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The rules and impact of nonsense-mediated mRNA decay in human cancers


Premature termination codons (PTCs) cause a large proportion of inherited human genetic diseases. PTC-containing transcripts can be degraded by an mRNA surveillance pathway termed nonsense-mediated mRNA decay (NMD). However, the efficiency of NMD varies; it is inefficient when a PTC is located downstream of the last exon junction complex (EJC). We used matched exome and transcriptome data from 9,769 human tumors to systematically elucidate the rules of NMD targeting in human cells. An integrated model incorporating multiple rules beyond the canonical EJC model explains approximately three-fourths of the non-random variance in NMD efficiency across thousands of PTCs. We also show that dosage compensation may sometimes mask the effects of NMD. Applying the NMD model identifies signatures of both positive and negative selection on NMD-triggering mutations in human tumors and provides a classification for tumor-suppressor genes.

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Figure 1: Study overview.
Figure 2: A downstream EJC and proximity to the start codon are widespread signals for NMD.
Figure 3: Exon length, distance to the stop codon, mRNA decay rate and RNA-binding proteins influence NMD efficiency.
Figure 4: The identified NMD rules explain a large part of NMD efficiency.
Figure 5: Signatures of negative and positive selection on somatic nonsense mutations.
Figure 6: Model summarizing the rules governing NMD in human cells.


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This work was supported by a European Research Council (ERC) Consolidator grant (616434), the Spanish Ministry of Economy and Competitiveness (BFU2011-26206 and 'Centro de Excelencia Severo Ochoa 2013–2017' SEV-2012-0208), the AXA Research Fund, the Agencia de Gestio d'Ajuts Universitaris i de Recerca (AGAUR), FP7 project 4DCellFate (277899) and the EMBL-CRG Systems Biology Program. F.S. was also supported by FP7 grants MAESTRA (ICT-2013-612944) and InnoMol (FP7-REGPOT-2012-2013-1-316289).

Author information

Authors and Affiliations



R.G.H.L. performed all analyses. R.G.H.L., F.S. and B.L. designed analyses, interpreted the data and wrote the manuscript. B.L. conceived the project.

Corresponding authors

Correspondence to Fran Supek or Ben Lehner.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Data sources and validation of known NMD rules in human genomic data sets.

(a) Overview of the data preprocessing pipeline. (b) Somatic nonsense mutation tally across cancer types in the TCGA. (c,d) The standard EJC model applied to frameshift somatic mutations in TCGA (c) and germline truncating variants in Geuvadis (d). (eh) The faux 3′ UTR model is not broadly supported in TCGA somatic nonsense variants when examining the last exon (e) or intronless genes (f), and similarly so in the TCGA frameshift data set (g) and in the Geuvadis germline variants (h). In all panels, the blue line is a fit using loess or generalized additive models (Online Methods) and the shaded area is its 95% confidence interval.

Supplementary Figure 2 Effects of start-proximal PTCs on NMD efficiency.

(a) In genes with 3′ UTR introns, the penultimate EJC becomes the NMD-inducing EJC, as observed in Geuvadis germline variants. (be) The start-proximal effect on NMD evasion is evident in additional data sets and is associated with downstream in-frame stop codons, demonstrated for somatic frameshifts (b,d) and for germline variants (c,e). (f,g) There is no clear effect of distance to the downstream in-frame start codon (f) or the Kozak sequence (g). (hk) There is no decrease in NMD efficiency for shorter distances between the PTC (h) and putative PABPC1-binding motifs (i–k) in a hypothetical looped mRNA conformation. In all panels, the blue line is a fit using loess or generalized additive models (Online Methods) and the shaded area is its 95% confidence interval.

Supplementary Figure 3 Other influences on measured NMD efficiency in human genomic data.

(a) Somatic mutations with high allelic frequency cause NMD estimates to appear more efficient. (be) Long exons and very large distances to the normal stop codon are associated with inefficient NMD in additional data sets consisting of TCGA somatic frameshift mutations (b,d) and Geuvadis germline truncating variants (c,e). (f,g) The standard EJC model and start-proximal NMD evasion have a dominant effect on NMD efficiency (f) and were thus factored out (g) using regression, facilitating discovery of further rules. (h,i) Rapid mRNA turnover attenuates the effects of NMD in somatic frameshifts (h) and germline variants (i). (j) NMD is slightly more efficient in highly expressed genes. (k) Reduced NMD efficiency in transcripts with short half-lives is also observed when using mRNA half-life measures from HeLa cells. The x axis shows the median mRNA half-life in minutes in each box plot. (l,m) The observed reduction in NMD efficiency in genes with fast mRNA turnover in B cells (l) is still observed upon explicitly factoring out gene expression levels from the NMD efficiency measure (m). In all panels with a continuous x axis, the axis was square root transformed. In all panels, the blue line is a fit using loess or generalized additive models (Online Methods) and the shaded area is its 95% confidence interval.

Supplementary Figure 4 RNA-binding protein motifs associated with differential NMD efficiency in additional data sets.

(a) Each row corresponds to one motif, which was detected near (±100 nt) the PTC in the case of SRSF1, PABPN1 and SNRPB2 and within the original 3′ UTR for ACO1. The leftmost column corresponds to the discovery data set consisting of nonsense somatic mutations in TCGA tumors (red); the middle column is the validation data set with frameshift mutations in TCGA (blue); and the rightmost column is the independent data set with truncating germline variants in the Geuvadis cohort (green). The “FALSE” and “TRUE” labels correspond to the motif being absent or present, respectively, in the particular location of the transcript. P values were calculated by Mann–Whitney U tests, two-tailed. An association was considered validated in additional data sets (frameshift and germline variants) if the pooled P value across the three data sets was <0.005. (b) Data are presented as in a. Motifs shown here were significant in the discovery set with TCGA nonsense somatic mutations and in the validation data set with TCGA somatic frameshifts. In the Geuvadis validation set with germline truncating variants, we observed a trend in the correct direction for these motifs but significance was not reached (P > 0.10).

Supplementary Figure 5 Some RNA-binding protein motifs associated with differential NMD efficiency have a general effect on mRNA turnover.

(ac) Association of the RBP motifs in Supplementary Figure 4 with mRNA half-life measures from B cells (left box plot) and HeLa cells (right box plot). Motifs that were associated with NMD when located near a PTC were tested for a different mRNA half-life when the motif was detected near the wild-type stop codon, and the GATCAA motif was tested around the most 3′ exon junction. The remaining RBP motifs were tested in the location of the transcript where they also showed an association with differential NMD (Supplementary Fig. 4). The “FALSE” and “TRUE” labels correspond to the motif being absent or present, respectively, in the particular location of the transcript. P values were calculated by Mann–Whitney U tests, two-tailed. (d,e) A marginally significant difference in NMD efficiency is observed for the 20% most biased transcripts. Codon optimality bias was defined by counting the codon usage that corresponds to highly abundant tRNAs (Online Methods) in either the whole coding region (d) or the first 50 codons (e). Bins are equally populated and P values were calculated by Mann–Whitney U tests, two-tailed.

Supplementary Figure 6 Tumor-suppressor gene candidates examined for mechanisms of inactivation via NMD and copy number changes.

(a) Principal-components plot as in Figure 5g, with nine additional genes overlaid (blue points). The additional genes were proposed to be possible tumor suppressors in Davoli et al.37, on the basis of their enrichment for deleterious over benign somatic mutations (the TUSON Explorer method, FDR ≤ 20% for all except ZFHX3 (22%) and MKI67 (31%)). Examined genes have ≥50 nonsense mutations in our data set. (b) Relative frequencies of NMD-inducing versus NMD-evading nonsense mutations (“NMD” and “noNMD”, respectively) and of gene deletions versus absence thereof (“Del” and “noDel”, respectively). Importantly, in the predictor from Davoli et al., nonsense mutations were not subclassified by their putative effects on NMD nor were copy number alterations used as input.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–6 and Supplementary Note. (PDF 3191 kb)

Supplementary Table 1

Importance of various sequence features for predicting NMD efficiency before adjusting for the prevalent NMD rules. (XLSX 122 kb)

Supplementary Table 2

Importance of various sequence features for predicting NMD efficiency after adjusting for the prevalent NMD rules. (XLSX 123 kb)

Supplementary Table 3

Significant RF features after adjusting for the prevalent NMD rules (permutation based P value < 0.05). (XLSX 8 kb)

Supplementary Table 4

Genes with >50 somatic nonsense mutations in TCGA. (XLSX 13 kb)

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Lindeboom, R., Supek, F. & Lehner, B. The rules and impact of nonsense-mediated mRNA decay in human cancers. Nat Genet 48, 1112–1118 (2016).

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