival across cohorts and predicted immunotherapy resistance. Targeting cellular senescence inhibited LAR subtype organoid growth and when combined with anti-PD-1 therapy synergistically suppressed tumor growth in vivo.

The LAR subtype harbors two therapeutic vulnerabilities: ERBB2 mutation-driven kinase activation; and senescence-mediated immune evasion. The LAR-S signature enables precise patient stratification and supports senescence-targeted and immunotherapy combination strategies as promising approaches for this refractory TNBC subtype.

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.

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.

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.

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.