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Robust metabolic transcriptional components in 34,494 patient-derived samples and cell lines

View ORCID ProfileV.C. Leeuwenburgh, View ORCID ProfileC.G. Urzúa-Traslaviña, View ORCID ProfileA. Bhattacharya, View ORCID ProfileM.T.C. Walvoort, View ORCID ProfileM. Jalving, View ORCID ProfileS. de Jong, View ORCID ProfileR.S.N. Fehrmann
doi: https://doi.org/10.1101/2020.10.01.321950
V.C. Leeuwenburgh
1Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
2Department of Chemical Biology, Stratingh Institute for Chemistry, University of Groningen, the Netherlands
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C.G. Urzúa-Traslaviña
1Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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A. Bhattacharya
1Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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M.T.C. Walvoort
2Department of Chemical Biology, Stratingh Institute for Chemistry, University of Groningen, the Netherlands
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M. Jalving
1Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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S. de Jong
1Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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R.S.N. Fehrmann
1Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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  • For correspondence: r.s.n.fehrmann@umcg.nl
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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.

  • http://www.themetaboliclandscapeofcancer.com

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Posted April 26, 2021.
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Robust metabolic transcriptional components in 34,494 patient-derived samples and cell lines
V.C. Leeuwenburgh, C.G. Urzúa-Traslaviña, A. Bhattacharya, M.T.C. Walvoort, M. Jalving, S. de Jong, R.S.N. Fehrmann
bioRxiv 2020.10.01.321950; doi: https://doi.org/10.1101/2020.10.01.321950
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Robust metabolic transcriptional components in 34,494 patient-derived samples and cell lines
V.C. Leeuwenburgh, C.G. Urzúa-Traslaviña, A. Bhattacharya, M.T.C. Walvoort, M. Jalving, S. de Jong, R.S.N. Fehrmann
bioRxiv 2020.10.01.321950; doi: https://doi.org/10.1101/2020.10.01.321950

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