Proteogenomic analysis of cancer aneuploidy and normal tissues reveals divergent modes of gene regulation across cellular pathways
- Institute for Systems Genetics and Department of Biochemistry and Molecular Pharmacology, NYU School of Medicine, United States
- Moores Cancer Center, University of California San Diego, United States
- Würzburg Institute of Systems Immunology, Max Planck Research Group at the Julius-Maximilians-Universität Würzburg, Germany
- International Max Planck Research School for Immunobiology, Epigenetics, and Metabolism, Germany
- Faculty of Biology, University of Freiburg, Germany
- Department of Pathology, NYU School of Medicine, United States
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Center for Infection Research, Germany
Figures
Figure 1 with 2 supplements
RNA-level and protein-level gene compensation after somatic copy number alteration (SCNA) across tumor types.
(A) Schematic of RNA-level and protein-level gene compensation as a result of DNA gains (red) or losses (light blue). RNA and protein abundance change proportionally to the DNA change when gene …
Figure 1—figure supplement 1
Gene compensation is not biased by genes encoding ribosome subunits, technical limitations of proteome detection, or genome doubling.
(A) Box plots showing the profiles of DNA, RNA, and protein log2 fold change (log2FC) of representative genes for the indicated cancer type. Samples were separated in different groups based on the …
Figure 1—figure supplement 2
Gene compensation at the RNA and protein levels in cancer cell lines and isogenic human colon epithelial cell (hCEC).
(A) Box plots showing the Cancer Cell Line Encyclopedia (CCLE) pan-cancer profile of DNA, RNA, and protein log2 fold change (log2FC) in five groups of DNA change. The genes of each group were …
Figure 2 with 1 supplement
Protein complex genes have a stronger protein-level regulation but a weaker RNA-level regulation than non-complex genes.
(A) Schematic representing the strategy to infer the degree of RNA- or protein-level regulation by Spearman’s correlation analysis between DNA and RNA (DNA–RNA) or between RNA and protein …
Figure 2—figure supplement 1
DNA–RNA and RNA–protein correlations across complex and non-complex genes among cell lines and normal tissues.
(A) Density distribution of DNA–RNA and RNA–protein correlations among different Clinical Proteomic Tumor Analysis Consortium (CPTAC) cancer types after removing bottom 10% of low expressed genes. (B…
Figure 3 with 2 supplements
Negative association between RNA- and protein-level regulation across genes and pathways.
(A) Dot plot showing the association between DNA–RNA and RNA–protein correlation, where each point is a gene. Density distribution is shown and a density distribution-dependent slope was calculated …
Figure 3—figure supplement 1
Enriched gene sets among genes with different regulation in primary tumors and cell lines.
(A) Top 10 Gene Ontology enriched gene sets of genes in Group 1 (high DNA–RNA correlation, low RNA–protein correlation; high protein-level regulation) in dark green and in Group 2 (low DNA–RNA …
Figure 3—figure supplement 2
DNA–RNA and RNA–protein correlations among genes localized in different cellular compartments.
Density plots of DNA–RNA and RNA–protein correlations for all cell compartments in Clinical Proteomic Tumor Analysis Consortium (CPTAC) pan-cancer analysis. Numbers at the right side of the figure …
Figure 4
Analysis of pathways dysregulated at the RNA and protein levels in high aneuploidy tumor samples.
(A) Schematic of the method used to identify pathways changing at the RNA and protein levels in samples of high aneuploidy. The aneuploidy degree of primary tumors (Clinical Proteomic Tumor Analysis …
Figure 5
Negative association between RNA- and protein-level regulation across cellular pathways.
(A) Schematics representing the negative correlation between protein-level regulation and RNA-level regulation across pathways (see also Figure 3B). (B) Schematics of representative cellular …
Author response image 1
Additional files
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Supplementary file 1
Gene compensation analysis for Figure 1, Figure 1—figure supplement 1, and Figure 1—figure supplement 2.
- https://cdn.elifesciences.org/articles/75227/elife-75227-supp1-v3.xlsx
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Supplementary file 2
DNA–RNA correlation and RNA–protein correlation analysis for Figure 2 and Figure 2—figure supplement 1.
- https://cdn.elifesciences.org/articles/75227/elife-75227-supp2-v3.xlsx
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Supplementary file 3
Complete list of cellular pathways and related analyses for Figure 3, Figure 3—figure supplement 1, and Figure 3—figure supplement 2.
- https://cdn.elifesciences.org/articles/75227/elife-75227-supp3-v3.xlsx
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Supplementary file 4
Complete list of t-value and Gene Set Enrichment Analysis (GSEA) results for Figure 4.
- https://cdn.elifesciences.org/articles/75227/elife-75227-supp4-v3.xlsx
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Transparent reporting form
- https://cdn.elifesciences.org/articles/75227/elife-75227-transrepform1-v3.docx
