Analysis

Reduced mutation rate in exons due to differential mismatch repair

Received:
Accepted:
Published online:

Abstract

While recent studies have identified higher than anticipated heterogeneity of mutation rate across genomic regions, mutations in exons and introns are assumed to be generated at the same rate. Here we find fewer somatic mutations in exons than expected from their sequence content and demonstrate that this is not due to purifying selection. Instead, we show that it is caused by higher mismatch-repair activity in exonic than in intronic regions. Our findings have important implications for understanding of mutational and DNA repair processes and knowledge of the evolution of eukaryotic genes, and they have practical ramifications for the study of evolution of both tumors and species.

Main

Genetic variation in exonic regions is lower than in intronic ones both across species and within populations. This differential exon–intron variation rate is attributed to the action of stronger purifying selection on exonic nucleotide changes, whereas the rate of generation of variants—which precedes the effect of selection—is generally assumed to be overall homogeneous between these two genic regions. This assumption lies at the heart of evolutionary biology and cancer genomics approaches that compare the rates of intronic and exonic variation to estimate the strength of selection acting on coding genes1,2,3,4,5.

Recent studies have shown that the rate of mutations across genomic regions is highly heterogeneous. Replication timing6,7, the level of gene expression8 and the degree of chromatin compaction9,10 have been described as features that affect mutation rate at the megabase scale. Our group and others recently demonstrated that the local efficiency of DNA repair is influenced by factors that affect accessibility of the repair machinery11,12,13,14.

The assumption that introns and exons have similar basal rates of mutation before the action of purifying selection is a reasonable one because exonic and intronic regions are replicated at the same time and transcribed equally. DNA repair mechanisms associated with the advance of the replication fork, as well as transcription-coupled repair, are therefore expected to have equivalent access to both regions. Nevertheless, several features of the chromatin structure—including some that have been related to the recruitment of DNA repair machineries15,16,17—vary widely between exons and introns18,19. This motivated us to question the long-standing assumption that introns and exons have similar rates of mutation before selection.

Somatic mutations detected in tumors20 are an ideal ground to explore whether exonic and intronic variants appear at the same rate. Tumor cells, upon clonal expansion, accumulate somatic mutations at accelerated rates as compared to the germ line. We demonstrate here that, even in the absence of purifying selection, exons acquire fewer mutations than expected given their nucleotide composition. We show that this decreased exonic mutation burden is detectable across seven tumor types. We also demonstrate that the cause of this reduction is that the mismatch repair (MMR) system acts more efficiently in exons than in introns, and we propose that this differential repair is caused by the differential positioning of histone marks in these two genic regions.

These findings imply that the differential genetic variation in exonic and intronic regions across species and within populations is caused by a combination of different sequence context, rate of DNA repair and purifying selection. This has ramifications of a technical nature for evolutionary methods that rely on the calculation of intronic variation to estimate the strength of selection on genes or to detect cancer driver genes1,2,3,5,21,22. More generally, these findings have profound implications for knowledge of gene evolution and DNA repair mechanisms.

Results

Differential distribution of chromatin features in exons and introns

We first sought to identify chromatin features with the most different distributions between exons and introns, using data generated by the Roadmap Epigenomics23 and the Encyclopedia of DNA Elements (ENCODE)24. We analyzed 32 chromatin features—comprising 30 histone modifications, the presence of a histone variant (H2A.Z) and DNase I–hypersensitive sites (DHSs)—in 127 cell lines and primary cells from different tissue types and nucleosome density obtained in a lymphoblastoid cell line (Supplementary Table 1). We computed the coverage (fraction of bases overlapping peaks) of each feature on exons and introns located at different positions along the structure of genes (the results of this calculation for three chromatin features are shown in Fig. 1a; Online Methods). Then, we defined the difference in the exonic and intronic coverage of each mark in each cell type as the P value of the two-tailed Mann–Whitney test of their comparison (box plots in Fig. 1a). Several chromatin marks exhibited a significant overall difference in exonic and intronic coverage (Fig. 1a,b). In particular, nucleosome density and trimethylation of histone H3 at lysine 36 (H3K36me3) were significantly higher in exons than in introns across the gene, and H3K36me3 was the histone mark with higher coverage across all exons in the gene. This behavior of H3K36me3 was consistent across the majority of the 127 cell types in Roadmap Epigenomics (Fig. 1b and Supplementary Tables 1 and 2). Moreover, H3K36me3 coverage decreased steeply in flanking introns (Fig. 1c). Interestingly, the hMutSα protein of MMR machinery, involved in the recognition of mismatches, has recently been described as being recruited to the chromatin through interaction of its hMSH6 subunit with H3K36me3 (refs. 15,17).

Figure 1: Exonic enrichment for several histone marks.
Figure 1

(a) Exonic and intronic coverage of H3K4me3 and H3K36me3 peaks in primary cells of the normal colon mucosa (E075) and of nucleosome-covered regions in GM12878 (lymphoblastoid cell line). Each bar represents the coverage of the mark in exons or introns at different positions of genes, depicted by the schematic structure of the genes at the bottom of the figure. The distribution of the exonic and intronic coverage of each chromatin feature across the gene structure is represented by the box plots at the right of the panel. The P value from a two-tailed Mann–Whitney test comparing the two distributions is shown. (b) Scatterplot representing the difference in exonic and intronic coverage of each histone mark (−log10 two-tailed Mann–Whitney P value corrected for multiple testing) on the y axis and the median coverage across all exons of genes on the x axis. Each dot represents one chromatin mark in one cell type, colored according to the former. All data on exonic and intronic coverage of all marks across cell types is available in Supplementary Tables 1 and 2. (c) Proportion of bases covered by H3K36me3 at internal exons and flanking introns in primary cells of the normal colon mucosa (E075).

We therefore hypothesized that the exonic enrichment of certain chromatin features, in particular H3K36me3, might result in increased recruitment of the MMR machinery to exons. This, in turn, would lead to a reduction in the quantity of exonic mutations with respect to the number of mismatches expected from the exonic sequence content alone.

Internal exons exhibit decreased exonic mutation burden in POLE-mutant tumors

POLE-mutant tumors, owing to the decreased proofreading capabilities of DNA polymerase ɛ, sustain a substantial number of mismatches during DNA replication, which make up a sizable part of their somatic mutations. Therefore, to determine whether the rate of somatic mutations caused by mismatches differs in exonic and intronic regions, we first explored the mutations detected across the whole genomes of six POLE-mutant colorectal tumors, sequenced by The Cancer Genome Atlas (TCGA). We stacked exon-centered 2,001-nucleotide (nt) sequences and computed the mutation burden at each position of this window as the number of mutations overlapping the position. This analysis showed that the mutation burden in positions dominated by exonic sequences was lower than that observed along flanking intronic regions (Fig. 2a, red line).

Figure 2: Decreased exonic mutation burden in POLE-mutant colorectal tumors.
Figure 2

(a) Exon-centered 2,001-nt-wide observed and expected profiles of mutations in six POLE-mutant colorectal tumors. The light red line represents the actual number of mutations at each position, while the red and black lines represent smoothed mutation numbers using a polynomial fit. Bottom, distribution of the percentage of exonic bases at each position across the 2,001-nt window. (b) Schematic of the method used to compute observed and expected numbers of mutations in exons and introns at the gene level. Rescfi represents the rescaled expected frequency of mutation of each nucleotide in the gene. The conditional probability of mutation of each site (P in the figure) is proportional to this quantity. EExonic and EIntronic represent the expected numbers of exonic and intronic mutations, respectively (see Online Methods for details). (c) Density plot representation of the distribution of gene-level differences in the numbers of observed and expected exonic mutations versus total mutations in POLE-mutant tumors. The distribution of exonic mutation difference (right-hand 1D density plot) is biased toward negative values, indicating that a majority of genes possess lower than expected numbers of mutations in exons. An analogous plot, restricted to genes with at least one expected exonic mutation (Supplementary Fig. 2b) shows similar results. (d) An overall highly significant 37.6% decreased in exonic mutation burden (3,380 fewer observed exonic mutations than the 8,996 expected) is observed in these tumors (top). Both synonymous and nonsynonymous mutations account for this decrease (bottom), with their observed values significantly below expected (left and middle plots), and no fewer nonsynonymous mutations than expected when the latter are computed solely from observed exonic mutations (right plot). (e) The decreased exonic mutation burden is maintained around the overall computed value (37.6%) across groups of genes with different mutation rate (first row), level of expression (second row), replication time (third row), the number of genic bases covered by H3K36me3 peaks (fourth row) and across exons at different positions in the gene (fifth row). The values in the abscissa in each graph represent the ordinal number of the bins of genes, sorted in the direction indicated in each panel.

The mutation probability at individual DNA positions is influenced by sequence context. Therefore, differences in nucleotide composition between exons and introns could provide a plausible explanation for the observed difference between exonic and intronic mutation counts. To compute the expected mutation burden at each position of the 2,001-nt exon-centered window, we distributed the mutations observed in each sequence in the stack, taking into account the conditional probability that each of the 2,001 positions was mutated given the adjoining 5′ and 3′ bases. This sequence-wise distribution of expected mutations (details in the Online Methods) avoids potential biases resulting from aggregating genic regions with different mutation rates and exon/intron proportions (Supplementary Fig. 1). The distribution of these synthetically generated 'expected' mutations in POLE-mutant tumors across exons and their flanking introns showed that more mutations are expected in exons than in introns, as represented by the black line in Figure 2a.

We then set out to compare the number of observed exonic mutations to the expected quantity in POLE-mutant tumors and to assess the statistical significance of the deviation between the two (Fig. 2b and Online Methods). We carried out this comparison at the level of individual genes to guarantee that its results were free from the aforementioned caveat. (Known cancer driver genes25,26 were excluded from this and subsequent analyses to eliminate any deviation due to positive selection.) First, we randomly distributed a number of mutations equal to that observed in each gene across its exons and introns, according to the probability of each nucleotide being mutated. A second method to obtain expected mutation burden based on permutations of observed mutations yielded similar results (Online Methods, Supplementary Fig. 2 and Supplementary Table 3). We then computed the difference between the observed and expected mutation burdens for each gene (Fig. 2c). Most genes (77%) possessed fewer exonic mutations than expected from their sequence content, resulting in a negative difference. After aggregating the numbers of observed and expected mutations across all genes (Fig. 2d), we discovered that, whereas internal exons bore only 5,616 mutations in the six POLE-mutant tumors, 8,996 exonic mutations were expected, given (i) the total number of genic mutations, (ii) the nucleotide composition of exons and introns, and (iii) the mutational processes operating in these tumors. This represents a decrease of 37.6% for the observed exonic mutation burden with respect to the expected burden. Employing a likelihood-based statistical approach (Online Methods), we found this decrease to be statistically significant (P < 0.0001). We have named this phenomenon 'decreased exonic mutation burden', and we quantify it globally as the percentage decrease with respect to the expected mutation burden.

We next tested whether the decreased exonic mutation burden was due to increased selective pressure on exons resulting in purifying selection of mutations in these regions during tumor evolution. To determine the impact of purifying selection on the exonic mutation burden, we separated exonic mutations on the basis of their consequence types. We found that the 5,616 exonic mutations in the six POLE-mutant tumors corresponded to 950 synonymous and 4,666 nonsynonymous mutations. If the decreased exonic mutation burden were caused by purifying selection, we would expect it to consist mostly of a decrease in nonsynonymous mutations. Nevertheless, when redistributing genic mutations across intronic, synonymous and nonsynonymous sites according to their mutational probabilities, we found a 35.7% decrease in nonsynonymous mutations, along with a 45.4% decrease in synonymous mutations (P < 0.0001; Fig. 2d). On the other hand, when redistributing solely exonic mutations on the basis of their mutational probability, we found that the expected number of nonsynonymous mutations was very close to the actual number observed: 4,562 (with the remaining 1,054 expected to yield synonymous variants). The results of these two tests support the conclusion that the decrease in the exonic mutation burden is not due to negative selection (Fig. 2d). This result was maintained across bins of genes with different mutation rates and was observable for all individual POLE-mutant tumors (Supplementary Tables 4 and 5).

We then checked that the decreased exonic mutation rate was not driven by a subset of genes at either extreme of the mutation rate range. To do this, we binned the genes into ten groups of increasing mutation rate (Fig. 2e, top). We then aggregated the mutations of the genes in each bin and confirmed that the decreased exonic mutation burden remained around 40% across all bins. Finally, we found that very similar values of decreased exonic mutation burden were observed across groups of genes with increasing replication times, expression levels and H3K36me3 coverage, and also across exons at different positions along genes (Fig. 2e, second to bottom panel). Furthermore, the decrease in exonic mutation burden was not driven by one or few POLE-mutant tumors, as it was observable and significant for each of them (Fig. 3a; this analysis also included a POLE-mutant tumor of uterine adenocarcinoma origin).

Figure 3: Decreased exonic mutation burden across scenarios of MMR activity.
Figure 3

(a) Tumor-level decreased exonic mutation burden in POLE-mutant (left) and, MSI-H (center) colorectal and uterine tumors and bMMRD glioblastomas (right). Dots represent individual tumors. Broken red lines represent the median decreased exonic mutation burden of each group of tumors. (b) Exon-centered 2,001-nt-wide observed and expected profiles of mutations in six colorectal and ten uterine MSI-H tumors and two POLE-mutant and two POLD-mutant bMMRD glioblastomas. (c) Schematic showing the increased efficiency of MMR at exons and the decreased exonic mutation burden.

In summary, we found a significant decrease in the exonic mutation rate in POLE-mutant tumors. This decrease is not due to sequence content and cannot be explained by negative selection acting on exonic mutations, and it is maintained across genes with all levels of mutation rate and across exons at different positions of the gene.

Decreased exonic mutation rate is caused by differential mismatch repair

We reasoned that the decreased exonic mutation burden observed in POLE-mutant tumors could be caused by elevated activity of MMR in exons with respect to their neighboring introns. MMR is the main mechanism responsible for the repair of errors generated by the polymerase during DNA replication. Colorectal tumors, as well as other cancer types, acquire a microsatellite instability (MSI) phenotype when mismatches introduced by the DNA polymerase are not corrected, owing to deficiencies in the MMR system27. MSI tumors are normally classified on the basis of the level of five biomarkers into MSI-H (high, with over 40% of the biomarkers of MSI) and MSI-L (low, with less than 40%), although the latter have recently been shown to not significantly differ from microsatellite stable (MSS) tumors in numbers of gained microsatellite alleles28. Thus, if our hypothesis were true, we would expect tumors with an impaired MMR function (MSI-H) to show lower decreased exonic mutation burden than MMR-competent tumors, such as POLE-mutant or MSS tumors.

We proceeded to compute the decreased exonic mutation burden of six colorectal and eight uterine MSI-H tumors in the TCGA cohort. We found, as predicted by our hypothesis, that MSI-H tumors exhibited a decreased exonic mutation burden of around 20% (Fig. 3a,b), close to half of the decrease observed in the MMR-proficient POLE-mutant tumors. Several reasons may explain why the decreased exonic mutation burden did not disappear completely in MSI-H tumors. On the one hand, the impairment of the MMR system may not be complete and has probably not existed throughout the entire history of the tumor. On the other hand, alternative mutational processes may also contribute to the mutation load.

Then, we computed the decreased exonic mutation burden of two POLE-mutant and two POLD-mutant glioblastomas from children with inherited biallelic mismatch-repair deficiency (bMMRD) sequenced by the International BMMRD Consortium29. These tumors have been MMR-deficient throughout their entire history, and their POLE or POLD mutations guarantee a preponderance of mismatch-caused mutations. Their decreased exonic mutation burden was indeed close to zero (Fig. 3a,b), with independence of the mutation rate of genes (Supplementary Fig. 3 and Supplementary Table 4). Mismatches in these tumors were generated at a rate comparable to that in previously analyzed POLE-mutant tumors. However, most of these mismatches remained uncorrected and turned into mutations. In other words, the mutations observed in these tumors follow the pattern of mismatch generation, corroborating our hypothesis that they appear with higher probability in exons than introns and that it is MMR, with its increased efficiency in the former, that causes the decreased exonic mutation burden.

In summary, the decreased exonic mutation burden differs between three different scenarios of MMR activity, with higher decrease in MMR-proficient tumors to none in MMR-deficient tumors. These results indicate that the increased activity of MMR in exons is the cause of the decrease in exonic mutation burden in POLE-mutant tumors (Fig. 3c).

A role for H3K36me3 in the differential activity of MMR in exons and introns

The results of the previous two sections demonstrate that the enhanced efficiency of the MMR system in exons is the cause of the observed decreased exonic mutation burden of colorectal POLE-mutant tumors. On the basis of formerly established mechanistic links between H3K36me3 and the recognition of mismatches, we then hypothesized that the decreased exonic mutation burden could be explained, at least in part, by the exonic enrichment of this histone mark in cells of the colon epithelium. If true, we should be able to observe the biggest decrease in exonic mutation burden in genes with the strongest exonic enrichment for H3K36me3 in MMR-proficient tumors. To test this, we first computed the exon-to-intron ratio of H3K36me3 read count for primary cells from the colonic mucosa (E075; Fig. 4a)23. Then, we grouped the genes into bins of increasing H3K36me3 exon-to-intron ratio, and we computed the aggregated decrease in exonic mutation burden of the genes in each bin for POLE-mutant colorectal tumors (Fig. 4a and Supplementary Figs. 4 and 5). As predicted by our hypothesis, we found a significant negative correlation between the H3K36me3 exon-to-intron ratio and the decrease in exonic mutation burden (correlation coefficient = −0.68, P = 6.7 × 10−8). A much weaker, non-significant correlation (Supplementary Fig. 5, bottom) was observed between the exon-to-intron ratio of nucleosomes and the decrease in exonic mutation burden. This suggests that the H3K36me3 histone mark and not just the presence of nucleosomes underlies the increased level of MMR in exons that results in the decreased exonic mutation burden. The correlation with other histone marks was also lower (Supplementary Table 6). On the other hand, the negative correlation between the H3K36me3 exon-to-intron ratio and the decreased exonic mutation burden was absent in MSI-H colorectal tumors (correlation coefficient = 0.12, P = 0.46) and bMMRD tumors (exon-to-intron H3K36me3 read count ratio computed from cells of the brain angular gyrus, E067; correlation coefficient = 0.07, P = 0.64) (Fig. 4b,c).

Figure 4: Decreased exonic mutation burden and H3K36me3 exon-to-intron ratio.
Figure 4

(ac) Decreased exonic mutation burden computed in POLE-mutant colorectal tumors (a), MSI-H colorectal tumors (b) and bMMRD glioblastoma tumors (c) for 50 groups of genes with increasing exon-to-intron ratios of H3K36me3 coverage (in the corresponding cell of origin; Online Methods). The trendline and its confidence interval in graphs was added using the seaborn package of Python, while the correlation coefficient and its significance were computed using an iteratively reweighted least-squares approach.

These results indicate that the exonic enrichment for H3K36me3, possibly in combination with other chromatin features, could act as a driver of the enhanced MMR activity in exons that ultimately results in the decreased exonic mutation rate of POLE-mutant tumors. When cells become MMR deficient, either during tumor evolution (MSI-H colorectal samples) or before its emergence (bMMRD glioblastomas), the link between the H3K36me3 exonic enrichment and the decreased exonic mutation burden is thus severed. This results in uncorrected mismatches accumulating and, ultimately, mutations appearing more frequently in exons.

Tumors of other cancer types also exhibit decreased exonic mutation rate

Our observations in previous sections have been limited to colorectal and uterine carcinomas, the mutational spectra of which are dominated by the interplay between the generation of mismatches in the course of DNA replication and their correction by the MMR machinery. The mutational processes of other somatic tissues are dominated by different types of damage dealt with by other DNA repair systems. Nevertheless, somatic cells in a human body, as well as the gametes, are the result of millions of cell divisions involved in organism development and tissue renewal. Therefore, MMR must have a role—although with different relative contribution—in shaping the mutational processes of all human tissues. We then asked whether tumors originated from other tissues exhibit a decreased exonic mutation rate. To do this, we first clustered the samples of eight tumor types on the basis of their mutational signatures (Supplementary Fig. 6).

For the tumors in each cluster, we next computed the decreased exonic mutation burden (Fig. 5a, top). All clusters except the one grouping POLE-mutant bMMRD glioblastomas exhibited significantly decreased exonic mutation burden. This global trend was corroborated for individual samples (Fig. 5b). Interestingly, we found that the decreased exonic mutation rate was apparent also in the somatic mutations detected in a normal skin sample (Fig. 5b, white-crossed black dot)30, indicating that this phenomenon is not a pathological effect caused by tumorigenesis. In none of the clusters could the decreased exonic mutation burden be attributed to negative selection acting on exonic mutations (Fig. 5a, middle). We also computed the exon-to-intron mutation rate ratio as explained in the first section for the chromatin features (Online Methods). In coherence with the decreased exonic mutation burden, in most clusters, exons showed fewer mutations than their intronic counterparts (Fig. 5a, bottom).

Figure 5: Decreased exonic mutation burden across cancer types.
Figure 5

(a) Top, decreased exonic mutation burden of groups of tumors clustered according to their underlying mutational processes. Dots represent clusters of tumors denoted in the bottom panel. Middle, decreased nonsynonymous mutation burden of the same groups of tumors, computed as in Figure 2d. Bottom, ratio of exonic and intronic mutation rates of the same groups of tumors. Clusters of tumors in the three panels are sorted following their decreased exonic mutation burden. (b) Decreased exonic mutation burden of individual tumors versus their mutational burden. Dots representing individual tumors are colored according to their cancer type; dots of tumors with significantly decreased exonic mutation burden are encircled by a black ring. The table to the top right presents the total number of samples, the subset of them with decreased exonic mutation burden and the subset of these with a significant decrease.

Strikingly, even melanomas and lung carcinomas, whose mutations arise mostly as a consequence of DNA damage caused by UV light or tobacco, respectively, repaired via nucleotide-excision repair (NER)31,32, exhibited a clearly decreased exonic mutation burden. Two explanations are plausible for the pervasive decreased exonic mutation rate identified. The first, as pointed out above, is that, although modest in relative terms, the MMR still has a role in DNA repair in these tumors. Nevertheless, a second intriguing possibility is that other DNA repair machineries, also acting with higher efficiency in exons, contribute to the reduced exonic mutation rate. Exploring this prospect in the case of NER in melanomas, we indeed found higher activity in exonic regions (Supplementary Fig. 7), although we cannot rule out the possibility that this is due to a higher exonic rate of UV-induced damage.

To summarize, the decrease in somatic mutation burden in exonic regions with respect to the expectations and to neighboring introns is apparent across cancer types. While we have demonstrated that MMR has a role in shaping this decrease, other DNA repair mechanisms may also contribute to it.

Discussion

In this work, we provide, to the best of our knowledge, the first demonstration that the generation of somatic mutations—in the absence of negative selection—is lower in exons than expected given their nucleotide composition. In other words, somatic cells exhibit a decreased exonic mutation burden. We have also shown that the reason is that mismatches in exonic DNA are repaired more efficiently than their intronic counterparts. These results represent an important contribution to the body of research that in recent years has revealed higher than anticipated heterogeneity in the mutation rate across different regions of the genome. Several recent seminal studies exploiting whole-genome germline and somatic mutations and the availability of nucleotide-resolution maps of DNA repair33,34 have provided glimpses at a complex relationship between chromatin conformation, basic cellular processes like gene expression, DNA replication, the binding of transcription factors, and DNA repair5,7,10,11,12,14,32,35,36,37,38,39. It is the complicated interplay between these processes that determines that mutations accumulate heterogeneously across the genome. The results we present here show that the interaction of the most basic structural feature of eukaryotic genes, namely their segmentation in exons and introns, and its correlative chromatin structural differences, results in these two regions being repaired at very different rates.

As a possible explanation of the mechanisms through which the segmented structure of genes influences the activity of the DNA repair machinery, we have shown a pervasive enrichment of the trimethylation of H3K36 in exons of normal tissues, which correlates with the decrease in exonic mutation burden in the corresponding tumors. As we show here, H3K36me3, possibly in combination with other chromatin features, may participate in shaping the observed depletion of exonic mutations. The enrichment of H3K36me3 for exonic regions, which appears in both germline and somatic tissues, has been proposed to be ultimately responsible for the correct recognition of exon–intron boundaries by the splicing machinery18,19,40. Nevertheless, H3K36me3 is bound by the MutSα protein via the PWWP domain of its MSH6 subunit15. A concomitant factor may thus have acted in the evolution of H3K36me3-enriched exons. Indeed, our results suggest that the increased recruitment of the MMR machinery to exonic regions as a result of higher levels of this histone mark would result in a reduction of the exonic mutation burden after DNA replication and, ultimately, in an increase of fitness.

Our results demonstrate that the decreased exonic mutation burden is not due to negative selection in the generation of cancer somatic mutations across all tumor types analyzed. This finding suggests that the mutational landscape of cancer-related genes is not strongly influenced by negative selection, in agreement with a recent report41. Nevertheless, we expect that, in the germ line, purifying selection has a predominant role, filtering out all variants that prevent the development of a viable individual42. Given that MMR components are highly conserved across evolution, and that the exonic enrichment for H3K36me3 and other chromatin marks has been observed across species18,43, it is reasonable to assume that the enhanced exonic MMR observed in human somatic cells is also present in germline cells and in other organisms. Therefore, intronic regions could accumulate more nucleotide changes across evolution as a result not only of intense purifying selection on exonic variants but also of this differential repair. This, in turn, would bring into question the use of rates of intronic substitution (Ki) as a proxy for neutral evolution44,45,46, with important implications for understanding of the evolution of genes. Further implications may be extracted for methods aimed at detecting cancer driver genes that model the background mutation rate of exonic elements from their surrounding areas to identify signals of positive selection in the coding region of genes. Some of these methods5,21,22, which use intronic mutations as estimators of the exonic background mutation rate, may be strongly affected by the differential generation of mutations in these regions.

In summary, we demonstrate that the differential MMR in exons and introns in somatic cells causes the former to harbor fewer mutations than expected from their nucleotide composition. This finding advances knowledge of the interplay between mutational processes and the DNA repair machinery. Moreover, our results have important implications regarding the way we study the forces that shape the development of tumors and the evolution of the genome.

Methods

Online Methods

Whole-genome expression and mutation data.

Whole-genome somatic mutations and expression data for 38 skin cutaneous melanomas (SKCM), 46 lung adenocarcinomas (LUAD), 45 lung squamous cell carcinomas (LUSC), 42 colorectal adenocarcinomas (CRC), 96 breast carcinomas (BRCA), 21 bladder carcinomas (BLCA), 47 uterine corpus squamous cell carcinomas (UCEC), 27 glioblastomas (GBM), 18 low-grade gliomas (LGG), 20 prostate adenocarcinomas (PRAD), 34 thyroid carcinomas (THCA) and 27 head and neck squamous cell carcinomas (HNSC) probed by TCGA were obtained from Fredriksson et al.20. Cohorts of tumors with fewer than 5,000 genic mutations or fewer than 1,000 exonic mutations (HNSC, GBM, KIRC, THCA, LGG and PRAD) were discarded from the analysis. The somatic mutations detected in four bMMRD pediatric glioblastomas sequenced by the International BMMRD Consortium29 were obtained through personal communication from the authors. Finally, we obtained the somatic mutations detected across the whole genome of a normal skin sample from Martincorena et al.30.

Genomic coordinates of exons and introns.

GENCODE47 v19 coordinates for 20,345 protein-coding genes were retrieved. Genes without introns, overlapping genes and cancer driver genes, according to the Cancer Gene Census and other sources25,26, were discarded, leaving a filtered set of 12,104 genes. All transcripts per gene were merged, generating meta-exon and meta-intron coordinates. Finally, 5′ and 3′ exons were removed, as well as all UTRs (except for the analysis shown in Fig. 1), thus leaving only internal exons and their flanking introns. We then identified all genic regions where mutation calling would be technically challenging because of low sequence complexity, ambiguous mappability of sequencing reads or low sequencing coverage. Regions of low complexity or low mappability were obtained from the UCSC Genome Browser (http://genome.ucsc.edu/cgi-bin/hgFileUi?db=hg19&g=wgEncodeMapability). The former included repetitive regions defined by RepeatMasker, while the latter comprised regions with low unique mappability for 36-mer sequences (CRG Alignability 36′ track, score <1). Finally, regions covered by fewer than eight reads in any of five randomly selected tumor samples of each tumor type (the requirement to make somatic calling in Fredriksson et al.20) were considered of low coverage. Regions of any of these three types were removed from introns and exons.

Clusters of tumors with different somatic mutational processes.

To group the tumors of each cancer type in the cohort according to their underlying mutational processes, we first built a matrix of the frequencies of the 96 trinucleotide changes across tumors, as in a previous work12. We carried out hierarchical clustering (using a Euclidean distance and average method to compute the similarity between clusters) of this matrix. We then manually separated the clusters of tumors and identified their underlying mutational processes through visual comparison with previously obtained32 mutational signatures across cancer types. Clusters of tumors with fewer than 5,000 genic mutations or fewer than 1,000 exonic mutations were discarded for downstream analyses.

Chromatin features.

We downloaded peak (narrow) coordinates and genome-wide read coverage for the 32 chromatin features presented in Supplementary Table 1 across 127 cell lines and primary cell types from Roadmap Epigenomics23 and the nucleosome density from ENCODE24. Peaks and reads (see below) obtained from http://egg2.wustl.edu/roadmap/data/byFileType/peaks/consolidated/narrowPeak and http://egg2.wustl.edu/roadmap/data/byFileType/alignments/consolidated, respectively, for each feature were mapped to intronic and exonic regions of genes. The primary cell types closest to colorectal tumors and glioblastomas in Roadmap Epigenomics were selected to represent the exon–intron distribution of chromatin features. Genome-wide nucleosome positioning signals (density graphs) for the ENCODE cell line GM12878 (lymphoblastoid cell line) were obtained via the UCSC Genome Browser (http://hgdownload.cse.ucsc.edu/). Further, by using the bwtool find program (with parameters local-extrema -maxima -min-sep = 150), nucleosome peak regions were identified across the genome, and the 146-bp regions flanking each peak (73 bp per side) were considered as regions covered by a nucleosome.

We numbered the exons and introns in each gene according to their positions with respect to the transcriptional start site (TSS). Exons and introns that occupied different positions in different transcripts and those in the lower quartile of length were discarded. We then stacked all exons and all introns separately and computed the aggregated coverage (fraction of bases covered by peaks for each mark) at the center of the stack corresponding to the number of bases of the shortest exon or intron remaining after filtering. Finally, the difference between exonic and intronic coverage was computed via the two-tailed Mann–Whitney P value for the comparison of the two distributions.

Classification of colorectal tumors according to MMR level.

Colorectal samples were separated into four subtypes on the basis of MMR levels. MSI-H (n = 6), MSI-L (n = 4) and MSS (n = 26) groups were defined on the basis of clinical information from TCGA (https://portal.gdc.cancer.gov/query). The POLE-mutant group (n = 6) was defined by identifying samples with missense mutations of the POLE (DNA polymerase ɛ) gene.

Exon-centered mutational analyses.

We stacked 2,001-nt sequences centered on the middle position of internal exons. In this analysis, we did not exclude regions that overlapped any of the three types of technically challenging regions mentioned above. Thus, we obtained a stack of 95,164 sequences centered on exons. We then counted the observed and expected (distributed across each sequence of the 2,001-nt window following the mutational probability for each nucleotide, as explained below for individual genes) mutations associated with each nucleotide of these sequences. With these counts across the selected windows, we produced exon-centered plots as shown in Figures 2a and 3b.

Computing decrease in exonic mutation burden.

We first computed the relative frequencies of the 192 possible trinucleotide changes, f(AiXjCkAiXlCk), across each cluster of tumors as

where N(AiXjCkAiXlCk) was the number of such changes among all mutations observed in the tumors and T was the total number of substitutions observed across tumors. Then, we made f relative to the abundance of each trinucleotide in the genome, G(AiXjCk).

Next, for each genic site, we summed the relative frequency of its three possible changes given its 5′ and 3′ flanking bases.

We rescaled the relative frequency of change for each site to 1 by multiplying each frequency by factor k.

The rescaled frequency (Rescf) of each nucleotide in the gene is proportional to the conditional probability that the reference nucleotide changes to the alternative given its 5′ and 3′ nucleotides. Finally, for each independent gene, we redistributed all observed mutations (Nmuts) across exonic and intronic sites following these summed rescaled frequencies of each site to be mutated.

Note that this redistribution process could be performed equivalently for the mutations observed in one tumor (for single-tumor analysis; Fig. 5b) or across a group of tumors (for group or cluster analyses; Figs. 2,3,4 and 5a). The process yielded the number of expected exonic (EExonic) and intronic (EIntronic) mutations in the gene. (We employed a second method to compute the expected number of exonic mutations based on the average of 1,000 random permutations of the observed mutations in each gene following the probability of each site to acquire a mutation (Supplementary Table 3).) Summing the observed and expected exonic mutations over all genes, we computed the difference between the observed and expected numbers of exonic mutations, which we refer to as the decrease in exonic mutation burden (as in most tumors there was a negative difference). Throughout the paper, we express this decrease as the percentage of the total number of observed exonic mutations.

To compute the significance of this decrease, we employed two tests: (i) a G test of independence comparing the numbers of observed and expected mutations in exons and introns, under the null hypothesis that the observed and theoretical distributions of the variables are equal, and (ii) for the expected number of exonic mutations computed using the permutations approach, we computed an empirical P value as the fraction of the iterations with fewer expected than observed exonic mutations.

Test for negative selection on exonic mutations.

The consequence type of all observed exonic mutations was obtained using the Ensembl Variant Effect Predictor48 (VEP; v.70). We subsequently separated exonic mutations into two groups: those with synonymous consequence and those with a consequence ranking higher than synonymous in the Ensembl Variation hierarchy (http://www.ensembl.org/info/genome/variation/predicted_data.html), which were collectively deemed nonsynonymous. All possible nucleotide changes in a gene were then divided into three categories: (i) synonymous; (ii) nonsynonymous (with the consequences defined above); and (iii) intronic. We redistributed the mutations observed in each gene across these three types of sites following the probability of occurrence of each change computed as explained above. Through the difference between observed and expected synonymous and nonsynonymous mutations, we were able to compute the decrease in the burden of both types of mutations (expressed as the percentage of the expected number, as explained above for all exonic mutations). Finally, a G test of independence was used on the null hypothesis that fewer nonsynonymous mutations should be observed than expected.

We also redistributed only the exonic mutations across synonymous and nonsynonymous sites according to the probability of change for each type of site. In this case, we used the G test of independence on the null hypothesis that the number of expected nonsynonymous mutations was not smaller than the observed number.

Stratification of genes by mutation rate and several covariates.

The mutation rate of each gene was computed as the quotient between the number of observed mutations and the number of bases in the gene. Genes were subsequently grouped into ten bins according to their mutation rate.

We computed the 75th percentile of the expression of each gene across the tumors in each cohort. Genes with a 75th percentile of expression equal to 0 were considered to be non-expressed and were grouped together. All other genes were sorted on the basis of their previously computed expression percentile and divided into nine bins of equal size. Non-expressed genes were subsequently added as a tenth bin.

Replication time data across the human genome measured in lymphoblastoid cell lines were obtained from Koren et al.6. Using these data, the mean replication time per gene was computed. Next, genes were sorted on the basis of this value and divided into ten groups of equal size.

Finally, we also grouped the genes into ten bins according to H3K36me3 peak coverage.

Relationship between decreased exonic mutation rate and exonic enrichment of nucleosomes and histone marks.

For each gene, we computed the read-count-based exonic enrichment for any chromatin feature as the ratio between the exonic and intronic read counts (total number of bases covered by reads of the chromatin feature). (This read-count-based exonic enrichment was used to compute the correlations shown in Fig. 4 and Supplementary Fig. 4.) We computed the peak-based exonic enrichment of any chromatin feature as the ratio of exonic and intronic bases covered by peaks of the feature (to compute the correlation shown in Supplementary Fig. 5). The exonic and intronic numbers of bases covered by reads or peaks of the chromatin feature for colorectal and bMMRD glioblastoma tumors were computed from colonic mucosa (E075) and brain angular gyrus (E067) cells, respectively, both obtained from Roadmap Epigenomics. (In the case of nucleosomes, peaks were obtained from occupancy values as explained above.) Genes were grouped into 10, 25 or 50 bins according to their exonic H3K36me3 enrichment, and the aggregated decrease in the exonic mutation rate of the genes in each bin was computed as explained above for colorectal POLE-mutant, MSI-H and bMMRD tumors. We then computed the correlation between the median exonic chromatin feature enrichment and the decreased exonic mutation rate across the bins. The trendline and its confidence intervals were added to each plot using the bootstrapping functions of the Python seaborn package, which confers equivalent weights in the regression to all points. To guarantee that the trend was not the result of a few outliers, the correlation coefficient and its significance were computed using an iteratively reweighted least-squares approach, letting the variance in exonic H3K36me3 enrichment of the bins influence the weight of each point.

Exon-to-intron mutation rate ratio.

As described above, we stacked all exon-centered and intron-centered sequences. Then, we averaged the total number of mutations observed at each of the 41 central positions of each stack. The selection of 41 central positions guaranteed both a vast majority of exonic sequences contributing mutations and enough mutations for calculation across all clusters at exon-centered stacks. The exon-centered and intron-centered mutation burden averages were then divided by the number of sequences included in each stack to make them comparable. Finally, we computed the exon-to-intron mutation rate ratio as the quotient between the corrected exon-centered and intron-centered mutation burden averages.

Computing the activity of NER from XR–seq data.

Genome-wide maps of NER for two UV-induced photoproducts, namely cyclobutane pyrimidine dimers (CPDs) and pyrimidine–pyrimidone (6,4) photoproducts (PP64s), in irradiated skin fibroblast cell lines were obtained from Hu et al.34. This data set comprises NER maps for the following three cell lines: (i) wild-type NHF1 skin fibroblasts, which have active global and transcription-coupled repair mechanisms; (ii) XP-C mutants, which are deficient in the global repair mechanism; and (iii) CS-B mutants, which are deficient in transcription-coupled repair. For each of these cell lines, we extracted the sequencing reads, processed and mapped to the human genome, following the steps mentioned in Hu et al. Further, we selected the mapped reads that were 26 nt in size, which is the typical size of NER-excised oligomers, and classified the reads on the basis of the presence of dipyrimidines (TT, CT, TC and CC) at positions 19–20 or 20–21 of the reads. In addition, we recorded the mapped genomic locations of the nucleotides in positions 19–20 or 20–21 of the reads. This way, we could predict the damage site according to the excised fragments. We mapped this information to the XR–seq exon-centered plot together with the frequencies of the dipyrimidines observed in each column (Supplementary Fig. 7).

Statistics.

We used the G test described above to compute P values to test the significance of decreased exonic mutation burden for groups of genes or all genes in a tumor or across groups or clusters of tumors. (All P values computed for all comparisons are provided in Supplementary Table 3, which also includes P values computed using a permutation-based test also described above.) When appropriate, P values computed with this test were corrected using the Benjamini–Hochberg approach. In Figure 1, we used the two-tailed Mann–Whitney test to compare the exonic and intronic distributions of chromatin features (and corrected them when appropriate). Above, we describe the approach employed to compute the correlation coefficient (and its associated P value) for the regression lines shown in Figure 4 and Supplementary Figures 4 and 5.

Code availability.

All code needed to reproduce the analyses described in the paper are available at the Bitbucket repository (https://bitbucket.org/bbglab/intron_exon_mutrate).

Data availability.

Preprocessed data needed to reproduce all analyses described here are provided together with the code at https://bitbucket.org/bbglab/intron_exon_mutrate. A Life Sciences Reporting Summary is available.

Additional information

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    et al. Initial genome sequencing and analysis of multiple myeloma. Nature 471, 467–472 (2011).

  2. 2.

    et al. Exome and whole-genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity. Nat. Genet. 45, 478–486 (2013).

  3. 3.

    et al. A comprehensive catalogue of somatic mutations from a human cancer genome. Nature 463, 191–196 (2010).

  4. 4.

    et al. A dual model for prioritizing cancer mutations in the non-coding genome based on germline and somatic events. PLoS Comput. Biol. 11, e1004583 (2015).

  5. 5.

    et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214–218 (2013).

  6. 6.

    et al. Differential relationship of DNA replication timing to different forms of human mutation and variation. Am. J. Hum. Genet. 91, 1033–1040 (2012).

  7. 7.

    et al. Human mutation rate associated with DNA replication timing. Nat. Genet. 41, 393–395 (2009).

  8. 8.

    & Variation in the mutation rate across mammalian genomes. Nat. Rev. Genet. 12, 756–766 (2011).

  9. 9.

    et al. Cell-of-origin chromatin organization shapes the mutational landscape of cancer. Nature 518, 360–364 (2015).

  10. 10.

    & Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature 488, 504–507 (2012).

  11. 11.

    et al. The topography of mutational processes in breast cancer genomes. Nat. Commun. 7, 11383 (2016).

  12. 12.

    et al. Differential DNA repair underlies mutation hotspots at active promoters in cancer genomes. Nature 532, 259–263 (2016).

  13. 13.

    et al. Reduced local mutation density in regulatory DNA of cancer genomes is linked to DNA repair. Nat. Biotechnol. 32, 71–75 (2014).

  14. 14.

    , , , & Nucleotide excision repair is impaired by binding of transcription factors to DNA. Nature 532, 264–267 (2016).

  15. 15.

    et al. The histone mark H3K36me3 regulates human DNA mismatch repair through its interaction with MutSα. Cell 153, 590–600 (2013).

  16. 16.

    & Evidence that the histone methyltransferase Dot1 mediates global genomic repair by methylating histone H3 on lysine 79. J. Biol. Chem. 286, 17530–17535 (2011).

  17. 17.

    , & Chromatin modifications and DNA repair: beyond double-strand breaks. Front. Genet. 5, 296 (2014).

  18. 18.

    , & Chromatin organization marks exon–intron structure. Nat. Struct. Mol. Biol. 16, 990–995 (2009).

  19. 19.

    , , & Reciprocal intronic and exonic histone modification regions in humans. Nat. Struct. Mol. Biol. 17, 1495–1499 (2010).

  20. 20.

    , , & Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types. Nat. Genet. 46, 1258–1263 (2014).

  21. 21.

    et al. A landscape of driver mutations in melanoma. Cell 150, 251–263 (2012).

  22. 22.

    et al. Discovery of cancer driver long noncoding RNAs across 1112 tumour genomes: new candidates and distinguishing features. Sci. Rep. 7, 41544 (2017).

  23. 23.

    et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  24. 24.

    ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  25. 25.

    et al. A census of human cancer genes. Nat. Rev. Cancer 4, 177–183 (2004).

  26. 26.

    et al. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell 27, 382–396 (2015).

  27. 27.

    Mechanisms and functions of DNA mismatch repair. Cell Res. 18, 85–98 (2008).

  28. 28.

    , , & Classification and characterization of microsatellite instability across 18 cancer types. Nat. Med. 22, 1342–1350 (2016).

  29. 29.

    et al. Combined hereditary and somatic mutations of replication error repair genes result in rapid onset of ultra-hypermutated cancers. Nat. Genet. 47, 257–262 (2015).

  30. 30.

    et al. High burden and pervasive positive selection of somatic mutations in normal human skin. Science 348, 880–886 (2015).

  31. 31.

    , , & Understanding nucleotide excision repair and its roles in cancer and ageing. Nat. Rev. Mol. Cell Biol. 15, 465–481 (2014).

  32. 32.

    et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

  33. 33.

    , , & Genome-wide kinetics of DNA excision repair in relation to chromatin state and mutagenesis. Proc. Natl. Acad. Sci. USA 113, E2124–E2133 (2016).

  34. 34.

    , , , & Genome-wide analysis of human global and transcription-coupled excision repair of UV damage at single-nucleotide resolution. Genes Dev. 29, 948–960 (2015).

  35. 35.

    et al. Mutational strand asymmetries in cancer genomes reveal mechanisms of DNA damage and repair. Cell 164, 538–549 (2016).

  36. 36.

    , , & Impact of chromatin structure on sequence variability in the human genome. Nat. Struct. Mol. Biol. 18, 510–515 (2011).

  37. 37.

    et al. Genome-wide patterns and properties of de novo mutations in humans. Nat. Genet. 47, 822–826 (2015).

  38. 38.

    & Differential DNA mismatch repair underlies mutation rate variation across the human genome. Nature 521, 81–84 (2015).

  39. 39.

    & Clustered mutation signatures reveal that error-prone DNA repair targets mutations to active genes. Cell 170, 534–547 (2017).

  40. 40.

    , , , & Pre-mRNA splicing is a determinant of histone H3K36 methylation. Proc. Natl. Acad. Sci. USA 108, 13564–13569 (2011).

  41. 41.

    et al. Universal patterns of selection in cancer and somatic tissues. Cell (2017).

  42. 42.

    Rate, molecular spectrum, and consequences of human mutation. Proc. Natl. Acad. Sci. USA 107, 961–968 (2010).

  43. 43.

    et al. Differential chromatin marking of introns and expressed exons by H3K36me3. Nat. Genet. 41, 376–381 (2009).

  44. 44.

    , & Hearing silence: non-neutral evolution at synonymous sites in mammals. Nat. Rev. Genet. 7, 98–108 (2006).

  45. 45.

    Chimpanzee Sequencing and Analysis Consortium. Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437, 69–87 (2005).

  46. 46.

    & Estimating the neutral rate of nucleotide substitution using introns. Mol. Biol. Evol. 24, 522–531 (2007).

  47. 47.

    et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

  48. 48.

    et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).

Download references

Acknowledgements

We acknowledge funding from the Spanish Ministry of Economy and Competitiveness (SAF2015-66084-R, MINECO/FEDER, UE), La Fundació la Marató de TV3, EU H2020 Programme 2014-2020 under grant agreements 634143 (MedBioinformatics) and by the European Research Council (Consolidator Grant 682398). IRB Barcelona is the recipient of a Severo Ochoa Centre of Excellence Award from the Spanish Ministry of Economy and Competitiveness (MINECO; Government of Spain) and is supported by CERCA (Generalitat de Catalunya). R.S. is supported by an EMBO Long-Term Fellowship (ALTF 568-2014) cofunded by the European Commission (EMBOCOFUND2012, GA-2012-600394) with support from Marie Curie Actions. A.G.-P. is supported by a Ramón y Cajal contract from the Spanish Ministry of Economy and Competitiveness (RYC-2013-14554). We acknowledge the contribution of I. Reyes-Salazar to refactoring and cleaning all code produced in the study for publication. We are grateful to B. Campbell and U. Tabori for help in obtaining the mutation calls for bMMRD samples sequenced by the International BMMRD Consortium. The results published here are in part based upon data generated by the TCGA Research Network (http://cancergenome.nih.gov/).

Author information

Author notes

    • Joan Frigola
    •  & Radhakrishnan Sabarinathan

    These authors contributed equally to this work.

Affiliations

  1. Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology (BIST), Barcelona, Spain.

    • Joan Frigola
    • , Radhakrishnan Sabarinathan
    • , Loris Mularoni
    • , Ferran Muiños
    • , Abel Gonzalez-Perez
    •  & Núria López-Bigas
  2. Research Program on Biomedical Informatics, Universitat Pompeu Fabra, Barcelona, Spain.

    • Joan Frigola
    • , Radhakrishnan Sabarinathan
    • , Loris Mularoni
    • , Ferran Muiños
    • , Abel Gonzalez-Perez
    •  & Núria López-Bigas
  3. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.

    • Núria López-Bigas

Authors

  1. Search for Joan Frigola in:

  2. Search for Radhakrishnan Sabarinathan in:

  3. Search for Loris Mularoni in:

  4. Search for Ferran Muiños in:

  5. Search for Abel Gonzalez-Perez in:

  6. Search for Núria López-Bigas in:

Contributions

J.F. and R.S. participated in the design and execution of analyses, produced the figures, participated in the interpretation of results and edited the manuscript. L.M. developed computational code employed in the analyses. F.M. developed the statistical framework to compute the significance of the decreased exonic mutation burden and its correlation with chromatin features. A.G.-P. participated in the design of analyses, the interpretation of results, the oversight of analyses, and drafted and edited the manuscript. N.L.-B. conceived the study, participated in the design of analyses, oversaw the study and the interpretation of results, and drafted and edited the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Núria López-Bigas.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–7

  2. 2.

    Life Sciences Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Coverage of several chromatin features of exons and introns across the structure of genes.

  2. 2.

    Supplementary Table 2

    Difference in exonic and intronic coverage (Mann–Whitney P value) and mean exonic coverage across the genic structure.

  3. 3.

    Supplementary Table 3

    Decreased exonic mutation rate across clusters of tumors.

  4. 4.

    Supplementary Table 4

    Decreased exonic mutation burden across groups of genes with different mutation rate covariate values.

  5. 5.

    Supplementary Table 5

    Decreased exonic mutation burden for individual tumors.

  6. 6.

    Supplementary Table 6

    Correlation of decreased exonic mutation burden and the exonic enrichment for several histone marks.