PURPOSE: 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. EXPERIMENTAL DESIGN: 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). RESULTS: 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. CONCLUSIONS: 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.
PURPOSE: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. EXPERIMENTAL DESIGN: 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). RESULTS: 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. CONCLUSIONS: 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.
Authors: Diana Whitaker-Menezes; Ubaldo E Martinez-Outschoorn; Zhao Lin; Adam Ertel; Neal Flomenberg; Agnieszka K Witkiewicz; Ruth C Birbe; Anthony Howell; Stephanos Pavlides; Ricardo Gandara; Richard G Pestell; Federica Sotgia; Nancy J Philp; Michael P Lisanti Journal: Cell Cycle Date: 2011-06-01 Impact factor: 4.534
Authors: Sung Soo Kang; Yi Kyeong Chun; Min Hee Hur; Hae Kyung Lee; Yee Jeong Kim; Sung Ran Hong; Jee Hyun Lee; Sung Gong Lee; Yong Koo Park Journal: Jpn J Cancer Res Date: 2002-10
Authors: Sneha Sundaram; Alex J Freemerman; Amy R Johnson; J Justin Milner; Kirk K McNaughton; Joseph A Galanko; Katharine M Bendt; David B Darr; Charles M Perou; Melissa A Troester; Liza Makowski Journal: Breast Cancer Res Treat Date: 2013-11-12 Impact factor: 4.872
Authors: Hongyun Zhao; Lifeng Yang; Joelle Baddour; Abhinav Achreja; Vincent Bernard; Tyler Moss; Juan C Marini; Thavisha Tudawe; Elena G Seviour; F Anthony San Lucas; Hector Alvarez; Sonal Gupta; Sourindra N Maiti; Laurence Cooper; Donna Peehl; Prahlad T Ram; Anirban Maitra; Deepak Nagrath Journal: Elife Date: 2016-02-27 Impact factor: 8.140
Authors: Guangxia Chen; Wan Feng; Shu Zhang; Kangqi Bian; Yan Yang; Cheng Fang; Min Chen; Jun Yang; Xiaoping Zou Journal: Am J Cancer Res Date: 2015-03-15 Impact factor: 6.166
Authors: Irina N Druzhkova; Marina V Shirmanova; Maria M Lukina; Varvara V Dudenkova; Nataliya M Mishina; Elena V Zagaynova Journal: Cell Cycle Date: 2016-05-02 Impact factor: 4.534