Clinical Implementation of MetaFusion for Accurate Cancer-Driving Fusion Detection from RNA Sequencing.

Oncogenic fusion genes may be identified from next-generation sequencing data, typically RNA-sequencing. However, in a clinical setting, identifying these alterations is challenging against a background of nonrelevant fusion calls that reduce workflow precision and specificity. Furthermore, although numerous algorithms have been developed to detect fusions in RNA-sequencing, there are variations in their individual sensitivities. Here this problem was addressed by introducing MetaFusion into clinical use. Its utility was illustrated when applied to both whole-transcriptome and targeted sequencing data sets. MetaFusion combines ensemble fusion calls from eight individual fusion-calling algorithms with practice-informed identification of gene fusions that are known to be clinically relevant. In doing so, it allows oncogenic fusions to be identified with near-perfect sensitivity and high precision and specificity, significantly outperforming the individual fusion callers it uses as well as existing clinical-grade software. MetaFusion enhances clinical yield over existing methods and is able to identify fusions that have patient relevance for the purposes of diagnosis, prognosis, and treatment.

Conserved transcriptional programming across sex and species after peripheral nerve injury predicts treatments for neuropathic pain.

BACKGROUND AND PURPOSE: Chronic pain is a devastating problem affecting one in five individuals around the globe, with neuropathic pain the most debilitating and poorly treated type of chronic pain. Advances in transcriptomics have contributed to cataloguing diverse cellular pathways and transcriptomic alterations in response to peripheral nerve injury but have focused on phenomenology and classifying transcriptomic responses. EXPERIMENTAL APPROACH: To identifying new types of pain-relieving agents, we compared transcriptional reprogramming changes in the dorsal spinal cord after peripheral nerve injury cross-sex and cross-species, and imputed commonalities, as well as differences in cellular pathways and gene regulation. KEY RESULTS: We identified 93 transcripts in the dorsal horn that were increased by peripheral nerve injury in male and female mice and rats. Following gene ontology and transcription factor analyses, we constructed a pain interactome for the proteins encoded by the differentially expressed genes, discovering new, conserved signalling nodes. We investigated the interactome with the Drug-Gene database to predict FDA-approved medications that may modulate key nodes within the network. The top hit from the analysis was fostamatinib, the molecular target of which is the non-receptor spleen associated tyrosine kinase (Syk), which our analysis had identified as a key node in the interactome. We found that intrathecally administrating the active metabolite of fostamatinib, R406 and another Syk inhibitor P505-15, significantly reversed pain hypersensitivity in both sexes. CONCLUSIONS AND IMPLICATIONS: Thus, we have identified and shown the efficacy of an agent that could not have been previously predicted to have analgesic properties.

The clinical utility of integrative genomics in childhood cancer extends beyond targetable mutations.

We conducted integrative somatic-germline analyses by deeply sequencing 864 cancer-associated genes, complete genomes and transcriptomes for 300 mostly previously treated children and adolescents/young adults with cancer of poor prognosis or with rare tumors enrolled in the SickKids Cancer Sequencing (KiCS) program. Clinically actionable variants were identified in 56% of patients. Improved diagnostic accuracy led to modified management in a subset. Therapeutically targetable variants (54% of patients) were of unanticipated timing and type, with over 20% derived from the germline. Corroborating mutational signatures (SBS3/BRCAness) in patients with germline homologous recombination defects demonstrates the potential utility of PARP inhibitors. Mutational burden was significantly elevated in 9% of patients. Sequential sampling identified changes in therapeutically targetable drivers in over one-third of patients, suggesting benefit from rebiopsy for genomic analysis at the time of relapse. Comprehensive cancer genomic profiling is useful at multiple points in the care trajectory for children and adolescents/young adults with cancer, supporting its integration into early clinical management.

Bridging clinical care and research in Ontario, Canada: Maximizing diagnoses from reanalysis of clinical exome sequencing data.

We examined the utility of clinical and research processes in the reanalysis of publicly-funded clinical exome sequencing data in Ontario, Canada. In partnership with eight sites, we recruited 287 families with suspected rare genetic diseases tested between 2014 and 2020. Data from seven laboratories was reanalyzed with the referring clinicians. Reanalysis of clinically relevant genes identified diagnoses in 4% (13/287); four were missed by clinical testing. Translational research methods, including analysis of novel candidate genes, identified candidates in 21% (61/287). Of these, 24 families have additional evidence through data sharing to support likely diagnoses (8% of cohort). This study indicates few diagnoses are missed by clinical laboratories, the incremental gain from reanalysis of clinically-relevant genes is modest, and the highest yield comes from validation of novel disease-gene associations. Future implementation of translational research methods, including continued reporting of compelling genes of uncertain significance by clinical laboratories, should be considered to maximize diagnoses.

Evaluation of single-cell RNA-seq clustering algorithms on cancer tumor datasets.

Tumors are complex biological entities that comprise cell types of different origins, with different mutational profiles and different patterns of transcriptional dysregulation. The exploration of data related to cancer biology requires careful analytical methods to reflect the heterogeneity of cell populations in cancer samples. Single-cell techniques are now able to capture the transcriptional profiles of individual cells. However, the complexity of RNA-seq data, especially in cancer samples, makes it challenging to cluster single-cell profiles into groups that reflect the underlying cell types. We have developed a framework for a systematic examination of single-cell RNA-seq clustering algorithms for cancer data, which uses a range of well-established metrics to generate a unified quality score and algorithm ranking. To demonstrate this framework, we examined clustering performance of 15 different single-cell RNA-seq clustering algorithms on eight different cancer datasets. Our results suggest that the single-cell RNA-seq clustering algorithms fall into distinct groups by performance, with the highest clustering quality on non-malignant cells achieved by three algorithms: Seurat, bigSCale and Cell Ranger. However, for malignant cells, two additional algorithms often reach a better performance, namely Monocle and SC3. Their ability to detect known rare cell types was also among the best, along with Seurat. Our approach and results can be used by a broad audience of practitioners who analyze single-cell transcriptomic data in cancer research.

Genomics4RD: An integrated platform to share Canadian deep-phenotype and multiomic data for international rare disease gene discovery.

Despite recent progress in the understanding of the genetic etiologies of rare diseases (RDs), a significant number remain intractable to diagnostic and discovery efforts. Broad data collection and sharing of information among RD researchers is therefore critical. In 2018, the Care4Rare Canada Consortium launched the project C4R-SOLVE, a subaim of which was to collect, harmonize, and share both retrospective and prospective Canadian clinical and multiomic data. Here, we introduce Genomics4RD, an integrated web-accessible platform to share Canadian phenotypic and multiomic data between researchers, both within Canada and internationally, for the purpose of discovering the mechanisms that cause RDs. Genomics4RD has been designed to standardize data collection and processing, and to help users systematically collect, prioritize, and visualize participant information. Data storage, authorization, and access procedures have been developed in collaboration with policy experts and stakeholders to ensure the trusted and secure access of data by external researchers. The breadth and standardization of data offered by Genomics4RD allows researchers to compare candidate disease genes and variants between participants (i.e., matchmaking) for discovery purposes, while facilitating the development of computational approaches for multiomic data analyses and enabling clinical translation efforts for new genetic technologies in the future.

MetaFusion: a high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates.

MOTIVATION: Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. RESULTS: MetaFusion is a flexible metacalling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format. Calls are annotated, merged using graph clustering, filtered and ranked to provide a final output of high-confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high-confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases and is provided with a Benchmarking Toolkit to calibrate new callers. AVAILABILITY AND IMPLEMENTATION: MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Basophil activation test shows high accuracy in the diagnosis of peanut and tree nut allergy: The Markers of Nut Allergy Study.

BACKGROUND: Peanut and tree nut allergies are the most important causes of anaphylaxis. Co-reactivity to more than one nut is frequent, and co-sensitization in the absence of clinical data is often obtained. Confirmatory oral food challenges (OFCs) are inconsistently performed. OBJECTIVE: To investigate the utility of the basophil activation test (BAT) in diagnosing peanut and tree nut allergies. METHODS: The Markers Of Nut Allergy Study (MONAS) prospectively enrolled patients aged 0.5-17 years with confirmed peanut and/or tree nut (almond, cashew, hazelnut, pistachio, walnut) allergy or sensitization from Canadian (n = 150) and Austrian (n = 50) tertiary pediatric centers. BAT using %CD63+ basophils (SSClow/CCR3pos) as outcome was performed with whole blood samples stimulated with allergen extracts of each nut (0.001-1000 ng/mL protein). BAT results were assessed against confirmed allergic status in a blinded fashion to develop a generalizable statistical model for comparison to extract and marker allergen-specific IgE. RESULTS: A mixed effect model integrating BAT results for 10 and 100 ng/mL of peanut and individual tree nut extracts was optimal. The area under the ROC curve (AUROC) was 0.98 for peanut, 0.97 for cashew, 0.92 for hazelnut, 0.95 for pistachio, and 0.97 for walnut. The BAT outperformed sIgE testing for peanut or hazelnut and was comparable for walnut (AUROC 0.95, 0.94, 0.92) in a sub-analysis in sensitized patients undergoing OFC. CONCLUSIONS: Basophil activation test can predict allergic clinical status to peanut and tree nuts in multi-nut-sensitized children and may reduce the need for high-risk OFCs in patients.

CReSCENT: CanceR Single Cell ExpressioN Toolkit.

CReSCENT: CanceR Single Cell ExpressioN Toolkit (https://crescent.cloud), is an intuitive and scalable web portal incorporating a containerized pipeline execution engine for standardized analysis of single-cell RNA sequencing (scRNA-seq) data. While scRNA-seq data for tumour specimens are readily generated, subsequent analysis requires high-performance computing infrastructure and user expertise to build analysis pipelines and tailor interpretation for cancer biology. CReSCENT uses public data sets and preconfigured pipelines that are accessible to computational biology non-experts and are user-editable to allow optimization, comparison, and reanalysis for specific experiments. Users can also upload their own scRNA-seq data for analysis and results can be kept private or shared with other users.

Prevalence and Clinical Features of Inflammatory Bowel Diseases Associated With Monogenic Variants, Identified by Whole-Exome Sequencing in 1000 Children at a Single Center.

BACKGROUND & AIMS: A proportion of infants and young children with inflammatory bowel diseases (IBDs) have subtypes associated with a single gene variant (monogenic IBD). We aimed to determine the prevalence of monogenic disease in a cohort of pediatric patients with IBD. METHODS: We performed whole-exome sequencing analyses of blood samples from an unselected cohort of 1005 children with IBD, aged 0-18 years (median age at diagnosis, 11.96 years) at a single center in Canada and their family members (2305 samples total). Variants believed to cause IBD were validated using Sanger sequencing. Biopsies from patients were analyzed by immunofluorescence and histochemical analyses. RESULTS: We identified 40 rare variants associated with 21 monogenic genes among 31 of the 1005 children with IBD (including 5 variants in XIAP, 3 in DOCK8, and 2 each in FOXP3, GUCY2C, and LRBA). These variants occurred in 7.8% of children younger than 6 years and 2.3% of children aged 6-18 years. Of the 17 patients with monogenic Crohn's disease, 35% had abdominal pain, 24% had nonbloody loose stool, 18% had vomiting, 18% had weight loss, and 5% had intermittent bloody loose stool. The 14 patients with monogenic ulcerative colitis or IBD-unclassified received their diagnosis at a younger age, and their most predominant feature was bloody loose stool (78%). Features associated with monogenic IBD, compared to cases of IBD not associated with a single variant, were age of onset younger than 2 years (odds ratio [OR], 6.30; P = .020), family history of autoimmune disease (OR, 5.12; P = .002), extra-intestinal manifestations (OR, 15.36; P < .0001), and surgery (OR, 3.42; P = .042). Seventeen patients had variants in genes that could be corrected with allogeneic hematopoietic stem cell transplantation. CONCLUSIONS: In whole-exome sequencing analyses of more than 1000 children with IBD at a single center, we found that 3% had rare variants in genes previously associated with pediatric IBD. These were associated with different IBD phenotypes, and 1% of the patients had variants that could be potentially corrected with allogeneic hematopoietic stem cell transplantation. Monogenic IBD is rare, but should be considered in analysis of all patients with pediatric onset of IBD.

Utilization of Whole Exome Sequencing Data to Identify Clinically Relevant Pharmacogenomic Variants in Pediatric Inflammatory Bowel Disease.

INTRODUCTION: We hypothesized that variants within clinically relevant pharmacogenes could be identified using a whole exome sequencing data set derived from a cohort of more than 1,000 patients with inflammatory bowel disease (IBD). METHODS: Pediatric patients diagnosed with IBD underwent whole exome sequencing. We selected 18 genes with supporting literature where specific exonic variants would influence clinical care. RESULTS: We identified actionable pharmacogenomic variants in 63% of patients. Importantly, 5% of patients with IBD were at risk for serious adverse effects from anesthesia and 3% were at increased risk for thrombosis. DISCUSSION: We identified exonic variants in most of our patients with IBD that directly impact clinical care.

Control of Long-Term Synaptic Potentiation and Learning by Alternative Splicing of the NMDA Receptor Subunit GluN1.

NMDA receptors (NMDARs) are critical for physiological synaptic plasticity, learning, and memory and for pathological plasticity and neuronal death. The GluN1 subunit is encoded by a single gene, GRIN1, with 8 splice variants, but whether the diversity generated by this splicing has physiological consequences remains enigmatic. Here, we generate mice lacking from the GluN1 exon 5-encoded N1 cassette (GluN1a mice) or compulsorily expressing this exon (GluN1b mice). Despite no differences in basal synaptic transmission, long-term potentiation in the hippocampus is significantly enhanced in GluN1a mice compared with that in GluN1b mice. Furthermore, GluN1a mice learn more quickly and have significantly better spatial memory performance than do GluN1b mice. In addition, in human iPSC-derived neurons in autism spectrum disorder NMDARs show characteristics of N1-lacking GluN1. Our findings indicate that alternative splicing of GluN1 is a mechanism for controlling physiological long-lasting synaptic potentiation, learning, and memory.

Alterations in ALK/ROS1/NTRK/MET drive a group of infantile hemispheric gliomas.

Infant gliomas have paradoxical clinical behavior compared to those in children and adults: low-grade tumors have a higher mortality rate, while high-grade tumors have a better outcome. However, we have little understanding of their biology and therefore cannot explain this behavior nor what constitutes optimal clinical management. Here we report a comprehensive genetic analysis of an international cohort of clinically annotated infant gliomas, revealing 3 clinical subgroups. Group 1 tumors arise in the cerebral hemispheres and harbor alterations in the receptor tyrosine kinases ALK, ROS1, NTRK and MET. These are typically single-events and confer an intermediate outcome. Groups 2 and 3 gliomas harbor RAS/MAPK pathway mutations and arise in the hemispheres and midline, respectively. Group 2 tumors have excellent long-term survival, while group 3 tumors progress rapidly and do not respond well to chemoradiation. We conclude that infant gliomas comprise 3 subgroups, justifying the need for specialized therapeutic strategies.

Expanding the Boundaries of RNA Sequencing as a Diagnostic Tool for Rare Mendelian Disease.

Gene-panel and whole-exome analyses are now standard methodologies for mutation detection in Mendelian disease. However, the diagnostic yield achieved is at best 50%, leaving the genetic basis for disease unsolved in many individuals. New approaches are thus needed to narrow the diagnostic gap. Whole-genome sequencing is one potential strategy, but it currently has variant-interpretation challenges, particularly for non-coding changes. In this study we focus on transcriptome analysis, specifically total RNA sequencing (RNA-seq), by using monogenetic neuromuscular disorders as proof of principle. We examined a cohort of 25 exome and/or panel "negative" cases and provided genetic resolution in 36% (9/25). Causative mutations were identified in coding and non-coding exons, as well as in intronic regions, and the mutational pathomechanisms included transcriptional repression, exon skipping, and intron inclusion. We address a key barrier of transcriptome-based diagnostics: the need for source material with disease-representative expression patterns. We establish that blood-based RNA-seq is not adequate for neuromuscular diagnostics, whereas myotubes generated by transdifferentiation from an individual's fibroblasts accurately reflect the muscle transcriptome and faithfully reveal disease-causing mutations. Our work confirms that RNA-seq can greatly improve diagnostic yield in genetically unresolved cases of Mendelian disease, defines strengths and challenges of the technology, and demonstrates the suitability of cell models for RNA-based diagnostics. Our data set the stage for development of RNA-seq as a powerful clinical diagnostic tool that can be applied to the large population of individuals with undiagnosed, rare diseases and provide a framework for establishing minimally invasive strategies for doing so.

C. elegans SUP-46, an HNRNPM family RNA-binding protein that prevents paternally-mediated epigenetic sterility.

BACKGROUND: In addition to DNA, gametes contribute epigenetic information in the form of histones and non-coding RNA. Epigenetic programs often respond to stressful environmental conditions and provide a heritable history of ancestral stress that allows for adaptation and propagation of the species. In the nematode C. elegans, defective epigenetic transmission often manifests as progressive germline mortality. We previously isolated sup-46 in a screen for suppressors of the hexosamine pathway gene mutant, gna-2(qa705). In this study, we examine the role of SUP-46 in stress resistance and progressive germline mortality. RESULTS: We identified SUP-46 as an HNRNPM family RNA-binding protein, and uncovered a highly novel role for SUP-46 in preventing paternally-mediated progressive germline mortality following mating. Proximity biotinylation profiling of human homologs (HNRNPM, MYEF2) identified proteins of ribonucleoprotein complexes previously shown to contain non-coding RNA. Like HNRNPM and MYEF2, SUP-46 was associated with multiple RNA granules, including stress granules, and also formed granules on active chromatin. SUP-46 depletion disrupted germ RNA granules and caused ectopic sperm, increased sperm transcripts, and chronic heat stress sensitivity. SUP-46 was also required for resistance to acute heat stress, and a conserved "MYEF2" motif was identified that was needed for stress resistance. CONCLUSIONS: In mammals, non-coding RNA from the sperm of stressed males has been shown to recapitulate paternal stress phenotypes in the offspring. Our results suggest that HNRNPM family proteins enable stress resistance and paternally-mediated epigenetic transmission that may be conserved across species.

Identification of complex genomic rearrangements in cancers using CouGaR.

The genomic alterations associated with cancers are numerous and varied, involving both isolated and large-scale complex genomic rearrangements (CGRs). Although the underlying mechanisms are not well understood, CGRs have been implicated in tumorigenesis. Here, we introduce CouGaR, a novel method for characterizing the genomic structure of amplified CGRs, leveraging both depth of coverage (DOC) and discordant pair-end mapping techniques. We applied our method to whole-genome sequencing (WGS) samples from The Cancer Genome Atlas and identify amplified CGRs in at least 5.2% (10+ copies) to 17.8% (6+ copies) of the samples. Furthermore, ∼95% of these amplified CGRs contain genes previously implicated in tumorigenesis, indicating the importance and widespread occurrence of CGRs in cancers. Additionally, CouGaR identified the occurrence of 'chromoplexy' in nearly 63% of all prostate cancer samples and 30% of all bladder cancer samples. To further validate the accuracy of our method, we experimentally tested 17 predicted fusions in two pediatric glioma samples and validated 15 of these (88%) with precise resolution of the breakpoints via qPCR experiments and Sanger sequencing, with nearly perfect copy count concordance. Additionally, to further help display and understand the structure of CGRs, we have implemented CouGaR-viz, a generic stand-alone tool for visualization of the copy count of regions, breakpoints, and relevant genes.

Erratum: Spatial genomic heterogeneity in diffuse intrinsic pontine and midline high-grade glioma: implications for diagnostic biopsy and targeted therapeutics.

Through inadvertent oversight of the authors, the paper failed to acknowledge funding support from Genome Canada. The Acknowledgement section should include the text: "This work was supported by the Canadian Centre for Computational Genomics (C3G), part of the Genome Innovation Network (GIN), funded by Genome Canada through Genome Quebec and Ontario Genomics".

A pair of RNA-binding proteins controls networks of splicing events contributing to specialization of neural cell types.

Alternative splicing is important for the development and function of the nervous system, but little is known about the differences in alternative splicing between distinct types of neurons. Furthermore, the factors that control cell-type-specific splicing and the physiological roles of these alternative isoforms are unclear. By monitoring alternative splicing at single-cell resolution in Caenorhabditis elegans, we demonstrate that splicing patterns in different neurons are often distinct and highly regulated. We identify two conserved RNA-binding proteins, UNC-75/CELF and EXC-7/Hu/ELAV, which regulate overlapping networks of splicing events in GABAergic and cholinergic neurons. We use the UNC-75 exon network to discover regulators of synaptic transmission and to identify unique roles for isoforms of UNC-64/Syntaxin, a protein required for synaptic vesicle fusion. Our results indicate that combinatorial regulation of alternative splicing in distinct neurons provides a mechanism to specialize metazoan nervous systems.