Candidate Cancer Driver Mutations in Distal Regulatory Elements and Long-Range Chromatin Interaction Networks

Multi-omic analysis of longitudinal acute myeloid leukemia patient samples reveals potential prognostic markers linked to disease progression

Relapse remains a determinant of treatment failure and contributes significantly to mortality in acute myeloid leukemia (AML) patients. Despite efforts to understand AML progression and relapse mechanisms, findings on acquired gene mutations in relapse vary, suggesting inherent genetic heterogeneity and emphasizing the role of epigenetic modifications. We conducted a multi-omic analysis using Omni-C, ATAC-seq, and RNA-seq on longitudinal samples from two adult AML patients at diagnosis and relapse. Herein, we characterized genetic and epigenetic changes in AML progression to elucidate the underlying mechanisms of relapse. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples. Comparing global open chromatin profiles revealed that relapse samples had significantly fewer accessible chromatin regions than diagnosis samples. In addition, we discovered that relapse-related upregulation was achieved either by forming new active enhancer contacts or by losing interactions with poised enhancers/potential silencers. Altogether, our study highlights the impact of genetic and epigenetic changes on AML progression, underlining the importance of multi-omic approaches in understanding disease relapse mechanisms and guiding potential therapeutic interventions.

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of variants occur within non-coding genomic regions. We designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Applying this framework to sequencing data from a large prostate cancer patient cohort revealed a large set of candidate drivers. We used (1) in silico analyses, (2) massively parallel reporter assays, and (3) in vivo CRISPR interference screens to systematically validate metastatic castration-resistant prostate cancer (mCRPC) drivers. One identified enhancer region, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of the U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. SF3A1 and CCDC157 promote tumor growth in vivo. We nominated a number of transcription factors, notably SOX6, to regulate expression of SF3A1 and CCDC157. Our integrative approach enables the systematic detection of non-coding regulatory regions that drive human cancers.

Prognostic genome and transcriptome signatures in colorectal cancers

Colorectal cancer is caused by a sequence of somatic genomic alterations affecting driver genes in core cancer pathways1. Here, to understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. From the 96 mutated driver genes, 9 were not previously implicated in colorectal cancer and 24 had not been linked to any cancer. Two distinct patterns of pathway co-mutations were observed, timing analyses identified nine early and three late driver gene mutations, and several signatures of colorectal-cancer-specific mutational processes were identified. Mutations in WNT, EGFR and TGFβ pathway genes, the mitochondrial CYB gene and 3 regulatory elements along with 21 copy-number variations and the COSMIC SBS44 signature correlated with survival. Gene expression classification yielded five prognostic subtypes with distinct molecular features, in part explained by underlying genomic alterations. Microsatellite-instable tumours divided into two classes with different levels of hypoxia and infiltration of immune and stromal cells. To our knowledge, this study constitutes the largest integrated genome and transcriptome analysis of colorectal cancer, and interlinks mutations, gene expression and patient outcomes. The identification of prognostic mutations and expression subtypes can guide future efforts to individualize colorectal cancer therapy.

Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of follicular lymphoma

Mutations in DNA regulatory regions are increasingly being recognized as important drivers of cancer and other complex diseases. These mutations can regulate gene expression by affecting DNA-protein binding and epigenetic profiles, such as DNA methylation in genome regulatory elements. However, identifying mutation hotspots associated with expression regulation and disease progression in non-coding DNA remains a challenge. Unlike most existing approaches that assign a mutation score to individual single nucleotide polymorphisms (SNP), a mutation block (MB)-based approach was introduced in this study to assess the collective impact of a cluster of SNPs on transcription factor-DNA binding affinity, differential gene expression (DEG), and nearby DNA methylation. Moreover, the long-distance target genes of functional MBs were identified using a new permutation-based algorithm that assessed the significance of correlations between DNA methylation at regulatory regions and target gene expression. Two new Python packages were developed. The Differential Methylation Region (DMR-analysis) analysis tool was used to detect DMR and map them to regulatory elements. The second tool, an integrated DMR, DEG, and SNP analysis tool (DDS-analysis), was used to combine the omics data to identify functional MBs and long-distance target genes. Both tools were validated in follicular lymphoma (FL) cohorts, where not only known functional MBs and their target genes (BCL2 and BCL6) were recovered, but also novel genes were found, including CDCA4 and JAG2, which may be associated with FL development. These genes are linked to target gene expression and are significantly correlated with the methylation of nearby DNA sequences in FL. The proposed computational integrative analysis of multiomics data holds promise for identifying regulatory mutations in cancer and other complex diseases.

Whole genome sequencing refines stratification and therapy of patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing for a detailed description of the somatic mutational landscape of ccRCC. We identify candidate driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for therapeutic interventions. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The observations that higher T-cell infiltration is associated with better overall survival and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients.

Assessing the validity of driver gene identification tools for targeted genome sequencing data

Motivation

Most cancer driver gene identification tools have been developed for whole-exome sequencing data. Targeted sequencing is a popular alternative to whole-exome sequencing for large cancer studies due to its greater depth at a lower cost per tumor. Unlike whole-exome sequencing, targeted sequencing only enables mutation calling for a selected subset of genes. Whether existing driver gene identification tools remain valid in that context has not previously been studied.

Results

We evaluated the validity of seven popular driver gene identification tools when applied to targeted sequencing data. Based on whole-exome data of 14 different cancer types from TCGA, we constructed matching targeted datasets by keeping only the mutations overlapping with the pan-cancer MSK-IMPACT panel and, in the case of breast cancer, also the breast-cancer-specific B-CAST panel. We then compared the driver gene predictions obtained on whole-exome and targeted mutation data for each of the seven tools. Differences in how the tools model background mutation rates were the most important determinant of their validity on targeted sequencing data. Based on our results, we recommend OncodriveFML, OncodriveCLUSTL, 20/20+, dNdSCv, and ActiveDriver for driver gene identification in targeted sequencing data, whereas MutSigCV and DriverML are best avoided in that context.

Availability and implementation

Code for the analyses is available at https://github.com/SchmidtGroupNKI/TGSdrivergene_validity.

Genome-wide somatic mutation analysis of sinonasal adenocarcinoma with and without wood dust exposure

BACKGROUND

Sinonasal adenocarcinoma is a rare cancer, encompassing two different entities, the intestinal-type sinonasal adenocarcinoma (ITAC) and the non-intestinal-type sinonasal adenocarcinoma (non-ITAC). Occurrence of ITAC is strongly associated with exposure to hardwood dusts. In countries with predominant exposure to softwood dust the occurrence of sinonasal adenocarcinomas is lower and the relative amount of non-ITACs to ITACs is higher. The molecular mechanisms behind the tumorigenic effects of wood dust remain largely unknown.

METHODS

We carried out whole-genome sequencing of formalin-fixed paraffin-embedded (FFPE) samples of sinonasal adenocarcinomas from ten wood dust-exposed and six non-exposed individuals, with partial tobacco exposure data. Sequences were analyzed for the presence of mutational signatures matching COSMIC database signatures. Driver mutations and CN variant regions were characterized.

RESULTS

Mutation burden was higher in samples of wood dust-exposed patients (p = 0.016). Reactive oxygen species (ROS) damage-related mutational signatures were almost exclusively identified in ITAC subtype samples (p = 0.00055). Tobacco smoke mutational signatures were observed in samples of patients with tobacco exposure or missing information, but not in samples from non-exposed patients. A tetraploidy copy number (CN) signature was enriched in ITAC subtype (p = 0.042). CN variation included recurrent gains in COSMIC Cancer Gene Census genes TERT, SDHA, RAC1, ETV1, PCM1, and MYC. Pathogenic variants were observed most frequently in TP53, NF1, CHD2, BRAF, APC, and LRP1B. Driver mutations and copy number gains did not segregate by subtype.

CONCLUSIONS

Our analysis identified distinct mutational characteristics in ITAC and non-ITAC. Mutational signature analysis may eventually become useful for documentation of occupation-related cancer, while the exact mechanisms behind wood dust-driven carcinogenesis remain elusive. The presence of homologous recombination deficiency signatures implies a novel opportunity for treatment, but further studies are needed.

Deep whole-genome analysis of 494 hepatocellular carcinomas

Over half of hepatocellular carcinoma (HCC) cases diagnosed worldwide are in China1-3. However, whole-genome analysis of hepatitis B virus (HBV)-associated HCC in Chinese individuals is limited4-8, with current analyses of HCC mainly from non-HBV-enriched populations9,10. Here we initiated the Chinese Liver Cancer Atlas (CLCA) project and performed deep whole-genome sequencing (average depth, 120×) of 494 HCC tumours. We identified 6 coding and 28 non-coding previously undescribed driver candidates. Five previously undescribed mutational signatures were found, including aristolochic-acid-associated indel and doublet base signatures, and a single-base-substitution signature that we termed SBS_H8. Pentanucleotide context analysis and experimental validation confirmed that SBS_H8 was distinct to the aristolochic-acid-associated SBS22. Notably, HBV integrations could take the form of extrachromosomal circular DNA, resulting in elevated copy numbers and gene expression. Our high-depth data also enabled us to characterize subclonal clustered alterations, including chromothripsis, chromoplexy and kataegis, suggesting that these catastrophic events could also occur in late stages of hepatocarcinogenesis. Pathway analysis of all classes of alterations further linked non-coding mutations to dysregulation of liver metabolism. Finally, we performed in vitro and in vivo assays to show that fibrinogen alpha chain (FGA), determined as both a candidate coding and non-coding driver, regulates HCC progression and metastasis. Our CLCA study depicts a detailed genomic landscape and evolutionary history of HCC in Chinese individuals, providing important clinical implications.

Genetic and transcriptomic analyses of diffuse large B-cell lymphoma patients with poor outcomes within two years of diagnosis

Despite the improvements in clinical outcomes for DLBCL, a significant proportion of patients still face challenges with refractory/relapsed (R/R) disease after receiving first-line R-CHOP treatment. To further elucidate the underlying mechanism of R/R disease and to develop methods for identifying patients at risk of early disease progression, we integrated clinical, genetic and transcriptomic data derived from 2805 R-CHOP-treated patients from seven independent cohorts. Among these, 887 patients exhibited R/R disease within two years (poor outcome), and 1918 patients remained in remission at two years (good outcome). Our analysis identified four preferentially mutated genes (TP53, MYD88, SPEN, MYC) in the untreated (diagnostic) tumor samples from patients with poor outcomes. Furthermore, transcriptomic analysis revealed a distinct gene expression pattern linked to poor outcomes, affecting pathways involved in cell adhesion/migration, T-cell activation/regulation, PI3K, and NF-κB signaling. Moreover, we developed and validated a 24-gene expression score as an independent prognostic predictor for treatment outcomes. This score also demonstrated efficacy in further stratifying high-risk patients when integrated with existing genetic or cell-of-origin subtypes, including the unclassified cases in these models. Finally, based on these findings, we developed an online analysis tool ( https://lymphprog.serve.scilifelab.se/app/lymphprog ) that can be used for prognostic prediction for DLBCL patients.

Identification of somatic mutation-driven enhancers and their clinical utility in breast cancer

Somatic mutations contribute to cancer development by altering the activity of enhancers. In the study, a total of 135 mutation-driven enhancers, which displayed significant chromatin accessibility changes, were identified as candidate risk factors for breast cancer (BRCA). Furthermore, we identified four mutation-driven enhancers as independent prognostic factors for BRCA subtypes. In Her2 subtype, enhancer G > C mutation was associated with poorer prognosis through influencing its potential target genes FBXW9, TRIR, and WDR83. We identified aminoglutethimide and quinpirole as candidate drugs targeting the mutated enhancer. In normal subtype, enhancer G > A mutation was associated with poorer prognosis through influencing its target genes ALOX15B, LINC00324, and MPDU1. We identified eight candidate drugs such as erastin, colforsin, and STOCK1N-35874 targeting the mutated enhancer. Our findings suggest that somatic mutations contribute to breast cancer subtype progression by altering enhancer activity, which could be potential candidates for cancer therapy.

Canonical BAF complex regulates the oncogenic program in human T-cell acute lymphoblastic leukemia

ABSTRACT

Acute leukemia cells require bone marrow microenvironments, known as niches, which provide leukemic cells with niche factors that are essential for leukemic cell survival and/or proliferation. However, it remains unclear how the dynamics of the leukemic cell-niche interaction are regulated. Using a genome-wide CRISPR screen, we discovered that canonical BRG1/BRM-associated factor (cBAF), a variant of the switch/sucrose nonfermenting chromatin remodeling complex, regulates the migratory response of human T-cell acute lymphoblastic leukemia (T-ALL) cells to a niche factor CXCL12. Mechanistically, cBAF maintains chromatin accessibility and allows RUNX1 to bind to CXCR4 enhancer regions. cBAF inhibition evicts RUNX1 from the genome, resulting in CXCR4 downregulation and impaired migration activity. In addition, cBAF maintains chromatin accessibility preferentially at RUNX1 binding sites, ensuring RUNX1 binding at these sites, and is required for expression of RUNX1-regulated genes, such as CDK6; therefore, cBAF inhibition negatively impacts cell proliferation and profoundly induces apoptosis. This anticancer effect was also confirmed using T-ALL xenograft models, suggesting cBAF as a promising therapeutic target. Thus, we provide novel evidence that cBAF regulates the RUNX1-driven leukemic program and governs migration activity toward CXCL12 and cell-autonomous growth in human T-ALL.

Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks

The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and describing the complex alterations driving cancer. While the knowledge of cancer development increases daily, many bioinformatic pipelines rely on single nucleotide variants or alterations in a vacuum without accounting for cellular compartments, mutational burden or disease progression. Even within bioinformatics and computational cancer biology, the research fields work in silos, risking overlooking potential synergies or breakthroughs. Here, we provide an overview of databases and datasets for building or testing predictive cancer driver tools. Furthermore, we introduce predictive tools for driver genes, driver mutations, and the impact of these based on structural analysis. Additionally, we suggest and recommend directions in the field to avoid silo-research, moving towards integrative frameworks.

Whole genome sequencing refines stratification and therapy of patients with clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common form of kidney cancer, but a comprehensive description of its genomic landscape is lacking. We report the whole genome sequencing of 778 ccRCC patients enrolled in the 100,000 Genomes Project, providing the most detailed somatic mutational landscape to date. We identify new driver genes, which as well as emphasising the major role of epigenetic regulation in ccRCC highlight additional biological pathways extending opportunities for drug repurposing. Genomic characterisation identified patients with divergent clinical outcome; higher number of structural copy number alterations associated with poorer prognosis, whereas VHL mutations were independently associated with a better prognosis. The twin observations that higher T-cell infiltration is associated with better outcome and that genetically predicted immune evasion is not common supports the rationale for immunotherapy. These findings should inform personalised surveillance and treatment strategies for ccRCC patients.

Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes

Genetic studies have associated thousands of enhancers with breast cancer. However, the vast majority have not been functionally characterized. Thus, it remains unclear how variant-associated enhancers contribute to cancer. Here, we perform single-cell CRISPRi screens of 3,512 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of >500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of variant-associated enhancers disrupts breast cancer gene programs. We observe variant-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple variant-associated enhancers indirectly regulate TP53. Comparative studies illustrate sub-type specific functions between enhancers in ER+ and ER- cells. Finally, we developed the pySpade package to facilitate analysis of single-cell enhancer screens. Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Tumour mutations in long noncoding RNAs enhance cell fitness

Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness.

Integrative identification of non-coding regulatory regions driving metastatic prostate cancer

Large-scale sequencing efforts of thousands of tumor samples have been undertaken to understand the mutational landscape of the coding genome. However, the vast majority of germline and somatic variants occur within non-coding portions of the genome. These genomic regions do not directly encode for specific proteins, but can play key roles in cancer progression, for example by driving aberrant gene expression control. Here, we designed an integrative computational and experimental framework to identify recurrently mutated non-coding regulatory regions that drive tumor progression. Application of this approach to whole-genome sequencing (WGS) data from a large cohort of metastatic castration-resistant prostate cancer (mCRPC) revealed a large set of recurrently mutated regions. We used (i) in silico prioritization of functional non-coding mutations, (ii) massively parallel reporter assays, and (iii) in vivo CRISPR-interference (CRISPRi) screens in xenografted mice to systematically identify and validate driver regulatory regions that drive mCRPC. We discovered that one of these enhancer regions, GH22I030351, acts on a bidirectional promoter to simultaneously modulate expression of U2-associated splicing factor SF3A1 and chromosomal protein CCDC157. We found that both SF3A1 and CCDC157 are promoters of tumor growth in xenograft models of prostate cancer. We nominated a number of transcription factors, including SOX6, to be responsible for higher expression of SF3A1 and CCDC157. Collectively, we have established and confirmed an integrative computational and experimental approach that enables the systematic detection of non-coding regulatory regions that drive the progression of human cancers.

Pan-cancer whole-genome comparison of primary and metastatic solid tumours

Metastatic cancer remains an almost inevitably lethal disease1-3. A better understanding of disease progression and response to therapies therefore remains of utmost importance. Here we characterize the genomic differences between early-stage untreated primary tumours and late-stage treated metastatic tumours using a harmonized pan-cancer analysis (or reanalysis) of two unpaired primary4 and metastatic5 cohorts of 7,108 whole-genome-sequenced tumours. Metastatic tumours in general have a lower intratumour heterogeneity and a conserved karyotype, displaying only a modest increase in mutations, although frequencies of structural variants are elevated overall. Furthermore, highly variable tumour-specific contributions of mutational footprints of endogenous (for example, SBS1 and APOBEC) and exogenous mutational processes (for example, platinum treatment) are present. The majority of cancer types had either moderate genomic differences (for example, lung adenocarcinoma) or highly consistent genomic portraits (for example, ovarian serous carcinoma) when comparing early-stage and late-stage disease. Breast, prostate, thyroid and kidney renal clear cell carcinomas and pancreatic neuroendocrine tumours are clear exceptions to the rule, displaying an extensive transformation of their genomic landscape in advanced stages. Exposure to treatment further scars the tumour genome and introduces an evolutionary bottleneck that selects for known therapy-resistant drivers in approximately half of treated patients. Our data showcase the potential of pan-cancer whole-genome analysis to identify distinctive features of late-stage tumours and provide a valuable resource to further investigate the biological basis of cancer and resistance to therapies.

Cancer driver mutations: predictions and reality

Cancer cells accumulate many genetic alterations throughout their lifetime, but only a few of them drive cancer progression, termed driver mutations. Driver mutations may vary between cancer types and patients, can remain latent for a long time and become drivers at particular cancer stages, or may drive oncogenesis only in conjunction with other mutations. The high mutational, biochemical, and histological tumor heterogeneity makes driver mutation identification very challenging. In this review we summarize recent efforts to identify driver mutations in cancer and annotate their effects. We underline the success of computational methods to predict driver mutations in finding novel cancer biomarkers, including in circulating tumor DNA (ctDNA). We also report on the boundaries of their applicability in clinical research.

Dr.Nod: computational framework for discovery of regulatory non-coding drivers in tissue-matched distal regulatory elements

The discovery of cancer driver mutations is a fundamental goal in cancer research. While many cancer driver mutations have been discovered in the protein-coding genome, research into potential cancer drivers in the non-coding regions showed limited success so far. Here, we present a novel comprehensive framework Dr.Nod for detection of non-coding cis-regulatory candidate driver mutations that are associated with dysregulated gene expression using tissue-matched enhancer-gene annotations. Applying the framework to data from over 1500 tumours across eight tissues revealed a 4.4-fold enrichment of candidate driver mutations in regulatory regions of known cancer driver genes. An overarching conclusion that emerges is that the non-coding driver mutations contribute to cancer by significantly altering transcription factor binding sites, leading to upregulation of tissue-matched oncogenes and down-regulation of tumour-suppressor genes. Interestingly, more than half of the detected cancer-promoting non-coding regulatory driver mutations are over 20 kb distant from the cancer-associated genes they regulate. Our results show the importance of tissue-matched enhancer-gene maps, functional impact of mutations, and complex background mutagenesis model for the prediction of non-coding regulatory drivers. In conclusion, our study demonstrates that non-coding mutations in enhancers play a previously underappreciated role in cancer and dysregulation of clinically relevant target genes.

Chromosome conformation capture approaches to investigate 3D genome architecture in Ankylosing Spondylitis

Ankylosing Spondylitis (AS) is a chronic inflammatory arthritis of the spine exhibiting a strong genetic background. The mechanistic and functional understanding of the AS-associated genomic loci, identified with Genome Wide Association Studies (GWAS), remains challenging. Chromosome conformation capture (3C) and derivatives are recent techniques which are of great help in elucidating the spatial genome organization and of enormous support in uncover a mechanistic explanation for disease-associated genetic variants. The perturbation of three-dimensional (3D) genome hierarchy may lead to a plethora of human diseases, including rheumatological disorders. Here we illustrate the latest approaches and related findings on the field of genome organization, highlighting how the instability of 3D genome conformation may be among the causes of rheumatological disease phenotypes. We suggest a new perspective on the inclusive potential of a 3C approach to inform GWAS results in rheumatic diseases. 3D genome organization may ultimately lead to a more precise and comprehensive functional interpretation of AS association, which is the starting point for emerging and more specific therapies.

The sodium new houttuyfonate suppresses NSCLC via activating pyroptosis through TCONS-14036/miR-1228-5p/PRKCDBP pathway

Several studies have suggested the potential value of Houttuynia cordata as a therapeutic agent in lung cancer, but direct evidence is still lacking. The study aimed to determine the regulatory impact of a major H. cordata constituent derivative (sodium new houttuyfonate [SNH]) on lncRNA networks in non-small cell lung cancer (NSCLC) to identify new potential therapeutic targets. After exposing NSCLC cells to SNH, we analysed the following: cell death (via flow cytometry, TUNEL and ASC speck formation assays), immune factors (via ELISA), gene transcription (via RT-qPCR), subcellular localisation (via FISH), gene-gene and gene-protein interactions (via dual-luciferase reporter and RNA immunoprecipitation assays, respectively) and protein expression and distribution (via western blotting and immunocytochemistry or immunohistochemistry). In addition, statistical analysis (via one-way ANOVA or unpaired t-tests) was performed. Exposure to SNH promoted NSCLC cell pyroptosis, concomitant with significant up-regulation of TCONS-14036, a novel lncRNA. Mechanistic research demonstrated that TCONS-14036 functions as a competing endogenous (ce)RNA by sequestering microRNA (miR)-1228-5p, thereby up-regulating PRKCDBP-encoding transcript levels. Indeed, PRKCDBP promoted pyroptosis by activating the NLRP3 inflammasome, resulting in CASP1, IL-1β and GSDMD cleavage. Our findings elucidate the potential molecular mechanisms underlying the ability of SNH to suppress NSCLC growth through activation of pyroptosis via the TCONS-14036/miR-1228-5p/PRKCDBP pathway. Thus, we identify a new potential therapeutic targets for NSCLC.

maxATAC: Genome-scale transcription-factor binding prediction from ATAC-seq with deep neural networks

Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built "maxATAC", a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.

Comparative Genomics Provides Etiologic and Biological Insight into Melanoma Subtypes

Melanoma is a cancer of melanocytes, with multiple subtypes based on body site location. Cutaneous melanoma is associated with skin exposed to ultraviolet radiation; uveal melanoma occurs in the eyes; mucosal melanoma occurs in internal mucous membranes; and acral melanoma occurs on the palms, soles, and nail beds. Here, we present the largest whole-genome sequencing study of melanoma to date, with 570 tumors profiled, as well as methylation and RNA sequencing for subsets of tumors. Uveal melanoma is genomically distinct from other melanoma subtypes, harboring the lowest tumor mutation burden and with significantly mutated genes in the G-protein signaling pathway. Most cutaneous, acral, and mucosal melanomas share alterations in components of the MAPK, PI3K, p53, p16, and telomere pathways. However, the mechanism by which these pathways are activated or inactivated varies between melanoma subtypes. Additionally, we identify potential novel germline predisposition genes for some of the less common melanoma subtypes.

SIGNIFICANCE

This is the largest whole-genome analysis of melanoma to date, comprehensively comparing the genomics of the four major melanoma subtypes. This study highlights both similarities and differences between the subtypes, providing insights into the etiology and biology of melanoma. This article is highlighted in the In This Issue feature, p. 2711.

Somatic mutation distribution across tumour cohorts provides a signal for positive selection in cancer

Cancer gene discovery is reliant on distinguishing driver mutations from a multitude of passenger mutations in tumour genomes. While driver genes may be revealed based on excess mutation recurrence or clustering, there is a need for orthogonal principles. Here, we take advantage of the fact that non-cancer genes, containing only passenger mutations under neutral selection, exhibit a likelihood of mutagenesis in a given tumour determined by the tumour's mutational signature and burden. This relationship can be disrupted by positive selection, leading to a difference in the distribution of mutated cases across a cohort for driver and passenger genes. We apply this principle to detect cancer drivers independently of recurrence in large pan-cancer cohorts, and show that our method (SEISMIC) performs comparably to traditional approaches and can provide resistance to known confounding mutational phenomena. Being based on a different principle, the approach provides a much-needed complement to existing methods for detecting signals of selection.

Untangling the roles of TOP2A and TOP2B in transcription and cancer

Type II topoisomerases (TOP2) are conserved regulators of chromatin topology that catalyze reversible DNA double-strand breaks (DSBs) and are essential for maintaining genomic integrity in diverse dynamic processes such as transcription, replication, and cell division. While controlled TOP2-mediated DSBs are an elegant solution to topological constraints of DNA, DSBs also contribute to the emergence of chromosomal translocations and mutations that drive cancer. The central importance of TOP2 enzymes as frontline chemotherapeutic targets is well known; however, their precise biological functions and impact in cancer development are still poorly understood. In this review, we provide an updated overview of TOP2A and TOP2B in the regulation of chromatin topology and transcription, and discuss the recent discoveries linking TOP2 activities with cancer pathogenesis.

Genome-wide mapping of somatic mutation rates uncovers drivers of cancer

Identification of cancer driver mutations that confer a proliferative advantage is central to understanding cancer; however, searches have often been limited to protein-coding sequences and specific non-coding elements (for example, promoters) because of the challenge of modeling the highly variable somatic mutation rates observed across tumor genomes. Here we present Dig, a method to search for driver elements and mutations anywhere in the genome. We use deep neural networks to map cancer-specific mutation rates genome-wide at kilobase-scale resolution. These estimates are then refined to search for evidence of driver mutations under positive selection throughout the genome by comparing observed to expected mutation counts. We mapped mutation rates for 37 cancer types and applied these maps to identify putative drivers within intronic cryptic splice regions, 5' untranslated regions and infrequently mutated genes. Our high-resolution mutation rate maps, available for web-based exploration, are a resource to enable driver discovery genome-wide.

Interpretation of the role of germline and somatic non-coding mutations in cancer: expression and chromatin conformation informed analysis

BACKGROUND

There has been extensive scrutiny of cancer driving mutations within the exome (especially amino acid altering mutations) as these are more likely to have a clear impact on protein functions, and thus on cell biology. However, this has come at the neglect of systematic identification of regulatory (non-coding) variants, which have recently been identified as putative somatic drivers and key germline risk factors for cancer development. Comprehensive understanding of non-coding mutations requires understanding their role in the disruption of regulatory elements, which then disrupt key biological functions such as gene expression.

MAIN BODY

We describe how advancements in sequencing technologies have led to the identification of a large number of non-coding mutations with uncharacterized biological significance. We summarize the strategies that have been developed to interpret and prioritize the biological mechanisms impacted by non-coding mutations, focusing on recent annotation of cancer non-coding variants utilizing chromatin states, eQTLs, and chromatin conformation data.

CONCLUSION

We believe that a better understanding of how to apply different regulatory data types into the study of non-coding mutations will enhance the discovery of novel mechanisms driving cancer.

Comprehensive characterization of posttranscriptional impairment-related 3'-UTR mutations in 2413 whole genomes of cancer patients

The 3' untranslated region (3'-UTR) is the vital element regulating gene expression, but most studies have focused on variations in RNA-binding proteins (RBPs), miRNAs, alternative polyadenylation (APA) and RNA modifications. To explore the posttranscriptional function of 3'-UTR somatic mutations in tumorigenesis, we collected whole-genome data from 2413 patients across 18 cancer types. Our updated algorithm, PIVar, revealed 25,216 3'-UTR posttranscriptional impairment-related SNVs (3'-UTR piSNVs) spanning 2930 genes; 24 related RBPs were significantly enriched. The somatic 3'-UTR piSNV ratio was markedly increased across all 18 cancer types, which was associated with worse survival for four cancer types. Several cancer-related genes appeared to facilitate tumorigenesis at the protein and posttranscriptional regulation levels, whereas some 3'-UTR piSNV-affected genes functioned mainly via posttranscriptional mechanisms. Moreover, we assessed immune cell and checkpoint characteristics between the high/low 3'-UTR piSNV ratio groups and predicted 80 compounds associated with the 3'-UTR piSNV-affected gene expression signature. In summary, our study revealed the prevalence and clinical relevance of 3'-UTR piSNVs in cancers, and also demonstrates that in addition to affecting miRNAs, 3'-UTR piSNVs perturb RBPs binding, APA and m6A RNA modification, which emphasized the importance of considering 3'-UTR piSNVs in cancer biology.

Alterations in transcriptional networks in cancer: the role of noncoding somatic driver mutations

Aberrant gene expression is a cancer hallmark and it is known that almost every tumor acquires somatic mutations in transcription factors, chromatin regulators, or the DNA regulatory elements that are critical for transcriptional control and cell phenotype. While the role of transcription factors and chromatin regulators has been widely studied, relatively few noncoding driver mutations have been identified and functionally characterized to date. Here, we review the current understanding of somatic variants in noncoding regions of the cancer genome and their impact on chromatin architecture and transcriptional networks. We also discuss approaches and ongoing challenges for noncoding driver discovery, and highlight insights gained from recent studies exploring the nature and impact of noncoding drivers on tumor formation.

Computational analysis of cancer genome sequencing data

Distilling biologically meaningful information from cancer genome sequencing data requires comprehensive identification of somatic alterations using rigorous computational methods. As the amount and complexity of sequencing data have increased, so has the number of tools for analysing them. Here, we describe the main steps involved in the bioinformatic analysis of cancer genomes, review key algorithmic developments and highlight popular tools and emerging technologies. These tools include those that identify point mutations, copy number alterations, structural variations and mutational signatures in cancer genomes. We also discuss issues in experimental design, the strengths and limitations of sequencing modalities and methodological challenges for the future.

Pan-cancer analysis of mutations in open chromatin regions and their possible association with cancer pathogenesis

BACKGROUND

Open chromatin is associated with gene transcription. Previous studies have shown that the density of mutations in open chromatin regions is lower than that in flanking regions because of the higher accessibility of DNA repair machinery. However, in several cancer types, open chromatin regions show an increased local density of mutations in activated regulatory regions. Although the mutation distribution within open chromatin regions in cancer cells has been investigated, only few studies have focused on their functional implications in cancer. To reveal the impact of highly mutated open chromatin regions on cancer, we investigated the association between mutations in open chromatin regions and their possible functions.

METHODS

Whole-genome sequencing data of 18 cancer types were downloaded from the PanCancer Analysis of Whole Genomes and Catalog of Somatic Mutations in Cancer. We quantified the mutations located in open chromatin regions defined by The Cancer Genome Atlas and classified open chromatin regions into three categories based on the number of mutations. Then, we investigated the chromatin state, amplification, and possible target genes of the open chromatin regions with a high number of mutations. We also analyzed the association between the number of mutations in open chromatin regions and patient prognosis.

RESULTS

In some cancer types, the proportion of promoter or enhancer chromatin state in open chromatin regions with a high number of mutations was significantly higher than that in the regions with a low number of mutations. The possible target genes of open chromatin regions with a high number of mutations were more strongly associated with cancer than those of other open chromatin regions. Moreover, a high number of mutations in open chromatin regions was significantly associated with a poor prognosis in some cancer types.

CONCLUSIONS

These results suggest that highly mutated open chromatin regions play an important role in cancer pathogenesis and can be effectively used to predict patient prognosis.

Exploring the effects of genetic variation on gene regulation in cancer in the context of 3D genome structure

Background

Numerous genome-wide association studies (GWAS) conducted to date revealed genetic variants associated with various diseases, including breast and prostate cancers. Despite the availability of these large-scale data, relatively few variants have been functionally characterized, mainly because the majority of single-nucleotide polymorphisms (SNPs) map to the non-coding regions of the human genome. The functional characterization of these non-coding variants and the identification of their target genes remain challenging.

Results

In this communication, we explore the potential functional mechanisms of non-coding SNPs by integrating GWAS with the high-resolution chromosome conformation capture (Hi-C) data for breast and prostate cancers. We show that more genetic variants map to regulatory elements through the 3D genome structure than the 1D linear genome lacking physical chromatin interactions. Importantly, the association of enhancers, transcription factors, and their target genes with breast and prostate cancers tends to be higher when these regulatory elements are mapped to high-risk SNPs through spatial interactions compared to simply using a linear proximity. Finally, we demonstrate that topologically associating domains (TADs) carrying high-risk SNPs also contain gene regulatory elements whose association with cancer is generally higher than those belonging to control TADs containing no high-risk variants.

Conclusions

Our results suggest that many SNPs may contribute to the cancer development by affecting the expression of certain tumor-related genes through long-range chromatin interactions with gene regulatory elements. Integrating large-scale genetic datasets with the 3D genome structure offers an attractive and unique approach to systematically investigate the functional mechanisms of genetic variants in disease risk and progression.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-021-01021-x.

Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource

Background

Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development.

Results

Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation.

Conclusions

Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02607-z.

Mutations in Noncoding Cis-Regulatory Elements Reveal Cancer Driver Cistromes in Luminal Breast Cancer

Whole-genome sequencing of primary breast tumors enabled the identification of cancer driver genes and noncoding cancer driver plexuses from somatic mutations. However, differentiating driver from passenger events among noncoding genetic variants remains a challenge. Herein, we reveal cancer-driver cis-regulatory elements linked to transcription factors previously shown to be involved in development of luminal breast cancers by defining a tumor-enriched catalogue of approximately 100,000 unique cis-regulatory elements from 26 primary luminal estrogen receptor (ER)⁺ progesterone receptor (PR)⁺ breast tumors. Integrating this catalog with somatic mutations from 350 publicly available breast tumor whole genomes, we uncovered cancer driver cistromes, defined as the sum of binding sites for a transcription factor, for ten transcription factors in luminal breast cancer such as FOXA1 and ER, nine of which are essential for growth in breast cancer with four exclusive to the luminal subtype. Collectively, we present a strategy to find cancer driver cistromes relying on quantifying the enrichment of noncoding mutations over cis-regulatory elements concatenated into a functional unit. IMPLICATIONS: Mapping the accessible chromatin of luminal breast cancer led to discovery of an accumulation of mutations within cistromes of transcription factors essential to luminal breast cancer. This demonstrates coopting of regulatory networks to drive cancer and provides a framework to derive insight into the noncoding space of cancer.

Decoding human cancer with whole genome sequencing: a review of PCAWG Project studies published in February 2020

Cancer is underlined by genetic changes. In an unprecedented international effort, the Pan-Cancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) sequenced the tumors of over two thousand five hundred patients across 38 different cancer types, as well as the corresponding healthy tissue, with the aim of identifying genome-wide mutations exclusively found in cancer and uncovering new genetic changes that drive tumor formation. What set this project apart from earlier efforts is the use of whole genome sequencing (WGS) that enabled to explore alterations beyond the coding DNA, into cancer's non-coding genome. WGS of the entire cohort allowed to tease apart driving mutations that initiate and support carcinogenesis from passenger mutations that do not play an overt role in the disease. At least one causative mutation was found in 95% of all cancers, with many tumors showing an average of 5 driver mutations. The PCAWG Project also assessed the transcriptional output altered in cancer and rebuilt the evolutionary history of each tumor showing that initial driver mutations can occur years if not decades prior to a diagnosis. Here, I provide a concise review of the Pan-Cancer Project papers published on February 2020, along with key computational tools and the digital framework generated as part of the project. This represents an historic effort by hundreds of international collaborators, which provides a comprehensive understanding of cancer genetics, with publicly available data and resources representing a treasure trove of information to advance cancer research for years to come.

Non-Coding Variants in Cancer: Mechanistic Insights and Clinical Potential for Personalized Medicine

The cancer genome is characterized by extensive variability, in the form of Single Nucleotide Polymorphisms (SNPs) or structural variations such as Copy Number Alterations (CNAs) across wider genomic areas. At the molecular level, most SNPs and/or CNAs reside in non-coding sequences, ultimately affecting the regulation of oncogenes and/or tumor-suppressors in a cancer-specific manner. Notably, inherited non-coding variants can predispose for cancer decades prior to disease onset. Furthermore, accumulation of additional non-coding driver mutations during progression of the disease, gives rise to genomic instability, acting as the driving force of neoplastic development and malignant evolution. Therefore, detection and characterization of such mutations can improve risk assessment for healthy carriers and expand the diagnostic and therapeutic toolbox for the patient. This review focuses on functional variants that reside in transcribed or not transcribed non-coding regions of the cancer genome and presents a collection of appropriate state-of-the-art methodologies to study them.

Non-coding driver mutations in human cancer

Tumour formation involves random mutagenic events and positive evolutionary selection acting on a subset of such events, referred to as driver mutations. A decade of careful surveying of tumour DNA using exome-based analyses has revealed a multitude of protein-coding somatic driver mutations, some of which are clinically actionable. Today, a transition towards whole-genome analysis is well under way, technically enabling the discovery of potential driver mutations occurring outside protein-coding sequences. Mutations are abundant in this vast non-coding space, which is more than 50 times larger than the coding exome, but reliable identification of selection signals in non-coding DNA remains a challenge. In this Review, we discuss recent findings in the field, where the emerging landscape is one in which non-coding driver mutations appear to be relatively infrequent. Nevertheless, we highlight several notable discoveries. We consider possible reasons for the relative absence of non-coding driver events, as well as the difficulties associated with detecting signals of positive selection in non-coding DNA.

Functional annotation of noncoding mutations in cancer

Recurrent regulatory mutations affecting transcription factor binding sites in 2,500 cancer samples.

In a cancer genome, the noncoding sequence contains the vast majority of somatic mutations. While very few are expected to be cancer drivers, those affecting regulatory elements have the potential to have downstream effects on gene regulation that may contribute to cancer progression. To prioritize regulatory mutations, we screened somatic mutations in the Pan-Cancer Analysis of Whole Genomes cohort of 2,515 cancer genomes on individual bases to assess their potential regulatory roles in their respective cancer types. We found a highly significant enrichment of regulatory mutations associated with the deamination signature overlapping a CpG site in the CCAAT/Enhancer Binding Protein β recognition sites in many cancer types. Overall, 5,749 mutated regulatory elements were identified in 1,844 tumor samples from 39 cohorts containing 11,962 candidate regulatory mutations. Our analysis indicated 20 or more regulatory mutations in 5.5% of the samples, and an overall average of six per tumor. Several recurrent elements were identified, and major cancer-related pathways were significantly enriched for genes nearby the mutated regulatory elements. Our results provide a detailed view of the role of regulatory elements in cancer genomes.

Whole-genome profiling of nasopharyngeal carcinoma reveals viral-host co-operation in inflammatory NF-κB activation and immune escape

Interplay between EBV infection and acquired genetic alterations during nasopharyngeal carcinoma (NPC) development remains vague. Here we report a comprehensive genomic analysis of 70 NPCs, combining whole-genome sequencing (WGS) of microdissected tumor cells with EBV oncogene expression to reveal multiple aspects of cellular-viral co-operation in tumorigenesis. Genomic aberrations along with EBV-encoded LMP1 expression underpin constitutive NF-κB activation in 90% of NPCs. A similar spectrum of somatic aberrations and viral gene expression undermine innate immunity in 79% of cases and adaptive immunity in 47% of cases; mechanisms by which NPC may evade immune surveillance despite its pro-inflammatory phenotype. Additionally, genomic changes impairing TGFBR2 promote oncogenesis and stabilize EBV infection in tumor cells. Fine-mapping of CDKN2A/CDKN2B deletion breakpoints reveals homozygous MTAP deletions in 32-34% of NPCs that confer marked sensitivity to MAT2A inhibition. Our work concludes that NPC is a homogeneously NF-κB-driven and immune-protected, yet potentially druggable, cancer.

Selective requirement of MYB for oncogenic hyperactivation of a translocated enhancer in leukemia

In acute myeloid leukemia (AML) with inv(3)(q21;q26) or t(3;3)(q21;q26), a translocated GATA2 enhancer drives oncogenic expression of EVI1. We generated an EVI1-GFP AML model and applied an unbiased CRISPR/Cas9 enhancer scan to uncover sequence motifs essential for EVI1 transcription. Using this approach, we pinpointed a single regulatory element in the translocated GATA2 enhancer that is critically required for aberrant EVI1 expression. This element contained a DNA binding motif for the transcription factor MYB which specifically occupied this site at the translocated allele and was dispensable for GATA2 expression. MYB knockout as well as peptidomimetic blockade of CBP/p300-dependent MYB functions resulted in downregulation of EVI1 but not of GATA2. Targeting MYB or mutating its DNA-binding motif within the GATA2 enhancer resulted in myeloid differentiation and cell death, suggesting that interference with MYB-driven EVI1 transcription provides a potential entry point for therapy of inv(3)/t(3;3) AMLs.

Decoding the organization, dynamics, and function of the 4D genome

Understanding how complex cell-fate decisions emerge at the molecular level is a key challenge in developmental biology. Despite remarkable progress in decoding the contribution of the linear epigenome, how spatial genome architecture functionally informs changes in gene expression remains unclear. In this review, we discuss recent insights in elucidating the molecular landscape of genome folding, emphasizing the multilayered nature of the 3D genome, its importance for gene regulation, and its spatiotemporal dynamics. Finally, we discuss how these new concepts and emergent technologies will enable us to address some of the outstanding questions in development and disease.

Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes

Background

Cancer genomes are shaped by mutational processes with complex spatial variation at multiple scales. Entire classes of regulatory elements are affected by local variations in mutation frequency. However, the underlying mechanisms with functional and genetic determinants remain poorly understood.

Results

We characterise the mutational landscape of 1.3 million gene-regulatory and chromatin architectural elements in 2419 whole cancer genomes with transcriptional and pathway activity, functional conservation and recurrent driver events. We develop RM2, a statistical model that quantifies mutational enrichment or depletion in classes of genomic elements through genetic, trinucleotide and megabase-scale effects. We report a map of localised mutational processes affecting CTCF binding sites, transcription start sites (TSS) and tissue-specific open-chromatin regions. Increased mutation frequency in TSSs associates with mRNA abundance in most cancer types, while open-chromatin regions are generally enriched in mutations. We identify ~ 10,000 CTCF binding sites with core DNA motifs and constitutive binding in 66 cell types that represent focal points of mutagenesis. We detect site-specific mutational signature enrichments, such as SBS40 in open-chromatin regions in prostate cancer and SBS17b in CTCF binding sites in gastrointestinal cancers. Candidate drivers of localised mutagenesis are also apparent: BRAF mutations associate with mutational enrichments at CTCF binding sites in melanoma, and ARID1A mutations with TSS-specific mutagenesis in pancreatic cancer.

Conclusions

Our method and catalogue of localised mutational processes provide novel perspectives to cancer genome evolution, mutagenesis, DNA repair and driver gene discovery. The functional and genetic correlates of mutational processes suggest mechanistic hypotheses for future studies.

Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers

BACKGROUND

Inflammatory breast cancer (IBC) has a highly invasive and metastatic phenotype. However, little is known about its genetic drivers. To address this, we report the largest cohort of whole-genome sequencing (WGS) of IBC cases.

METHODS

We performed WGS of 20 IBC samples and paired normal blood DNA to identify genomic alterations. For comparison, we used 23 matched non-IBC samples from the Cancer Genome Atlas Program (TCGA). We also validated our findings using WGS data from the International Cancer Genome Consortium (ICGC) and the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We examined a wide selection of genomic features to search for differences between IBC and conventional breast cancer. These include (i) somatic and germline single-nucleotide variants (SNVs), in both coding and non-coding regions; (ii) the mutational signature and the clonal architecture derived from these SNVs; (iii) copy number and structural variants (CNVs and SVs); and (iv) non-human sequence in the tumors (i.e., exogenous sequences of bacterial origin).

RESULTS

Overall, IBC has similar genomic characteristics to non-IBC, including specific alterations, overall mutational load and signature, and tumor heterogeneity. In particular, we observed similar mutation frequencies between IBC and non-IBC, for each gene and most cancer-related pathways. Moreover, we found no exogenous sequences of infectious agents specific to IBC samples. Even though we could not find any strongly statistically distinguishing genomic features between the two groups, we did find some suggestive differences in IBC: (i) The MAST2 gene was more frequently mutated (20% IBC vs. 0% non-IBC). (ii) The TGF β pathway was more frequently disrupted by germline SNVs (50% vs. 13%). (iii) Different copy number profiles were observed in several genomic regions harboring cancer genes. (iv) Complex SVs were more frequent. (v) The clonal architecture was simpler, suggesting more homogenous tumor-evolutionary lineages.

CONCLUSIONS

Whole-genome sequencing of IBC manifests a similar genomic architecture to non-IBC. We found no unique genomic alterations shared in just IBCs; however, subtle genomic differences were observed including germline alterations in TGFβ pathway genes and somatic mutations in the MAST2 kinase that could represent potential therapeutic targets.

Interpretation of allele-specific chromatin accessibility using cell state-aware deep learning

Genomic sequence variation within enhancers and promoters can have a significant impact on the cellular state and phenotype. However, sifting through the millions of candidate variants in a personal genome or a cancer genome, to identify those that impact cis-regulatory function, remains a major challenge. Interpretation of noncoding genome variation benefits from explainable artificial intelligence to predict and interpret the impact of a mutation on gene regulation. Here we generate phased whole genomes with matched chromatin accessibility, histone modifications, and gene expression for 10 melanoma cell lines. We find that training a specialized deep learning model, called DeepMEL2, on melanoma chromatin accessibility data can capture the various regulatory programs of the melanocytic and mesenchymal-like melanoma cell states. This model outperforms motif-based variant scoring, as well as more generic deep learning models. We detect hundreds to thousands of allele-specific chromatin accessibility variants (ASCAVs) in each melanoma genome, of which 15%-20% can be explained by gains or losses of transcription factor binding sites. A considerable fraction of ASCAVs are caused by changes in AP-1 binding, as confirmed by matched ChIP-seq data to identify allele-specific binding of JUN and FOSL1. Finally, by augmenting the DeepMEL2 model with ChIP-seq data for GABPA, the TERT promoter mutation, as well as additional ETS motif gains, can be identified with high confidence. In conclusion, we present a new integrative genomics approach and a deep learning model to identify and interpret functional enhancer mutations with allelic imbalance of chromatin accessibility and gene expression.

Genomic and transcriptomic alterations associated with drug vulnerabilities and prognosis in adenocarcinoma at the gastroesophageal junction

Adenocarcinoma at the gastroesophageal junction (ACGEJ) has dismal clinical outcomes, and there are currently few specific effective therapies because of limited knowledge on its genomic and transcriptomic alterations. The present study investigates genomic and transcriptomic changes in ACGEJ from Chinese patients and analyzes their drug vulnerabilities and associations with the survival time. Here we show that the major genomic changes of Chinese ACGEJ patients are chromosome instability promoted tumorigenic focal copy-number variations and COSMIC Signature 17-featured single nucleotide variations. We provide a comprehensive profile of genetic changes that are potentially vulnerable to existing therapeutic agents and identify Signature 17-correlated IFN-α response pathway as a prognostic marker that might have practical value for clinical prognosis of ACGEJ. These findings further our understanding on the molecular biology of ACGEJ and may help develop more effective therapeutic strategies.

Adenocarcinoma at the gastroesophageal junction has a dismal prognosis and few drug options. Here, the authors present genomic and transcriptomic features and potential therapeutic targets and prognostic biomarkers of Chinese and Caucasian tumours, and reveal the molecular similarities.

Genome-Wide Somatic Alterations in Multiple Myeloma Reveal a Superior Outcome Group

PURPOSE

Multiple myeloma (MM) is accompanied by heterogeneous somatic alterations. The overall goal of this study was to describe the genomic landscape of myeloma using deep whole-genome sequencing (WGS) and develop a model that identifies patients with long survival.

METHODS

We analyzed deep WGS data from 183 newly diagnosed patients with MM treated with lenalidomide, bortezomib, and dexamethasone (RVD) alone or RVD + autologous stem cell transplant (ASCT) in the IFM/DFCI 2009 study (ClinicalTrials.gov identifier: NCT01191060). We integrated genomic markers with clinical data.

RESULTS

We report significant variability in mutational load and processes within MM subgroups. The timeline of observed activation of mutational processes provides the basis for 2 distinct models of acquisition of mutational changes detected at the time of diagnosis of myeloma. Virtually all MM subgroups have activated DNA repair-associated signature as a prominent late mutational process, whereas APOBEC signature targeting C>G is activated in the intermediate phase of disease progression in high-risk MM. Importantly, we identify a genomically defined MM subgroup (17% of newly diagnosed patients) with low DNA damage (low genomic scar score with chromosome 9 gain) and a superior outcome (100% overall survival at 69 months), which was validated in a large independent cohort. This subgroup allowed us to distinguish patients with low- and high-risk hyperdiploid MM and identify patients with prolongation of progression-free survival. Genomic characteristics of this subgroup included lower mutational load with significant contribution from age-related mutations as well as frequent NRAS mutation. Surprisingly, their overall survival was independent of International Staging System and minimal residual disease status.

CONCLUSION

This is a comprehensive study identifying genomic markers of a good-risk group with prolonged survival. Identification of this patient subgroup will affect future therapeutic algorithms and research planning.

Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes.. [DOI: 10.1101/2020.07.29.226373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background

Cancer genomes are shaped by mutational processes with complex spatial variation at multiple scales. Entire classes of regulatory elements are affected by local variations in mutation frequency. However, the underlying mutational mechanisms with functional and genetic determinants remain poorly understood.

Results

We characterised the mutational landscape of 1.3 million gene regulatory and chromatin architectural elements in 2,419 whole cancer genomes with transcriptional and pathway activity, functional conservation and recurrent driver events. We developed RM2, a statistical model that quantifies mutational enrichment or depletion in classes of genomic elements through genetic, trinucleotide and megabase-scale effects. We report a map of localised mutational processes affecting CTCF binding sites, transcription start sites (TSS) and tissue-specific open-chromatin regions. We show that increased mutational frequency in TSSs correlates with mRNA abundance in most cancer types, while open-chromatin regions are generally enriched in mutations. We identified ∼10,000 CTCF binding sites with core DNA motifs and constitutive binding in 66 cell types that represent focal points of local mutagenesis. We detected site-specific mutational signatures, such as SBS40 in open-chromatin regions in prostate cancer and SBS17b in CTCF binding sites in gastrointestinal cancers. We also proposed candidate drivers of localised mutagenesis: BRAF mutations associate with mutational enrichments at CTCF binding sites in melanoma, and ARID1A mutations with TSS-specific mutations in pancreatic cancer.

Conclusions

Our method and catalogue of localised mutational processes provide novel perspectives to cancer genome evolution, mutagenesis, DNA repair and driver discovery. Functional and genetic correlates of localised mutagenesis provide mechanistic hypotheses for future studies.

MutSpot: detection of non-coding mutation hotspots in cancer genomes

Recurrence and clustering of somatic mutations (hotspots) in cancer genomes may indicate positive selection and involvement in tumorigenesis. MutSpot performs genome-wide inference of mutation hotspots in non-coding and regulatory DNA of cancer genomes. MutSpot performs feature selection across hundreds of epigenetic and sequence features followed by estimation of position- and patient-specific background somatic mutation probabilities. MutSpot is user-friendly, works on a standard workstation, and scales to thousands of cancer genomes.

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.