Dataset Information

GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity.

ABSTRACT: Breast cancers possess fundamentally altered metabolism that fuels their pathogenicity. While many metabolic drivers of breast cancers have been identified, the metabolic pathways that mediate breast cancer malignancy and poor prognosis are less well understood. Here, we used a reactivity-based chemoproteomic platform to profile metabolic enzymes that are enriched in breast cancer cell types linked to poor prognosis, including triple-negative breast cancer (TNBC) cells and breast cancer cells that have undergone an epithelial-mesenchymal transition-like state of heightened malignancy. We identified glutathione S-transferase Pi 1 (GSTP1) as a novel TNBC target that controls cancer pathogenicity by regulating glycolytic and lipid metabolism, energetics, and oncogenic signaling pathways through a protein interaction that activates glyceraldehyde-3-phosphate dehydrogenase activity. We show that genetic or pharmacological inactivation of GSTP1 impairs cell survival and tumorigenesis in TNBC cells. We put forth GSTP1 inhibitors as a novel therapeutic strategy for combatting TNBCs through impairing key cancer metabolism and signaling pathways.

SUBMITTER: Louie SM

PROVIDER: S-EPMC4876719 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity.

Louie Sharon M SM Grossman Elizabeth A EA Crawford Lisa A LA Ding Lucky L Camarda Roman R Huffman Tucker R TR Miyamoto David K DK Goga Andrei A Weerapana Eranthie E Nomura Daniel K DK

Cell chemical biology 20160512 5

Breast cancers possess fundamentally altered metabolism that fuels their pathogenicity. While many metabolic drivers of breast cancers have been identified, the metabolic pathways that mediate breast cancer malignancy and poor prognosis are less well understood. Here, we used a reactivity-based chemoproteomic platform to profile metabolic enzymes that are enriched in breast cancer cell types linked to poor prognosis, including triple-negative breast cancer (TNBC) cells and breast cancer cells th ...[more]

PMID: 27185638

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Project description:BackgroundThe high heterogeneity of triple negative breast cancer (TNBC) is the main clinical challenge for individualized therapy. Considering that fatty acid metabolism (FAM) plays an indispensable role in tumorigenesis and development of TNBC, we proposed a novel FAM-based classification to characterize the tumor microenvironment immune profiles and heterogeneous for TNBC.MethodsWeighted gene correlation network analysis (WGCNA) was performed to identify FAM-related genes from 221 TNBC samples in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. Then, non-negative matrix factorization (NMF) clustering analysis was applied to determine FAM clusters based on the prognostic FAM-related genes, which chosen from the univariate/multivariate Cox regression model and the least absolute shrinkage and selection operator (LASSO) regression algorithm. Then, a FAM scoring scheme was constructed to further quantify FAM features of individual TNBC patient based on the prognostic differentially expressed genes (DEGs) between different FAM clusters. Systematically analyses were performed to evaluate the correlation between the FAM scoring system (FS) with survival outcomes, genomic characteristics, tumor microenvironment (TME) features and immunotherapeutic response for TNBC, which were further validated in the Cancer Genome Atlas (TCGA) and GSE58812 datasets. Moreover, the expression level and clinical significancy of the selected FS gene signatures were further validated in our cohort.Results1860 FAM-genes were screened out using WGCNA. Three distinct FAM clusters were determined by NMF clustering analysis, which allowed to distinguish different groups of patients with distinct clinical outcomes and tumor microenvironment (TME) features. Then, prognostic gene signatures based on the DEGs between different FAM clusters were identified using univariate Cox regression analysis and Lasso regression algorithm. A FAM scoring scheme was constructed, which could divide TNBC patients into high and low-FS subgroups. Low FS subgroup, characterized by better prognosis and abundance with effective immune infiltration. While patients with higher FS were featured with poorer survival and lack of effective immune infiltration. In addition, two independent immunotherapy cohorts (Imvigor210 and GSE78220) confirmed that patients with lower FS demonstrated significant therapeutic advantages from anti-PD-1/PD-L1 immunotherapy and durable clinical benefits. Further analyses in our cohort found that the differential expression of CXCL13, FBP1 and PLCL2 were significantly associated with clinical outcomes of TNBC samples.ConclusionsThis study revealed FAM plays an indispensable role in formation of TNBC heterogeneity and TME diversity. The novel FAM-based classification could provide a promising prognostic predictor and guide more effective immunotherapy strategies for TNBC.

Dataset Information

GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity.

Publications

GSTP1 Is a Driver of Triple-Negative Breast Cancer Cell Metabolism and Pathogenicity.

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