Identification of coding and non-coding mutational hotspots in cancer genomes

A di-acetyl-decorated chromatin signature couples liquid condensation to suppress DNA end synapsis

Appropriate DNA end synapsis, regulated by core components of the synaptic complex including KU70-KU80, LIG4, XRCC4, and XLF, is central to non-homologous end joining (NHEJ) repair of chromatinized DNA double-strand breaks (DSBs). However, it remains enigmatic whether chromatin modifications can influence the formation of NHEJ synaptic complex at DNA ends, and if so, how this is achieved. Here, we report that the mitotic deacetylase complex (MiDAC) serves as a key regulator of DNA end synapsis during NHEJ repair in mammalian cells. Mechanistically, MiDAC removes combinatorial acetyl marks on histone H2A (H2AK5acK9ac) around DSB-proximal chromatin, suppressing hyperaccumulation of bromodomain-containing protein BRD4 that would otherwise undergo liquid-liquid phase separation with KU80 and prevent the proper installation of LIG4-XRCC4-XLF onto DSB ends. This study provides mechanistic insight into the control of NHEJ synaptic complex assembly by a specific chromatin signature and highlights the critical role of H2A hypoacetylation in restraining unscheduled compartmentalization of DNA repair machinery.

Integrating Clinical Cancer and PTM Proteomics Data Identifies a Mechanism of ACK1 Kinase Activation

Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases.

IMPLICATIONS

This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study.

Widespread perturbation of ETS factor binding sites in cancer

Although >90% of somatic mutations reside in non-coding regions, few have been reported as cancer drivers. To predict driver non-coding variants (NCVs), we present a transcription factor (TF)-aware burden test based on a model of coherent TF function in promoters. We apply this test to NCVs from the Pan-Cancer Analysis of Whole Genomes cohort and predict 2555 driver NCVs in the promoters of 813 genes across 20 cancer types. These genes are enriched in cancer-related gene ontologies, essential genes, and genes associated with cancer prognosis. We find that 765 candidate driver NCVs alter transcriptional activity, 510 lead to differential binding of TF-cofactor regulatory complexes, and that they primarily impact the binding of ETS factors. Finally, we show that different NCVs within a promoter often affect transcriptional activity through shared mechanisms. Our integrated computational and experimental approach shows that cancer NCVs are widespread and that ETS factors are commonly disrupted.

The MLL3/4 complexes and MiDAC co-regulate H4K20ac to control a specific gene expression program

The mitotic deacetylase complex MiDAC has recently been shown to play a vital physiological role in embryonic development and neurite outgrowth. However, how MiDAC functionally intersects with other chromatin-modifying regulators is poorly understood. Here, we describe a physical interaction between the histone H3K27 demethylase UTX, a complex-specific subunit of the enhancer-associated MLL3/4 complexes, and MiDAC. We demonstrate that UTX bridges the association of the MLL3/4 complexes and MiDAC by interacting with ELMSAN1, a scaffolding subunit of MiDAC. Our data suggest that MiDAC constitutes a negative genome-wide regulator of H4K20ac, an activity which is counteracted by the MLL3/4 complexes. MiDAC and the MLL3/4 complexes co-localize at many genomic regions, which are enriched for H4K20ac and the enhancer marks H3K4me1, H3K4me2, and H3K27ac. We find that MiDAC antagonizes the recruitment of UTX and MLL4 and negatively regulates H4K20ac, and to a lesser extent H3K4me2 and H3K27ac, resulting in transcriptional attenuation of associated genes. In summary, our findings provide a paradigm how the opposing roles of chromatin-modifying components, such as MiDAC and the MLL3/4 complexes, balance the transcriptional output of specific gene expression programs.

Enhanced characterization of the thyA system for mutational analysis in Escherichia coli: Defining mutationally "hot" regions of the gene

We have extensively characterized base substitution mutations in the 795 base pair (bp) long E. coli thyA gene to define as many of the base substitution mutational sites that inactivate the gene as possible. The resulting catalog of mutational sites constitutes a system with up to 5 times as many sites for monitoring each of the six base substitution mutations as the widely used rpoB/Rif^r system. We have defined 75 sites for the G:C -> A:T transition, 68 sites for the G:C -> T:A transversion, 53 sites for the G:C -> C:G transversion, 49 sites for the A:T -> G:C transition, 39 sites for the A:T -> T:A transversion, and 59 sites for the A:T -> C:G transversion. The system is thus comprised of 343 base substitution mutations at 232 different base pairs, all of which can be sequenced with a single primer pair. This allows for the examination of mutational spectra using a more detailed probe of known mutations, while still allowing one to compare the number of repeated occurrences at specific sites. We have examined several mutagens and mutators with this system, and show its utility by looking at the spectrum of cisplatin, that has a single hotspot, underscoring the value of having as large an array of sites as possible at which one can monitor repeat occurrences. To test for regions of the gene that might be hotspots for a number of mutagens, or "hot" (mutaphilic) regions, we have looked at the ratio of mutations per set of an equal number of mutational sites throughout the gene. The resulting graphs suggest that there are "hot" regions at intervals, and this may reflect aspects of secondary structures, of the higher order structure of the chromosome, or perhaps the nucleoid structure of the chromosome plus histone-like protein complexes.

Cis-regulatory mutations with driver hallmarks in major cancers

Despite the recent availability of complete genome sequences of tumors from thousands of patients, isolating disease-causing (driver) non-coding mutations from the plethora of somatic variants remains challenging, and only a handful of validated examples exist. By integrating whole-genome sequencing, genetic data, and allele-specific gene expression from TCGA, we identified 320 somatic non-coding mutations that affect gene expression in cis (FDR<0.25). These mutations cluster into 47 cis-regulatory elements that modulate expression of their subject genes through diverse molecular mechanisms. We further show that these mutations have hallmark features of non-coding drivers; namely, that they preferentially disrupt transcription factor binding motifs, are associated with a selective advantage, increased oncogene expression and decreased tumor suppressor expression.

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Enrichment of functional non-coding somatic mutations predicts drivers

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Elevated variant allele frequencies are consistent with roles in tumorigenesis

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Putative non-coding drivers disrupt transcription factor binding motifs

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Predicted drivers associate with increased oncogene and decreased TSG expression

Long Noncoding RNAs: New Regulators of Resistance to Systemic Therapies for Gastric Cancer

Gastric cancer (GC) is the second leading cause of cancer mortality and the fourth most commonly diagnosed malignant disease, with approximately 951,000 new cases diagnosed and approximately 723,000 cases of mortality each year. The highest mortality rate of GC is in East Asia, and the lowest is in North America. A large number of studies have demonstrated that GC patients are characterized by higher morbidity, metastasis rates, and mortality and lower early diagnosis rates, radical resection rates, and 5-year survival rates. All cases of GC can be divided into two important stages, namely, early- and advanced-stage GC, and the stage mainly determines the treatment strategy for and the therapeutic effect in GC patients. Patients with early-stage GC undergo radical surgery followed by chemotherapy, and the 5-year survival rate can be as high as 90%. However, patients with advanced-stage GC cannot undergo radical surgery because they are at risk for metastasis; therefore, they can choose only radiotherapy or chemotherapy and have a poor prognosis. Based on the lack of specific clinical manifestations and detection methods, most GC patients (>70%) are diagnosed in the advanced stage; therefore, continued efforts toward developing treatments have been focused on advanced-stage GC patients and include molecular targeted therapy, immunotherapy, and small molecular therapy. Nevertheless, in recent years, accumulating evidence has indicated that small molecules, especially long noncoding RNAs (lncRNAs), are involved in the occurrence, development, and progression of GC, and their abundantly dysregulated expression has been identified in GC tissues and cell lines. Therefore, lncRNAs are considered easily detectable molecules and ideal biomarkers or target-specific agents for the future diagnosis or treatment of GC. In this review, we primarily discuss the status of GC, the role of lncRNAs in GC, and the emerging systemic treatments for GC.

Computational methods for detecting cancer hotspots

Cancer mutations that are recurrently observed among patients are known as hotspots. Hotspots are highly relevant because they are, presumably, likely functional. Known hotspots in BRAF, PIK3CA, TP53, KRAS, IDH1 support this idea. However, hundreds of hotspots have never been validated experimentally. The detection of hotspots nevertheless is challenging because background mutations obscure their statistical and computational identification. Although several algorithms have been applied to identify hotspots, they have not been reviewed before. Thus, in this mini-review, we summarize more than 40 computational methods applied to detect cancer hotspots in coding and non-coding DNA. We first organize the methods in cluster-based, 3D, position-specific, and miscellaneous to provide a general overview. Then, we describe their embed procedures, implementations, variations, and differences. Finally, we discuss some advantages, provide some ideas for future developments, and mention opportunities such as application to viral integrations, translocations, and epigenetics.

Genomic Instability Signature of Palindromic Non-Coding Somatic Mutations in Bladder Cancer

Numerous pan-genomic studies identified alterations in protein-coding genes and signaling pathways involved in bladder carcinogenesis, while non-coding somatic alterations remain weakly explored. The goal of this study was to identify clinical biomarkers in non-coding regions for bladder cancer patients. We have previously identified in bladder tumors two non-coding mutational hotspots occurring at high frequencies (≥30%). These mutations are located close to the GPR126 and PLEKHS1 genes, at the guanine or the cytosine of a TGAACA core motif flanked, on both sides, by a stretch of palindromic sequences. Here, we hypothesize that such a pattern of recurrent non-coding mutations could be a signature of somatic genomic instability specifically involved in bladder cancer. We analyzed 26 additional mutable non-coding sites with the same core motif in a cohort of 103 bladder cancers composed of 44 NMIBC cases and 59 MIBC cases using high-resolution melting (HRM) and Sanger sequencing. Five bladder cancers were additionally analyzed for protein-coding gene mutations using a targeted NGS panel composed of 571 genes. Expression levels of three members of the APOBEC3 family genes were assessed using real-time quantitative RT-PCR. Non-coding somatic mutations were observed for at least one TGAACA core motif locus in 62.1% (64/103) of bladder tumor samples. These non-coding mutations co-occurred in the bladder tumors but were absent in prostate tumor, HPV-positive Head and Neck Squamous Cell Carcinoma, and high microsatellite instability (MSI-H) colorectal tumor series. This signature of palindromic non-coding somatic mutations, specific to bladder tumors, was not associated with patients' outcome and was more frequent in females. Interestingly, this signature was associated with high tumor mutational burden (TMB) and high expression levels of APOBEC3B and interferon inducible genes. We identified a new type of somatic genomic instability targeting the TGAACA core motif loci flanked by palindromic sequences in bladder cancer. This mutational signature is a promising candidate clinical biomarker for the early detection of relapse and a major low-cost alternative to the TMB to monitor the response to immunotherapy for bladder cancer patients.

The MiDAC histone deacetylase complex is essential for embryonic development and has a unique multivalent structure

MiDAC is one of seven distinct, large multi-protein complexes that recruit class I histone deacetylases to the genome to regulate gene expression. Despite implications of involvement in cell cycle regulation and in several cancers, surprisingly little is known about the function or structure of MiDAC. Here we show that MiDAC is important for chromosome alignment during mitosis in cancer cell lines. Mice lacking the MiDAC proteins, DNTTIP1 or MIDEAS, die with identical phenotypes during late embryogenesis due to perturbations in gene expression that result in heart malformation and haematopoietic failure. This suggests that MiDAC has an essential and unique function that cannot be compensated by other HDAC complexes. Consistent with this, the cryoEM structure of MiDAC reveals a unique and distinctive mode of assembly. Four copies of HDAC1 are positioned at the periphery with outward-facing active sites suggesting that the complex may target multiple nucleosomes implying a processive deacetylase function.

Mutations induced by Bleomycin, 4-nitroquinoline-1-oxide, and hydrogen peroxide in the rpoB gene of Escherichia coli: Perspective on Mutational Hotspots

We report the mutational spectra in a segment of the E. coli rpoB gene of bleomycin (BLEO), 4-nitroquinoline-1-oxide (NQO), and hydrogen peroxide (H₂O₂). We compare these spectra with those of other mutagens and repair deficient strains in the same rpoB system, and review the key elements determining mutational hotspots and outline the questions that remain unanswered. We consider three tiers of hotspots that derive from 1) the nature of the sequence change at a specific base, 2) the direct nearest neighbors and 3) some aspect of the larger sequence context or the local 3D-structure of segments of DNA. This latter tier can have a profound effect on mutation frequencies, even among sites with identical nearest neighbor sequences. BLEO is dependent on the SOS-induced translesion Pol V for mutagenesis, and has a dramatic hotspot at a single mutational site in rpoB. NQO is not dependent on any of the translesion polymerases, in contrast to findings with plasmids treated in vitro and transformed into E. coli. The rpoB system allows one to monitor both G:C -> A:T transitions and G:C -> T:A transversions at the same site in 11 cases, each site having the identical sequence context for each of the two mutations. The combined preference for G:C -> A:T transitions at these sites is 20-fold. Several of the favored sites for hydrogen peroxide mutagenesis are not seen in the spectra of BLEO and NQO mutations, indicating that mutagenesis from reactive oxygen species is not a major cause of BLEO or NQO mutagenesis, but rather specific adducts. The variance in mutation rates at sites with identical nearest neighbors suggests that the local structure of different DNA segments is an important factor in mutational hotspots.

Non-coding somatic mutations converge on the PAX8 pathway in ovarian cancer

The functional consequences of somatic non-coding mutations in ovarian cancer (OC) are unknown. To identify regulatory elements (RE) and genes perturbed by acquired non-coding variants, here we establish epigenomic and transcriptomic landscapes of primary OCs using H3K27ac ChIP-seq and RNA-seq, and then integrate these with whole genome sequencing data from 232 OCs. We identify 25 frequently mutated regulatory elements, including an enhancer at 6p22.1 which associates with differential expression of ZSCAN16 (P = 6.6 × 10-4) and ZSCAN12 (P = 0.02). CRISPR/Cas9 knockout of this enhancer induces downregulation of both genes. Globally, there is an enrichment of single nucleotide variants in active binding sites for TEAD4 (P = 6 × 10-11) and its binding partner PAX8 (P = 2×10-10), a known lineage-specific transcription factor in OC. In addition, the collection of cis REs associated with PAX8 comprise the most frequently mutated set of enhancers in OC (P = 0.003). These data indicate that non-coding somatic mutations disrupt the PAX8 transcriptional network during OC development.

Structural basis for adhesion G protein-coupled receptor Gpr126 function

Many drugs target the extracellular regions (ECRs) of cell-surface receptors. The large and alternatively-spliced ECRs of adhesion G protein-coupled receptors (aGPCRs) have key functions in diverse biological processes including neurodevelopment, embryogenesis, and tumorigenesis. However, their structures and mechanisms of action remain unclear, hampering drug development. The aGPCR Gpr126/Adgrg6 regulates Schwann cell myelination, ear canal formation, and heart development; and GPR126 mutations cause myelination defects in human. Here, we determine the structure of the complete zebrafish Gpr126 ECR and reveal five domains including a previously unknown domain. Strikingly, the Gpr126 ECR adopts a closed conformation that is stabilized by an alternatively spliced linker and a conserved calcium-binding site. Alternative splicing regulates ECR conformation and receptor signaling, while mutagenesis of the calcium-binding site abolishes Gpr126 function in vivo. These results demonstrate that Gpr126 ECR utilizes a multi-faceted dynamic approach to regulate receptor function and provide relevant insights for ECR-targeted drug design.

The extracellular regions (ECRs) of adhesion GPCRs have diverse biological functions, but their structures and mechanisms of action remain unclear. Here, the authors solve the ECR structure of the Gpr126 receptor and show that ECR conformation and signaling functions are regulated by alternative splicing.

Identification of putative regulatory regions and transcription factors associated with intramuscular fat content traits

Background

Integration of high throughput DNA genotyping and RNA-sequencing data allows for the identification of genomic regions that control gene expression, known as expression quantitative trait loci (eQTL), on a whole genome scale. Intramuscular fat (IMF) content and carcass composition play important roles in metabolic and physiological processes in mammals because they influence insulin sensitivity and consequently prevalence of metabolic diseases such as obesity and type 2 diabetes. However, limited information is available on the genetic variants and mechanisms associated with IMF deposition in mammals. Thus, our hypothesis was that eQTL analyses could identify putative regulatory regions and transcription factors (TFs) associated with intramuscular fat (IMF) content traits.

Results

We performed an integrative eQTL study in skeletal muscle to identify putative regulatory regions and factors associated with intramuscular fat content traits. Data obtained from skeletal muscle samples of 192 animals was used for association analysis between 461,466 SNPs and the transcription level of 11,808 genes. This yielded 1268 cis- and 10,334 trans-eQTLs, among which we identified nine hotspot regions that each affected the expression of > 119 genes. These putative regulatory regions overlapped with previously identified QTLs for IMF content. Three of the hotspots respectively harbored the transcription factors USF1, EGR4 and RUNX1T1, which are known to play important roles in lipid metabolism. From co-expression network analysis, we further identified modules significantly correlated with IMF content and associated with relevant processes such as fatty acid metabolism, carbohydrate metabolism and lipid metabolism.

Conclusion

This study provides novel insights into the link between genotype and IMF content as evident from the expression level. It thereby identifies genomic regions of particular importance and associated regulatory factors. These new findings provide new knowledge about the biological processes associated with genetic variants and mechanisms associated with IMF deposition in mammals.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4871-y) contains supplementary material, which is available to authorized users.

Inferring the effect of genomic variation in the new era of genomics

Accurate and detailed understanding of the effects of variants in the coding and noncoding regions of the genome is the next big challenge in the new genomic era of personalized medicine, especially to tackle newer findings of genetic and phenotypic heterogeneity of diseases. This is necessary to resolve the gene-variant-disease relationship, the pathogenic variant spectrum of genes, pathogenic variants with variable clinical consequences, and multiloci diseases. In turn, this will facilitate patient recruitment for relevant clinical trials. In this review, we describe the trends in research at the intersection of basic and clinical genomics aiming to (a) overcome molecular diagnostic challenges and increase the clinical utility of next-generation sequencing (NGS) platforms, (b) elucidate variants associated with disease, (c) determine overall genomic complexity including epistasis, complex inheritance patterns such as "synergistic heterozygosity," digenic/multigenic inheritance, modifier effect, and rare variant load. We describe the newly emerging field of integrated functional genomics, in vivo or in vitro large-scale functional approaches, statistical bioinformatics algorithms that support NGS genomics data to interpret variants for timely clinical diagnostics and disease management. Thus, facilitating the discovery of new therapeutic or biomarker options, and their roles in the future of personalized medicine.

Identification of Single Nucleotide Non-coding Driver Mutations in Cancer

Recent whole-genome sequencing studies have identified millions of somatic variants present in tumor samples. Most of these variants reside in non-coding regions of the genome potentially affecting transcriptional and post-transcriptional gene regulation. Although a few hallmark examples of driver mutations in non-coding regions have been reported, the functional role of the vast majority of somatic non-coding variants remains to be determined. This is because the few driver variants in each sample must be distinguished from the thousands of passenger variants and because the logic of regulatory element function has not yet been fully elucidated. Thus, variants prioritized based on mutational burden and location within regulatory elements need to be validated experimentally. This is generally achieved by combining assays that measure physical binding, such as chromatin immunoprecipitation, with those that determine regulatory activity, such as luciferase reporter assays. Here, we present an overview of in silico approaches used to prioritize somatic non-coding variants and the experimental methods used for functional validation and characterization.

microRNAs in Cancer Susceptibility

microRNAs (miRNAs) are a small RNA species without protein-coding potential. However, they are key modulators of protein translation. Many studies have linked miRNAs with cancer initiation, progression, diagnosis, and prognosis, and recent studies have also linked them with cancer etiology and susceptibility, especially through single-nucleotide polymorphisms (SNPs). This review discusses some of the recent advances in miRNA-SNP literature-including SNPs in miRNA genes, miRNA target sites, and the processing machinery. In addition, we highlight some emerging areas of interest, including isomiRs and non-3'UTR focused miRNA-binding mechanisms that could provide further novel insight into the relationship between miR-SNPs and cancer. Finally, we note that additional epidemiological and experimental research is needed to close the gap in our understanding of the genotype-phenotype relationship between miRNA-SNPs and cancer.