Integrated Metabolic Profiling and Transcriptional Analysis Reveals Therapeutic Modalities for Targeting Rapidly Proliferating Breast Cancers

Metabolic dysregulation is a prominent feature in breast cancer, but it remains poorly characterized in patient tumors. In this study, untargeted metabolomics analysis of triple-negative breast cancer (TNBC) and patient with estrogen receptor (ER)-positive breast cancer samples, as well as TNBC patient-derived xenografts (PDX), revealed two major metabolic groups independent of breast cancer histologic subtypes: a Nucleotide/Carbohydrate-Enriched group and a Lipid/Fatty Acid-Enriched group. Cell lines grown in vivo more faithfully recapitulated the metabolic profiles of patient tumors compared with those grown in vitro. Integrated metabolic and gene expression analyses identified genes that strongly correlate with metabolic dysregulation and predict patient prognosis. As a proof of principle, targeting Nucleotide/Carbohydrate-Enriched TNBC cell lines or PDX xenografts with a pyrimidine biosynthesis inhibitor or a glutaminase inhibitor led to therapeutic efficacy. In multiple in vivo models of TNBC, treatment with the pyrimidine biosynthesis inhibitor conferred better therapeutic outcomes than chemotherapeutic agents. This study provides a metabolic stratifi- cation of breast tumor samples that can guide the selection of effective therapeutic strategies targeting breast cancer subsets. In addition, we have developed a public, interactive data visualization portal (http://brcametab.org) based on the data generated from this study to facilitate future research. Significance: A multiomics strategy that integrates metabolic and gene expression profiling in patient tumor samples and animal models identifies effective pharmacologic approaches to target rapidly proliferating breast tumor subtypes. [GRAPHICS] .

Automated summary

Metabolic dysregulation in breast cancer was characterized and divided into two major metabolic groups. Genes strongly correlated with metabolic dysregulation and predicted patient prognosis were identified. Targeting metabolic dysregulation led to effective therapeutic outcomes. This study is of great significance in guiding therapeutic strategies for breast cancer subsets.