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Impact of tumor microenvironment and epithelial phenotypes on metabolism in breast cancer.

ABSTRACT: Cancer cells have altered metabolism, with increased glucose uptake, glycolysis, and biomass production. This study conducted genomic and metabolomic analyses to elucidate how tumor and stromal genomic characteristics influence tumor metabolism.Thirty-three breast tumors and six normal breast tissues were analyzed by gene expression microarray and by mass spectrometry for metabolites. Gene expression data and clinical characteristics were evaluated in association with metabolic phenotype. To evaluate the role of stromal interactions in altered metabolism, cocultures were conducted using breast cancer cells and primary cancer-associated fibroblasts (CAF).Across all metabolites, unsupervised clustering resulted in two main sample clusters. Normal breast tissue and a subset of tumors with less aggressive clinical characteristics had lower levels of nucleic and amino acids and glycolysis byproducts, whereas more aggressive tumors had higher levels of these Warburg-associated metabolites. While tumor-intrinsic subtype did not predict metabolic phenotype, metabolic cluster was significantly associated with expression of a wound response signature. In cocultures, CAFs from basal-like breast cancers increased glucose uptake and basal-like epithelial cells increased glucose oxidation and glycogen synthesis, suggesting interplay of stromal and epithelial phenotypes on metabolism. Cytokine arrays identified hepatocyte growth factor (HGF) as a potential mediator of stromal-epithelial interaction and antibody neutralization of HGF resulted in reduced expression of glucose transporter 1 (GLUT1) and decreased glucose uptake by epithelium.Both tumor/epithelial and stromal characteristics play important roles in metabolism. Warburg-like metabolism is influenced by changes in stromal-epithelial interactions, including altered expression of HGF/Met pathway and GLUT1 expression.

SUBMITTER: Brauer HA

PROVIDER: S-EPMC3684709 | biostudies-literature | 2013 Feb

REPOSITORIES: biostudies-literature

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Impact of tumor microenvironment and epithelial phenotypes on metabolism in breast cancer.

Brauer Heather Ann HA Makowski Liza L Hoadley Katherine A KA Casbas-Hernandez Patricia P Lang Lindsay J LJ Romàn-Pèrez Erick E D'Arcy Monica M Freemerman Alex J AJ Perou Charles M CM Troester Melissa A MA

Clinical cancer research : an official journal of the American Association for Cancer Research 20121212 3

<h4>Purpose</h4>Cancer cells have altered metabolism, with increased glucose uptake, glycolysis, and biomass production. This study conducted genomic and metabolomic analyses to elucidate how tumor and stromal genomic characteristics influence tumor metabolism.<h4>Experimental design</h4>Thirty-three breast tumors and six normal breast tissues were analyzed by gene expression microarray and by mass spectrometry for metabolites. Gene expression data and clinical characteristics were evaluated in ...[more]

PMID: 23236214

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Project description:BackgroundThe abundance of immune and stromal cells in the tumor microenvironment (TME) is informative of levels of inflammation, angiogenesis, and desmoplasia. Radiomics, an approach of extracting quantitative features from radiological imaging to characterize diseases, have been shown to predict molecular classification, cancer recurrence risk, and many other disease outcomes. However, the ability of radiomics methods to predict the abundance of various cell types in the TME remains unclear. In this study, we employed a radio-genomics approach and machine learning models to predict the infiltration of 10 cell types in breast cancer lesions utilizing radiomic features extracted from breast Dynamic Contrast Enhanced Magnetic Resonance Imaging.MethodsWe performed a retrospective study utilizing 73 patients from two independent institutions with imaging and gene expression data provided by The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA), respectively. A set of 199 radiomic features including shape-based, morphological, texture, and kinetic characteristics were extracted from the lesion volumes. To capture one-to-one relationships between radiomic features and cell type abundance, we performed linear regression on each radiomic feature/cell type abundance combination. Each regression model was tested for statistical significance. In addition, multivariate models were built for the cell type infiltration status (i.e. "high" vs "low") prediction. A feature selection process via Recursive Feature Elimination was applied to the radiomic features on the training set. The classification models took the form of a binary logistic extreme gradient boosting framework. Two evaluation methods including leave-one-out cross validation and external independent test, were used for radiomic model learning and testing. The models' performance was measured via area under the receiver operating characteristic curve (AUC).ResultsUnivariate relationships were identified between a set of radiomic features and the abundance of fibroblasts. Multivariate models yielded leave-one-out cross validation AUCs ranging from 0.5 to 0.83, and independent test AUCs ranging from 0.5 to 0.68 for the multiple cell type invasion predictions.ConclusionsOn two independent breast cancer cohorts, breast MRI-derived radiomics are associated with the tumor's microenvironment in terms of the abundance of several cell types. Further evaluation with larger cohorts is needed.

Project description:Background:Cellular heterogeneity within the tumor microenvironment is essential to tumorigenesis and tumor development. A high-resolution global view of the tumor-infiltrating immune and stromal cells in breast tumors is needed. Methods:xCell was used to create a cellular heterogeneity map of 64 cell types in 1,092 breast tumor and adjacent normal tissues. xCell digitally dissects tissue cellular heterogeneity based on gene expression. Integrated statistical analyses were then performed. Results:There were noticeable differences between the cell fractions in tumor tissues and normal tissues. Tumors displayed higher proportions of immune cells, including CD4+ Tem, CD8+ naïve T cells, and CD8+ Tcm compared with normal tissues. Immune inhibitory receptors (PD1, CTLA4, LAG3 and TIM3) were co-expressed on certain subtypes of T cells in breast tumors, and PD1 and CTLA4 were both positively correlated with CD8+ Tcm and CD8+ T cells. 28 cell types were significantly associated with overall survival in univariate analysis. CD4+ Tem, CD8+ Tcm, CD8+ T-cells, CD8+ naive T-cells, and B cells were positive prognostic factors but CD4+ naive T-cells were negative prognostic factors for breast cancer patients. TDRD6 and TTK are promising T cell and B cell targets for tumor vaccines. Endothelial cells and fibroblasts were significantly less prevalent in tumor tissues; astrocytes and mesangial cells were negatively correlated with the T stage. Mesangial cells and keratinocytes were found to be favorable prognostic factors and myocytes were negative prognostic factors. Five cell types were found to be independent prognostic factors and we used these to create a reliable prognostic model for breast cancer patients. Cellular heterogeneity was discovered among different breast cancer subtypes by Her2, ER, and PR status. Tri-negative patients had the highest fraction of immune cells while luminal type patients had the lowest. The various cells may have diverse or opposing roles in the prognosis of breast cancer patients. Conclusions:We created a uniquecellular map for the diverse heterogeneity of immune and stromal phenotypes within the breast tumor microenvironment. This map may lead to potential therapeutic targets and biomarkers with prognostic utility.

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Impact of tumor microenvironment and epithelial phenotypes on metabolism in breast cancer.

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Impact of tumor microenvironment and epithelial phenotypes on metabolism in breast cancer.

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