Modeling double strand break susceptibility to interrogate structural variation in cancer

Abstract Background Structural variants (SVs) are known to play important roles in a variety of cancers, but their origins and functional consequences are still poorly understood. Many SVs are thought to emerge from errors in the repair processes following DNA double strand breaks (DSBs). Results We used experimentally quantified DSB frequencies in cell lines with matched chromatin and sequence features to derive the first quantitative genome-wide models of DSB susceptibility. These models are accurate and provide novel insights into the mutational mechanisms generating DSBs. Models trained in one cell type can be successfully applied to others, but a substantial proportion of DSBs appear to reflect cell type-specific processes. Using model predictions as a proxy for susceptibility to DSBs in tumors, many SV-enriched regions appear to be poorly explained by selectively neutral mutational bias alone. A substantial number of these regions show unexpectedly high SV breakpoint frequencies given their predicted susceptibility to mutation and are therefore credible targets of positive selection in tumors. These putatively positively selected SV hotspots are enriched for genes previously shown to be oncogenic. In contrast, several hundred regions across the genome show unexpectedly low levels of SVs, given their relatively high susceptibility to mutation. These novel coldspot regions appear to be subject to purifying selection in tumors and are enriched for active promoters and enhancers. Conclusions We conclude that models of DSB susceptibility offer a rigorous approach to the inference of SVs putatively subject to selection in tumors

Breaking point: the genesis and impact of structural variation in tumours

Somatic structural variants undoubtedly play important roles in driving tumourigenesis. This is evident despite the substantial technical challenges that remain in accurately detecting structural variants and their breakpoints in tumours and in spite of our incomplete understanding of the impact of structural variants on cellular function. Developments in these areas of research contribute to the ongoing discovery of structural variation with a clear impact on the evolution of the tumour and on the clinical importance to the patient. Recent large whole genome sequencing studies have reinforced our impression of each tumour as a unique combination of mutations but paradoxically have also discovered similar genome-wide patterns of single-nucleotide and structural variation between tumours. Statistical methods have been developed to deconvolute mutation patterns, or signatures, that recur across samples, providing information about the mutagens and repair processes that may be active in a given tumour. These signatures can guide treatment by, for example, highlighting vulnerabilities in a particular tumour to a particular chemotherapy. Thus, although the complete reconstruction of the full evolutionary trajectory of a tumour genome remains currently out of reach, valuable data are already emerging to improve the treatment of cancer

Simultaneous DNA and RNA Mapping of Somatic Mitochondrial Mutations across Diverse Human Cancers

<div><p>Somatic mutations in the nuclear genome are required for tumor formation, but the functional consequences of somatic mitochondrial DNA (mtDNA) mutations are less understood. Here we identify somatic mtDNA mutations across 527 tumors and 14 cancer types, using an approach that takes advantage of evidence from both genomic and transcriptomic sequencing. We find that there is selective pressure against deleterious coding mutations, supporting that functional mitochondria are required in tumor cells, and also observe a strong mutational strand bias, compatible with endogenous replication-coupled errors as the major source of mutations. Interestingly, while allelic ratios in general were consistent in RNA compared to DNA, some mutations in tRNAs displayed strong allelic imbalances caused by accumulation of unprocessed tRNA precursors. The effect was explained by altered secondary structure, demonstrating that correct tRNA folding is a major determinant for processing of polycistronic mitochondrial transcripts. Additionally, the data suggest that tRNA clusters are preferably processed in the 3′ to 5′ direction. Our study gives insights into mtDNA function in cancer and answers questions regarding mitochondrial tRNA biogenesis that are difficult to address in controlled experimental systems.</p></div

Frameshift indels show reduced heteroplasmy levels indicative of negative selection.

<p>Cumulative distributions of mutant allele frequencies (heteroplasmy levels) for different mutational categories. Frameshift indels showed significantly reduced levels of heteroplasmy, and never reach above 85%. A similar trend, although non-significant, was seen for missense (stop-inducing) mutations. In contrast, D-loop mutations, which in general should be more tolerable, showed significantly elevated levels. <i>P</i>-values were calculated using the two-sample Kolmogorov-Smirnov test, comparing the tumor set of interest to remaining samples. Missense PP2 refers to a subset of missense mutations predicted to be “probably damaging” by PolyPhen-2 [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005333#pgen.1005333.ref029" target="_blank">29</a>].</p

Proposed model of mt-tRNA processing in light of observed RNA species in human cancers.

<p>The normal processing cascade is depicted (left-hand side). The data in <b><a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005333#pgen.1005333.g004" target="_blank">Fig 4</a></b> suggests that proper folding of the pre-tRNAs is important for RNAse P/Z processing. Structure-disrupting mutations in <i>tRNA</i>^<i>Ile</i> allow for normal processing of the <i>tRNA</i>^<i>Met</i>, but leave polyA+ processing-intermediate products with mutation-bearing <i>tRNA</i>^<i>Ile</i> and the antisense-<i>tRNA</i>^<i>Gln</i> sequences on the 3’ end of <i>ND1</i> (middle). This implies that the antisense <i>tRNA</i>^<i>Gln</i> is not a substrate for tRNA processing endonucleases. Structure-disrupting mutations in the <i>tRNA</i>^<i>Met</i> gene lead to the accumulation of intermediates in which the antisense <i>tRNA</i>^<i>Gln</i> and mutation-bearing tRNA^<i>Met</i> sequences remain attached to the 5’ end of the <i>ND2</i> gene (right-hand side). Processing of the wild type <i>tRNA</i>^<i>Ile</i> still occurs but at reduced efficacy, consistent with a model whereby processing of a multi-tRNA cluster occurs preferably in the 3’ to 5’ direction.</p

Comparison of allelic ratios in DNA and RNA reveals allelic imbalances consistent with impaired tRNA processing.

<p>(<b>a</b>) Scatter plot of allele frequencies (heteroplasmy levels) in DNA vs. RNA for all 616 mutations (<i>r</i> = 0.91). 15 mutations with marked accumulation in polyA+ RNA relative to DNA (frequency difference > 0.3) are indicated in red. 12 of these 15 mutations were in tRNAs regions (numbered 1–12 in superscript), indicative of impaired processing of the polyA+ precursor RNA to a mature polyA- tRNA. (<b>b</b>) tRNA mutations accumulated in polyA+ RNA (red in panel a) showed elevated predicted RNA structural impact, determined using the RNAsnp tool [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005333#pgen.1005333.ref035" target="_blank">35</a>,<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005333#pgen.1005333.ref036" target="_blank">36</a>], compared to other tRNA mutations (<i>P</i> = 0.038, Wilcoxon rank sum test). The comparison was based on 9 and 9 inhibiting/non-inhibiting mutations (cases where the wild-type sequence failed to fold into a tRNA-like structure were excluded). The dotted line indicates an RNAsnp <i>P</i>-value (structural impact score) of 0.2 (<b>c</b>) Example RNAsnp result for a U to C mutation in position 37 of the mitochondrial isoleucine tRNA. The dot-plot shows the ensemble base-pair probabilities of the wild type (upper triangle) and mutant (lower triangle) sequences, with the altered local region indicated in gray. Wild type and mutant minimum free energy structures are shown (altered local region in color). (<b>d</b>) Normalized RNA read coverage, showing relative (per-tumor normalized) polyA+ expression levels across the mitochondrial genome in mutated tRNA regions for the 12 tRNA mutations indicated in panel a (each identifiable by a superscript number). Mutated cases (yellow) are compared to controls (green, median of all non-mutated cases). Gene strand orientation is indicated by arrows (right-facing: L-strand). Mutated positions are indicated by triangles. Samples IDs are shown in gray. tRNA genes are referred to as “TX”, where X = the single letter amino acid code.</p

Overview of mtDNA mutational signatures.

<p>Substitution patterns (mutational signatures) are shown for each cancer type, which each substitution class labeled by the pyrimidine of the Watson-Crick pair [<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1005333#pgen.1005333.ref031" target="_blank">31</a>] but with sense and antisense patterns shown separately to reveal strand biases. Bars indicate enrichment relative to the expected frequency (observed/expected ratio) for all possible substitutions, taking into account the nucleotide composition of mtDNA and assuming equal probability for all three substitutions. L, light strand; H, heavy strand.</p

Mutational density across the mitochondrial genome.

<p>Inward-facing thick bars indicate the number of mutations per 331 nt segment (50 segments), with substitutions and indels shown in gray and orange, respectively. 616 somatic mutations, identified across 527 tumors, are shown. Outward-facing bars thin bars indicate individual recurrently mutated positions (> = 2 tumors).</p