ABSTRACT
Patient-derived 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. 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 and their activation score in individual samples were defined. Here we demonstrate how this landscape can be used to explore associations between the metabolic transcriptome and drug sensitivities, patient outcomes, and the composition of the immune tumor microenvironment. The metabolic landscape can be explored at http://www.themetaboliclandscapeofcancer.com.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The paper was revised to put emphasis on the advantages of using c-ICA to analyze the transcriptome, as opposed to analyzing raw gene expression data. This includes the rephrasing of some paragraphs in the introduction and discussion, a new added analysis of which transcriptional components in GEO and TCGA datasets might capture batch effects (Figure S2), an analysis on which transcriptional components are highly similar in all four used datasets (Figure 1D and S3), and changes in figure 4 which add information that shows the robustness of drug sensitivity associations.
