Analysis of somatic microsatellite indels identifies driver events in human tumors

Maruvka, Yosef E.; Mouw, Kent W.; Karlić, Rosa; Parasuraman, Prasanna; Kamburov, Atanas; Polak, Paz; Haradhvala, Nicholas J.; Hess, Julian M.; Rheinbay, Esther; Brody, Yehuda; Koren, Amnon; Braunstein, Lior Z.; D’Andrea, Alan D.; Lawrence, Michael S.; Bass, Adam J.; Bernards, André; Michor, Franziska; Getz, Gad

doi:10.1038/nbt.3966

Cited by 116 publications

(152 citation statements)

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“…Using sequence data of chrX from 32 normal tissues of male individuals, we estimated the error rate among the different types and lengths of microsatellites. As the male chrX is hemizygotic, we can infer the error rate without the influence of heterozygous polymorphisms (19,20). As expected, error rates depended on the unit and length of the microsatellites, and longer microsatellites had higher error rates ( Supplementary Fig.…”

Section: Error Rate Estimation Of Microsatellitesmentioning

confidence: 54%

“…We compared recurrently mutated microsatellites in the coding regions between MSS and MSI samples (Fig 4, Supplementary Table 7). Microsatellites in ACVR2A and TGFBR2, which have been reported to be frequently mutated in MSI tumors (13,14,20), were recurrently mutated in 60% and 47% of the MSI samples, respectively. In addition, microsatellites in ASTE1, KIAA2018, LIN1 and CDH26 were also mutated in more than 50% of the MSI samples.…”

Section: Recurrently Mutated Microsatellites In the Coding Regionmentioning

confidence: 95%

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Comprehensive Analysis of Indels in Whole-genome Microsatellite Regions and Microsatellite Instability across 21 Cancer Types

Fujimoto

Fujita

Hasegawa

et al. 2018

Preprint

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20Microsatellites are repeats of 1-6bp units and ~10 million microsatellites have been 21 identified across the human genome. Microsatellites are vulnerable to DNA mismatch 22 germline variation of the microsatellites suggested that the amount of germline variations 34 and somatic mutation rates were correlated. Lastly, analysis of mutations in mismatch 35 repair genes showed that somatic SNVs and short indels had larger functional impact than 36 germline mutations and structural variations. Our analysis provides a comprehensive 37 picture of mutations in the microsatellite regions, and reveals possible causes of mutations, 38as well as provides a useful marker set for MSI detection. 39 40Introduction 41Recent large-scale whole genome sequencing studies have revealed the complexity of the 42 mutational landscape of the cancer genome (1-4). In cancer genomes, various types of 43 mutations, such as SNVs (single nucleotide variants), short indels (insertions and 44 deletions), genomic rearrangements, copy number alterations, insertion of 45 retrotransposons, and virus genome integrations, have been identified, and their 46 oncogenic roles have been characterized (1-5). Additionally, genome sequencing studies 47 have revealed the molecular basis of somatic mutations (6-9). However, somatic 48 mutations in microsatellites or repeat sequences have not been well-characterized in a 49 large whole genome sequencing cohort due to difficulties in accurately detecting 50 mutations using presently available short-read sequencing technologies. 51A microsatellite is defined as a tract of repetitive DNA motif composed of short 52 repeating units (10). The mutation rate of microsatellites has been known to be higher 53 than other genomic regions due to DNA polymerase slippage during DNA replication and 54 repair (10). Due to their fragility, microsatellites are used as markers of genomic 55 instability in cancer (11). In cancer genetics studies, microsatellite instability (MSI) has 56 been used for molecular diagnosis of Lynch syndrome and cancers with mismatch repair 57 deficiency (11). Furthermore, MSI-positive tumors are generally burdened with higher 58 numbers of somatic mutations and present many mutation-associated neo-antigens, which 59 might be recognized by the immune system. Presently, MSI can also be used as a marker 60 to predict the effect of immune therapy (12). The MSI phenotype is most common in 61 colorectal cancers, stomach cancers and uterine endometrial cancers (10-15%), although 62 it has also been observed across many tumor types at a few % (11). The MSI phenotype 63 is defined by the presence of somatic indels of the 2-5 microsatellite makers, whereby 64 BAT25/26 mononucleotide microsatellites are widely used to establish MSI status (11). 65Irrespective of the clinical importance of microsatellite, large-scale analysis of 66 somatic changes in microsatellites across various type of cancers is limited for whole 67 genome sequencing (WGS) data (13, 14). In the current study, we analyzed indels in 68 microsat...

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Section: Error Rate Estimation Of Microsatellitesmentioning

confidence: 54%

Section: Recurrently Mutated Microsatellites In the Coding Regionmentioning

confidence: 95%

Comprehensive Analysis of Indels in Whole-genome Microsatellite Regions and Microsatellite Instability across 21 Cancer Types

Fujimoto

Fujita

Hasegawa

et al. 2018

Preprint

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“…One explanation for this is that existing methods systematically miscall a large number of indels as SNVs in tandem repeat regions leading to under representation in the truth sets. Although both representations result in the same haplotype sequence, the distinction could have important clinical consequences, such as for mutation signature profiling 29 or microsatellite instability analysis 39,40 . Moreover, around half of our manually curated de novo calls occur in microsatellites.…”

Section: Discussionmentioning

confidence: 99%

A unified haplotype-based method for accurate and comprehensive variant calling

Cooke

Wedge

Lunter

2018

Preprint

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Haplotype-based variant callers, which consider physical linkage between variant sites, are currently among the best tools for germline variation discovery and genotyping from short-read sequencing data. However, almost all such tools were designed specifically for detecting common germline variation in diploid populations, and give sub-optimal results in other scenarios. Here we present Octopus, a versatile haplotype-based variant caller that uses a polymorphic Bayesian genotyping model capable of modeling sequencing data from a range of experimental designs within a unified haplotype-aware framework. We show that Octopus accurately calls de novo mutations in parent-offspring trios and germline variants in individuals, including SNVs, indels, and small complex replacements such as microinversions. In addition, using a carefully designed synthetic-tumour data set derived from clean sequencing data from a sample with known germline haplotypes, and observed mutations in large cohort of tumour samples, we show that Octopus accurately characterizes germline and somatic variation in tumours, both with and without a paired normal sample. Sequencing reads and prior information are combined to phase called genotypes of arbitrary ploidy, including those with somatic mutations. Octopus also outputs realigned evidence BAMs to aid validation and interpretation.Haplotype-based approaches have emerged as the method of choice for calling germline variants because these methods are robust to alignment errors from read mappers and have better signal-to-noise characteristics than positional approaches 1-7 . However, existing haplotype-based variant callers have several limitations. First, existing tools are sub-optimal for many problems as most implement models that assume either diploidy 1-3 or constant copy number 4-6 , and assume that samples are selected from an idealized population of unrelated individuals. Such models are appropriate for calling germline variants in small cohorts, but provide a poorer fit to data generated in other experimental designs, such as studies involving samples with known relatedness such as paired tumours, single-cells, and parent-offspring trios, or in pooled tumour and bacterial sequencing where samples are often heterogeneous. These limitations cause researchers to implement custom pipelines that may integrate various callers and involve post hoc filtering and interpretation [8][9][10][11][12][13][14][15][16] . Second, existing haplotype-based methods suffer from windowing artifacts as variants are evaluated in independent non-overlapping regions. This can lead to false calls in complex regions where reads support variants that fall outside the region being evaluated. Third, existing methods do not make a clear distinction between the haplotype sequence supported by the read data, and the mutation events that gave rise to it. This makes it challenging to assign appropriate prior probabilities to these haplotype sequences, because different sets of mutations can have very different biological pla...

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“…Therefore, the application of more sensitive 4 somatic indel calling strategies most likely will induce striking changes in the spectrum of somatic indels so far detected. As indicated in earlier work [Maruvka et al, 2017], this has the potential to deepen our understanding of the origin and effects of somatic indels, beyond just balancing count statistics.…”

Section: Introductionmentioning

confidence: 91%

“…The fraction of somatic variants discovered, however, has left room for improvement across the whole range of possible variant types [Alioto et al, 2015]. Thereby, somatic insertions and deletions (indels) 2 have proven to pose particular challenges when belonging to certain classes or length ranges [Hause et al, 2016, Maruvka et al, 2017. Indels of length approximately 30 -250 bp, termed "the next-generation sequencing (NGS) twilight zone of indels" in other contexts [Mandoiu and Zelikovsky, 2016, Trappe et al, 2014, have resisted their discovery in particular also in somatic variant calling: while the COSMIC database 3 counts 1,879,044 indels of length 1-30 bp, it only counts 17 793 indels of length 31-60 bp, 3758 indels of length 61-100 bp and 2483 indels of length 101-250 bp.…”

Section: Introductionmentioning

confidence: 99%

Enhancing sensitivity and controlling false discovery rate in somatic indel discovery

Köster

Dijkstra

Marschall

et al. 2019

Preprint

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As witnessed by various population-scale cancer genome sequencing projects, accurate discovery of somatic variants has become of central importance in modern cancer research. However, count statistics on somatic insertions and deletions (indels) discovered so far point out that large amounts of discoveries must have been missed. The reason is that the combination of uncertainties relating to, for example, gap and alignment ambiguities, twilight zone indels, cancer heterogeneity, sample purity, sampling and strand bias are hard to accurately quantify. Here, a unifying statistical model is provided whose dependency structures enable to accurately quantify all inherent uncertainties in short time. As major consequence, false discovery rate (FDR) in somatic indel discovery can now be controlled at utmost accuracy. As demonstrated on simulated and real data, this enables to dramatically increase the amount of true discoveries while safely suppressing the FDR. Specifically supported by workflow design, our approach can be integrated as a post-processing step in large-scale projects.The software is publicly available at https://varlociraptor.github.io and can be easily installed via Bioconda 1 [Grüning et al., 2018].

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Analysis of somatic microsatellite indels identifies driver events in human tumors

Cited by 116 publications

References 64 publications

Comprehensive Analysis of Indels in Whole-genome Microsatellite Regions and Microsatellite Instability across 21 Cancer Types

Comprehensive Analysis of Indels in Whole-genome Microsatellite Regions and Microsatellite Instability across 21 Cancer Types

A unified haplotype-based method for accurate and comprehensive variant calling

Enhancing sensitivity and controlling false discovery rate in somatic indel discovery

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