AUTHOR=Li Lijuan , Wu Nan , Zhuang Gaojian , Geng Lin , Zeng Yu , Wang Xuan , Wang Shuang , Ruan Xianhui , Zheng Xiangqian , Liu Juntian , Gao Ming 

TITLE=Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic‐associated molecular subtypes and genomic mutations

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1224828

DOI=10.3389/fphar.2023.1224828

ISSN=1663-9812

ABSTRACT=Objective Due to a lack of effective therapy, triple-negative breast cancer (TNBC) has a poor prognosis. Metabolic reprogramming is an important hallmark in tumorigenesis, cancer diagnosis, prognosis, and treatment. Categorizing metabolic patterns in TNBC is critical to combat heterogeneity and targeted therapeutics. Methods A total of 115 TNBC patients from The Cancer Genome Atlas (TCGA) were merged into a virtual cohort for metabolic clustering and verified by other verification sets, discovering differentially expressed genes (DEGs). To identify reliable metabolic features, we applied the same procedures to five independent datasets to verify the identified TNBC subtypes, which differed in terms of prognosis, metabolic characteristics, immune infiltration, clinical features, somatic mutation, and drug sensitivity. Results In general, TNBC could be classified into two metabolically distinct subtypes. C1 had relatively high expression of immune checkpoint genes and high immune and stromal scores, indicating drug sensitivity to PD-1 inhibitors. The C2 subtype, on the other hand, displayed a high variation in metabolism pathways involved in lipid, carbohydrate, and amino acid metabolism. More importantly, C2 was a lack of immune signatures, with an advanced pathological stage, low immune infiltration and poor prognosis. Interestingly, C2 had a high mutation frequency in PIK3CA, KMT2D, and KMT2C and displayed significant activation of the PI3K and angiogenesis pathways. As a final output, we created a 100-gene classifier to reliably differentiate the TNBC subtypes and AKR1B10 was a potential biomarker for C2 subtypes. Conclusions In conclusion, we identified two subtypes with distinct metabolic phenotypes, provided novel insights into TNBC heterogeneity, and provided a theoretical foundation for therapeutic strategies.