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Background The classical genetic model of colorectal cancer presents APC mutations as the earliest genomic alterations, followed by KRAS and TP53 mutations. However, the timing and relative order of clonal expansion and other types of genomic alterations, such as genomic rearrangements, are still unclear. Results Here, we perform comprehensive bioinformatic analysis to dissect the relative timing of somatic genetic alterations in 63 colorectal cancers with whole-genome sequencing data. Utilizing allele fractions of somatic single nucleotide variants as molecular clocks while accounting for the presence of copy number changes and structural alterations, we identify key events in the evolution of colorectal tumors. We find that driver point mutations, gene fusions, and arm-level copy losses typically arise early in tumorigenesis; different mechanisms act on distinct genomic regions to drive DNA copy changes; and chromothripsis—clustered rearrangements previously thought to occur as a single catastrophic event—is frequent and may occur multiple times independently in the same tumor through different mechanisms. Furthermore, our computational approach reveals that, in contrast to recent studies, selection is often present on subclones and that multiple evolutionary models can operate in a single tumor at different stages. Conclusion Combining these results, we present a refined tumor progression model which significantly expands our understanding of the tumorigenic process of human colorectal cancer. Electronic supplementary material The online version of this article (10.1186/s13059-019-1782-4) contains supplementary material, which is available to authorized users.

Malignant rhabdoid tumors (MRT) are highly aggressive pediatric cancers that respond poorly to current therapies. In this study, we screened several MRT cell lines with large-scale RNAi, CRISPR-Cas9, and small-molecule libraries to identify potential drug targets specific for these cancers. We discovered MDM2 and MDM4, the canonical negative regulators of p53, as significant vulnerabilities. Using two compounds currently in clinical development, idasanutlin (MDM2-specific) and ATSP-7041 (MDM2/4-dual), we show that MRT cells were more sensitive than other p53 wild-type cancer cell lines to inhibition of MDM2 alone as well as dual inhibition of MDM2/4. These compounds caused significant upregulation of the p53 pathway in MRT cells, and sensitivity was ablated by CRISPR-Cas9–mediated inactivation of TP53. We show that loss of SMARCB1, a subunit of the SWI/SNF (BAF) complex mutated in nearly all MRTs, sensitized cells to MDM2 and MDM2/4 inhibition by enhancing p53-mediated apoptosis. Both MDM2 and MDM2/4 inhibition slowed MRT xenograft growth in vivo, with a 5-day idasanutlin pulse causing marked regression of all xenografts, including durable complete responses in 50% of mice. Together, these studies identify a genetic connection between mutations in the SWI/SNF chromatin-remodeling complex and the tumor suppressor gene TP53 and provide preclinical evidence to support the targeting of MDM2 and MDM4 in this often-fatal pediatric cancer. Significance: This study identifies two targets, MDM2 and MDM4, as vulnerabilities in a deadly pediatric cancer and provides preclinical evidence that compounds inhibiting these proteins have therapeutic potential.

Mutations in BRCA1 and/or BRCA2 (BRCA1/2) are the most common indication of deficiency in the homologous recombination (HR) DNA repair pathway. However, recent genome-wide analyses have shown that the same pattern of mutations found in BRCA1/2-mutant tumors is also present in several other tumors. Here, we present a new computational tool called Signature Multivariate Analysis (SigMA), which can be used to accurately detect the mutational signature associated with HR deficiency from targeted gene panels. Whereas previous methods require whole-genome or whole-exome data, our method detects the HR-deficiency signature even from low mutation counts, by using a likelihood-based measure combined with machine-learning techniques. Cell lines that we identify as HR deficient show a significant response to poly (ADP-ribose) polymerase (PARP) inhibitors; patients with ovarian cancer whom we found to be HR deficient show a significantly longer overall survival with platinum regimens. By enabling panel-based identification of mutational signatures, our method substantially increases the number of patients that may be considered for treatments targeting HR deficiency.

Cell behaviors are dictated by epigenetic and transcriptional programs. Little is known about how extracellular stimuli modulate these programs to reshape gene expression and control cell behavioral responses. Here, we interrogated the epigenetic and transcriptional response of endothelial cells to VEGFA treatment and found rapid chromatin changes that mediate broad transcriptomic alterations. VEGFA-responsive genes were associated with active promoters, but changes in promoter histone marks were not tightly linked to gene expression changes. VEGFA altered transcription factor occupancy and the distal epigenetic landscape, which profoundly contributed to VEGFA-dependent changes in gene expression. Integration of gene expression, dynamic enhancer, and transcription factor occupancy changes induced by VEGFA yielded a VEGFA-regulated transcriptional regulatory network, which revealed that the small MAF transcription factors are master regulators of the VEGFA transcriptional program and angiogenesis. Collectively these results revealed that extracellular stimuli rapidly reconfigure the chromatin landscape to coordinately regulate biological responses.

Background Glioblastoma is a malignant brain tumor characterized by rapid growth, diffuse invasion and therapeutic resistance. We recently used microRNA expression profiles to subclassify glioblastoma into five genetically and clinically distinct subclasses, and showed that microRNAs both define and contribute to the phenotypes of these subclasses. Here we show that miR-29a activates a multi-faceted growth and invasion program that promotes glioblastoma aggressiveness. Methods microRNA expression profiles from 197 glioblastomas were analyzed to identify the candidate miRNAs that are correlated to glioblastoma aggressiveness. The candidate miRNA, miR-29a, was further studied in vitro and in vivo. Results Members of the miR-29 subfamily display increased expression in the two glioblastoma subclasses with the worst prognoses (astrocytic and neural). We observed that miR-29a is among the microRNAs that are most positively-correlated with PTEN copy number in glioblastoma, and that miR-29a promotes glioblastoma growth and invasion in part by targeting PTEN. In PTEN-deficient glioblastoma cells, however, miR-29a nevertheless activates AKT by downregulating the metastasis suppressor, EphB3. In addition, miR-29a robustly promotes invasion in PTEN-deficient glioblastoma cells by repressing translation of the Sox4 transcription factor, and this upregulates the invasion-promoting protein, HIC5. Indeed, we identified Sox4 as the most anti-correlated predicted target of miR-29a in glioblastoma. Importantly, inhibition of endogenous miR-29a decreases glioblastoma growth and invasion in vitro and in vivo, and increased miR-29a expression in glioblastoma specimens correlates with decreased patient survival. Conclusions Taken together, these data identify miR-29a as a master regulator of glioblastoma growth and invasion. Electronic supplementary material The online version of this article (10.1186/s13046-019-1026-1) contains supplementary material, which is available to authorized users.

For the information content of microscopy images to be appropriately interpreted, reproduced, and meet FAIR (Findable Accessible Interoperable and Reusable) principles, they should be accompanied by detailed descriptions of microscope hardware, image acquisition settings, image pixel and dimensional structure, and instrument performance. Nonetheless, the thorough documentation of imaging experiments is significantly impaired by the lack of community-sanctioned easy-to-use software tools to facilitate the extraction and collection of relevant microscopy metadata. Here we presentMicro-Meta App, an intuitive open-source software designed to tackle these issues that was developed in the context of nascent global bioimaging community organizations, includingBioImagingNorthAmerica (BINA) andQUAlity Assessment andREProducibility inLightMicroscopy (QUAREP-LiMi), whose goal is to improve reproducibility, data quality and sharing value for imaging experiments. The App provides a user-friendly interface for building comprehensive descriptions of the conditions utilized to produce individual microscopy datasets as specified by the recently proposed 4DN-BINA-OME tiered-system of Microscopy Metadata model. To achieve this goal the App provides a visual guide for a microscope-user to: 1) interactively build diagrammatic representations of hardware configurations of given microscopes that can be easily reused and shared with colleagues needing to document similar instruments. 2) Automatically extracts relevant metadata from image files and facilitates the collection of missing image acquisition settings and calibration metrics associated with a given experiment. 3) Output all collected Microscopy Metadata to interoperable files that can be used for documenting imaging experiments and shared with the community. In addition to significantly lowering the burden of quality assurance, the visual nature of Micro-Meta App makes it particularly suited for training users that have limited knowledge of the intricacies of light microscopy experiments. To ensure wide-adoption by microscope-users with different needs Micro-Meta App closely interoperates withMethodsJ2andOMERO.mde, two complementary tools described in parallel manuscripts.

Primary template-directed amplification (PTA) is an improved amplification technique for single-cell DNA sequencing. We generated whole-genome analysis of 76 single neurons and developed SCAN2, a computational method to accurately identify both clonal and non-clonal somatic (i.e., limited to a single neuron) single nucleotide variants (SNVs) and small insertions and deletions (indels) using PTA data. Our analysis confirms an increase in non-clonal somatic mutation in single neurons with age, but revises estimates for the rate of this accumulation to be 15 SNVs per year. We also identify artifacts in other amplification methods. Most importantly, we show that somatic indels also increase by at least 2 indels per year per neuron and that indels may have a larger impact on gene function than somatic SNVs in human neurons.

Background Retrotransposons have been implicated as causes of Mendelian disease, but their role in autism spectrum disorder (ASD) has not been systematically defined, because they are only called with adequate sensitivity from whole genome sequencing (WGS) data and a large enough cohort for this analysis has only recently become available. Results We analyzed WGS data from a cohort of 2288 ASD families from the Simons Simplex Collection by establishing a scalable computational pipeline for retrotransposon insertion detection. We report 86,154 polymorphic retrotransposon insertions—including > 60% not previously reported—and 158 de novo retrotransposition events. The overall burden of de novo events was similar between ASD individuals and unaffected siblings, with 1 de novo insertion per 29, 117, and 206 births for Alu, L1, and SVA respectively, and 1 de novo insertion per 21 births total. However, ASD cases showed more de novo L1 insertions than expected in ASD genes. Additionally, we observed exonic insertions in loss-of-function intolerant genes, including a likely pathogenic exonic insertion in CSDE1, only in ASD individuals. Conclusions These findings suggest a modest, but important, impact of intronic and exonic retrotransposon insertions in ASD, show the importance of WGS for their analysis, and highlight the utility of specific bioinformatic tools for high-throughput detection of retrotransposon insertions.

Mutational activation of KRAS promotes the initiation and progression of cancers, especially in the colorectum, pancreas, lung, and blood plasma, with varying prevalence of specific activating missense mutations. Although epidemiological studies connect specific alleles to clinical outcomes, the mechanisms underlying the distinct clinical characteristics of mutant KRAS alleles are unclear. Here, we analyze 13,492 samples from these four tumor types to examine allele- and tissue-specific genetic properties associated with oncogenic KRAS mutations. The prevalence of known mutagenic mechanisms partially explains the observed spectrum of KRAS activating mutations. However, there are substantial differences between the observed and predicted frequencies for many alleles, suggesting that biological selection underlies the tissue-specific frequencies of mutant alleles. Consistent with experimental studies that have identified distinct signaling properties associated with each mutant form of KRAS, our genetic analysis reveals that each KRAS allele is associated with a distinct tissue-specific comutation network. Moreover, we identify tissue-specific genetic dependencies associated with specific mutant KRAS alleles. Overall, this analysis demonstrates that the genetic interactions of oncogenic KRAS mutations are allele- and tissue-specific, underscoring the complexity that drives their clinical consequences., The KRAS gene is often mutated at several hotspot codons in cancer, resulting in similar, yet distinct, functional impacts on the KRAS protein. Here, the authors examine the genetic interactions of the different KRAS mutations across multiple cancer types and discover that KRAS mutations have allele- and tissue-specific mutagenic origins, comutation patterns, and dependency interactions.

Idiopathic normal pressure hydrocephalus (iNPH) is a neurological disorder that occurs in about 1% of individuals over age 60 and is characterized by enlarged cerebral ventricles, gait difficulty, incontinence, and cognitive decline. The cause and pathophysiology of iNPH are largely unknown. We performed whole exome sequencing of DNA obtained from 53 unrelated iNPH patients. Two recurrent heterozygous loss of function deletions in CWH43 were observed in 15% of iNPH patients and were significantly enriched 6.6‐fold and 2.7‐fold, respectively, when compared to the general population. Cwh43 modifies the lipid anchor of glycosylphosphatidylinositol‐anchored proteins. Mice heterozygous for CWH43 deletion appeared grossly normal but displayed hydrocephalus, gait and balance abnormalities, decreased numbers of ependymal cilia, and decreased localization of glycosylphosphatidylinositol‐anchored proteins to the apical surfaces of choroid plexus and ependymal cells. Our findings provide novel mechanistic insights into the origins of iNPH and demonstrate that it represents a distinct disease entity.

Summary Despite the improvement in variant detection algorithms, visual inspection of the read-level data remains an essential step for accurate identification of variants in genome analysis. We developed BamSnap, an efficient BAM file viewer utilizing a graphics library and BAM indexing. In contrast to existing viewers, BamSnap can generate high-quality snapshots rapidly, with customized tracks and layout. As an example, we produced read-level images at 1000 genomic loci for >2500 whole-genomes. Availability and implementation BamSnap is freely available at https://github.com/parklab/bamsnap. Supplementary information Supplementary data are available at Bioinformatics online.

Gene fusions are widespread in tumor cells and can play important roles in tumor initiation and progression. Using full length single cell RNA sequencing (scRNA-seq), gene fusions can now be detected at single cell level by analyzing chimeric reads in scRNA-seq. However, scRNA-seq data has a high noise level and contains various technical artefacts. Direct application of fusion detection tools developed for bulk data can lead to spurious fusion discoveries and leave some true fusions undetected. In this paper, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. scFusion is composed of a statistical model and a deep learning model, both of which are designed to control for potential false discoveries. The statistical model models the background noise as zero inflated negative binomial and uses a statistical testing procedure to control for false positives. The deep learning model is trained to recognize technical chimeric artefacts and filter false fusion candidates generated by these artefacts. We compared scFusion with bulk fusion detection methods using simulation data created based on real scRNA-seq data and found that scFusion had superior performance. Applying scFusion to a T cell data, scFusion successfully detected the invariant TCR gene recombinations in Mucosal-associated invariant T cells that many bulk methods failed to detect. In a multiple myeloma data, scFusion detected the known recurrent fusion IgH-WHSC1, which was associated with overexpression of the WHSC1 oncogene.

BackgroundThe importance of ambulatory hypoxia without resting hypoxia in COVID-19 is unknown. Ambulatory hypoxia without resting hypoxia may help objectively identify high-risk patients hospitalized with COVID-19. Interventions may be initiated earlier with sufficient lead-time between development of ambulatory hypoxia and other outcome measures.MethodsWe performed a retrospective study of adult patients hospitalized with COVID-19 from March 1, 2020 to October 30, 2020 in ten hospitals in an integrated academic medical system in the Chicagoland area. We analyzed patients who had daily ambulatory oximetry measurements, excluding patients who had first ambulatory oximetry measurements after the use of oxygen therapies (nasal cannula or advanced oxygen therapies). We determined the association of ambulatory hypoxia without resting hypoxia with the eventual need for nasal cannula or advanced oxygen therapies (defined as high flow nasal cannula, Bi-PAP, ventilator, or extracorporeal membrane oxygenation). We also calculated the time between development of ambulatory hypoxia and the need for oxygen therapies.ResultsOf 531 patients included in the study, 132 (24.9%) had ambulatory hypoxia. Presence of ambulatory hypoxia was strongly associated with subsequent use of nasal cannula (OR 4.8, 95% CI 2.8 – 8.4) and advanced oxygen therapy (IRR 7.7, 95% CI 3.4 – 17.5). Ambulatory hypoxia preceded nasal cannula use by a median 12.5 hours [IQR 3.25, 29.25] and advanced oxygenation therapies by 54 hours [IQR 25, 82].ConclusionAmbulatory hypoxia without resting hypoxia may serve as an early, non-invasive physiologic marker for the likelihood of developing moderate to severe COVID-19 and help clinicians triage patients and initiate earlier interventions.

The vertebrate retina is generated by retinal progenitor cells (RPCs), which produce >100 cell types. Although some RPCs produce many cell types, other RPCs produce restricted types of daughter cells, such as a cone photoreceptor and a horizontal cell (HC). We used genome-wide assays of chromatin structure to compare the profiles of a restricted cone/HC RPC and those of other RPCs in chicks. These data nominated regions of regulatory activity, which were tested in tissue, leading to the identification of many cis-regulatory modules (CRMs) active in cone/HC RPCs and developing cones. Two transcription factors, Otx2 and Oc1, were found to bind to many of these CRMs, including those near genes important for cone development and function, and their binding sites were required for activity. We also found that Otx2 has a predicted autoregulatory CRM. These results suggest that Otx2, Oc1 and possibly other Onecut proteins have a broad role in coordinating cone development and function. The many newly discovered CRMs for cones are potentially useful reagents for gene therapy of cone diseases.

SUMMARYHistone chaperones are critical for controlling chromatin integrity during transcription, DNA replication, and DNA repair. We have discovered that the physical interaction between two essential histone chaperones, Spt6 and Spn1/Iws1, is required for transcriptional accuracy and nucleosome organization. To understand this requirement, we have isolated suppressors of anspt6mutation that disrupts the Spt6-Spn1 interaction. Several suppressors are in a third essential histone chaperone, FACT, while another suppressor is in the transcription elongation factor Spt5/DSIF. The FACT suppressors weaken FACT-nucleosome interactions and bypass the requirement for Spn1, possibly by restoring a necessary balance between Spt6 and FACT on chromatin. In contrast, the Spt5 suppressor modulates Spt6 function in a Spn1-dependent manner. Despite these distinct mechanisms, both suppressors alleviate the nucleosome organization defects caused by disruption of the Spt6-Spn1 interaction. Taken together, we have uncovered a network in which histone chaperones and other elongation factors coordinate transcriptional integrity and chromatin structure.

Genome sequencing is often pivotal in the diagnosis of rare diseases, but many of these conditions lack specific treatments. We describe how molecular diagnosis of a rare, fatal neurodegenerative condition led to the rational design, testing, and manufacture of milasen, a splice-modulating antisense oligonucleotide drug tailored to a particular patient. Proof-of-concept experiments in cell lines from the patient served as the basis for launching an "N-of-1" study of milasen within 1 year after first contact with the patient. There were no serious adverse events, and treatment was associated with objective reduction in seizures (determined by electroencephalography and parental reporting). This study offers a possible template for the rapid development of patient-customized treatments. (Funded by Mila's Miracle Foundation and others.).

Post-zygotic mutations incurred during DNA replication, DNA repair, and other cellular processes lead to somatic mosaicism. Somatic mosaicism is an established cause of various diseases, including cancers. However, detecting mosaic variants in DNA from non-cancerous somatic tissues poses significant challenges, particularly if the variants only are present in a small fraction of cells. Here, the Brain Somatic Mosaicism Network conducted a coordinated, multi-institutional study to: (i) examine the ability of existing methods to detect simulated somatic single nucleotide variants (SNVs) in DNA mixing experiments; (ii) generate multiple replicates of whole genome sequencing data from the dorsolateral prefrontal cortex, other brain regions, dura mater, and dural fibroblasts of a single neurotypical individual; (iii) devise strategies to discover somatic SNVs; and (iv) apply various approaches to validate somatic SNVs. These efforts led to the identification of 43 bona fide somatic SNVs that ranged in variant allele fractions from ~0.005 to ~0.28. Guided by these results, we devised best practices for calling mosaic SNVs from 250X whole genome sequencing data in the accessible portion of the human genome that achieve 90% specificity and sensitivity. Finally, we demonstrated that analysis of multiple bulk DNA samples from a single individual allows the reconstruction of early developmental cell lineage trees. Thus, this study provides a unified set of best practices to detect somatic SNVs in non-cancerous tissues. The data and methods are freely available to the scientific community and should serve as a guide to assess the contributions of somatic SNVs to neuropsychiatric diseases.

Understanding variations in the performance of serological tests for SARS-CoV-2 across varying demographics is relevant to clinical interpretations and public policy derived from their results. Appropriate use of serological assays to detect anti-SARS-CoV-2 antibodies requires estimation of their accuracy over large populations and an understanding of the variance in performance over time and across demographic groups. In this manuscript we focus on anti-SARS-CoV-2 IgG, IgA, and IgM antibody tests approved under emergency use authorizations and determine the recall of the serological tests compared to RT-PCR tests by Logical Observation Identifiers Names and Codes (LOINCs). Variability in test performance was further examined over time and by demographics. The recall of the most common IgG assay (LOINC 94563-4) was 91.2% (95% CI: 90.5%, 91.9%). IgA (LOINC 94562-6) and IgM (94564-2) assays performed significantly worse than IgG assays with estimated recall rates of 20.6% and 27.3%, respectively. A statistically significant difference in recall (p = 0.019) was observed across sex with a higher recall in males than females, 92.1% and 90.4%, respectively. Recall also differed significantly by age group, with higher recall in those over 45 compared to those under 45, 92.9% and 88.0%, respectively (p< 0.001). While race was unavailable for the majority of the individuals, a significant difference was observed between recall in White individuals and Black individuals (p = 0.007) and White individuals and Hispanic individuals (p = 0.001). The estimates of recall were 89.3%, 95.9%, and 94.2% for White, Black, and Hispanic individuals respectively.

Homologous recombination (HR)-deficient cancers are sensitive to inhibitors of Poly-ADP Ribose Polymerase (PARPi), which have shown clinical efficacy in the treatment of high-grade serous cancers (HGSC). However, the majority of patients will relapse, and acquired PARPi resistance is emerging as a pressing clinical problem. Here we generated seven single-cell clones with acquired PARPi resistance derived from a PARPi-sensitive, TP53−/− and BRCA1−/− epithelial cell line generated using CRISPR/Cas9. These clones showed diverse resistance mechanisms, and some clones presented with multiple mechanisms of resistance at the same time. Genomic analysis of the clones revealed unique transcriptional and mutational profiles and increased genomic instability in comparison to a PARPi-sensitive cell line. Clonal evolutionary analyses suggested that acquired PARPi resistance arose via clonal selection from an intrinsically unstable and heterogenous cell population in the sensitive cell line, which contained pre-existing drug tolerant cells. Similarly, clonal and spatial heterogeneity in tumor biopsies from a clinical BRCA1-mutant HGSC patient with acquired PARPi resistance were observed. In an imaging-based drug screening, the clones showed heterogenous responses to targeted therapeutic agents, indicating that not all PARPi-resistant clones can be targeted with just one therapy. Furthermore, PARPi-resistant clones showed mechanism-dependent vulnerabilities to the selected agents, demonstrating that a deeper understanding on the mechanisms of resistance could lead to improved targeting and biomarkers for HGSC with acquired PARPi resistance.

As the coronavirus cases continue to surge, the urgent need for universal testing to identify positive cases for effective containment of this highly contagious pandemic has become the center of attention worldwide. However, in spite of extensive discussions, very few places have even attempted to implement it. We evaluated the efficacy of widespread testing in creating a safe workplace in our electrophysiology (EP) community. Furthermore, we assessed the new infection rate in patients undergoing EP procedure, to see if identification and exclusion of positive cases facilitated establishment of a risk-free operating environment. Viral-RNA and serology tests were conducted in 1670 asymptomatic subjects including patients and their caregivers and staff in our EP units along with the Emergency Medical Service (EMS) staff. Of 1670, 758 (45.4%) were patients and the remaining 912 were caregivers, EMS staff, and staff from EP clinic and lab. Viral-RNA test revealed 64 (3.8%) positives in the population. A significant increase in positivity rate was observed from April to June 2020 (p = 0.02). Procedures of positive cases (n = 31) were postponed until they tested negative at retesting. Staff testing positive (n = 33) were retested before going back to work after 2 weeks. Because of suspected exposure, 67 staff were retested and source was traced. No new infections were reported in patients during or within 2 weeks after the hospital-stay. Universal testing to identify positive cases was helpful in creating and maintaining a safe working environment without exposing patients and staff to new infections in the EP units. Trial Registration Number: clinicaltrials.gov : NCT04352764

Transposable element (TE) mobilization is a significant source of genomic variation and has been associated with various human diseases. The exponential growth of population-scale whole-genome sequencing and rapid innovations in long-read sequencing technologies provide unprecedented opportunities to study TE insertions and their functional impact in human health and disease. Identifying TE insertions, however, is challenging due to the repetitive nature of the TE sequences. Here, we review computational approaches to detecting and genotyping TE insertions using short- and long-read sequencing and discuss the strengths and weaknesses of different approaches. © 2020 Wiley Periodicals LLC.

Funder: U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI), Funder: Ontario Institute for Cancer Research (Institut Ontarien de Recherche sur le Cancer); doi: https://doi.org/10.13039/100012118, Funder: EMBL Member States EU FP7 Programme projects EurocanPlatform (260791) CAGEKID (241669), Funder: European Union’s Framework Programme For Research and Innovation Horizon 2020 under the Marie Sklodowska-Curie grant agreement no. 703543, Funder: Michael & Susan Dell Foundation; Mary K. Chapman Foundation; CCSG Grant P30 CA016672 (Bioinformatics Shared Resource); ITCR U24 CA199461; GDAN U24 CA210949; GDAN U24 CA210950, Funder: European Commission's H2020 Programme, project SOUND, Grant Agreement no 633974, Funder: Spanish Government (SEV 2015-0493) BSC-Lenovo Master Collaboration Agreement (2015), The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user’s guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.

Spn1/Iws1 is a conserved protein involved in transcription and chromatin dynamics, yet its generalin vivorequirement for these functions is unknown. Using a Spn1 depletion system inS. cerevisiae,we demonstrate that Spn1 broadly influences several aspects of gene expression on a genome-wide scale. We show that Spn1 is globally required for normal mRNA levels and for normal splicing of ribosomal protein transcripts. Furthermore, Spn1 maintains the localization of H3K36 and H3K4 methylation across the genome and is required for normal histone levels at highly expressed genes. Finally, we show that the association of Spn1 with the transcription machinery is strongly dependent on its binding partner, Spt6, while the association of Spt6 and Set2 with transcribed regions is partially dependent on Spn1. Taken together, our results show that Spn1 affects multiple aspects of gene expression and provide additional evidence that it functions as a histone chaperonein vivo.

The Wilms tumor-1 transcription factor regulates a repair process, providing insights on the pathogenesis of kidney disease., In the context of human disease, the mechanisms whereby transcription factors reprogram gene expression in reparative responses to injury are not well understood. We have studied the mechanisms of transcriptional reprogramming in disease using murine kidney podocytes as a model for tissue injury. Podocytes are a crucial component of glomeruli, the filtration units of each nephron. Podocyte injury is the initial event in many processes that lead to end-stage kidney disease. Wilms tumor-1 (WT1) is a master regulator of gene expression in podocytes, binding nearly all genes known to be crucial for maintenance of the glomerular filtration barrier. Using murine models and human kidney organoids, we investigated WT1-mediated transcriptional reprogramming during the course of podocyte injury. Reprogramming the transcriptome involved highly dynamic changes in the binding of WT1 to target genes during a reparative injury response, affecting chromatin state and expression levels of target genes.

The vertebrate retina is a highly organized structure of approximately 110 cell types. Retinal progenitor cells (RPCs) produce these cell types in a temporal order that is highly conserved. While some RPCs produce many cell types, some terminally dividing RPCs produce restricted types of daughter cells, such as a cone photoreceptor and a horizontal cell (HC). Here, we compared the transcriptomes and chromatin profiles of such a restricted cone/HC RPC with those of other RPCs. We identified many cis-regulatory modules (CRMs) active in cone/HC RPCs and developing cones. We then showed that Otx2 and Oc1 directly regulate the activity of multiple CRMs genome-wide, including near genes important for cone development, such as Rxrg and Neurod1. In addition, we found that Otx2 regulates itself. These results suggest that Otx2 and Oc1 have a broader role than previously appreciated, and deepen our understanding of retinal development, which may benefit therapies for retinal diseases.

Elucidating the lineage relationships among different cell types is key to understanding human brain development. Here we developedParallelRNA andDNA analysis afterDeep-sequencing (PRDD-seq), which combines RNA analysis of neuronal cell types with analysis of nested spontaneous DNA somatic mutations as cell lineage markers, identified from joint analysis of single cell and bulk DNA sequencing by single-cell MosaicHunter (scMH). PRDD-seq enables the first-ever simultaneous reconstruction of neuronal cell type, cell lineage, and sequential neuronal formation (“birthdate”) in postmortem human cerebral cortex. Analysis of two human brains showed remarkable quantitative details that relate mutation mosaic frequency to clonal patterns, confirming an early divergence of precursors for excitatory and inhibitory neurons, and an “inside-out” layer formation of excitatory neurons as seen in other species. In addition our analysis allows the first estimate of excitatory neuron-restricted precursors (about 10) that generate the excitatory neurons within a cortical column. Inhibitory neurons showed complex, subtype-specific patterns of neurogenesis, including some patterns of development conserved relative to mouse, but also some aspects of primate cortical interneuron development not seen in mouse. PRDD-seq can be broadly applied to characterize cell identity and lineage from diverse archival samples with single-cell resolution and in potentially any developmental or disease condition.Significance StatementStem cells and progenitors undergo a series of cell divisions to generate the neurons of the brain, and understanding this sequence is critical to studying the mechanisms that control cell division and migration in developing brain. Mutations that occur as cells divide are known as the basis of cancer, but have more recently been shown to occur with normal cell divisions, creating a permanent, forensic map of the clonal patterns that define the brain. Here we develop new technology to analyze both DNA mutations and RNA gene expression patterns in single cells from human postmortem brain, allowing us to define clonal patterns among different types of human brain neurons, gaining the first direct insight into how they form.

Chromothripsis is a mutational phenomenon characterized by massive, clustered genomic rearrangements that occurs in cancer and other diseases. Recent studies in selected cancer types have suggested that chromothripsis may be more common than initially inferred from low-resolution copy-number data. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we analyze patterns of chromothripsis across 2,658 tumors from 38 cancer types using whole-genome sequencing data. We find that chromothripsis events are pervasive across cancers, with a frequency of more than 50% in several cancer types. Whereas canonical chromothripsis profiles display oscillations between two copy-number states, a considerable fraction of events involve multiple chromosomes and additional structural alterations. In addition to non-homologous end joining, we detect signatures of replication-associated processes and templated insertions. Chromothripsis contributes to oncogene amplification and to inactivation of genes such as mismatch-repair-related genes. These findings show that chromothripsis is a major process that drives genome evolution in human cancer.

International audience; Negative elongation factor (NELF) is a critical transcriptional regulator that stabilizes paused RNA polymerase to permit rapid gene expression changes in response to environmental cues. Although NELF is essential for embryonic development, its role in adult stem cells remains unclear. In this study, through a muscle-stem-cell-specific deletion, we showed that NELF is required for efficient muscle regeneration and stem cell pool replenishment. In mechanistic studies using PRO-seq, single-cell trajectory analyses and myofiber cultures revealed that NELF works at a specific stage of regeneration whereby it modulates p53 signaling to permit massive expansion of muscle progenitors. Strikingly, transplantation experiments indicated that these progenitors are also necessary for stem cell pool repopulation, implying that they are able to return to quiescence. Thus, we identified a critical role for NELF in the expansion of muscle progenitors in response to injury and revealed that progenitors returning to quiescence are major contributors to the stem cell pool repopulation.

Hi-C is a common technique for assessing 3D chromatin conformation. Recent studies have shown that long-range interaction information in Hi-C data can be used to generate chromosome-length genome assemblies and identify large-scale structural variations. Here, we demonstrate the use of Hi-C data in detecting mobile transposable element (TE) insertions genome-wide. Our pipeline Hi-C-based TE analyzer (HiTea) capitalizes on clipped Hi-C reads and is aided by a high proportion of discordant read pairs in Hi-C data to detect insertions of three major families of active human TEs. Despite the uneven genome coverage in Hi-C data, HiTea is competitive with the existing callers based on whole-genome sequencing (WGS) data and can supplement the WGS-based characterization of the TE-insertion landscape. We employ the pipeline to identify TE-insertions from human cell-line Hi-C samples. Availability and implementation HiTea is available at https://github.com/parklab/HiTea and as a Docker image. Supplementary information Supplementary data are available at Bioinformatics online.

The Genomics of Malignant Peripheral Nerve Sheath Tumor (GeM) Consortium is an international collaboration focusing on multi-omic analysis of malignant peripheral nerve sheath tumors (MPNSTs), the most aggressive tumor associated with neurofibromatosis type 1 (NF1). Here we present a summary of current knowledge gaps, a description of our consortium and the cohort we have assembled, and an overview of our plans for multi-omic analysis of these tumors. We propose that our analysis will lead to a better understanding of the order and timing of genetic events related to MPNST initiation and progression. Our ten institutions have assembled 96 fresh frozen NF1-related (63%) and sporadic MPNST specimens from 86 subjects with corresponding clinical and pathological data. Clinical data have been collected as part of the International MPNST Registry. We will characterize these tumors with bulk whole genome sequencing, RNAseq, and DNA methylation profiling. In addition, we will perform multiregional analysis and temporal sampling, with the same methodologies, on a subset of nine subjects with NF1-related MPNSTs to assess tumor heterogeneity and cancer evolution. Subsequent multi-omic analyses of additional archival specimens will include deep exome sequencing (500×) and high density copy number arrays for both validation of results based on fresh frozen tumors, and to assess further tumor heterogeneity and evolution. Digital pathology images are being collected in a cloud-based platform for consensus review. The result of these efforts will be the largest MPNST multi-omic dataset with correlated clinical and pathological information ever assembled.

The three-dimensional conformation of a genome can be profiled using Hi-C, a technique that combines chromatin conformation capture with high-throughput sequencing. However, structural variations often yield features that can be mistaken for chromosomal interactions. Here, we describe a computational method HiNT (Hi-C for copy Number variation and Translocation detection), which detects copy number variations and interchromosomal translocations within Hi-C data with breakpoints at single base-pair resolution. We demonstrate that HiNT outperforms existing methods on both simulated and real data. We also show that Hi-C can supplement whole-genome sequencing in structure variant detection by locating breakpoints in repetitive regions. Electronic supplementary material The online version of this article (10.1186/s13059-020-01986-5) contains supplementary material, which is available to authorized users.

Purpose: Uterine artery embolization (UAE) is an effective treatment for the management of symptomatic uterine fibroids. We aim to evaluate the efficacy of superior hypogastric nerve block (SHNB) in reducing narcotic analgesia use for postprocedural pain after UAE. Methods: A retrospective review of 88 consecutive patients with symptomatic fibroids who underwent UAE between August 2015 and August 2018 was performed. A total of 44 patients had intraprocedural SHNB and 44 patients had no SHNB. They were placed on a morphine patient-controlled anesthesia (PCA) pump after the procedure and were admitted for overnight observation. The total amount of PCA narcotic analgesia received was recorded for each patient. Additional factors including fibroid size, fibroid location, and patient age were evaluated to determine the predictors of narcotic use reduction after SHNB. Results: The mean amount of postprocedural morphine use was 51.7 mg in patients who did not receive an SHNB versus 35.9 mg in patients who did receive an SHNB ( P = .008), resulting in a 47.2% reduction in narcotic analgesia use in the SHNB cohort. A significant reduction in morphine use with SHNB was associated with fibroid size greater than 5 cm ( P = .009), intramural fibroid location ( P = .04), and patients 45 years or younger ( P = .006). Conclusion: The use of SHNB could significantly reduce the amount of narcotic analgesia required for pain control after UAE with larger intramural fibroids and younger patients as predictors of increased efficacy.

Although somatic mutations have well-established roles in cancer and certain focal epilepsies, the extent to which mutational mosaicism shapes the developing human brain is poorly understood. Here we characterize the landscape of somatic mutations in the human brain using ultra-deep (~250×) whole-genome sequencing of brains from 59 autism spectrum disorder (ASD) cases and 15 controls. We observe a mean of 26 (±10, range 10-60) somatic single nucleotide variants (sSNVs) per brain present in ≥4% of cells, with enrichment of mutations in coding and putative regulatory regions. Our analysis reveals that the first cell division after fertilization produces ~3.4 mutations, followed by 2-3 mutations in subsequent generations. This rate suggests that a typical individual possesses ~80 sSNVs present in ≥2% of cells—comparable to the number of de novo germline mutations per generation—with about half of individuals having at least one potentially function-altering somatic mutation somewhere in the cortex. Although limited by sample size, ASD brains show an excess of somatic mutations in neural enhancer sequences compared to controls, suggesting that mosaic enhancer mutations may contribute to ASD risk.

Although germline de novo copy number variants are a known cause of autism spectrum disorder (ASD), the contribution of mosaic (early-developmental) copy number variants (mCNVs) has not been explored. Here, we assessed the contribution of mCNVs to ASD by ascertaining mCNVs in genotype array intensity data from 12,077 ASD probands and 5,500 unaffected siblings in the Simons Simplex Collection (SSC) and Simons Powering Autism Research for Knowledge (SPARK) cohorts. We detected 46 mCNVs in probands and 19 mCNVs in siblings ranging from 49 kb to 249 Mb and affecting 2.8-73.8% of cells. In both cohorts, probands carried a significant burden of large (>4 Mb) mCNVs (P = 0.043 and P = 6.6 × 10−3 in SSC and SPARK, respectively), which were present in a total of 25 probands but only 1 sibling (OR=11.4, 95% CI=1.5-84.2). Surprisingly, we did not observe mosaic analogues of the short de novo CNVs recurrently observed in ASD. Event size positively correlated with severity of ASD symptoms (P = 0.016), and four probands exhibited clinical symptoms consistent with syndromes previously associated with genes or regions disrupted by their respective mosaic mutations. In analyses of post-mortem brain tissue from 60 additional probands, we further detected and experimentally validated two mCNVs including a complex 10.3 Mb duplication on chromosome 2. These results indicate that mosaic CNVs contribute a previously unexplained component of ASD risk.

Negative Elongation Factor (NELF) is a critical transcriptional regulator that works through stabilizing paused RNA Polymerase to permit rapid gene expression changes in response to environmental cues. Whilst NELF is essential for embryonic development, its role in adult stem cells remains unclear. Here, through a muscle stem cell-specific deletion, we show that NELF is required for efficient muscle regeneration and replenishing the stem cell pool. Mechanistic studies using PRO-seq, single cell trajectory analyses and myofiber cultures revealed that NELF works at a specific stage of muscle regeneration whereby it mediates massive expansion of muscle progenitors. Strikingly, transplantation experiments indicate that these progenitors are also necessary for stem cell pool repopulation implying they are able to return to quiescence. Thus, we identified a critical role for NELF in expansion of muscle progenitors in response to injury and revealed that progenitors returning to quiescence are major contributors to stem cell pool repopulation.

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Somatic mutations have been studied extensively in the context of cancer. Recent studies have demonstrated that high-throughput sequencing data can be used to detect somatic mutations in non-tumor cells. Analysis of such mutations allows us to better understand the mutational processes in normal cells, explore cell lineage in development, and examine potential associations with age-related disease. Here, we describe approaches for characterizing somatic mutations in normal and non-tumor disease tissues. We discuss several experimental designs and common pitfalls in somatic mutation detection, as well as more recent developments, such as phasing and linked-read technology. With the dramatically increasing numbers of samples undergoing genome sequencing, bioinformatic analysis will enable the characterization of somatic mutations and their impact in non-cancer tissues.

Summary: A systematic cataloging of genes affected by genomic rearrangement, using multiple patient cohorts and cancer types, can provide insight into cancer-relevant alterations outside of exomes. By integrative analysis of whole-genome sequencing (predominantly low pass) and gene expression data from 1,448 cancers involving 18 histopathological types in The Cancer Genome Atlas, we identified hundreds of genes for which the nearby presence (within 100 kb) of a somatic structural variant (SV) breakpoint is associated with altered expression. While genomic rearrangements are associated with widespread copy-number alteration (CNA) patterns, approximately 1,100 genes—including overexpressed cancer driver genes (e.g., TERT, ERBB2, CDK12, CDK4) and underexpressed tumor suppressors (e.g., TP53, RB1, PTEN, STK11)—show SV-associated deregulation independent of CNA. SVs associated with the disruption of topologically associated domains, enhancer hijacking, or fusion transcripts are implicated in gene upregulation. For cancer-relevant pathways, SVs considerably expand our understanding of how genes are affected beyond point mutation or CNA. : Zhang et al. analyzed over 1,400 cancers by high- or low-pass whole-genome sequencing, focusing on patterns of structural variation. They saw a widespread impact of somatic structural variants on gene expression patterns, independent of copy-number alterations, involving key oncogenes and tumor suppressor genes. Keywords: cancer, structural variation, genomic rearrangement, whole genome sequencing, pan-cancer, TCGA

Trauma is a significant contributor to mortality, especially in the young. Pelvic trauma with pelvic ring fractures may result in associated arterial injury, necessitating endovascular intervention. As a result, interventional radiology plays a critical role in partnering with trauma providers in the care of these patients. Management is determined by the acuity of the patient's clinical status, radiographs, ultrasound, and the results of computed tomography imaging when available. Numerous embolic agents are available for treatment of arterial hemorrhage.

Background: Poly (ADP-ribose) polymerase (PARP) inhibitors target cancers with homologous recombination deficiency (HRD). Preclinical data showed PI3K inhibitors impair homologous recombination (HR) pathways; exposure may sensitize HR-proficient cancers to PARP inhibition. Single-base substitution mutational signature 3 (Sig3) is strongly associated with HRD. We report correlative analysis of Sig3 as predictor of response from a Phase Ib trial of the PARP inhibitor olaparib in combination with the PI3K inhibitor buparlisib (BKM120) in patients (pts) with advanced high-grade serous ovarian cancer (HGSOC) and triple-negative breast cancer (TNBC). Methods The parent trial (NCT01623349) is a multicenter phase 1b trial of escalating doses of olaparib and buparlisib. Eligible pts enrolled in the trial carried a diagnosis of advanced TNBC, HGSOC, or recurrent breast cancer of any histology with a germline BRCA1 or BRCA2 (BRCA1/2) mutation. Archival tumor tissue was available from 39 pts, and was analyzed by panel sequencing and whole-exome sequencing (WES). A score for the Sig3 was calculated from panel sequencing data and WES using SigMA. Genomic Instability Score (GIS) based on copy-number variation (CNV) were also calculated from WES data using scarHRD. Survival analyses and Cox regression were performed.Results: Our cohort comprised 39 pts (28 pts with HGSOC and 11 pts with TNBC). The median age was 58 years (range 34 - 78). Pts with Sig3+ tumors had a progression-free survival (PFS) of 7.7 months vs 5.6 months for those with Sig3- tumors (log-rank test; p = 0.036). CNV+ group did not differ significantly from CNV- group (log-rank test; p = 0.18). Even though Sig3 identified the majority of cancers with BRCA1/2 mutations (18/24), the PFS for pts with BRCA1/2 mutations was not statistically different from those with BRCA wild type tumors (log-rank test; p = 0.15). In our Cox regression model, Sig3+ had a significant positive impact on PFS (HR 0.27, CI 0.092 - 0.77, p = 0.015) while GIS did not (HR 0.41, CI 0.13 - 1.3, p = 0.14). We tested the influence of copy number alterations of genes associated with PARPi resistance on PFS, and identified ATR amplification as a feature of PARPi resistance (HR 4.48, p = 0.002).Conclusions: Within a trial that combined the PARP inhibitor olaparib and the PI3K inhibitor BKM120 in advanced ovarian and breast cancer, Sig3 positivity from panel sequencing data was associated with longer PFS while the CNV-based HRD prediction score (i.e. GIS) from WES was not. SigMA identified Sig3 from readily-available clinical sequencing as a candidate biomarker to refine patient selection for therapy with PARP inhibitors. Mutational signatures may improve HRD identification compared to a selection solely based on the mutation status of BRCA1/2 genes. Clinical utility should be explored in the setting of a confirmatory clinical trial. Citation Format: Felipe Batalini, Doga Gulhan, Victor Mao, Madeline Polak, Eric P. Winer, Erica L. Mayer, Ursula A. Matulonis, Panagiotis A. Konstantinopoulos, Peter Park, Gerburg M. Wulf. Mutational signature 3 predicts responses to olaparib plus buparlisib in triple-negative breast cancer and high-grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 156.

Accurate detection of genomic alterations using high-throughput sequencing is an essential component of precision cancer medicine. We characterize the variant allele fractions (VAFs) of somatic single nucleotide variants and indels across 5095 clinical samples profiled using a custom panel, CancerSCAN. Our results demonstrate that a significant fraction of clinically actionable variants have low VAFs, often due to low tumor purity and treatment-induced mutations. The percentages of mutations under 5% VAF across hotspots in EGFR, KRAS, PIK3CA, and BRAF are 16%, 11%, 12%, and 10%, respectively, with 24% for EGFR T790M and 17% for PIK3CA E545. For clinical relevance, we describe two patients for whom targeted therapy achieved remission despite low VAF mutations. We also characterize the read depths necessary to achieve sensitivity and specificity comparable to current laboratory assays. These results show that capturing low VAF mutations at hotspots by sufficient sequencing coverage and carefully tuned algorithms is imperative for a clinical assay., High-throughput sequencing is used to identify somatic variants in cancer patients. Here, the authors perform panel-based profiling of 5095 clinical samples and demonstrate that many clinically-actionable variants have low variant allele fractions, requiring assays with high detection sensitivity.

Regulatory decisions in Drosophila require Polycomb group (PcG) proteins to maintain the silent state and Trithorax group (TrxG) proteins to oppose silencing. Since PcG and TrxG are ubiquitous and lack apparent sequence specificity, a long-standing model is that targeting occurs via protein interactions; for instance, between repressors and PcG proteins. Instead, we found that Pc-repressive complex 1 (PRC1) purifies with coactivators Fs(1)h [female sterile (1) homeotic] and Enok/Br140 during embryogenesis. Fs(1)h is a TrxG member and the ortholog of BRD4, a bromodomain protein that binds to acetylated histones and is a key transcriptional coactivator in mammals. Enok and Br140, another bromodomain protein, are orthologous to subunits of a mammalian MOZ/MORF acetyltransferase complex. Here we confirm PRC1–Br140 and PRC1–Fs(1)h interactions and identify their genomic binding sites. PRC1–Br140 bind developmental genes in fly embryos, with analogous co-occupancy of PRC1 and a Br140 ortholog, BRD1, at bivalent loci in human embryonic stem (ES) cells. We propose that identification of PRC1–Br140 “bivalent complexes” in fly embryos supports and extends the bivalency model posited in mammalian cells, in which the coexistence of H3K4me3 and H3K27me3 at developmental promoters represents a poised transcriptional state. We further speculate that local competition between acetylation and deacetylation may play a critical role in the resolution of bivalent protein complexes during development.

Purpose Histologic transformation of EGFR mutant lung adenocarcinoma (LADC) into small-cell lung cancer (SCLC) has been described as one of the major resistant mechanisms for epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs). However, the molecular pathogenesis is still unclear. Methods We investigated 21 patients with advanced EGFR-mutant LADCs that were transformed into EGFR TKI–resistant SCLCs. Among them, whole genome sequencing was applied for nine tumors acquired at various time points from four patients to reconstruct their clonal evolutionary history and to detect genetic predictors for small-cell transformation. The findings were validated by immunohistochemistry in 210 lung cancer tissues. Results We identified that EGFR TKI–resistant LADCs and SCLCs share a common clonal origin and undergo branched evolutionary trajectories. The clonal divergence of SCLC ancestors from the LADC cells occurred before the first EGFR TKI treatments, and the complete inactivation of both RB1 and TP53 were observed from the early LADC stages in sequenced tumors. We extended the findings by immunohistochemistry in the early-stage LADC tissues of 75 patients treated with EGFR TKIs; inactivation of both Rb and p53 was strikingly more frequent in the small-cell–transformed group than in the nontransformed group (82% v 3%; odds ratio, 131; 95% CI, 19.9 to 859). Among patients registered in a predefined cohort (n = 65), an EGFR mutant LADC that harbored completely inactivated Rb and p53 had a 43× greater risk of small-cell transformation (relative risk, 42.8; 95% CI, 5.88 to 311). Branch-specific mutational signature analysis revealed that apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like (APOBEC)–induced hypermutation was frequent in the branches toward small-cell transformation. Conclusion EGFR TKI–resistant SCLCs are branched out early from the LADC clones that harbor completely inactivated RB1 and TP53. The evaluation of RB1 and TP53 status in EGFR TKI–treated LADCs is informative in predicting small-cell transformation.

Release of promoter-proximally paused RNA polymerase II (RNAPII) is a recently recognized transcriptional regulatory checkpoint. The biological roles of RNAPII pause release and the mechanisms by which extracellular signals control it are incompletely understood. Here we show that VEGF stimulates RNAPII pause release by stimulating acetylation of ETS1, a master endothelial cell transcriptional regulator. In endothelial cells, ETS1 binds transcribed gene promoters and stimulates their expression by broadly increasing RNAPII pause release. 34 VEGF enhances ETS1 chromatin occupancy and increases ETS1 acetylation, enhancing its binding to BRD4, which recruits the pause release machinery and increases RNAPII pause release. Endothelial cell angiogenic responses in vitro and in vivo require ETS1-mediated transduction of VEGF signaling to release paused RNAPII. Our results define an angiogenic pathway in which VEGF enhances ETS1–BRD4 interaction to broadly promote RNAPII pause release and drive angiogenesis., Promoter proximal RNAPII pausing is a rate-limiting transcriptional mechanism. Chen et al. show that this process is essential in angiogenesis by demonstrating that the endothelial master transcription factor ETS1 promotes global RNAPII pause release, and that this process is governed by VEGF.

Chromatin plays a critical role in faithful implementation of gene expression programs. Different post-translational modifications (PTMs) of histone proteins reflect the underlying state of gene activity, and many chromatin proteins write, erase, bind, or are repelled by, these histone marks. One such protein is UpSET, the Drosophila homolog of yeast Set3 and mammalian KMT2E (MLL5). Here, we show that UpSET is necessary for the proper balance between active and repressed states. Using CRISPR/Cas-9 editing, we generated S2 cells that are mutant for upSET. We found that loss of UpSET is tolerated in S2 cells, but that heterochromatin is misregulated, as evidenced by a strong decrease in H3K9me2 levels assessed by bulk histone PTM quantification. To test whether this finding was consistent in the whole organism, we deleted the upSET coding sequence using CRISPR/Cas-9, which we found to be lethal in both sexes in flies. We were able to rescue this lethality using a tagged upSET transgene, and found that UpSET protein localizes to transcriptional start sites (TSS) of active genes throughout the genome. Misregulated heterochromatin is apparent by suppressed position effect variegation of the wm4 allele in heterozygous upSET-deleted flies. Using nascent-RNA sequencing in the upSET-mutant S2 lines, we show that this result applies to heterochromatin genes generally. Our findings support a critical role for UpSET in maintaining heterochromatin, perhaps by delimiting the active chromatin environment.

Genes encoding subunits of SWI/SNF (BAF) chromatin-remodeling complexes are collectively mutated in ∼20% of all human cancers. Although ARID1A is the most frequent target of mutations, the mechanism by which its inactivation promotes tumorigenesis is unclear. Here we demonstrate that Arid1a functions as a tumor suppressor in the mouse colon, but not the small intestine, and that invasive ARID1A-deficient adenocarcinomas resemble human colorectal cancer (CRC). These tumors lack deregulation of APC/β-catenin signaling components, which are crucial gatekeepers in common forms of intestinal cancer. We find that ARID1A normally targets SWI/SNF complexes to enhancers, where they function in coordination with transcription factors to facilitate gene activation. ARID1B preserves SWI/SNF function in ARID1A-deficient cells, but defects in SWI/SNF targeting and control of enhancer activity cause extensive dysregulation of gene expression. These findings represent an advance in colon cancer modeling and implicate enhancer-mediated gene regulation as a principal tumor-suppressor function of ARID1A.