Abstract
Patient-derived bulk expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. However, these bulk profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in the biopsy. Therefore, subtle transcriptional footprints of metabolic processes can be concealed by other biological processes and experimental artifacts. We therefore performed consensus Independent Component Analyses (c-ICA) with 34,494 bulk expression profiles of patient-derived tumor biopsies, non-cancer tissues and cell lines. c-ICA enabled us to create a transcriptional metabolic landscape in which many robust metabolic transcriptional components (mTCs) and their activation score in individual samples were defined. Here we demonstrate that this metabolic landscape can be used to explore associations between metabolic processes and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. The metabolic landscape can be explored at http://www.themetaboliclandscapeofcancer.com.
Highlights
Patient-derived bulk expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer
We demonstrate how this landscape can be used to explore associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of immune cells in the tumor microenvironment (TME)
A direct link between the functions of glutathione and Hsp90 in oxidative stress has been suggested, as well as a relationship between tanespimycin sensitivity and NQO1 expression, a gene coding for an enzyme reducing quinones to hydroquinones that is involved in detoxification pathways [26, 27]. In line with these findings, we found that the NQO1 gene is present near the top of Genomics of Drug Sensitivity in Cancer portal (GDSC) metabolic processes (mTCs) 18, Cell Line Encyclopedia (CCLE)
Summary
Patient-derived bulk expression profiles of cancers can provide insight into transcriptional changes that underlie reprogrammed metabolism in cancer. These profiles represent the average expression pattern of all heterogeneous tumor and non-tumor cells present in biopsies of tumor lesions. Reprogrammed energy metabolism is a hallmark of cancer [1]. Metabolic reprogramming supports the survival, proliferation, and maintenance of cancer cells by ensuring sufficient biosynthetic capacity, redox potential, and energy [2]. Metabolic reprogramming of cancer cells influences the composition and function of immune cells present in the tumor microenvironment (TME), affecting the anti-cancer immune response to immunotherapy [4, 5]. More recent knowledge about cancer cell metabolism has resulted in novel therapeutic targets, such as glutaminase and mutant forms of IDH1/2, currently being evaluated in pre-clinical models and phase I/II clinical trials [7, 8]
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