Unique Metabolic Contexts Sensitize Cancer Cells and Discriminate between Glycolytic Tumor Types

SIMPLE SUMMARY: We sought to assess cancer cell viability in the context of glycolytic versus oxidative phosphorylation carbon source availability from cell lines and expression data from variably glycolytic human tumors. We performed an RNAi screen of genes consisting of the cytosolic machinery for ATP production and regulation of bioenergetic output in cancer cells in glycolytic or oxidative phosphorylation (OXPHOS) conditions. We identified the pentose phosphate pathway as requisite for viability under glycolytic conditions and mTOR signaling as requisite for viability under OXPHOS conditions. We then characterized gene sets within this panel to identify similarities and differences amongst RNA-seq profiles across variably glycolytic cancer types. This analysis identified glycolytic tumor profiles from non-glycolytic tumor profiles. Our analyses support classification of tumors by metabolic phenotype. ABSTRACT: Cancer cells utilize variable metabolic programs in order to maintain homeostasis in response to environmental challenges. To interrogate cancer cell reliance on glycolytic programs under different nutrient availabilities, we analyzed a gene panel containing all glycolytic genes as well as pathways associated with glycolysis. Using this gene panel, we analyzed the impact of an siRNA library on cellular viability in cells containing only glucose or only pyruvate as the major bioenergetic nutrient source. From these panels, we aimed to identify genes that elicited conserved and glycolysis-dependent changes in cellular bioenergetics across glycolysis-promoting and OXPHOS-promoting conditions. To further characterize gene sets within this panel and identify similarities and differences amongst glycolytic tumor RNA-seq profiles across a pan-cancer cohort, we then used unsupervised statistical classification of RNA-seq profiles for glycolytic cancers and non-glycolytic cancer types. Here, Kidney renal clear cell carcinoma (KIRC); Head and Neck squamous cell carcinoma (HNSC); and Lung squamous cell carcinoma (LUSC) defined the glycolytic cancer group, while Prostate adenocarcinoma (PRAD), Thyroid carcinoma (THCA), and Thymoma (THYM) defined the non-glycolytic cancer group. These groups were defined based on glycolysis scoring from previous studies, where KIRC, HNSC, and LUSC had the highest glycolysis scores, meanwhile, PRAD, THCA, and THYM had the lowest. Collectively, these results aimed to identify multi-omic profiles across cancer types with demonstrated variably glycolytic rates. Our analyses provide further support for strategies aiming to classify tumors by metabolic phenotypes in order to therapeutically target tumor-specific vulnerabilities.