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Anthropogenic mercury (Hg) emissions to the atmosphere are a long-lived hazard to human and environmental health. The UN Minamata Convention on Mercury is seeking to lower anthropogenic mercury emissions through a mix of policies from banning certain Hg uses to reducing unintentional Hg release from different activities. In addition to independent Hg policy, strategies to mitigate greenhouse gases, particulate matter (PM) and SO2 may also lower Hg emissions as a co-benefit. This study uses the Greenhouse Gas–Air Pollution Interactions and Synergies (GAINS) model to examine the effect of different clean air and climate policies on future global Hg emissions. The baseline scenario assumes current trends for energy use and Hg emissions as well as current legislation for clean air, mercury and climate policy. In addition, we explore the impact of the Minamata Convention, the co-benefits of climate and stringent air pollution policies, and maximum feasible reduction measures for Hg. Hg emission projections until 2050 show noticeable reductions in combustion sectors for all scenarios due to a decrease in global fossil fuel and traditional biomass use, leading to emission reductions of 33 % at baseline and up to 90 % when combining stringent climate controls and the most efficient Hg controls. Cement and non-ferrous metal emissions increase in all scenarios with current air pollution policy but could be reduced by up to 72 % and 46 %, respectively, in 2050 with stringent Hg-specific measures. Other emissions (including waste) are a significant source of uncertainty in this study, and their projections range between a 22 % increase and a 54 % decrease in 2050, depending on both climate and clean air policy. The largest absolute reduction potential for Hg abatement but also the largest uncertainties regarding absolute emissions lie in small-scale and artisanal gold production, where abatement measures could eliminate annual Hg emissions in the range of 601–1371 t (95 % confidence interval), although the uncertainties in the estimate are so high that they might eclipse reduction efforts in all other sectors. In total, 90 % of Hg emissions are covered by provisions of the Minamata Convention. Overall, the findings emphasize the necessity to implement targeted Hg control policies in addition to stringent climate, PM and SO2 policies to achieve significant reductions in Hg emissions.

Surface meltwater is widespread around the Antarctic Ice Sheet margin and has the potential to influence ice shelf stability, ice flow and ice–albedo feedbacks. Our understanding of the seasonal and multi-year evolution of Antarctic surface meltwater is limited. Attempts to generate robust meltwater cover time series have largely been constrained by computational expense or limited ice surface visibility associated with mapping from optical satellite imagery. Here, we add a novel method for calculating visibility metrics to an existing meltwater detection method within Google Earth Engine. This enables us to quantify uncertainty induced by cloud cover and variable image data coverage, allowing time series of surface meltwater area to be automatically generated over large spatial and temporal scales. We demonstrate our method on the Amery Ice Shelf region of East Antarctica, analysing 4164 Landsat 7 and 8 optical images between 2005 and 2020. Results show high interannual variability in surface meltwater cover, with mapped cumulative lake area totals ranging from 384 to 3898 km2 per melt season. By incorporating image visibility assessments, however, we estimate that cumulative total lake areas are on average 42 % higher than minimum mapped values. We show that modelled melt predictions from a regional climate model provide a good indication of lake cover in the Amery region and that annual lake coverage is typically highest in years with a negative austral summer SAM index. Our results demonstrate that our method could be scaled up to generate a multi-year time series record of surface water extent from optical imagery at a continent-wide scale.

Positrons are key to the production of cold antihydrogen. Here the authors report the sympathetic cooling of positrons by interacting them with laser-cooled Be+ ions resulting in a three-fold reduction of the temperature of positrons for antihydrogen synthesis.

Abstract This study is an analysis of the 100 most cited articles in mitral valve surgery. A bibliometric analysis is a tool to evaluate research performance in a given field. It uses the number of times a publication is cited by others as a proxy marker of its impact. The most cited paper Carpentier et al. discusses mitral valve repair in terms of restoring the geometry of the entire valve rather than simply narrowing the annulus (Carpentier, J Thorac Cardiovasc Surg 86:23–37, 1983). The first successful mitral valve repair was performed by Elliot Cutler at Brigham and Women’s Hospital in 1923 (Cohn et al., Ann Cardiothorac Surg 4:315, 2015). More recently percutaneous and minimally invasive techniques that were originally designed as an option for high risk patients are being trialled in other patient groups (Hajar, Heart Views 19:160–3, 2018). Comparison of percutaneous method with open repair represents an expanding area of research (Hajar, Heart Views 19:160–3, 2018). This study will analyse the top 100 cited papers relevant to mitral valve surgery, identifying the most influential papers that guide current management, the institutions that produce them and the authors involved.

The ALPHA Collaboration, based at the CERN Antiproton Decelerator, has recently implemented a novel beamline for low energy (≲100 eV) positron and antiproton transport between cylindrical Penning traps that have strong axial magnetic fields. Here, we describe how a combination of semianalytical and numerical calculations was used to optimize the layout and design of this beamline. Using experimental measurements taken during the initial commissioning of the instrument, we evaluate its performance and validate the models used for its development. By combining data from a range of sources, we show that the beamline has a high transfer efficiency and estimate that the percentage of particles captured in the experiments from each bunch is (78±3)% for up to 10^{5} antiprotons and (71±5)% for bunches of up to 10^{7} positrons.

The drift of sea ice is an important geophysical process with widespread implications for the ocean energy budget and ecosystems. Drifting sea ice can also threaten marine operations and present a hazard for ocean vessels and installations. Here, we evaluate single-pass along-track synthetic aperture radar (SAR) interferometry (S-ATI) as a tool to assess ice drift while discussing possible applications and inherent limitations. Initial validation shows that TanDEM-X phase-derived drift speed corresponds well with drift products from a ground-based radar at Utqiaġvik, Alaska. Joint analysis of TanDEM-X and Sentinel-1 data covering the Fram Strait demonstrates that S-ATI can help quantify the opening/closing rate of leads with possible applications for navigation. S-ATI enables an instantaneous assessment of ice drift and dynamic processes that are otherwise difficult to observe. For instance, by evaluating sea ice drift through the Vilkitsky Strait, Russia, we identified short-lived transient convergence patterns. We conclude that S-ATI enables the identification and analysis of potentially important dynamic processes (e.g., drift, rafting, and ridging). However, current limitations of S-ATI are significant (e.g., data availability and they presently only provide the cross-track vector component of the ice drift field) but may be significantly reduced with future SAR systems.

Ice-nucleating particles can modify cloud properties with implications for climate and the hydrological cycle; hence, it is important to understand which aerosol particle types nucleate ice and how efficiently they do so. It has been shown that aerosol particles such as natural dusts, volcanic ash, bacteria and pollen can act as ice-nucleating particles, but the ice-nucleating ability of combustion ashes has not been studied. Combustion ashes are major by-products released during the combustion of solid fuels and a significant amount of these ashes are emitted into the atmosphere either during combustion or via aerosolization of bottom ashes. Here, we show that combustion ashes (coal fly ash, wood bottom ash, domestic bottom ash, and coal bottom ash) nucleate ice in the immersion mode at conditions relevant to mixed-phase clouds. Hence, combustion ashes could play an important role in primary ice formation in mixed-phase clouds, especially in clouds that are formed near the emission source of these aerosol particles. In order to quantitatively assess the impact of combustion ashes on mixed-phase clouds, we propose that the atmospheric abundance of combustion ashes should be quantified since up to now they have mostly been classified together with mineral dust particles. Also, in reporting ice residue compositions, a distinction should be made between natural mineral dusts and combustion ashes in order to quantify the contribution of combustion ashes to atmospheric ice nucleation.

Diagnosing the climate of New Zealand from low-resolution General Circulation Models (GCMs) is notoriously difficult due to the interaction of the complex topography and the Southern Hemisphere (SH) mid-latitude westerly winds. Therefore, methods of downscaling synoptic scale model data for New Zealand are useful to help understand past climate. New Zealand also has a wealth of palaeoclimate-proxy data to which the downscaled model output can be compared, and to provide a qualitative method of assessing the capability of GCMs to represent, in this case, the climate 6000 yr ago in the Mid-Holocene. In this paper, a synoptic weather and climate regime classification system using Empirical Orthogonal Function (EOF) analysis of GCM and reanalysis data was used. The climate regimes are associated with surface air temperature and precipitation anomalies over New Zealand. From the analysis in this study, we find at 6000 BP that increased trough activity in summer and autumn led to increased precipitation, with an increased north-south pressure gradient ("zonal events") in winter and spring leading to drier conditions. Opposing effects of increased (decreased) temperature are also seen in spring (autumn) in the South Island, which are associated with the increased zonal (trough) events; however, the circulation induced changes in temperature are likely to have been of secondary importance to the insolation induced changes. Evidence from the palaeoclimate-proxy data suggests that the Mid-Holocene was characterized by increased westerly wind events in New Zealand, which agrees with the preference for trough and zonal regimes in the models.

The advent of long-read (LR) sequencing technologies has provided a direct opportunity to determine the structure of transcripts with potential for end-to-end sequencing of full-length RNAs. LR methods that have been described to date include commercial offerings from Oxford Nanopore Technologies (ONT) and Pacific Biosciences. These kits are based on selection of polyadenylated (polyA+) RNAs and/or oligo-dT priming of reverse transcription. Thus, these approaches do not allow comprehensive interrogation of the transcriptome due to their exclusion of non-polyadenylated (polyA-) RNAs. In addition, polyA + specificity also results in 3′-biased measurements of PolyA+ RNAs especially when the RNA input is partially degraded. To address these limitations of current LR protocols, we modified rRNA depletion protocols that have been used in short-read sequencing: one approach representing a ligation-based method and the other a template-switch cDNA synthesis-based method to append ONT-specific adaptor sequences and by removing any deliberate fragmentation/shearing of RNA/cDNA. Here, we present comparisons with poly+ RNA-specific versions of the two approaches including the ONT PCR-cDNA Barcoding kit. The rRNA depletion protocols displayed higher proportions (30%–50%) of intronic content compared to that of the polyA-specific protocols (5%–8%). In addition, the rRNA depletion protocols enabled ∼20–50% higher detection of expressed genes. Other metrics that were favourable to the rRNA depletion protocols include better coverage of long transcripts, and higher accuracy and reproducibility of expression measurements. Overall, these results indicate that the rRNA depletion-based protocols described here allow the comprehensive characterization of polyadenylated and non-polyadenylated RNAs. While the resulting reads are long enough to help decipher transcript structures, future endeavors are warranted to improve the proportion of individual reads representing end-to-end spanning of transcripts.

Abstract Background Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance. Methods We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan–Meier and cox-proportional hazards analyses. Results TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012). Conclusions TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions.

The tumor suppressor gene, TP53, has the highest rate of mutation among all genes in human cancer. This transcription factor plays an essential role in the regulation of many cellular processes. Mutations in TP53 result in loss of wild-type p53 function in a dominant negative manner. Although TP53 is a well-studied gene, the transcriptome modifications caused by the mutations in this gene have not yet been explored in a pan-cancer study using both primary and metastatic samples. In this work, we used a random forest model to stratify tumor samples based on TP53 mutational status and detected a p53 transcriptional signature. We hypothesize that the existence of this transcriptional signature is due to the loss of wild-type p53 function and is universal across primary and metastatic tumors as well as different tumor types. Additionally, we showed that the algorithm successfully detected this signature in samples with apparent silent mutations that affect correct mRNA splicing. Furthermore, we observed that most of the highly ranked genes contributing to the classification extracted from the random forest have known associations with p53 within the literature. We suggest that other genes found in this list including GPSM2, OR4N2, CTSL2, SPERT, and RPE65 protein coding genes have yet undiscovered linkages to p53 function. Our analysis of time on different therapies also revealed that this signature is more effective than the recorded TP53 status in detecting patients who can benefit from platinum therapies and taxanes. Our findings delineate a p53 transcriptional signature, expand the knowledge of p53 biology and further identify genes important in p53 related pathways.

Abstract Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Specific Molecular Profile) is assigned after exclusion of the defining features of the other three molecular subtypes and includes patients with heterogeneous clinical outcomes. In this study, we employ artificial intelligence (AI)-powered histopathology image analysis to differentiate between p53abn and NSMP EC subtypes and consequently identify a sub-group of NSMP EC patients that has markedly inferior progression-free and disease-specific survival (termed ‘p53abn-like NSMP’), in a discovery cohort of 368 patients and two independent validation cohorts of 290 and 614 from other centers. Shallow whole genome sequencing reveals a higher burden of copy number abnormalities in the ‘p53abn-like NSMP’ group compared to NSMP, suggesting that this group is biologically distinct compared to other NSMP ECs. Our work demonstrates the power of AI to detect prognostically different and otherwise unrecognizable subsets of EC where conventional and standard molecular or pathologic criteria fall short, refining image-based tumor classification. This study’s findings are applicable exclusively to females.

Abstract The role for routine whole genome and transcriptome analysis (WGTA) for poor prognosis pediatric cancers remains undetermined. Here, we characterize somatic mutations, structural rearrangements, copy number variants, gene expression, immuno-profiles and germline cancer predisposition variants in children and adolescents with relapsed, refractory or poor prognosis malignancies who underwent somatic WGTA and matched germline sequencing. Seventy-nine participants with a median age at enrollment of 8.8 y (range 6 months to 21.2 y) are included. Germline pathogenic/likely pathogenic variants are identified in 12% of participants, of which 60% were not known prior. Therapeutically actionable variants are identified by targeted gene report and whole genome in 32% and 62% of participants, respectively, and increase to 96% after integrating transcriptome analyses. Thirty-two molecularly informed therapies are pursued in 28 participants with 54% achieving a clinical benefit rate; objective response or stable disease ≥6 months. Integrated WGTA identifies therapeutically actionable variants in almost all tumors and are directly translatable to clinical care of children with poor prognosis cancers.

Abstract In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell’s mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.

Immune checkpoint inhibitors (ICIs) are increasingly used in the treatment of many tumor types, and durable responses can be observed in select populations. However, patients may exhibit significant immune-related adverse events (irAEs) that may lead to morbidity. There is limited information on whether the presence of specific germline mutations may highlight those at elevated risk of irAEs. We evaluated 117 patients with metastatic solid tumors or hematologic malignancies who underwent genomic analysis through the ongoing Personalized OncoGenomics (POG) program at BC Cancer and received an ICI during their treatment history. Charts were reviewed for irAEs. Whole genome sequencing of a fresh biopsy and matched normal specimens (blood) was performed at the time of POG enrollment. Notably, we found that MHC class I alleles in the HLA-B27 family, which have been previously associated with autoimmune conditions, were associated with grade 3 hepatitis and pneumonitis (q = 0.007) in patients treated with combination PD-1/PD-L1 and CTLA-4 inhibitors, and PD-1 inhibitors in combination with IDO-1 inhibitors. These data highlight that some patients may have a genetic predisposition to developing irAEs.

Abstract Immune checkpoint inhibitors (ICI) are highly effective in specific cancers where canonical markers of antitumor immunity are used for patient selection. Improved predictors of T cell-inflammation are needed to identify ICI-responsive tumor subsets in additional cancer types. We investigated associations of a 4-chemokine expression signature (c-Score: CCL4, CCL5, CXCL9, CXCL10) with metrics of antitumor immunity across tumor types. Across cancer entities from The Cancer Genome Atlas, subgroups of tumors displayed high expression of the c-Score (c-Scorehi) with increased expression of immune checkpoint (IC) genes and transcriptional hallmarks of the cancer-immunity cycle. There was an incomplete association of the c-Score with high tumor mutation burden (TMB), with only 15% of c-Scorehi tumors displaying ≥10 mutations per megabase. In a heterogeneous pan-cancer cohort of 82 patients, with advanced and previously treated solid cancers, c-Scorehi tumors had a longer median time to progression (103 versus 72 days, P = 0.012) and overall survival (382 versus 196 days, P = 0.038) following ICI therapy initiation, compared to patients with low c-Score expression. We also found c-Score stratification to outperform TMB assignment for overall survival prediction (HR = 0.42 [0.22–0.79], P = 0.008 versus HR = 0.60 [0.29-1.27], P = 0.18, respectively). Assessment of the c-Score using the TIDE and PredictIO databases, which include ICI treatment outcomes from 10 tumor types, provided further support for the c-Score as a predictive ICI therapeutic biomarker. In summary, the c-Score identifies patients with hallmarks of T cell-inflammation and potential response to ICI treatment across cancer types, which is missed by TMB assignment.

Abstract Self-renewal is a crucial property of glioblastoma cells that is enabled by the choreographed functions of chromatin regulators and transcription factors. Identifying targetable epigenetic mechanisms of self-renewal could therefore represent an important step toward developing effective treatments for this universally lethal cancer. Here we uncover an epigenetic axis of self-renewal mediated by the histone variant macroH2A2. With omics and functional assays deploying patient-derived in vitro and in vivo models, we show that macroH2A2 shapes chromatin accessibility at enhancer elements to antagonize transcriptional programs of self-renewal. macroH2A2 also sensitizes cells to small molecule-mediated cell death via activation of a viral mimicry response. Consistent with these results, our analyses of clinical cohorts indicate that high transcriptional levels of this histone variant are associated with better prognosis of high-grade glioma patients. Our results reveal a targetable epigenetic mechanism of self-renewal controlled by macroH2A2 and suggest additional treatment approaches for glioblastoma patients.

Abstract There is emerging evidence about the predictive role of homologous recombination deficiency (HRD), but this is less defined in gastrointestinal (GI) and thoracic malignancies. We reviewed whole genome (WGS) and transcriptomic (RNA-Seq) data from advanced GI and thoracic cancers in the Personalized OncoGenomics trial (NCT02155621) to evaluate HRD scores and single base substitution (SBS)3, which is associated with BRCA1/2 mutations and potentially predictive of defective HRD. HRD scores were calculated by sum of loss of heterozygosity, telomeric allelic imbalance, and large-scale state transitions scores. Regression analyses examined the association between HRD and time to progression on platinum (TTPp). We included 223 patients with GI (n = 154) or thoracic (n = 69) malignancies. TTPp was associated with SBS3 (p

Abstract Specific classes of DNA damage repair (DDR) defect can drive sensitivity to emerging therapies for metastatic prostate cancer. However, biomarker approaches based on DDR gene sequencing do not accurately predict DDR deficiency or treatment benefit. Somatic alteration signatures may identify DDR deficiency but historically require whole-genome sequencing of tumour tissue. We assembled whole-exome sequencing data for 155 high ctDNA fraction plasma cell-free DNA and matched leukocyte DNA samples from patients with metastatic prostate or bladder cancer. Labels for DDR gene alterations were established using deep targeted sequencing. Per sample mutation and copy number features were used to train XGBoost ensemble models. Naive somatic features and trinucleotide signatures were associated with specific DDR gene alterations but insufficient to resolve each class. Conversely, XGBoost-derived models showed strong performance including an area under the curve of 0.99, 0.99 and 1.00 for identifying BRCA2, CDK12, and mismatch repair deficiency in metastatic prostate cancer. Our machine learning approach re-classified several samples exhibiting genomic features inconsistent with original labels, identified a metastatic bladder cancer sample with a homozygous BRCA2 copy loss, and outperformed an existing exome-based classifier for BRCA2 deficiency. We present DARC Sign (DnA Repair Classification SIGNatures); a public machine learning tool leveraging clinically-practical liquid biopsy specimens for simultaneously identifying multiple types of metastatic prostate cancer DDR deficiencies. We posit that it will be useful for understanding differential responses to DDR-directed therapies in ongoing clinical trials and may ultimately enable prospective identification of prostate cancers with phenotypic evidence of DDR deficiency.

KRAS wildtype metastatic pancreatic ductal adenocarcinoma (mPDAC) could represent a distinct molecular entity from other PDACs. Here, the authors analyse KRAS wildtype mPDAC tumours using genomics and transcriptomics and find molecular similarities with cholangiocarcinomas.

The interpretation of somatic variants in cancer is challenging due to the scale and complexity of sequencing data. Here, the authors present PORI, an open-source framework for interpreting somatic variants in cancer using graph knowledge base tools, automated reporting, and manual curation.

AbstractCoho salmon (Oncorhynchus kisutch

Abstract Poorly differentiated chordoma (PDC) is a recently recognized subtype of chordoma characterized by expression of the embryonic transcription factor, brachyury, and loss of INI1. PDC primarily affects children and is associated with a poor prognosis and limited treatment options. Here we describe the molecular and immune tumour microenvironment profiles of two paediatric PDCs produced using whole-genome, transcriptome and whole-genome bisulfite sequencing (WGBS) and multiplex immunohistochemistry. Our analyses revealed the presence of tumour-associated immune cells, including CD8+ T cells, and expression of the immune checkpoint protein, PD-L1, in both patient samples. Molecular profiling provided the rationale for immune checkpoint inhibitor (ICI) therapy, which resulted in a clinical and radiographic response. A dominant T cell receptor (TCR) clone specific for a brachyury peptide–MHC complex was identified from bulk RNA sequencing, suggesting that targeting of the brachyury tumour antigen by tumour-associated T cells may underlie this clinical response to ICI. Correlative analysis with rhabdoid tumours, another INI1-deficient paediatric malignancy, suggests that a subset of tumours may share common immune phenotypes, indicating the potential for a therapeutically targetable subgroup of challenging paediatric cancers.

Abstract Background Single‐arm trials are common in precision oncology. Owing to the lack of randomized counterfactual, resultant data are not amenable to comparative outcomes analyses. Difference‐in‐difference (DID) methods present an opportunity to generate causal estimates of time‐varying treatment outcomes. Using DID, our study estimates within‐cohort effects of genomics‐informed treatment versus standard care on clinical and cost outcomes. Methods We focus on adults with advanced cancers enrolled in the single‐arm BC Cancer Personalized OncoGenomics program between 2012 and 2017. All individuals had a minimum of 1‐year follow up. Logistic regression explored baseline differences across patients who received a genomics‐informed treatment versus a standard care treatment after genomic sequencing. DID estimated the incremental effects of genomics‐informed treatment on time to treatment discontinuation (TTD), time to next treatment (TTNT), and costs. TTD and TTNT correlate with improved response and survival. Results Our study cohort included 346 patients, of whom 140 (40%) received genomics‐informed treatment after sequencing and 206 (60%) received standard care treatment. No significant differences in baseline characteristics were detected across treatment groups. DID estimated that the incremental effect of genomics‐informed versus standard care treatment was 102 days (95% CI: 35, 167) on TTD, 91 days (95% CI: −9, 175) on TTNT, and CAD$91,098 (95% CI: $46,848, $176,598) on costs. Effects were most pronounced in gastrointestinal cancer patients. Conclusions Genomics‐informed treatment had a statistically significant effect on TTD compared to standard care treatment, but at increased treatment costs. Within‐cohort evidence generated through this single‐arm study informs the early‐stage comparative effectiveness of precision oncology.

Several genomic features have been found for acute myeloid leukaemia (AML) but targeted clinical genetic testing fails to predict prognosis. Here, the authors generate an AML prognostic score from RNA-seq data of patients, which successfully stratifies AML patients and which may provide guidance for therapeutic strategies.

Abstract This is the first report of a NACC2‐NTRK2 fusion in a histological glioblastoma. Oncogenomic analysis revealed this actionable fusion oncogene in a pediatric cerebellar glioblastoma, which would not have been identified through routine diagnostics, demonstrating the value of clinical genome profiling in cancer care.

Abstract The ability of nanopore sequencing to simultaneously detect modified nucleotides while producing long reads makes it ideal for detecting and phasing allele-specific methylation. However, there is currently no complete software for detecting SNPs, phasing haplotypes, and mapping methylation to these from nanopore sequence data. Here, we present NanoMethPhase, a software tool to phase 5-methylcytosine from nanopore sequencing. We also present SNVoter, which can post-process nanopore SNV calls to improve accuracy in low coverage regions. Together, these tools can accurately detect allele-specific methylation genome-wide using nanopore sequence data with low coverage of about ten-fold redundancy.

Elevated levels of interleukin-33 have been associated with poor prognosis in patients with glioma. Here the authors show that glioma-derived IL-33 modulates a pro-tumorigenic immune microenvironment by activating resident and recruiting peripheral innate immune cells.

AbstractThe highly diverse insect family of true weevils, Curculionidae, includes many agricultural and forest pests. Pissodes strobiPissodes strobiPissodes strobiWolbachiaWolbachia

Abstract Background Precision oncology involves analysis of individual cancer samples to understand the genes and pathways involved in the development and progression of a cancer. To improve patient care, knowledge of diagnostic, prognostic, predisposing, and drug response markers is essential. Several knowledgebases have been created by different groups to collate evidence for these associations. These include the open-access Clinical Interpretation of Variants in Cancer (CIViC) knowledgebase. These databases rely on time-consuming manual curation from skilled experts who read and interpret the relevant biomedical literature. Methods To aid in this curation and provide the greatest coverage for these databases, particularly CIViC, we propose the use of text mining approaches to extract these clinically relevant biomarkers from all available published literature. To this end, a group of cancer genomics experts annotated sentences that discussed biomarkers with their clinical associations and achieved good inter-annotator agreement. We then used a supervised learning approach to construct the CIViCmine knowledgebase. Results We extracted 121,589 relevant sentences from PubMed abstracts and PubMed Central Open Access full-text papers. CIViCmine contains over 87,412 biomarkers associated with 8035 genes, 337 drugs, and 572 cancer types, representing 25,818 abstracts and 39,795 full-text publications. Conclusions Through integration with CIVIC, we provide a prioritized list of curatable clinically relevant cancer biomarkers as well as a resource that is valuable to other knowledgebases and precision cancer analysts in general. All data is publically available and distributed with a Creative Commons Zero license. The CIViCmine knowledgebase is available at http://bionlp.bcgsc.ca/civicmine/.

Information on protein sequence variability and conservation can be leveraged to identify functionally important regions. Here, the authors develop new conservation measures that exploit taxonomy distances and LIST, a tool for predicting deleteriousness of human variants.

RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3′ or 5′ termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.

Pink salmon (Oncorhynchus gorbuscha) adults are the smallest of the five Pacific salmon native to the western Pacific Ocean. Pink salmon are also the most abundant of these species and account for a large proportion of the commercial value of the salmon fishery worldwide. A two-year life history of pink salmon generates temporally isolated populations that spawn either in even-years or odd-years. To uncover the influence of this genetic isolation, reference genome assemblies were generated for each year-class and whole genome re-sequencing data was collected from salmon of both year-classes. The salmon were sampled from six Canadian rivers and one Japanese river. At multiple centromeres we identified peaks of Fst between year-classes that were millions of base-pairs long. The largest Fst peak was also associated with a million base-pair chromosomal polymorphism found in the odd-year genome near a centromere. These Fst peaks may be the result of a centromere drive or a combination of reduced recombination and genetic drift, and they could influence speciation. Other regions of the genome influenced by odd-year and even-year temporal isolation and tentatively under selection were mostly associated with genes related to immune function, organ development/maintenance, and behaviour.

Population sequencing often requires collaboration across a distributed network of sequencing centers for the timely processing of thousands of samples. In such massive efforts, it is important that participating scientists can be confident that the accuracy of the sequence data produced is not affected by which center generates the data. A study was conducted across three established sequencing centers, located in Montreal, Toronto, and Vancouver, constituting Canada’s Genomics Enterprise (www.cgen.ca). Whole genome sequencing was performed at each center, on three genomic DNA replicates from three well-characterized cell lines. Secondary analysis pipelines employed by each site were applied to sequence data from each of the sites, resulting in three datasets for each of four variables (cell line, replicate, sequencing center, and analysis pipeline), for a total of 81 datasets. These datasets were each assessed according to multiple quality metrics including concordance with benchmark variant truth sets to assess consistent quality across all three conditions for each variable. Three-way concordance analysis of variants across conditions for each variable was performed. Our results showed that the variant concordance between datasets differing only by sequencing center was similar to the concordance for datasets differing only by replicate, using the same analysis pipeline. We also showed that the statistically significant differences between datasets result from the analysis pipeline used, which can be unified and updated as new approaches become available. We conclude that genome sequencing projects can rely on the quality and reproducibility of aggregate data generated across a network of distributed sites.

The driver mutations for the two main molecular subgroups of diffuse large B-cell lymphoma (DLBCL) are poorly defined. Here, an integrative genomics analysis identifies 3′ UTR NFKBIZ mutations within the activated B-cell DLBCL subgroup and small FCGR2B amplifications in the germinal centre B-cell DLBCL subgroup.

In melanoma, reduced ADAR1 impairs A-to-I microRNA editing. Here, the authors show that miR-378a-3p undergoes this editing in non-metastatic cells and the edited form of miR-378a-3p binds to the PARVA oncogene, inhibiting its expression and preventing melanoma progression and metastasis.

Abstract Background Intellectual Disability (ID) is among the most common global disorders, yet etiology is unknown in ~30% of patients despite clinical assessment. Whole genome sequencing (WGS) is able to interrogate the entire genome, providing potential to diagnose idiopathic patients. Methods We conducted WGS on eight children with idiopathic ID and brain structural defects, and their normal parents; carrying out an extensive data analyses, using standard and discovery approaches. Results We verified de novo pathogenic single nucleotide variants (SNV) in ARID1B c.1595delG and PHF6 c.820C > T, potentially causative de novo two base indels in SQSTM1 c.115_116delinsTA and UPF1 c.1576_1577delinsA, and de novo SNVs in CACNB3 c.1289G > A, and SPRY4 c.508 T > A, of uncertain significance. We report results from a large secondary control study of 2081 exomes probing the pathogenicity of the above genes. We analyzed structural variation by four different algorithms including de novo genome assembly. We confirmed a likely contributory 165 kb de novo heterozygous 1q43 microdeletion missed by clinical microarray. The de novo assembly resulted in unmasking hidden genome instability that was missed by standard re-alignment based algorithms. We also interrogated regulatory sequence variation for known and hypothesized ID genes and present useful strategies for WGS data analyses for non-coding variation. Conclusion This study provides an extensive analysis of WGS in the context of ID, providing genetic and structural insights into ID and yielding diagnoses.

Chardonnay is the basis of some of the world's most iconic wines and its success is underpinned by a historic program of clonal selection. There are numerous clones of Chardonnay available that exhibit differences in key viticultural and oenological traits that have arisen from the accumulation of somatic mutations during centuries of asexual propagation. However, the genetic variation that underlies these differences remains largely unknown. To address this knowledge gap, a high-quality, diploid-phased Chardonnay genome assembly was produced from single-molecule real time sequencing, and combined with re-sequencing data from 15 different Chardonnay clones. There were 1620 markers identified that distinguish the 15 clones. These markers were reliably used for clonal identification of independently sourced genomic material, as well as in identifying a potential genetic basis for some clonal phenotypic differences. The predicted parentage of the Chardonnay haplomes was elucidated by mapping sequence data from the predicted parents of Chardonnay (Gouais blanc and Pinot noir) against the Chardonnay reference genome. This enabled the detection of instances of heterosis, with differentially-expanded gene families being inherited from the parents of Chardonnay. Most surprisingly however, the patterns of nucleotide variation present in the Chardonnay genome indicate that Pinot noir and Gouais blanc share an extremely high degree of kinship that has resulted in the Chardonnay genome displaying characteristics that are indicative of inbreeding.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Background. Streptococcus pneumoniae can cause a wide spectrum of disease, including invasive pneumococcal disease (IPD). From 2005 to 2009 an outbreak of IPD occurred in Western Canada, caused by a S. pneumoniae strain with multilocus sequence type (MLST) 289 and serotype 5. We sought to investigate the incidence of IPD due to this S. pneumoniae strain and to characterize the outbreak in British Columbia using whole-genome sequencing. Methods. IPD was defined according to Public Health Agency of Canada guidelines. Two isolates representing the beginning and end of the outbreak were whole-genome sequenced. The sequences were analyzed for single nucleotide variants (SNVs) and putative genomic islands. Results. The peak of the outbreak in British Columbia was in 2006, when 57% of invasive S. pneumoniae isolates were serotype 5. Comparison of two whole-genome sequenced strains showed only 10 SNVs between them. A 15.5 kb genomic island was identified in outbreak strains, allowing the design of a PCR assay to track the spread of the outbreak strain. Discussion. We show that the serotype 5 MLST 289 strain contains a distinguishing genomic island, which remained genetically consistent over time. Whole-genome sequencing holds great promise for real-time characterization of outbreaks in the future and may allow responses tailored to characteristics identified in the genome.

The activities of pathways that regulate malignant transformation can be influenced by microRNAs (miRs). Recently, we showed that increased expression of five tumour-suppressor miRs, miR-508-3p, miR-508-5p, miR-509-3p, miR-509-5p and miR-130b-3p, correlate with improved clinical outcomes in human ovarian cancer patients, and that miR-509-3p attenuates invasion of ovarian cancer cell lines. Here, we investigate the mechanism underlying this reduced invasive potential by assessing the impact of these five miRs on the physical properties of cells. Human ovarian cancer cells (HEYA8, OVCAR8) that are transfected with miR mimics representing these five miRs exhibit decreased invasion through collagen matrices, increased cell size and reduced deformability as measured by microfiltration and microfluidic assays. To understand the molecular basis of altered invasion and deformability induced by these miRs, we use predicted and validated mRNA targets that encode structural and signalling proteins that regulate cell mechanical properties. Combined with analysis of gene transcripts by real-time PCR and image analysis of F-actin in single cells, our results suggest that these tumour-suppressor miRs may alter cell physical properties by regulating the actin cytoskeleton. Our findings provide biophysical insights into how tumour-suppressor miRs can regulate the invasive behaviour of ovarian cancer cells, and identify potential therapeutic targets that may be implicated in ovarian cancer progression.

The linked read sequencing library preparation platform by 10X Genomics produces barcoded sequencing libraries, which are subsequently sequenced using the Illumina short read sequencing technology. In this new approach, long fragments of DNA are partitioned into separate micro-reactions, where the same index sequence is incorporated into each of the sequencing fragment inserts derived from a given long fragment. In this study, we exploited this property by using reads from index sequences associated with a large number of reads, to assemble the chloroplast genome of the Sitka spruce tree (Picea sitchensis). Here we report on the first Sitka spruce chloroplast genome assembled exclusively from P. sitchensis genomic libraries prepared using the 10X Genomics protocol. We show that the resulting 124,049 base pair long genome shares high sequence similarity with the related white spruce and Norway spruce chloroplast genomes, but diverges substantially from a previously published P. sitchensis- P. thunbergii chimeric genome. The use of reads from high-frequency indices enabled separation of the nuclear genome reads from that of the chloroplast, which resulted in the simplification of the de Bruijn graphs used at the various stages of assembly.

We present a computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. The majority of therapeutics are designed to target single proteins, yet most diseased states are characterized by a combination of many interacting genes and proteins. Using the topology of protein-protein interaction networks, our methods can explicitly model the possible synergistic effect of targeting multiple proteins using drug combinations in different cancer types. The methodology can be conceptually split into two distinct stages. Firstly, we integrate protein interaction and gene expression data to develop network representations of different tissue types and cancer types. Secondly, we model network perturbations to search for target combinations which cause significant damage to a relevant cancer network but only minimal damage to an equivalent normal network. We have developed sets of predicted target and drug combinations for multiple cancer types, which are validated using known cancer and drug associations, and are currently in experimental testing for prostate cancer. Our methods also revealed significant bias in curated interaction data sources towards targets with associations compared with high-throughput data sources from model organisms. The approach developed can potentially be applied to many other diseased cell types.

Peritoneal mesothelioma is a rare and sometimes lethal malignancy that presents a clinical challenge for both diagnosis and management. Recent studies have led to a better understanding of the molecular biology of peritoneal mesothelioma. Translation of the emerging data into better treatments and outcome is needed. From two patients with peritoneal mesothelioma, we derived whole genome sequences, RNA expression profiles, and targeted deep sequencing data. Molecular data were made available for translation into a clinical treatment plan. Treatment responses and outcomes were later examined in the context of molecular findings. Molecular studies presented here provide the first reported whole genome sequences of peritoneal mesothelioma. Mutations in known mesothelioma-related genes NF2, CDKN2A, LATS2, amongst others, were identified. Activation of MET-related signaling pathways was demonstrated in both cases. A hypermutated phenotype was observed in one case (434 vs. 18 single nucleotide variants) and was associated with a favourable outcome despite sarcomatoid histology and multifocal disease. This study represents the first report of whole genome analyses of peritoneal mesothelioma, a key step in the understanding and treatment of this disease.

JAGuaR is an alignment protocol for RNA-seq reads that uses an extended reference to increase alignment sensitivity. It uses BWA to align reads to the genome and reference transcript models (including annotated exon-exon junctions) specifically allowing for the possibility of a single read spanning multiple exons. Reads aligned to the transcript models are then re-mapped on to genomic coordinates, transforming alignments that span multiple exons into large-gapped alignments on the genome. While JAGuaR does not detect novel junctions, we demonstrate how JAGuaR generates fast and accurate transcriptome alignments, which allows for both sensitive and specific SNV calling.

Malformations of the cardiovascular system are the most common type of birth defect in humans, frequently affecting the formation of valves and septa. During heart valve and septa formation, cells from the atrio-ventricular canal (AVC) and outflow tract (OFT) regions of the heart undergo an epithelial-to-mesenchymal transformation (EMT) and invade the underlying extracellular matrix to give rise to endocardial cushions. Subsequent maturation of newly formed mesenchyme cells leads to thin stress-resistant leaflets. TWIST1 is a basic helix-loop-helix transcription factor expressed in newly formed mesenchyme cells of the AVC and OFT that has been shown to play roles in cell survival, cell proliferation and differentiation. However, the downstream targets of TWIST1 during heart valve formation remain unclear. To identify genes important for heart valve development downstream of TWIST1, we performed global gene expression profiling of AVC, OFT, atria and ventricles of the embryonic day 10.5 mouse heart by tag-sequencing (Tag-seq). Using this resource we identified a novel set of 939 genes, including 123 regulators of transcription, enriched in the valve forming regions of the heart. We compared these genes to a Tag-seq library from the Twist1 null developing valves revealing significant gene expression changes. These changes were consistent with a role of TWIST1 in controlling differentiation of mesenchymal cells following their transformation from endothelium in the mouse. To study the role of TWIST1 at the DNA level we performed chromatin immunoprecipitation and identified novel direct targets of TWIST1 in the developing heart valves. Our findings support a role for TWIST1 in the differentiation of AVC mesenchyme post-EMT in the mouse, and suggest that TWIST1 can exert its function by direct DNA binding to activate valve specific gene expression.

Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.

Pyrosequencing technology has the potential to rapidly sequence HIV-1 viral quasispecies without requiring the traditional approach of cloning. In this study, we investigated the utility of ultra-deep pyrosequencing to characterize genetic diversity of the HIV-1 gag quasispecies and assessed the possible contribution of pyrosequencing technology in studying HIV-1 biology and evolution.HIV-1 gag gene was amplified from 96 patients using nested PCR. The PCR products were cloned and sequenced using capillary based Sanger fluorescent dideoxy termination sequencing. The same PCR products were also directly sequenced using the 454 pyrosequencing technology. The two sequencing methods were evaluated for their ability to characterize quasispecies variation, and to reveal sites under host immune pressure for their putative functional significance. A total of 14,034 variations were identified by 454 pyrosequencing versus 3,632 variations by Sanger clone-based (SCB) sequencing. 11,050 of these variations were detected only by pyrosequencing. These undetected variations were located in the HIV-1 Gag region which is known to contain putative cytotoxic T lymphocyte (CTL) and neutralizing antibody epitopes, and sites related to virus assembly and packaging. Analysis of the positively selected sites derived by the two sequencing methods identified several differences. All of them were located within the CTL epitope regions.Ultra-deep pyrosequencing has proven to be a powerful tool for characterization of HIV-1 genetic diversity with enhanced sensitivity, efficiency, and accuracy. It also improved reliability of downstream evolutionary and functional analysis of HIV-1 quasispecies.

The Y-box binding protein-1 (YB-1) is an oncogenic transcription/translation factor that is activated by phosphorylation at S102 whereby it induces the expression of growth promoting genes such as EGFR and HER-2. We recently illustrated by an in vitro kinase assay that a novel peptide to YB-1 was highly phosphorylated by the serine/threonine p90 S6 kinases RSK-1 and RSK-2, and to a lesser degree PKCα and AKT. Herein, we sought to develop this decoy cell permeable peptide (CPP) as a cancer therapeutic. This 9-mer was designed as an interference peptide that would prevent endogenous YB-1(S102) phosphorylation based on molecular docking. In cancer cells, the CPP blocked P-YB-1(S102) and down-regulated both HER-2 and EGFR transcript level and protein expression. Further, the CPP prevented YB-1 from binding to the EGFR promoter in a gel shift assay. Notably, the growth of breast (SUM149, MDA-MB-453, AU565) and prostate (PC3, LNCap) cancer cells was inhibited by ∼90% with the CPP. Further, treatment with this peptide enhanced sensitivity and overcame resistance to trastuzumab in cells expressing amplified HER-2. By contrast, the CPP had no inhibitory effect on the growth of normal immortalized breast epithelial (184htert) cells, primary breast epithelial cells, nor did it inhibit differentiation of hematopoietic progenitors. These data collectively suggest that the CPP is a novel approach to suppressing the growth of cancer cells while sparing normal cells and thereby establishes a proof-of-concept that blocking YB-1 activation is a new course of cancer therapeutics.

A crucial step in the development of muscle cells in all metazoan animals is the assembly and anchorage of the sarcomere, the essential repeat unit responsible for muscle contraction. In Caenorhabditis elegans, many of the critical proteins involved in this process have been uncovered through mutational screens focusing on uncoordinated movement and embryonic arrest phenotypes. We propose that additional sarcomeric proteins exist for which there is a less severe, or entirely different, mutant phenotype produced in their absence. We have used Serial Analysis of Gene Expression (SAGE) to generate a comprehensive profile of late embryonic muscle gene expression. We generated two replicate long SAGE libraries for sorted embryonic muscle cells, identifying 7,974 protein-coding genes. A refined list of 3,577 genes expressed in muscle cells was compiled from the overlap between our SAGE data and available microarray data. Using the genes in our refined list, we have performed two separate RNA interference (RNAi) screens to identify novel genes that play a role in sarcomere assembly and/or maintenance in either embryonic or adult muscle. To identify muscle defects in embryos, we screened specifically for the Pat embryonic arrest phenotype. To visualize muscle defects in adult animals, we fed dsRNA to worms producing a GFP-tagged myosin protein, thus allowing us to analyze their myofilament organization under gene knockdown conditions using fluorescence microscopy. By eliminating or severely reducing the expression of 3,300 genes using RNAi, we identified 122 genes necessary for proper myofilament organization, 108 of which are genes without a previously characterized role in muscle. Many of the genes affecting sarcomere integrity have human homologs for which little or nothing is known.