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

Background: Currently, 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. Method: Herein, 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). Results: Two 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. Conclusion: Collectively, 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.

Combination of Immune-Related Network and Molecular Typing Analysis Defines a Three-Gene Signature for Predicting Prognosis of Triple-Negative Breast Cancer.

Recent breakthroughs in immune checkpoint inhibitors (ICIs) have shown promise in triple-negative breast cancer (TNBC). Due to the intrinsic heterogeneity among TNBC, clinical response to ICIs varies greatly among individuals. Thus, discovering rational biomarkers to select susceptible patients for ICIs treatment is warranted. A total of 422 TNBC patients derived from The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset were included in this study. High immunogenic gene modules were identified using weighted gene co-expression network analysis (WGCNA). Immune-related genes (IRGs) expression patterns were generated by consensus clustering. We developed a three-gene signature named immune-related gene panel (IRGP) by Cox regression method. Afterward, the associations of IRGP with survival outcomes, infiltration of immune cells, drug sensitivity, and the response to ICIs therapy were further explored. We found five high immunogenic gene modules. Two distinct IRGclusters and IRG-related genomic clusters were identified. The IRGP was constructed based on TAPBPL, FBP1, and GPRC5C genes. TNBC patients were then subdivided into high- and low-IRGriskscore subgroups. TNBC patients with low IRGriskscore had a better survival outcome, higher infiltration of immune cells, lower TP53 mutation rate, and more benefit from ICIs treatment than high IRGriskscore patients. These findings offer novel insights into molecular subtype of TNBC and provided potential indicators for guiding ICIs treatment.

Aurora A-mediated pyruvate kinase M2 phosphorylation promotes biosynthesis with glycolytic metabolites and tumor cell cycle progression.

Cancer cells have distinctive demands for intermediates from glucose metabolism for biosynthesis and energy in different cell cycle phases. However, how cell cycle regulators and glycolytic enzymes coordinate to orchestrate the essential metabolic processes are still poorly characterized. Here, we report a novel interaction between the mitotic kinase, Aurora A, and the glycolytic enzyme, pyruvate kinase M2 (PKM2), in the interphase of the cell cycle. We found Aurora A-mediated phosphorylation of PKM2 at threonine 45. This phosphorylation significantly attenuated PKM2 enzymatic activity by reducing its tetramerization and also promoted glycolytic flux and the branching anabolic pathways. Replacing the endogenous PKM2 with a nonphosphorylated PKM2 T45A mutant inhibited glycolysis, glycolytic branching pathways, and tumor growth in both in vitro and in vivo models. Together, our study revealed a new protumor function of Aurora A through modulating a rate-limiting glycolytic enzyme, PKM2, mainly during the S phase of the cell cycle. Our findings also showed that although both Aurora A and Aurora B kinase phosphorylate PKM2 at the same residue, the spatial and temporal regulations of the specific kinase and PKM2 interaction are context dependent, indicating intricate interconnectivity between cell cycle and glycolytic regulators.