TY - JOUR
T1 - Integrated Metabolic Profiling and Transcriptional Analysis Reveals Therapeutic Modalities for Targeting Rapidly Proliferating Breast Cancers
AU - Liao, Chengheng
AU - Glodowski, Cherise Ryan
AU - Fan, Cheng
AU - Liu, Juan
AU - Mott, Kevin R.
AU - Kaushik, Akash
AU - Vu, Hieu
AU - Locasale, Jason W.
AU - McBrayer, Samuel K.
AU - DeBerardinis, Ralph J.
AU - Perou, Charles M.
AU - Zhang, Qing
N1 - Funding Information:
The authors thank all members of the Zhang and Perou laboratories for helpful discussions and suggestions. They thank Dr. Shunqiang Li and group from Washington University in St. Louis for their help with the PDX drug treatment. The authors thank Drs. Srinivas Malladi, Kangsan Kim, and Pravat Parida from UT Southwestern for their kind helps and suggestions during revision. This work was supported by Cancer Prevention and Research Institute of Texas (to Q. Zhang, CPRIT, RR190058) and ACS Research Scholar Award (to Q. Zhang, RSG-18-059-01-TBE), NCI Breast SPORE program (to C.M. Perou, P50-CA58223), NCI (to C.M. Perou, R01-CA148761; to Q. Zhang, R01CA256833), and BCRF (to C.M. Perou).
Publisher Copyright:
© 2022 American Association for Cancer Research Inc.. All rights reserved.
PY - 2022/2/15
Y1 - 2022/2/15
N2 - 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.
AB - 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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U2 - 10.1158/0008-5472.CAN-21-2745
DO - 10.1158/0008-5472.CAN-21-2745
M3 - Article
C2 - 34911787
AN - SCOPUS:85124883507
VL - 82
SP - 665
EP - 680
JO - Cancer Research
JF - Cancer Research
SN - 0008-5472
IS - 4
ER -