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

AI Article Synopsis

  • Metabolic dysregulation plays a significant role in breast cancer but is not well understood across different patient tumors; this study identifies two main metabolic groups in triple-negative and estrogen receptor-positive breast cancer.
  • Analysis showed that cell lines grown in vivo reflect patient tumor metabolism better than those grown in vitro, and specific gene expressions were found to correlate with metabolic changes and patient outcomes.
  • Targeting the identified "Nucleotide/Carbohydrate-Enriched" group with specific inhibitors demonstrated improved treatment results compared to traditional chemotherapy, highlighting potential for personalized therapeutic approaches in breast cancer.

Article Abstract

Unlabelled: 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 stratification 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8857046PMC
http://dx.doi.org/10.1158/0008-5472.CAN-21-2745DOI Listing

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