The mutational landscape of chromatin regulatory factors across 4,623 tumor samples

Abstract

Background: Chromatin regulatory factors are emerging as important genes in cancer development and are regarded as interesting candidates for novel targets for cancer treatment. However, we lack a comprehensive understanding of the role of this group of genes in different cancer types.

Results: We have analyzed 4,623 tumor samples from thirteen anatomical sites to determine which chromatin regulatory factors are candidate drivers in these different sites. We identify 34 chromatin regulatory factors that are likely drivers in tumors from at least one site, all with relatively low mutational frequency. We also analyze the relative importance of mutations in this group of genes for the development of tumorigenesis in each site, and indifferent tumor types from the same site.

Conclusions: We find that, although tumors from all thirteen sites show mutations in likely driver chromatin regulatory factors, these are more prevalent in tumors arising from certain tissues. With the exception of hematopoietic, liver and kidney tumors, as a median, the mutated factors are less than one fifth of all mutated drivers across all sites analyzed. We also show that mutations in two of these genes, MLL and EP300, correlate with broad expression changes across cancer cell lines, thus presenting at least one mechanism through which these mutations could contribute to tumorigenesis in cells of the corresponding tissues.

Figure 1

Likely driver chromatin regulatory factors across the datasets of somatic mutations in IntOGen-mutations. The heat-map in the top panel identifies FM-biased and CLUST-biased CRFs across the 31 datasets from 13 sites in IntOGen-mutations, whose original projects are detailed in the middle panel. The heat-map in the bottom panel contains the number of samples with mutations in each likely driver CRF in each site. Cells in the heat-map are colored following mutational frequency.

Figure 2

Chromatin regulatory factors within their context of functional interactions. Network of functional interactions among CRFs mapped to the Cytoscape FI plugin network. Square nodes represent likely driver CRFs, circle nodes other CRFs within the catalog, and diamond nodes represent linker genes. CRFs functions are color-coded, and genes in the same complex are grouped and circled.

Figure 3

FM bias, mutation frequencies and mutually exclusivity of chromatin regulatory factors as part of complexes. (A) Left heat-map shows the P value of FM bias analysis for all CRFs and for each complex. Right heat-map shows the number of samples with PAMs in the complex and the color indicates the mutation frequency (number of samples with PAMs divided by number of samples of this cancer type analyzed). (B) Heat-map of samples and genes of each complex, PAMs are represented as green cells in the heat-map. Tumor samples from each site have headers of the corresponding colors. Samples and genes in the heat-map are ordered based on mutually exclusive alterations within each site using Gitools built-in function for this purpose. Number of samples with PAMs in the gene (N) and the mutation frequency (Freq) of the gene in whole dataset are shown at the right of each heat-map. Gene names in bold indicated that the gene is one of the 34 detected as candidate drivers. PAM, protein-affecting mutations.

Figure 4

Relative importance of chromatin regulatory factors in tumorigenesis across sites. (A) Histograms of the fraction of samples with 0 (green) or at least one (red) likely driver CRF with PAMs in each site. (B) Boxplots representing the distribution of fraction of CRFs with PAMs with respect to all FM-biased genes with PAMs in each sample (CF ratios) of samples from each site with at least one mutation in a CRF (red fraction in panel A). (C) Boxplots representing the distribution of CF ratios of samples from each of the three projects focused on brain tumors. CRF, chromatin regulatory factors; DKFZ, German Cancer Research Center; JHU, Johns Hopkins University; TCGA, The Cancer Genome Atlas.

Figure 5

Mutational status of tumor samples from the three brain datasets included in IntOGen. The genes represented in the heat-map comprise all FM-biased CRFs that bear one mutation in at least one brain tumor sample (in bold typeface) plus the top 15 FM-biased genes in brain obtained from IntOGen. Mutations are represented by their MutationAssesor [109] functional impact scores (FIS). Samples and genes in the heat-map are ordered based on mutually exclusive alterations within dataset. FIS, functional impact score; MA, MutationAssessor score. JHU, Johns Hopkins University; TCGA, The Cancer Genome Atlas.

Figure 6

Effect of PAMs in EP300 and MLL3 on the transcription of broad gene modules across cancer cell lines. Cancer cell lines originated from solid tissues (Additional file 2: Figure S1) are enriched (SLEA) for regulatory modules (Additional file 1: Table S4) and selected pathways from Kyoto Encyclopedia of Genes and Genomes. The first two panels in both A and B correspond to mean enrichment z-scores in wild type and mutant cell lines. The difference between the two enrichment groups, assessed through a Wilcoxon-Mann–Whitney group comparison test, is indicated at the right. (A)EP300 mutation status. (B)MLL3 mutation status.