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Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types

Nature Genetics volume 46, pages 1258–1263 (2014)Cite this article

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Abstract

Somatic mutations in noncoding sequences are poorly explored in cancer, a rare exception being the recent identification of activating mutations in TERT regulatory DNA. Although this finding is suggestive of a general mechanism for oncogene activation, this hypothesis remains untested. Here we map somatic mutations in 505 tumor genomes across 14 cancer types and systematically screen for associations between mutations in regulatory regions and RNA-level changes. We identify recurrent promoter mutations in several genes but find that TERT mutations are exceptional in showing a strong and genome-wide significant association with increased expression. Detailed analysis of TERT across cancers shows that the strength of this association is highly variable and is strongest in copy number–stable cancers such as thyroid carcinoma. We additionally propose that TERT promoter mutations control expression of the nearby gene CLPTM1L. Our analysis provides a detailed pan-cancer view of TERT transcriptional activation but finds no clear evidence for frequent oncogenic promoter mutations beyond TERT.

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Figure 1: Whole-genome mapping of somatic mutations and screening for associations with RNA-level changes across 505 tumors.
Figure 2: Quantile-quantile plots of mutation-to-expression association scores using increasingly stringent search constraints.
Figure 3: Pan-cancer view of TERT expression levels and upstream DNA mutations.
Figure 4: Recurrent somatic mutations near TSSs.

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Acknowledgements

The results published here are based in whole or in part on data generated by the TCGA pilot project established by the National Cancer Institute and National Human Genome Research Institute. Information about TCGA and the investigators and institutions who constitute the TCGA research network can be found at http://cancergenome.nih.gov/. This work was supported by grants from the Swedish Foundation for Strategic Research (E.L. and N.J.F.); the Swedish Medical Research Council (E.L. and J.A.N.); the Swedish Cancer Society (E.L. and J.A.N.); the Magnus Bergvall Foundation (E.L.); the Åke Wiberg foundation (E.L.); the Lars Hierta Memorial Foundation (E.L.); Region Västra Götaland (J.A.N.); BioCARE (J.A.N.); and the Sahlgrenska Academy (J.A.N.). The computations were performed in part on resources provided by the Swedish National Infrastructure on Computing through the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX) under project b2012108. We thank the patients and their families; B. Einarsdottir and L. Nilsson for processing patient samples; R. Olofsson, U. Stierner and J. Mattsson for patient discussions; and members of our laboratory for valuable comments.

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Authors and Affiliations

  1. Department of Medical Biochemisty and Cell Biology, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Nils J Fredriksson & Erik Larsson

  2. Department of Oncology, Institute of Clinical Sciences, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Lars Ny

  3. Department of Surgery, Sahlgrenska Cancer Center, Institute of Clinical Sciences, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden

    Jonas A Nilsson

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Contributions

N.J.F. and E.L. analyzed data. N.J.F. performed experiments. J.A.N. collected and processed clinical material and information. L.N. recruited patients, evaluated clinical data and performed medical record follow up. E.L. and N.J.F. wrote the paper. E.L. conceived the study.

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Correspondence to Erik Larsson.

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Integrated supplementary information

Supplementary Figure 1 Quantile-quantile plots of mutation-to-expression association scores while taking copy-number changes into account.

Rank-based scores (Z-statistic) were used to assess associations between gene expression levels and presence/absence of somatic mutations in 100 bp genomic segments, such that activation gives positive scores and inactivation gives negative scores. Scores obtained in each cancer type were summarized into a pan-cancer score, considering the best-scoring genomic segment for each gene. The analysis is similar to that depicted in the main Fig. 2, but with additional measures taken to compensate for the impact of copy-number changes (see Methods).

Supplementary Figure 2 Positive association between TERT promoter mutations and mRNA levels of the neighboring CLPTM1L revealed by filtering for ETS-site forming mutations.

(a) Q-Q plot of expected vs. observed scores (Z-statistic for mutation-expression association summed across cancer types), when only considering mutations in DNase I hypersensitive regions that also contribute to formation of an ETS factor consensus sequence (GGAA or its reverse complement). A maximum distance of +/- 100 kb to the transcription start site was allowed. Gray lines indicate the 90% confidence interval, determined using 200 simulations (dots above the line are in the upper 5% percentile, and thus significant at the corrected P < 0.05 level). A similar screen for ETS-site disrupting mutations did not produce any significant associations. (b) Box plots showing that expression levels of CLPTML1, which is located close to TERT on chromosome 5, are positively correlated with mutations in the TERT promoter in several cancer types. The effect was strongest in LGG and THCA. The fraction of tumors with TERT promoter mutations is indicated in the figure titles. The central mark is the median and the box edges are the 25th and 75th percentiles, with individual data points indicated in red. P-values were calculated using the two-sided Wilcoxon rank sum test (unadjusted).

Supplementary Figure 3 Statistical power and sparseness of somatic mutations.

(a) Histogram showing the percentage of genomic tiles (100 bp genomic segments) that were somatically mutated in >1, >2 or more tumors, counted across all 505 analyzed tumor genomes. In the right panel, the percentages refer to tiles that, in addition to being mutated, also overlap with DNase I hypersensitive regions. (b) Theoretical power analysis using simulated mutations. Tumors were randomly selected to be targeted by artificial mutations at one specific tile in the ACTB promoter. The endogenous ACTB mRNA level was elevated by a factor 1.5 or 2 in mutated tumors, while otherwise retaining the normal gene expression profile. Mutations were spread randomly across all tumors and cancer types, to mimic a pan-cancer mutational pattern. The analysis was performed using DNase I HS filtering while including all genes (no COSMIC filter), with variable settings for the maximum TSS distance (y-axis). The number of introduced mutations was varied (x-axis), and the experiment was repeated 100 times for each set of parameters. Apart from the simulated mutations, the analysis was identical to the main screen, including correction for multiple testing using random permutations. Red tiles indicate conditions in which the compound score would, on average, reach significance at the corrected P < 0.05 level.

Supplementary Figure 4 No reproducible association with expression levels for PLEKHS1 or DPH3 noncoding mutations.

(a) PLEKHS1 mRNA levels were similar in 6 PLEKHS1 mutated vs. 15 non-mutated bladder carcinomas (BLCA; this cancer type had the highest fraction of PLEKHS1 mutated samples). (b) A positive association observed for DPH3 in 6 mutated vs. 32 non-mutated melanomas (SKCM) was too weak too reach genome-wide significance in the screen (main Fig. 2), and the effect was not replicated in 25/148 mutated/non-mutated melanomas genotyped using exome sequencing data (WXS) (Supplementary Table 7/Supplementary Fig. 6). No mRNA structural changes were revealed by inspection of RNA-seq alignments in mutated and non-mutated samples for these genes (based on WGS samples). P-values were calculated using the two-sided Wilcoxon rank sum test.

Supplementary Figure 5 Regulatory and sequence context of recurrently mutated noncoding positions in PLEKHS1 and DPH3.

(a) Somatically mutated positions in PLEKHS1, 70 and 73 bp into the first intron, are flanked by sequences of perfect reverse complementarity. Complementary sequences are 11 bp long including the mutated positions themselves. (b) Recurrent somatic mutations upstream of DPH3, located in a region of strong regulatory potential with multiple overlapping ChIP-seq peaks including ETS factors ELK4/ELF1 and ELK1. The mutations flank an ETS core motif sequence. In both panels ChIP-seq data for 161 transcription factors (TF) from ENCODE (Gerstein et al. Nature 2012, 489:91-100), visualized using the UCSC browser ENCODE Regulation track, is indicated at the bottom, with predicted TF binding sites from FactorBook (Wang et al. Nucleic Acids Res 2013, 41:D171-176) shown in green.

Supplementary Figure 6 Exome-based genotyping of DPH3 promoter mutations.

Exome data from TCGA was used to genotype the DPH3 promoter for mutations in the positions highlighted in main Fig. 4b. Data was obtained for 176 independent SKCM tumors (TCGA sample type 6, metastatis, which is the main type considered by TCGA for melanoma and the only type in our WGS analysis), and 242 SKCM normals (TCGA sample type 10). All included samples had at least 2 exome reads covering the positions of interest. Mutations were detected in 25/176 tumors and 0/242 normals. The histograms indicate the minimum read coverage in the three relevant positions (-25, -26 and -29) in each tumor. For validation we used a set of 25 tumors with both exome and WGS data, and found that the exome-based method could rediscover 4/5 mutations detected by WGS with no additional positives.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6, Supplementary Tables 3, 5, 6 and 8–11 and Supplementary Note (PDF 1321 kb)

Supplementary Table 1

Detailed information regarding included TCGA WGS and RNA-seq samples including UUID codes for cgHub retrevial. (XLSX 114 kb)

Supplementary Table 2

WGS read coverage information for the positions of recurrent mutations in the promoter regions of DPH3, TERT and PLEKHS1. (XLSX 131 kb)

Supplementary Table 4

Somatic mutations detected upstream of the TERT TSS. (XLSX 52 kb)

Supplementary Table 7

Genotyping of DPH3 promoter mutations using TCGA exome (WXS) data. (XLSX 76 kb)

Supplementary Data Set 1

Supplementary Data Set 1 (ZIP 6010 kb)

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Fredriksson, N., Ny, L., Nilsson, J. et al. Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types. Nat Genet 46, 1258–1263 (2014). https://doi.org/10.1038/ng.3141

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