Essential Amino Acid Metabolism-Related Molecular Classification in Triple-Negative Breast Cancer

Abstract

Aim: To develop an approach to characterize and classify triple-negative breast cancer (TNBC) tumors based upon their essential amino acid (EAA) metabolic activity. Methods: We performed bioinformatic analyses of genomic, transcriptomic and clinical data in an integrated cohort of 740 TNBC patients from public databases. Results: Based on EAA metabolism-related gene expression patterns, two TNBC subtypes were identified with distinct prognoses and genomic alterations. Patients exhibiting an upregulated EAA metabolism phenotype were more prone to chemoresistance but also expressed higher levels of immune checkpoint genes and may be better candidates for immune checkpoint inhibitor therapy. Conclusion: Metabolic classification based upon EAA profiles offers a novel biological insight into previously established TNBC subtypes and advances current understanding of TNBC’s metabolic heterogeneity.

Lay abstract

Breast cancer is the most common malignancy in women. Triple-negative breast cancer (TNBC) is a highly malignant subtype of breast cancer, accounting for about 12–17% of total breast cancer cases. This subtype is prone to liver and bone metastases, has a high risk of recurrence and carries a poor prognosis. In this study the authors explored the essential amino acid metabolism characteristics of TNBC tumors. They found that TNBC tumors exhibiting high essential amino acid metabolism were more malignant, associated with a worse prognosis and less sensitive to chemotherapy, but were also associated with better patient responses to immunotherapy. These results offer new insights into the precision treatment of TNBC. The results of the study are promising but require additional investigation.

Keywords: :

• bioinformatics
• chemoresistance
• classification
• epigenomics
• essential amino acid
• immunotherapy
• metabolic heterogeneity
• PD-1
• prognosis
• TNBC

Supplementary data

To view the supplementary data that accompany this paper please visit the journal website at: www.tandfonline.com/doi/suppl/10.2217/epi-2021-0210

Author contributions

Y Zhao and C Pu were responsible for the analysis, interpretation of data and graphing. Y Zhao wrote the manuscript. Z Liu supervised the whole analysis and contributed to data analysis and editing of the manuscript. All authors contributed to the article and approved the submitted version.

Financial & competing interests disclosure

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. The authors declare no potential conflicts of interest.

No writing assistance was utilized in the production of this manuscript.