Comprehensive metabolomics expands precision medicine for triple-negative breast cancer

Yi Xiao,Qiang Yu,Jia-Han Ding,Da-Qiang Li,Qiuzhuang Sun,Lin Jiang,François Bertucci,Song-Yang Wu,Qing Zhang,Zhi-Ming Shao,Li-Ping Ge,Fan Yang,Yun-Song Yang,Yue Gong,Ding Ma,Xin Hu,Yi-Zhou Jiang

doi:10.1038/s41422-022-00614-0

Abstract

Metabolic reprogramming is a hallmark of cancer. However, systematic characterizations of metabolites in triple-negative breast cancer (TNBC) are still lacking. Our study profiled the polar metabolome and lipidome in 330 TNBC samples and 149 paired normal breast tissues to construct a large metabolomic atlas of TNBC. Combining with previously established transcriptomic and genomic data of the same cohort, we conducted a comprehensive analysis linking TNBC metabolome to genomics. Our study classified TNBCs into three distinct metabolomic subgroups: C1, characterized by the enrichment of ceramides and fatty acids; C2, featured with the upregulation of metabolites related to oxidation reaction and glycosyl transfer; and C3, having the lowest level of metabolic dysregulation. Based on this newly developed metabolomic dataset, we refined previous TNBC transcriptomic subtypes and identified some crucial subtype-specific metabolites as potential therapeutic targets. The transcriptomic luminal androgen receptor (LAR) subtype overlapped with metabolomic C1 subtype. Experiments on patient-derived organoid and xenograft models indicate that targeting sphingosine-1-phosphate (S1P), an intermediate of the ceramide pathway, is a promising therapy for LAR tumors. Moreover, the transcriptomic basal-like immune-suppressed (BLIS) subtype contained two prognostic metabolomic subgroups (C2 and C3), which could be distinguished through machine-learning methods. We show that N-acetyl-aspartyl-glutamate is a crucial tumor-promoting metabolite and potential therapeutic target for high-risk BLIS tumors. Together, our study reveals the clinical significance of TNBC metabolomics, which can not only optimize the transcriptomic subtyping system, but also suggest novel therapeutic targets. This metabolomic dataset can serve as a useful public resource to promote precision treatment of TNBC.

Highlights

Triple-negative breast cancer (TNBC) is a subset of breast cancer defined by the lack of expression of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2.1 Clinical management of TNBC is a great challenge because of its high incidence of visceral metastases and the lack of well-recognized therapeutic targets.[1]
Metabolomic subtyping refines the transcriptomic subtyping of basal-like immune-suppressed (BLIS) tumors and can be achieved by machine learning We further explored the associations among metabolomic subtypes, previously defined transcriptomic subtypes[4] and metabolic-pathwaybased subtypes (MPSs).[9]
We constructed a large metabolomic dataset to systematically describe the metabolomic landscape of TNBC

Summary

Introduction

Triple-negative breast cancer (TNBC) is a subset of breast cancer defined by the lack of expression of estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2.1 Clinical management of TNBC is a great challenge because of its high incidence of visceral metastases and the lack of well-recognized therapeutic targets.[1]. We need to seek for a more multilayered understanding of TNBC for new target identification

Methods

Results

Conclusion