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=14
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) is extremely 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: 115 TNBC patients from TCGA were combined into a virtual cohort 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 high immune checkpoint genes expression and immune and stromal scores, demonstrating sensitivity to the treatment of PD-1 inhibitors. On the other hand, C2 displayed a high variation in metabolism pathways involved in carbohydrate, lipid, and amino acid metabolism. More importantly, C2 was a lack of immune signatures, with late 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.
Conclusion: In conclusion, we identified two subtypes with distinct metabolic phenotypes, provided novel insights into TNBC heterogeneity, and provided a theoretical foundation for therapeutic strategies.