Abstract
Recurrence is a hallmark of cancer-driving mutations. Recurrent mutations can arise at the same site or affect the same gene at different sites. Here we identified a set of mutations arising in individual samples and altering different cis-regulatory elements that converge on a common gene via chromatin interactions. The mutations and genes identified in this fashion showed strong relevance to cancer, in contrast to noncoding mutations with site-specific recurrence only. We developed a prediction method that identifies potentially recurrent mutations on the basis of the features shared by mutations whose recurrence is observed in a given cohort. Our method was capable of accurately predicting recurrent mutations at the level of target genes but not mutations recurring at the same site. We experimentally validated predicted mutations in distal regulatory regions of the TERT gene. In conclusion, we propose a novel approach to discovering potential cancer-driving mutations in noncoding regions.
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Acknowledgements
This work was supported by the Ministry of Health and Welfare funded through the Korea Health Industry Development Institute (HI13C2143), by the KAIST Future Systems Healthcare Project, and by the Ministry of Science, ICT and Future Planning (2013M3A9C4078139 and NRF-2015M3C9A4053251). Research facilities were supported by the CHUNG Moon Soul Centre of KAIST and by the KAIST Institute for the BioCentury.
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K.K., K.J., and W.Y. performed all analyses and drafted the manuscript. E.-Y.C., S.-M.P., M.B., and Y.-J.K. performed reporter assays. J.K.C. conceived the study, supervised data analyses, and wrote the manuscript.
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Integrated supplementary information
Supplementary Figure 1 Expression deviation of the genes mapped to gene-wise recurrent mutations in the TCGA breast cancer cohort (top) and the TCGA lung cancer cohort (bottom).
For each gene (each pair of red and blue bars), the average degree of expression deviation is compared between the mutated samples (red bar) and non-mutated samples (blue bar). For 72% (top) and 73% (bottom) of the cases, the mutated samples exhibited a higher degree of expression deviation. Genes were sorted according to the degree of the difference between the mutated and non-mutated groups. All genes are collapsed in the box plots on the right.
Supplementary Figure 2 Effects of copy number alterations versus mutations.
(a) The effects of copy number alterations for genes ordered as in Figure 1c. For each gene (each pair of red and blue bars), the percentage of samples with copy number alterations is compared between the mutated samples (red bar) and non-mutated samples (blue bar). All genes are collapsed in the box plots on the right. (b) Outcome of multiple linear regression quantitatively comparing the contribution of mutation recurrence and copy number alteration to expression perturbation. Copy number alteration was processed through the deviation metric used for expression perturbation. The contribution of mutation burden remained significant after adjusting for copy number alteration.
Supplementary Figure 3 Permutation tests for expression perturbation by mutations.
Gene expression profiles based on the RNA sequencing of 92 breast and 90 lung cancer samples were obtained from the TCGA and matched with the whole-genome sequences of the TCGA cohort samples. Array-based gene expression profiles of breast cancer samples were obtained from the ArrayExpress archive (E-MTAB-1088) and matched with the whole-genome sequences of the Sanger cohort samples. We obtained expression difference for each gene between the mutated and non-mutated groups as the t statistic. To estimate expected expression differences, the sample grouping (mutated versus non-mutated) was randomized while maintaining the sample size of each group. This permutation was repeated 1,000 times, and the t value was recalculated each time.
Supplementary Figure 4 Directionality of expression perturbation by mutations.
The x axis has genes ordered as in Figure 1c. The y axis indicates the fraction of cases (samples) that go in the same direction (either positive or negative expression deviation).
Supplementary Figure 5 Recurrence rate (the percentage of mutated samples in the given cohort) was obtained for each mutation (for site-specific recurrence) or each gene (for gene-level recurrence).
The site-specific recurrence rate was mapped to the target gene via the same chromatin interactome used for the gene-level recurrence analysis. P value indicates the differences in the recurrence rate between the known cancer genes and other genes.
Supplementary Figure 6 Permutation tests for biological validity of the chromatin interaction data.
We shuffled chromatin interaction links between enhancers and promoters. This procedure was repeated 1,000 times. High recurrence rates were observed for known cancer genes (Fig. 2c), cancer gene–interacting partners (Fig. 2d), and genes with relevant GO terms (Fig. 2e). We tested whether these patterns disappear as target genes are randomly mapped to mutations. To this end, the average recurrence rate of the above three groups of genes was obtained and divided by that of all genes. These relative recurrence rates were significantly higher with the real chromatin interactome map (black bars) than with the 1,000 randomized maps (gray density plots).
Supplementary Figure 7 Results of site-specific LOOCV
The proportion of positive votes by 1,000 decision trees in our random forest for site-specific LOOCV compared between truly recurrent mutations (green) and false, non-recurrent, mutations (red) depending on tumor and epigenome types.
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Supplementary Text and Figures
Supplementary Figures 1–7, Supplementary Tables 1, 2, 4, 7 and 9, and Supplementary Note. (PDF 2003 kb)
Supplementary Table 3
Cancer genes and interacting genes with >10% gene-wise recurrence in either cancer. (XLSX 37 kb)
Supplementary Table 5
Prediction performance (AUC). (XLSX 21 kb)
Supplementary Table 6
Variable importance. (XLSX 144 kb)
Supplementary Table 8
Random forest features of lung cancer mutations targeting TERT. (XLSX 21 kb)
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Kim, K., Jang, K., Yang, W. et al. Chromatin structure–based prediction of recurrent noncoding mutations in cancer. Nat Genet 48, 1321–1326 (2016). https://doi.org/10.1038/ng.3682
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DOI: https://doi.org/10.1038/ng.3682
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