Transposable elements (TEs) are abundant in the human genome, and some are capable of generating new insertions through RNA intermediates. In cancer, the disruption of cellular mechanisms that ...normally suppress TE activity may facilitate mutagenic retrotranspositions. We performed single-nucleotide resolution analysis of TE insertions in 43 high-coverage whole-genome sequencing data sets from five cancer types. We identified 194 high-confidence somatic TE insertions, as well as thousands of polymorphic TE insertions in matched normal genomes. Somatic insertions were present in epithelial tumors but not in blood or brain cancers. Somatic L1 insertions tend to occur in genes that are commonly mutated in cancer, disrupt the expression of the target genes, and are biased toward regions of cancer-specific DNA hypomethylation, highlighting their potential impact in tumorigenesis.
Somatic mutations have been studied extensively in the context of cancer. Recent studies have demonstrated that high-throughput sequencing data can be used to detect somatic mutations in non-tumor ...cells. Analysis of such mutations allows us to better understand the mutational processes in normal cells, explore cell lineages in development, and examine potential associations with age-related disease. We describe here approaches for characterizing somatic mutations in normal and non-tumor disease tissues. We discuss several experimental designs and common pitfalls in somatic mutation detection, as well as more recent developments such as phasing and linked-read technology. With the dramatically increasing numbers of samples undergoing genome sequencing, bioinformatic analysis will enable the characterization of somatic mutations and their impact on non-cancer tissues.
Somatic mosaicism resulting from post-zygotic mutations has been shown to contribute to many diseases including brain-related disorders, in addition to cancer. Emerging data also suggest that mosaicism is common in healthy individuals.
Mutations occurring late in development have very low allele fractions, and their detection requires specialized algorithms and filters that can remove artifacts that arise in sample handling, DNA sequencing, and analysis.
Emerging technologies, such as single-cell sequencing and linked-read sequencing, allow improved phasing of variants, thus increasing detection accuracy.
Neurons live for decades in a postmitotic state, their genomes susceptible to DNA damage. Here we survey the landscape of somatic single-nucleotide variants (SNVs) in the human brain. We identified ...thousands of somatic SNVs by single-cell sequencing of 36 neurons from the cerebral cortex of three normal individuals. Unlike germline and cancer SNVs, which are often caused by errors in DNA replication, neuronal mutations appear to reflect damage during active transcription. Somatic mutations create nested lineage trees, allowing them to be dated relative to developmental landmarks and revealing a polyclonal architecture of the human cerebral cortex. Thus, somatic mutations in the brain represent a durable and ongoing record of neuronal life history, from development through postmitotic function.
Recent advances in single cell technology have enabled dissection of cellular heterogeneity in great detail. However, analysis of single cell DNA sequencing data remains challenging due to bias and ...artifacts that arise during DNA extraction and whole-genome amplification, including allelic imbalance and dropout. Here, we present a framework for statistical estimation of allele-specific amplification imbalance at any given position in single cell whole-genome sequencing data by utilizing the allele frequencies of heterozygous single nucleotide polymorphisms in the neighborhood. The resulting allelic imbalance profile is critical for determining whether the variant allele fraction of an observed mutation is consistent with the expected fraction for a true variant. This method, implemented in SCAN-SNV (Single Cell ANalysis of SNVs), substantially improves the identification of somatic variants in single cells. Our allele balance framework is broadly applicable to genotype analysis of any variant type in any data that might exhibit allelic imbalance.
Detection of mosaic mutations that arise in normal development is challenging, as such mutations are typically present in only a minute fraction of cells and there is no clear matched control for ...removing germline variants and systematic artifacts. We present MosaicForecast, a machine-learning method that leverages read-based phasing and read-level features to accurately detect mosaic single-nucleotide variants and indels, achieving a multifold increase in specificity compared with existing algorithms. Using single-cell sequencing and targeted sequencing, we validated 80-90% of the mosaic single-nucleotide variants and 60-80% of indels detected in human brain whole-genome sequencing data. Our method should help elucidate the contribution of mosaic somatic mutations to the origin and development of disease.
A large database of copy number profiles from cancer genomes can facilitate the identification of recurrent chromosomal alterations that often contain key cancer-related genes. It can also be used to ...explore low-prevalence genomic events such as chromothripsis. In this study, we report an analysis of 8227 human cancer copy number profiles obtained from 107 array comparative genomic hybridization (CGH) studies. Our analysis reveals similarity of chromosomal arm-level alterations among developmentally related tumor types as well as a number of co-occurring pairs of arm-level alterations. Recurrent ("pan-lineage") focal alterations identified across diverse tumor types show an enrichment of known cancer-related genes and genes with relevant functions in cancer-associated phenotypes (e.g., kinase and cell cycle). Tumor type-specific ("lineage-restricted") alterations and their enriched functional categories were also identified. Furthermore, we developed an algorithm for detecting regions in which the copy number oscillates rapidly between fixed levels, indicative of chromothripsis. We observed these massive genomic rearrangements in 1%-2% of the samples with variable tumor type-specific incidence rates. Taken together, our comprehensive view of copy number alterations provides a framework for understanding the functional significance of various genomic alterations in cancer genomes.
DNA copy number variations (CNVs) play an important role in the pathogenesis and progression of cancer and confer susceptibility to a variety of human disorders. Array comparative genomic ...hybridization has been used widely to identify CNVs genome wide, but the next-generation sequencing technology provides an opportunity to characterize CNVs genome wide with unprecedented resolution. In this study, we developed an algorithm to detect CNVs from whole-genome sequencing data and applied it to a newly sequenced glioblastoma genome with a matched control. This read-depth algorithm, called BIC-seq, can accurately and efficiently identify CNVs via minimizing the Bayesian information criterion. Using BIC-seq, we identified hundreds of CNVs as small as 40 bp in the cancer genome sequenced at 10x coverage, whereas we could only detect large CNVs (> 15 kb) in the array comparative genomic hybridization profiles for the same genome. Eighty percent (14/16) of the small variants tested (110 bp to 14 kb) were experimentally validated by quantitative PCR, demonstrating high sensitivity and true positive rate of the algorithm. We also extended the algorithm to detect recurrent CNVs in multiple samples as well as deriving error bars for breakpoints using a Gibbs sampling approach. We propose this statistical approach as a principled yet practical and efficient method to estimate CNVs in whole-genome sequencing data.
Genomic rearrangements exert a heavy influence on the molecular landscape of cancer. New analytical approaches integrating somatic structural variants (SSVs) with altered gene features represent a ...framework by which we can assign global significance to a core set of genes, analogous to established methods that identify genes non-randomly targeted by somatic mutation or copy number alteration. While recent studies have defined broad patterns of association involving gene transcription and nearby SSV breakpoints, global alterations in DNA methylation in the context of SSVs remain largely unexplored.
By data integration of whole genome sequencing, RNA sequencing, and DNA methylation arrays from more than 1400 human cancers, we identify hundreds of genes and associated CpG islands (CGIs) for which the nearby presence of a somatic structural variant (SSV) breakpoint is recurrently associated with altered expression or DNA methylation, respectively, independently of copy number alterations. CGIs with SSV-associated increased methylation are predominantly promoter-associated, while CGIs with SSV-associated decreased methylation are enriched for gene body CGIs. Rearrangement of genomic regions normally having higher or lower methylation is often involved in SSV-associated CGI methylation alterations. Across cancers, the overall structural variation burden is associated with a global decrease in methylation, increased expression in methyltransferase genes and DNA damage response genes, and decreased immune cell infiltration.
Genomic rearrangement appears to have a major role in shaping the cancer DNA methylome, to be considered alongside commonly accepted mechanisms including histone modifications and disruption of DNA methyltransferases.
A systematic cataloging of genes affected by genomic rearrangement, using multiple patient cohorts 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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•Whole-genome analysis of >1,400 cancer cases by high- or low-pass sequencing•Hundreds of genes with overexpression associated with somatic structural variants•Structural variant breakpoints within specific tumor suppressors disrupt expression•No single mechanism involved with structural variant-mediated gene deregulation
Zhang et al. analyzed over 1,400 cancers by high- or low-pass whole-genome sequencing, focusing on patterns of structural variation. They saw a widespread impact of somatic structural variants on gene expression patterns, independent of copy-number alterations, involving key oncogenes and tumor suppressor genes.
The systematic identification of effective drug combinations has been hindered by the unavailability of methods that can explore the large combinatorial search space of drug interactions. Here we ...present multiplex screening for interacting compounds (MuSIC), which expedites the comprehensive assessment of pairwise compound interactions. We examined ∼500,000 drug pairs from 1,000 US Food and Drug Administration (FDA)-approved or clinically tested drugs and identified drugs that synergize to inhibit HIV replication. Our analysis reveals an enrichment of anti-inflammatory drugs in drug combinations that synergize against HIV. As inflammation accompanies HIV infection, these findings indicate that inhibiting inflammation could curb HIV propagation. Multiple drug pairs identified in this study, including various glucocorticoids and nitazoxanide (NTZ), synergize by targeting different steps in the HIV life cycle. MuSIC can be applied to a wide variety of disease-relevant screens to facilitate efficient identification of compound combinations.
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