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

A comprehensive catalog of cancer driver mutations is essential for understanding tumorigenesis and developing therapies. Exome-sequencing studies have mapped many protein-coding drivers, yet few non-coding drivers are known because genome-wide discovery is challenging. We developed a driver discovery method, ActiveDriverWGS, and analyzed 120,788 cis-regulatory modules (CRMs) across 1,844 whole tumor genomes from the ICGC-TCGA PCAWG project. We found 30 CRMs with enriched SNVs and indels (FDR < 0.05). These frequently mutated regulatory elements (FMREs) were ubiquitously active in human tissues, showed long-range chromatin interactions and mRNA abundance associations with target genes, and were enriched in motif-rewiring mutations and structural variants. Genomic deletion of one FMRE in human cells caused proliferative deficiencies and transcriptional deregulation of cancer genes CCNB1IP1, CDH1, and CDKN2B, validating observations in FMRE-mutated tumors. Pathway analysis revealed further sub-significant FMREs at cancer genes and processes, indicating an unexplored landscape of infrequent driver mutations in the non-coding genome.

The U1 spliceosomal RNA is recurrently mutated in multiple cancers.

Cancers are caused by genomic alterations known as drivers. Hundreds of drivers in coding genes are known but, to date, only a handful of noncoding drivers have been discovered-despite intensive searching1,2. Attention has recently shifted to the role of altered RNA splicing in cancer; driver mutations that lead to transcriptome-wide aberrant splicing have been identified in multiple types of cancer, although these mutations have only been found in protein-coding splicing factors such as splicing factor 3b subunit 1 (SF3B1)3-6. By contrast, cancer-related alterations in the noncoding component of the spliceosome-a series of small nuclear RNAs (snRNAs)-have barely been studied, owing to the combined challenges of characterizing noncoding cancer drivers and the repetitive nature of snRNA genes1,7,8. Here we report a highly recurrent A>C somatic mutation at the third base of U1 snRNA in several types of tumour. The primary function of U1 snRNA is to recognize the 5' splice site via base-pairing. This mutation changes the preferential A-U base-pairing between U1 snRNA and the 5' splice site to C-G base-pairing, and thus creates novel splice junctions and alters the splicing pattern of multiple genes-including known drivers of cancer. Clinically, the A>C mutation is associated with heavy alcohol use in patients with hepatocellular carcinoma, and with the aggressive subtype of chronic lymphocytic leukaemia with unmutated immunoglobulin heavy-chain variable regions. The mutation in U1 snRNA also independently confers an adverse prognosis to patients with chronic lymphocytic leukaemia. Our study demonstrates a noncoding driver in spliceosomal RNAs, reveals a mechanism of aberrant splicing in cancer and may represent a new target for treatment. Our findings also suggest that driver discovery should be extended to a wider range of genomic regions.

JBrowse Jupyter: a Python interface to JBrowse 2.

MOTIVATION: JBrowse Jupyter is a package that aims to close the gap between Python programming and genomic visualization. Web-based genome browsers are routinely used for publishing and inspecting genome annotations. Historically they have been deployed at the end of bioinformatics pipelines, typically decoupled from the analysis itself. However, emerging technologies such as Jupyter notebooks enable a more rapid iterative cycle of development, analysis and visualization. RESULTS: We have developed a package that provides a Python interface to JBrowse 2's suite of embeddable components, including the primary Linear Genome View. The package enables users to quickly set up, launch and customize JBrowse views from Jupyter notebooks. In addition, users can share their data via Google's Colab notebooks, providing reproducible interactive views. AVAILABILITY AND IMPLEMENTATION: JBrowse Jupyter is released under the Apache License and is available for download on PyPI. Source code and demos are available on GitHub at https://github.com/GMOD/jbrowse-jupyter.

Fourteen quick tips for crowdsourcing geographically linked data for public health advocacy.

This article presents 14 quick tips to build a team to crowdsource data for public health advocacy. It includes tips around team building and logistics, infrastructure setup, media and industry outreach, and project wrap-up and archival for posterity.

Gramene 2021: harnessing the power of comparative genomics and pathways for plant research.

Gramene (http://www.gramene.org), a knowledgebase founded on comparative functional analyses of genomic and pathway data for model plants and major crops, supports agricultural researchers worldwide. The resource is committed to open access and reproducible science based on the FAIR data principles. Since the last NAR update, we made nine releases; doubled the genome portal's content; expanded curated genes, pathways and expression sets; and implemented the Domain Informational Vocabulary Extraction (DIVE) algorithm for extracting gene function information from publications. The current release, #63 (October 2020), hosts 93 reference genomes-over 3.9 million genes in 122 947 families with orthologous and paralogous classifications. Plant Reactome portrays pathway networks using a combination of manual biocuration in rice (320 reference pathways) and orthology-based projections to 106 species. The Reactome platform facilitates comparison between reference and projected pathways, gene expression analyses and overlays of gene-gene interactions. Gramene integrates ontology-based protein structure-function annotation; information on genetic, epigenetic, expression, and phenotypic diversity; and gene functional annotations extracted from plant-focused journals using DIVE. We train plant researchers in biocuration of genes and pathways; host curated maize gene structures as tracks in the maize genome browser; and integrate curated rice genes and pathways in the Plant Reactome.

A renewed model of pancreatic cancer evolution based on genomic rearrangement patterns.

Pancreatic cancer, a highly aggressive tumour type with uniformly poor prognosis, exemplifies the classically held view of stepwise cancer development. The current model of tumorigenesis, based on analyses of precursor lesions, termed pancreatic intraepithelial neoplasm (PanINs) lesions, makes two predictions: first, that pancreatic cancer develops through a particular sequence of genetic alterations (KRAS, followed by CDKN2A, then TP53 and SMAD4); and second, that the evolutionary trajectory of pancreatic cancer progression is gradual because each alteration is acquired independently. A shortcoming of this model is that clonally expanded precursor lesions do not always belong to the tumour lineage, indicating that the evolutionary trajectory of the tumour lineage and precursor lesions can be divergent. This prevailing model of tumorigenesis has contributed to the clinical notion that pancreatic cancer evolves slowly and presents at a late stage. However, the propensity for this disease to rapidly metastasize and the inability to improve patient outcomes, despite efforts aimed at early detection, suggest that pancreatic cancer progression is not gradual. Here, using newly developed informatics tools, we tracked changes in DNA copy number and their associated rearrangements in tumour-enriched genomes and found that pancreatic cancer tumorigenesis is neither gradual nor follows the accepted mutation order. Two-thirds of tumours harbour complex rearrangement patterns associated with mitotic errors, consistent with punctuated equilibrium as the principal evolutionary trajectory. In a subset of cases, the consequence of such errors is the simultaneous, rather than sequential, knockout of canonical preneoplastic genetic drivers that are likely to set-off invasive cancer growth. These findings challenge the current progression model of pancreatic cancer and provide insights into the mutational processes that give rise to these aggressive tumours.

Aberrant PRDM9 expression impacts the pan-cancer genomic landscape.

The binding of PRDM9 to chromatin is a key step in the induction of DNA double-strand breaks associated with meiotic recombination hotspots; it is normally expressed solely in germ cells. We interrogated 1879 cancer samples in 39 different cancer types and found that PRDM9 is unexpectedly expressed in 20% of these tumors even after stringent gene homology correction. The expression levels of PRDM9 in tumors are significantly higher than those found in healthy neighboring tissues and in healthy nongerm tissue databases. Recurrently mutated regions located within 5 Mb of the PRDM9 loci, as well as differentially expressed genes in meiotic pathways, correlate with PRDM9 expression. In samples with aberrant PRDM9 expression, structural variant breakpoints frequently neighbor the DNA motif recognized by PRDM9, and there is an enrichment of structural variants at sites of known meiotic PRDM9 activity. This study is the first to provide evidence of an association between aberrant expression of the meiosis-specific gene PRDM9 with genomic instability in cancer.

Whole genomes define concordance of matched primary, xenograft, and organoid models of pancreas cancer.

Pancreatic ductal adenocarcinoma (PDAC) has the worst prognosis among solid malignancies and improved therapeutic strategies are needed to improve outcomes. Patient-derived xenografts (PDX) and patient-derived organoids (PDO) serve as promising tools to identify new drugs with therapeutic potential in PDAC. For these preclinical disease models to be effective, they should both recapitulate the molecular heterogeneity of PDAC and validate patient-specific therapeutic sensitivities. To date however, deep characterization of the molecular heterogeneity of PDAC PDX and PDO models and comparison with matched human tumour remains largely unaddressed at the whole genome level. We conducted a comprehensive assessment of the genetic landscape of 16 whole-genome pairs of tumours and matched PDX, from primary PDAC and liver metastasis, including a unique cohort of 5 'trios' of matched primary tumour, PDX, and PDO. We developed a pipeline to score concordance between PDAC models and their paired human tumours for genomic events, including mutations, structural variations, and copy number variations. Tumour-model comparisons of mutations displayed single-gene concordance across major PDAC driver genes, but relatively poor agreement across the greater mutational load. Genome-wide and chromosome-centric analysis of structural variation (SV) events highlights previously unrecognized concordance across chromosomes that demonstrate clustered SV events. We found that polyploidy presented a major challenge when assessing copy number changes; however, ploidy-corrected copy number states suggest good agreement between donor-model pairs. Collectively, our investigations highlight that while PDXs and PDOs may serve as tractable and transplantable systems for probing the molecular properties of PDAC, these models may best serve selective analyses across different levels of genomic complexity.

Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia.

In acute myeloid leukaemia (AML), the cell of origin, nature and biological consequences of initiating lesions, and order of subsequent mutations remain poorly understood, as AML is typically diagnosed without observation of a pre-leukaemic phase. Here, highly purified haematopoietic stem cells (HSCs), progenitor and mature cell fractions from the blood of AML patients were found to contain recurrent DNMT3A mutations (DNMT3A(mut)) at high allele frequency, but without coincident NPM1 mutations (NPM1c) present in AML blasts. DNMT3A(mut)-bearing HSCs showed a multilineage repopulation advantage over non-mutated HSCs in xenografts, establishing their identity as pre-leukaemic HSCs. Pre-leukaemic HSCs were found in remission samples, indicating that they survive chemotherapy. Therefore DNMT3A(mut) arises early in AML evolution, probably in HSCs, leading to a clonally expanded pool of pre-leukaemic HSCs from which AML evolves. Our findings provide a paradigm for the detection and treatment of pre-leukaemic clones before the acquisition of additional genetic lesions engenders greater therapeutic resistance.

Gramene 2018: unifying comparative genomics and pathway resources for plant research.

Gramene (http://www.gramene.org) is a knowledgebase for comparative functional analysis in major crops and model plant species. The current release, #54, includes over 1.7 million genes from 44 reference genomes, most of which were organized into 62,367 gene families through orthologous and paralogous gene classification, whole-genome alignments, and synteny. Additional gene annotations include ontology-based protein structure and function; genetic, epigenetic, and phenotypic diversity; and pathway associations. Gramene's Plant Reactome provides a knowledgebase of cellular-level plant pathway networks. Specifically, it uses curated rice reference pathways to derive pathway projections for an additional 66 species based on gene orthology, and facilitates display of gene expression, gene-gene interactions, and user-defined omics data in the context of these pathways. As a community portal, Gramene integrates best-of-class software and infrastructure components including the Ensembl genome browser, Reactome pathway browser, and Expression Atlas widgets, and undergoes periodic data and software upgrades. Via powerful, intuitive search interfaces, users can easily query across various portals and interactively analyze search results by clicking on diverse features such as genomic context, highly augmented gene trees, gene expression anatomograms, associated pathways, and external informatics resources. All data in Gramene are accessible through both visual and programmatic interfaces.

Mutations in Mitochondrial DNA From Pancreatic Ductal Adenocarcinomas Associate With Survival Times of Patients and Accumulate as Tumors Progress.

Somatic mutations have been found in the mitochondria in different types of cancer cells, but it is not clear whether these affect tumorigenesis or tumor progression. We analyzed mitochondrial genomes of 268 early-stage, resected pancreatic ductal adenocarcinoma tissues and paired non-tumor tissues. We defined a mitochondrial somatic mutation (mtSNV) as a position where the difference in heteroplasmy fraction between tumor and normal sample was ≥0.2. Our analysis identified 304 mtSNVs, with at least 1 mtSNV in 61% (164 of 268) of tumor samples. The noncoding control region had the greatest proportion of mtSNVs (60 of 304 mutations); this region contains sites that regulate mitochondrial DNA transcription and replication. Frequently mutated genes included ND5, RNR2, and CO1, plus 29 mutations in transfer RNA genes. mtSNVs in 2 separate mitochondrial genes (ND4 and ND6) were associated with shorter overall survival time. This association appeared to depend on the level of mtSNV heteroplasmy. Non-random co-occurrence between mtSNVs and mutations in nuclear genes indicates interactions between nuclear and mitochondrial DNA. In an analysis of primary tumors and metastases from 6 patients, we found tumors to accumulate mitochondrial mutational mutations as they progress.

Plant Reactome: a resource for plant pathways and comparative analysis.

Plant Reactome (http://plantreactome.gramene.org/) is a free, open-source, curated plant pathway database portal, provided as part of the Gramene project. The database provides intuitive bioinformatics tools for the visualization, analysis and interpretation of pathway knowledge to support genome annotation, genome analysis, modeling, systems biology, basic research and education. Plant Reactome employs the structural framework of a plant cell to show metabolic, transport, genetic, developmental and signaling pathways. We manually curate molecular details of pathways in these domains for reference species Oryza sativa (rice) supported by published literature and annotation of well-characterized genes. Two hundred twenty-two rice pathways, 1025 reactions associated with 1173 proteins, 907 small molecules and 256 literature references have been curated to date. These reference annotations were used to project pathways for 62 model, crop and evolutionarily significant plant species based on gene homology. Database users can search and browse various components of the database, visualize curated baseline expression of pathway-associated genes provided by the Expression Atlas and upload and analyze their Omics datasets. The database also offers data access via Application Programming Interfaces (APIs) and in various standardized pathway formats, such as SBML and BioPAX.

TDP-1, the Caenorhabditis elegans ortholog of TDP-43, limits the accumulation of double-stranded RNA.

Caenorhabditis elegans mutants deleted for TDP-1, an ortholog of the neurodegeneration-associated RNA-binding protein TDP-43, display only mild phenotypes. Nevertheless, transcriptome sequencing revealed that many RNAs were altered in accumulation and/or processing in the mutant. Analysis of these transcriptional abnormalities demonstrates that a primary function of TDP-1 is to limit formation or stability of double-stranded RNA. Specifically, we found that deletion of tdp-1: (1) preferentially alters the accumulation of RNAs with inherent double-stranded structure (dsRNA); (2) increases the accumulation of nuclear dsRNA foci; (3) enhances the frequency of adenosine-to-inosine RNA editing; and (4) dramatically increases the amount of transcripts immunoprecipitable with a dsRNA-specific antibody, including intronic sequences, RNAs with antisense overlap to another transcript, and transposons. We also show that TDP-43 knockdown in human cells results in accumulation of dsRNA, indicating that suppression of dsRNA is a conserved function of TDP-43 in mammals. Altered accumulation of structured RNA may account for some of the previously described molecular phenotypes (e.g., altered splicing) resulting from reduction of TDP-43 function.

Full-genome evolutionary histories of selfing, splitting, and selection in Caenorhabditis.

The nematode Caenorhabditis briggsae is a model for comparative developmental evolution with C. elegans. Worldwide collections of C. briggsae have implicated an intriguing history of divergence among genetic groups separated by latitude, or by restricted geography, that is being exploited to dissect the genetic basis to adaptive evolution and reproductive incompatibility; yet, the genomic scope and timing of population divergence is unclear. We performed high-coverage whole-genome sequencing of 37 wild isolates of the nematode C. briggsae and applied a pairwise sequentially Markovian coalescent (PSMC) model to 703 combinations of genomic haplotypes to draw inferences about population history, the genomic scope of natural selection, and to compare with 40 wild isolates of C. elegans. We estimate that a diaspora of at least six distinct C. briggsae lineages separated from one another approximately 200,000 generations ago, including the "Temperate" and "Tropical" phylogeographic groups that dominate most samples worldwide. Moreover, an ancient population split in its history approximately 2 million generations ago, coupled with only rare gene flow among lineage groups, validates this system as a model for incipient speciation. Low versus high recombination regions of the genome give distinct signatures of population size change through time, indicative of widespread effects of selection on highly linked portions of the genome owing to extreme inbreeding by self-fertilization. Analysis of functional mutations indicates that genomic context, owing to selection that acts on long linkage blocks, is a more important driver of population variation than are the functional attributes of the individually encoded genes.

Pathway and network analysis of cancer genomes.

Genomic information on tumors from 50 cancer types cataloged by the International Cancer Genome Consortium (ICGC) shows that only a few well-studied driver genes are frequently mutated, in contrast to many infrequently mutated genes that may also contribute to tumor biology. Hence there has been large interest in developing pathway and network analysis methods that group genes and illuminate the processes involved. We provide an overview of these analysis techniques and show where they guide mechanistic and translational investigations.

Redefining genomic privacy: trust and empowerment.

Fulfilling the promise of the genetic revolution requires the analysis of large datasets containing information from thousands to millions of participants. However, sharing human genomic data requires protecting subjects from potential harm. Current models rely on de-identification techniques in which privacy versus data utility becomes a zero-sum game. Instead, we propose the use of trust-enabling techniques to create a solution in which researchers and participants both win. To do so we introduce three principles that facilitate trust in genetic research and outline one possible framework built upon those principles. Our hope is that such trust-centric frameworks provide a sustainable solution that reconciles genetic privacy with data sharing and facilitates genetic research.

RNASequel: accurate and repeat tolerant realignment of RNA-seq reads.

RNA-seq is a key technology for understanding the biology of the cell because of its ability to profile transcriptional and post-transcriptional regulation at single nucleotide resolutions. Compared to DNA sequencing alignment algorithms, RNA-seq alignment algorithms have a diminished ability to accurately detect and map base pair substitutions, gaps, discordant pairs and repetitive regions. These shortcomings adversely affect experiments that require a high degree of accuracy, notably the ability to detect RNA editing. We have developed RNASequel, a software package that runs as a post-processing step in conjunction with an RNA-seq aligner and systematically corrects common alignment artifacts. Its key innovations are a two-pass splice junction alignment system that includes de novo splice junctions and the use of an empirically determined estimate of the fragment size distribution when resolving read pairs. We demonstrate that RNASequel produces improved alignments when used in conjunction with STAR or Tophat2 using two simulated datasets. We then show that RNASequel improves the identification of adenosine to inosine RNA editing sites on biological datasets. This software will be useful in applications requiring the accurate identification of variants in RNA sequencing data, the discovery of RNA editing sites and the analysis of alternative splicing.

Downregulation of histone H2A and H2B pathways is associated with anthracycline sensitivity in breast cancer.

BACKGROUND: Drug resistance in breast cancer is the major obstacle to effective treatment with chemotherapy. While upregulation of multidrug resistance genes is an important component of drug resistance mechanisms in vitro, their clinical relevance remains to be determined. Therefore, identifying pathways that could be targeted in the clinic to eliminate anthracycline-resistant breast cancer remains a major challenge. METHODS: We generated paired native and epirubicin-resistant MDA-MB-231, MCF7, SKBR3 and ZR-75-1 epirubicin-resistant breast cancer cell lines to identify pathways contributing to anthracycline resistance. Native cell lines were exposed to increasing concentrations of epirubicin until resistant cells were generated. To identify mechanisms driving epirubicin resistance, we used a complementary approach including gene expression analyses to identify molecular pathways involved in resistance, and small-molecule inhibitors to reverse resistance. In addition, we tested its clinical relevance in a BR9601 adjuvant clinical trial. RESULTS: Characterisation of epirubicin-resistant cells revealed that they were cross-resistant to doxorubicin and SN-38 and had alterations in apoptosis and cell-cycle profiles. Gene expression analysis identified deregulation of histone H2A and H2B genes in all four cell lines. Histone deacetylase small-molecule inhibitors reversed resistance and were cytotoxic for epirubicin-resistant cell lines, confirming that histone pathways are associated with epirubicin resistance. Gene expression of a novel 18-gene histone pathway module analysis of the BR9601 adjuvant clinical trial revealed that patients with low expression of the 18-gene histone module benefited from anthracycline treatment more than those with high expression (hazard ratio 0.35, 95 % confidence interval 0.13-0.96, p = 0.042). CONCLUSIONS: This study revealed a key pathway that contributes to anthracycline resistance and established model systems for investigating drug resistance in all four major breast cancer subtypes. As the histone modification can be targeted with small-molecule inhibitors, it represents a possible means of reversing clinical anthracycline resistance. TRIAL REGISTRATION: ClinicalTrials.gov identifier NCT00003012 . Registered on 1 November 1999.

Computational approaches to identify functional genetic variants in cancer genomes.

The International Cancer Genome Consortium (ICGC) aims to catalog genomic abnormalities in tumors from 50 different cancer types. Genome sequencing reveals hundreds to thousands of somatic mutations in each tumor but only a minority of these drive tumor progression. We present the result of discussions within the ICGC on how to address the challenge of identifying mutations that contribute to oncogenesis, tumor maintenance or response to therapy, and recommend computational techniques to annotate somatic variants and predict their impact on cancer phenotype.