Global analysis of somatic structural genomic alterations and their impact on gene expression in diverse human cancers

Alaei-Mahabadi, Babak; Bhadury, Joydeep; Karlsson, Jan Ch; Nilsson, Jonas A.; Larsson, Erik

doi:10.1073/pnas.1606220113

Cited by 54 publications

(57 citation statements)

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“…SVs may exert a heavy influence on the expression of genes through various mechanisms, including CNAs, direct disruption of the gene by breakpoint falling within the coding region, formation of fusion transcripts involving two genes, disruption or repositioning of cis -regulatory elements near genes, formation of cryptic promoters, placement of genes into anomalous chromatin environments, and disruption of topologically associated domain (TAD) organization affecting long-range enhancer-promoter interactions (Dekker and Heard, 2015; Harewood and Fraser, 2014). Whole-genome sequencing (WGS) enables the accurate detection of somatic rearrangements in cancer; somatic SVs were characterized in several previous studies, for both individual cancer types (Bass et al, 2011; Berger et al, 2011; Campbell et al, 2010; Davis et al, 2014; Stephens et al, 2011) and pan-cancer studies (Alaei-Mahabadi et al, 2016; Drier et al, 2013; Hillmer et al, 2011; Yang et al, 2013). These studies typically involved a relatively modest number of cancer cases.…”

Section: Introductionmentioning

confidence: 99%

“…An analogous list of candidate driver genes resulting from rearrangement could provide further insight into cancer-related processes and pathways and could be relevant from the standpoint of personalized or precision medicine approaches, which typically focus primarily on point mutations within the coding region of genes. Previous studies have, for example, defined broad patterns of association involving genomic rearrangements and transcription collectively involving large groups of genes (Alaei-Mahabadi et al, 2016; Drier et al, 2013), defined the landscape of gene fusions in cancer (Hu et al, 2017; Stransky et al, 2014; Yoshihara et al, 2015), and documented cases of individual genes altered by the rearrangement of cis -regulatory elements within specific cancer types (Davis et al, 2014; Gröschel et al, 2014; Northcott et al, 2014; Peifer et al, 2015). However, a pan-cancer, gene-by-gene assessment of which ones appear recurrently deregulated by genomic rearrangement, using sizable sample numbers for greater power (Lawrence et al, 2014), remains to be carried out.…”

Section: Introductionmentioning

confidence: 99%

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A Pan-Cancer Compendium of Genes Deregulated by Somatic Genomic Rearrangement across More Than 1,400 Cases

et al. 2018

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SUMMARY A systematic cataloging of genes affected by genomic rearrangement, using multiple patient co-horts and cancer types, can provide insight into cancer-relevant alterations outside of exomes. By integrative analysis of whole-genome sequencing (predominantly low pass) and gene expression data from 1,448 cancers involving 18 histopathological types in The Cancer Genome Atlas, we identified hundreds of genes for which the nearby presence (within 100 kb) of a somatic structural variant (SV) breakpoint is associated with altered expression. While genomic rearrangements are associated with widespread copy-number alteration (CNA) patterns, approximately 1,100 genes—including overexpressed cancer driver genes (e.g., TERT, ERBB2, CDK12, CDK4) and underexpressed tumor suppressors (e.g., TP53, RB1, PTEN, STK11)—show SV-associated deregulation independent of CNA. SVs associated with the disruption of topologically associated domains, enhancer hijacking, or fusion transcripts are implicated in gene upregulation. For cancer-relevant pathways, SVs considerably expand our understanding of how genes are affected beyond point mutation or CNA.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Pan-Cancer Compendium of Genes Deregulated by Somatic Genomic Rearrangement across More Than 1,400 Cases

et al. 2018

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“…When compared with single nucleotide polymorphisms (SNPs), the larger size of SVs makes them more likely to alter genomic structures and have functional consequences. In many diseases, such as Alzheimer's disease and many types of cancer, SVs have been found as important contributors [1,2,3,4]. To identify SV patterns associated with diseases, complete discovery and accurate genotyping of SVs is the first and foremost step.…”

Section: Introductionmentioning

confidence: 99%

Enhancing breakpoint resolution with deep segmentation model: a general refinement method for read-depth based structural variant callers

Zhang

Imoto

Miyano

et al. 2019

Preprint

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Motivation: Structure variants (SVs) are complex genomic alterations with affected genome sizes larger than 50 nucleotides. They have been found in both healthy individuals and patients, which contribute to phenotypic traits and are highly correlated with diseases, such as cancer. With the advance of sequencing technologies, more precise profiling of SVs becomes available. It is still a challenging task to determine SVs in nucleotide-resolution with satisfying both high accuracy and coverage for a single sample. Result: In this paper, we proposed a novel nucleotide-resolution SV detection method for determining SV fragment inside of a bin with base-pair read-depth (RD) information. Distinguished from traditional RD based SV detectors, which usually require grouping and smoothing RD signals at the scale of a bin, we use a U-net model to directly process base-pair RD signals inside of a bin and learn feature representations for intra-bin SV segmentation. The proposed method can be used solely for searching target SVs with RD signature or integrated with previous bin-based SV detection methods to exceed the bin-size limitation. We did a systematic evaluation of the method using WGS data from 1000 Genomes Project. In both single-sample and cross-sample experiments, the U-net model achieves significantly better performances on detecting intra-bin SV fragments when compared with convolutional neural networks (CNN). Furthermore, the model shows a good ability to detect specific SV subtypes, such as ALU deletion.

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“…Structural variation (SV) in tumour genomes is known to play important roles in disease progression and may be critical in driving the development of certain cancer types (1–3). However, challenges remain not only in ascertaining accurate SV calls, as evidenced by the compendium of SV calling algorithms used in many projects (4–6), but also in predicting their functional impact. Some SVs have apparently direct consequences; for example, amplification of oncogenes leading to overexpression, deletion of tumor suppressors leading to dysfunction, and translocations generating oncogenic fusion proteins (4).…”

Section: Introductionmentioning

confidence: 99%

Modelling double strand break susceptibility to interrogate structural variation in cancer

Ballinger

Bouwman

Mirzazadeh

et al. 2018

Preprint

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26Background: Structural variants (SVs) are known to play important roles in a 27 variety of cancers, but their origins and functional consequences are still poorly 28 understood. Many SVs are thought to emerge via errors in the repair processes 29 following DNA double strand breaks (DSBs) and previous studies have 30 experimentally measured DSB frequencies across the genome in cell lines. 31Results: Using these data we derive the first quantitative genome-wide models 32 of DSB susceptibility, based upon underlying chromatin and sequence features. 33These models are accurate and provide novel insights into the mutational 34 mechanisms generating DSBs. Models trained in one cell type can be successfully 35 applied to others, but a substantial proportion of DSBs appear to reflect cell type 36 specific processes. Using model predictions as a proxy for susceptibility to DSBs 37 in tumours, many SV enriched regions appear to be poorly explained by 38 selectively neutral mutational bias alone. A substantial number of these regions 39show unexpectedly high SV breakpoint frequencies given their predicted 40 susceptibility to mutation, and are therefore credible targets of positive selection 41 in tumours. These putatively positively selected SV hotspots are enriched for 42 3 genes previously shown to be oncogenic. In contrast, several hundred regions 43 across the genome show unexpectedly low levels of SVs, given their relatively 44 high susceptibility to mutation. These novel 'coldspot' regions appear to be 45 subject to purifying selection in tumours and are enriched for active promoters 46 and enhancers. 47 Conclusions:We conclude that models of DSB susceptibility offer a rigorous 48 approach to the inference of SVs putatively subject to selection in tumours. 49 50 Keywords: Double strand break, cancer, structural variaton, chromatin, 51 modelling 52 53 Background 54 55 Structural variation (SV) in tumour genomes is known to play important roles in 56 disease progression and may be critical in driving the development of certain 57cancer types (1-3). However, challenges remain not only in ascertaining accurate 58 SV calls, as evidenced by the compendium of SV calling algorithms used in many 59 projects (4-6), but also in predicting their functional impact. Some SVs have 60 apparently direct consequences; for example, amplification of oncogenes leading 61 to overexpression, deletion of tumor suppressors leading to dysfunction, and 62 translocations generating oncogenic fusion proteins (4). Reportedly indirect 63 consequences of SVs include changes in enhancer targeting, affecting the 64 expression of nearby genes, or "enhancer hijacking" (7). However, it remains 65 Project (30) and others, allowing well-matched models to be constructed for all 131 datasets. We demonstrate that these models provide accurate estimates for the 132 expected rate of DSBs in a given region and can be cross applied between DSB 133 datasets. In addition the models can be used to explore tumour SV breakpoint 134 data, to nominate novel regions p...

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Global analysis of somatic structural genomic alterations and their impact on gene expression in diverse human cancers

Cited by 54 publications

References 65 publications

A Pan-Cancer Compendium of Genes Deregulated by Somatic Genomic Rearrangement across More Than 1,400 Cases

A Pan-Cancer Compendium of Genes Deregulated by Somatic Genomic Rearrangement across More Than 1,400 Cases

Enhancing breakpoint resolution with deep segmentation model: a general refinement method for read-depth based structural variant callers

Modelling double strand break susceptibility to interrogate structural variation in cancer

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