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Advances in genomics have transformed our ability to identify the genetic causes of rare diseases (RDs), yet we have a limited understanding of the mechanistic roles of most genes in health and disease. When a novel RD gene is first discovered, there is minimal insight into its biological function, the pathogenic mechanisms of disease-causing variants, and how therapy might be approached. To address this gap, the Canadian Rare Diseases Models and Mechanisms (RDMM) Network was established to connect clinicians discovering new disease genes with Canadian scientists able to study equivalent genes and pathways in model organisms (MOs). The Network is built around a registry of more than 500 Canadian MO scientists, representing expertise for over 7,500 human genes. RDMM uses a committee process to identify and evaluate clinician-MO scientist collaborations and approve 25,000 Canadian dollars in catalyst funding. To date, we have made 85 clinician-MO scientist connections and funded 105 projects. These collaborations help confirm variant pathogenicity and unravel the molecular mechanisms of RD, and also test novel therapies and lead to long-term collaborations. To expand the impact and reach of this model, we made the RDMM Registry open-source, portable, and customizable, and we freely share our committee structures and processes. We are currently working with emerging networks in Europe, Australia, and Japan to link international RDMM networks and registries and enable matches across borders. We will continue to create meaningful collaborations, generate knowledge, and advance RD research locally and globally for the benefit of patients and families living with RD.

Next-generation sequencing is a prevalent diagnostic tool for undiagnosed diseases, and has played a significant role in rare disease gene discovery. While this technology resolves some cases, others are given a list of possibly damaging genetic variants necessitating functional studies. Productive collaborations between scientists, clinicians, and patients can help resolve such medical mysteries, and provide insights into in vivo function of human genes. Furthermore, facilitating interactions between scientists and research funders, including non-profit organizations or commercial entities, can dramatically reduce the time to translate discoveries from bench to bedside. Several systems designed to connect clinicians and researchers with a shared gene of interest have been successful. However, these platforms exclude some stakeholders based on their role or geography. Here we describe ModelMatcher, a global online matchmaking tool designed to facilitate cross-disciplinary collaborations, especially between scientists and other stakeholders of rare and undiagnosed disease research. ModelMatcher is integrated into the Rare Diseases Models and Mechanisms Network and Matchmaker Exchange, allowing users to identify potential collaborators in other registries. This living database decreases the time from when a scientist or clinician is making discoveries regarding their genes of interest, to when they identify collaborators and sponsors to facilitate translational and therapeutic research.

Dipeptidyl peptidase 4 (DPP-4) is a multi-functional protein that, besides its catalytic activity, also functions as a binding protein and a ligand for a variety of extracellular molecules. One of its most substantial roles is impact on glucose homeostasis by regulating the biological activities of two incretins, glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP). Thus, DPP-4 enzyme inhibitors, collectively called glyptins, have been successfully used in treatment of type 2 diabetes mellitus. 3D-quantitative structure-activity relationship study (3D-QSAR) was conducted on a series of 48 molecules with more than 85% structural similarity to sitagliptin in order to determine the influence of the chemical structure on the biological activity. Structures and data on the DPP-4 inhibitory activity (IC50) of these molecules were collected from the ChemBL database. Molecular structures were previously optimized using the semiempirical PM3 method, and the modeling was performed using PLS regression analysis. Based on the calculated statistical parameters, R2 = 0.96, Q2 = 0.71 and \({\rm{R}}_{{{\rm{pred}}}}^{2}\) = 0.610, it can be concluded that the formed 3D-QSAR model has good predictive power and can be used to predict DPP-4 inhibitory activity of structurally related compounds. Variables obtained from QSAR contain information of crucial structural determinants that impact activity and provide guidelines for future design of more potent inhibitors.

SYNGAP1 is a Ras and Rap GTPase with important roles in regulating excitatory synaptic plasticity. While manySYNGAP1missense and nonsense mutations have been associated with intellectual disability, epilepsy, schizophrenia and autism spectrum disorder (ASD), there are many variants of unknown significance (VUS). In this report, we characterize 58 variants in nine assays that examine multiple aspects of SYNGAP1 function. Specifically, we used multiplex phospho-flow cytometry to measure the impact of variants on pERK, pGSK3β and pCREB and high-content imaging to examine their subcellular localization. We find variants ranging from complete loss-of-function (LoF) to wildtype (WT)-like in their ability to regulate pERK and pGSK3β, while all variants retain at least partial ability to regulate pCREB. Interestingly, our assays reveal that a high percentage of variants located within the disordered domain of unknown function that makes up the C-terminal half of SYNGAP1 exhibited LoF, compared to the more well studied catalytic domain. Moreover, we find protein instability to be a major contributor to dysfunction only for two missense variants both located within the catalytic domain. Using high-content imaging, we find variants with nuclear enrichment/exclusion and aberrant nuclear speckle localization. These variants are primarily located within the C2 domain known to mediate membrane lipid interactions. We find that mislocalization is distinct from altered catalytic activity, highlighting multiple independent molecular mechanisms underlying variant dysfunction. Our multidimensional dataset allows clustering of variants based on functional phenotypes and provides high-confidence pathogenicity classification.

Summary SYNGAP1 is a neuronal Ras and Rap GTPase-activating protein with important roles in regulating excitatory synaptic plasticity. While many SYNGAP1 missense and nonsense mutations have been associated with intellectual disability, epilepsy, schizophrenia, and autism spectrum disorder (ASD), whether and how they contribute to individual disease phenotypes is often unknown. Here, we characterize 57 variants in seven assays that examine multiple aspects of SYNGAP1 function. Specifically, we used multiplex phospho-flow cytometry to measure variant impact on protein stability, pERK, pGSK3β, pp38, pCREB, and high-content imaging to examine subcellular localization. We find variants ranging from complete loss-of-function (LoF) to wild-type (WT)-like in their regulation of pERK and pGSK3β, while all variants retain at least partial ability to dephosphorylate pCREB. Interestingly, our assays reveal that a larger proportion of variants located within the disordered domain of unknown function (DUF) comprising the C-terminal half of SYNGAP1 exhibited higher LoF, compared to variants within the better studied catalytic domain. Moreover, we find protein instability to be a major contributor to dysfunction for only two missense variants, both located within the catalytic domain. Using high-content imaging, we find variants located within the C2 domain known to mediate membrane lipid interactions exhibit significantly larger cytoplasmic speckles than WT SYNGAP1. Moreover, this subcellular phenotype shows both correlation with altered catalytic activity and unique deviation from signaling assay results, highlighting multiple independent molecular mechanisms underlying variant dysfunction. Our multidimensional dataset allows clustering of variants based on functional phenotypes and provides high-confidence, multi-functional measures for making pathogenicity predictions.

Functional variomics provides the foundation for personalized medicine by linking genetic variation to disease expression, outcome and treatment, yet its utility is dependent on appropriate assays to evaluate mutation impact on protein function. To fully assess the effects of 106 missense and nonsense variants of PTEN associated with autism spectrum disorder, somatic cancer and PTEN hamartoma syndrome (PHTS), we take a deep phenotypic profiling approach using 18 assays in 5 model systems spanning diverse cellular environments ranging from molecular function to neuronal morphogenesis and behavior. Variants inducing instability occur across the protein, resulting in partial-to-complete loss-of-function (LoF), which is well correlated across models. However, assays are selectively sensitive to variants located in substrate binding and catalytic domains, which exhibit complete LoF or dominant negativity independent of effects on stability. Our results indicate that full characterization of variant impact requires assays sensitive to instability and a range of protein functions., Mutations in PTEN have been associated with various human disease, including autism spectrum disorder (ASD) and cancer. Here, the authors assess the function of 106 PTEN variants in yeast, invertebrate models and cell culture and report that PTEN variants generally decrease protein stability.

Recent years have seen a boom in the application of the next-generation sequencing technology to the study of human disorders, including Autism Spectrum Disorder (ASD), where the focus has been on identifying rare, possibly causative genomic variants in ASD individuals. Because of the high genetic heterogeneity of ASD, a large number of subjects is needed to establish evidence for a variant or gene ASD-association, thus aggregating data across cohorts and studies is necessary. However, methodological inconsistencies and subject overlap across studies complicate data aggregation. Here we present VariCarta, a web-based database developed to address these challenges by collecting, reconciling, and consistently cataloging literature-derived genomic variants found in ASD subjects using ongoing semi-manual curation. The careful manual curation combined with a robust data import pipeline rectifies errors, converts variants into a standardized format, identifies and harmonizes cohort overlaps, and documents data provenance. The harmonization aspect is especially important since it prevents the potential double counting of variants, which can lead to inflation of gene-based evidence for ASD-association. The database currently contains 170,416 variant events from 10,893 subjects, collected across 61 publications, and reconciles 16,202 variants that have been reported in literature multiple times. VariCarta is freely accessible at http://varicarta.msl.ubc.ca. Autism Res 2019, 12: 1728-1736. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The search for genetic factors underlying Autism Spectrum Disorder (ASD) yielded numerous studies reporting potentially causative genomic variants found in ASD individuals. However, methodological differences and subject overlap across studies complicate the assembly of these data, diminishing its utility and accessibility. We developed VariCarta, a web-based database that aggregates carefully curated, annotated, and harmonized literature-derived variants identified in individuals with ASD using ongoing semi-manual curation.

BackgroundRecent years has seen a boom in the application of the next-generation sequencing technology to the study of human diseases, including Autism Spectrum Disorder (ASD), where the focus has been on identifying rare, possibly causative genomic variants in ASD individuals. Because of the high genetic heterogeneity of ASD, a large number of subjects is needed to establish evidence for a variant or gene ASD-association, thus aggregating data across cohorts and studies is necessary. However, methodological inconsistencies and subject overlap across studies complicate data aggregation.DescriptionHere we present VariCarta, a web-based database developed to address these challenges by collecting, reconciling and consistently cataloguing literature-derived genomic variants found in ASD subjects using ongoing semi-manual curation. The careful manual curation combined with a robust data import pipeline rectifies errors, converts variants into a standardized format, identifies and harmonizes cohort overlaps and documents data provenance. The harmonization aspect is especially important since it prevents the potential double-counting of variants which can lead to inflation of gene-based evidence for ASD-association.ConclusionVariCarta is the largest collection of systematically curated, harmonized and comprehensively annotated literature-derived ASD-associated variants. The database currently contains 35,615 variant events from 8,044 subjects, collected across 50 publications, and reconciles 6,057 variants that have been reported in literature multiple times. VariCarta is freely accessible at http://varicarta.msl.ubc.ca.

Autism spectrum disorder (ASD) is a highly heterogeneous genetic disorder with strong evidence of ASD-association currently available only for a small number of genes. This makes it challenging to identify the underlying genetic cause in many cases of ASD, and there is a continuing need for further discovery efforts. We sequenced whole genomes of 119 deeply phenotyped ASD probands in order to identify likely pathogenic variants. We prioritized variants found in each subject by predicted damage, population frequency, literature evidence, and phenotype concordance. We used Sanger sequencing to determine the inheritance status of high-priority variants where possible. We report five novel de novo damaging variants as well as several likely damaging variants of unknown inheritance; these include two novel de novo variants in the well-established ASD gene SCN2A. The availability of rich phenotypic information and its concordance with the literature allowed us to increase our confidence in pathogenicity of discovered variants, especially in probands without parental DNA. Our results contribute to the documentation of potential pathogenic variants and their associated phenotypes in individuals with ASD.

Background Prenatal alcohol exposure (PAE) can result in an array of morphological, behavioral, and neurobiological deficits that can range in their severity. Despite extensive research in the field and a significant progress made, especially in understanding the range of possible malformations and neurobehavioral abnormalities, the molecular mechanisms of alcohol responses in development are still not well understood. There have been multiple transcriptomic studies looking at the changes in gene expression after PAE in animal models; however, there is a limited apparent consensus among the reported findings. In an effort to address this issue, we performed a comprehensive re-analysis and meta-analysis of all suitable, publically available expression data sets. Methods We assembled 10 microarray data sets of gene expression after PAE in mouse and rat models consisting of samples from a total of 63 ethanol (EtOH)-exposed and 80 control animals. We re-analyzed each data set for differential expression and then used the results to perform meta-analyses considering all data sets together or grouping them by time or duration of exposure (pre- and postnatal, acute and chronic, respectively). We performed network and Gene Ontology enrichment analysis to further characterize the identified signatures. Results For each subanalysis, we identified signatures of differential expressed genes that show support from multiple studies. Overall, the changes in gene expression were more extensive after acute EtOH treatment during prenatal development than in other models. Considering the analysis of all the data together, we identified a robust core signature of 104 genes down-regulated after PAE, with no up-regulated genes. Functional analysis reveals over representation of genes involved in protein synthesis, mRNA splicing, and chromatin organization. Conclusions Our meta-analysis shows that existing studies, despite superficial dissimilarity in findings, share features that allow us to identify a common core signature set of transcriptome changes in PAE. This is an important step to identifying the biological processes that underlie the etiology of fetal alcohol spectrum disorders.

Autism spectrum disorders (ASD) are clinically heterogeneous and biologically complex. In general it remains unclear, what biological factors lead to changes in the brains of autistic individuals. A considerable number of transcriptome analyses have been performed in attempts to address this question, but their findings lack a clear consensus. As a result, each of these individual studies has not led to any significant advance in understanding the autistic phenotype as a whole. Here, we report a meta-analysis of more than 1000 microarrays across twelve independent studies on expression changes in ASD compared to unaffected individuals, in both blood and brain tissues. We identified a number of known and novel genes that are consistently differentially expressed across three studies of the brain (71 samples in total). A subset of the highly ranked genes is suggestive of effects on mitochondrial function. In blood, consistent changes were more difficult to identify, despite individual studies tending to exhibit larger effects than the brain studies. Our results are the strongest evidence to date of a common transcriptome signature in the brains of individuals with ASD.

Effective treatment of diffuse large B-cell lymphoma (DLBCL) is plagued by heterogeneous responses to standard therapy, and molecular mechanisms underlying unfavorable outcomes in lymphoma patients remain elusive. Here, we profiled 148 genomes with 91 matching transcriptomes in a DLBCL cohort treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisolone (R-CHOP) to uncover molecular subgroups linked to treatment failure. Systematic integration of high-resolution genotyping arrays and RNA sequencing data revealed novel deletions in RCOR1 to be associated with unfavorable progression-free survival (P = .001). Integration of expression data from the clinical samples with data from RCOR1 knockdowns in the lymphoma cell lines KM-H2 and Raji yielded an RCOR1 loss-associated gene signature comprising 233 genes. This signature identified a subgroup of patients with unfavorable overall survival (P = .023). The prognostic significance of the 233-gene signature for overall survival was reproduced in an independent cohort comprising 195 R-CHOP-treated patients (P = .039). Additionally, we discovered that within the International Prognostic Index low-risk group, the gene signature provides additional prognostic value that was independent of the cell-of-origin phenotype. We present a novel and reproducible molecular subgroup of DLBCL that impacts risk-stratification of R-CHOP-treated DLBCL patients and reveals a possible new avenue for therapeutic intervention strategies.

Classical Hodgkin lymphoma and primary mediastinal B cell lymphoma (PMBCL) are related lymphomas sharing pathological, molecular and clinical characteristics. Here we discovered by whole-genome and whole-transcriptome sequencing recurrent somatic coding-sequence mutations in the PTPN1 gene. Mutations were found in 6 of 30 (20%) Hodgkin lymphoma cases, in 6 of 9 (67%) Hodgkin lymphoma-derived cell lines, in 17 of 77 (22%) PMBCL cases and in 1 of 3 (33%) PMBCL-derived cell lines, consisting of nonsense, missense and frameshift mutations. We demonstrate that PTPN1 mutations lead to reduced phosphatase activity and increased phosphorylation of JAK-STAT pathway members. Moreover, silencing of PTPN1 by RNA interference in Hodgkin lymphoma cell line KM-H2 resulted in hyperphosphorylation and overexpression of downstream oncogenic targets. Our data establish PTPN1 mutations as new drivers in lymphomagenesis.

We have recently reported the application of RNAseq to mantle cell lymphoma (MCL) transcriptomes revealing recurrent mutations in NOTCH1. Here we describe the targeted resequencing of 18 genes mutated in this discovery cohort using a larger cohort of MCL tumors. In addition to frequent mutations in ATM, CCND1, TP53, and NOTCH1, mutations were also observed recurrently in MEF2B, TRAF2, and TET2. Interestingly, the third most frequently mutated gene was UBR5, a gene encoding a 2799aa protein, with multiple functions, including E3 ligase activity based on a conserved cysteine residue at the C-terminus. Nonsynonymous mutations were detected in 18% (18/102) of tumors, with 61% of the mutations resulting in frameshifts in, or around, exon 58, predicted to result in the loss of this conserved cysteine residue. The recurrence and clustering of deleterious mutations implicate UBR5 mutations as a critical pathogenic event in a subgroup of MCL.

Purpose Diffuse large B-cell lymphoma (DLBCL) is curable in 60% of patients treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). MYC translocations, with or without BCL2 translocations, have been associated with inferior survival in DLBCL. We investigated whether expression of MYC protein, with or without BCL2 protein expression, could risk-stratify patients at diagnosis. Patients and Methods We determined the correlation between presence of MYC and BCL2 proteins by immunohistochemistry (IHC) with survival in two independent cohorts of patients with DLBCL treated with R-CHOP. We further determined if MYC protein expression correlated with high MYC mRNA and/or presence of MYC translocation. Results In the training cohort (n = 167), MYC and BCL2 proteins were detected in 29% and 44% of patients, respectively. Concurrent expression (MYC positive/BCL2 positive) was present in 21% of patients. MYC protein correlated with presence of high MYC mRNA and MYC translocation (both P < .001), but the latter was less frequent (both 11%). MYC protein expression was only associated with inferior overall and progression-free survival when BCL2 protein was coexpressed (P < .001). Importantly, the poor prognostic effect of MYC positive/BCL2 positive was validated in an independent cohort of 140 patients with DLBCL and remained significant (P < .05) after adjusting for presence of high-risk features in a multivariable model that included elevated international prognostic index score, activated B-cell molecular subtype, and presence of concurrent MYC and BCL2 translocations. Conclusion Assessment of MYC and BCL2 expression by IHC represents a robust, rapid, and inexpensive approach to risk-stratify patients with DLBCL at diagnosis.

Follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL) are the two most common non-Hodgkin lymphomas (NHLs). Here we sequenced tumour and matched normal DNA from 13 DLBCL cases and one FL case to identify genes with mutations in B-cell NHL. We analysed RNA-seq data from these and another 113 NHLs to identify genes with candidate mutations, and then re-sequenced tumour and matched normal DNA from these cases to confirm 109 genes with multiple somatic mutations. Genes with roles in histone modification were frequent targets of somatic mutation. For example, 32% of DLBCL and 89% of FL cases had somatic mutations in MLL2, which encodes a histone methyltransferase, and 11.4% and 13.4% of DLBCL and FL cases, respectively, had mutations in MEF2B, a calcium-regulated gene that cooperates with CREBBP and EP300 in acetylating histones. Our analysis suggests a previously unappreciated disruption of chromatin biology in lymphomagenesis.

Identifying variants causal for complex genetic disorders is challenging. With the advent of whole-exome and whole-genome sequencing, computational tools are needed to explore and analyze the list of variants for further validation. Correlating genetic variants with subject phenotype is crucial for the interpretation of the disease-causing mutations. Often such work is done by teams of researchers who need to share information and coordinate activities. To this end, we have developed a powerful, easy to use Web application, ASPIREdb, which allows researchers to search, organize, analyze, and visualize variants and phenotypes associated with a set of human subjects. Investigators can annotate variants using publicly available reference databases and build powerful queries to identify subjects or variants of interest. Functional information and phenotypic associations of these genes are made accessible as well. Burden analysis and additional reporting tools allow investigation of variant properties and phenotype characteristics. Projects can be shared, allowing researchers to work collaboratively to build queries and annotate the data. We demonstrate ASPIREdb's functionality using publicly available data sets, showing how the software can be used to accomplish goals that might otherwise require specialized bioinformatics expertise. ASPIREdb is available at http://aspiredb.chibi.ubc.ca.

Gene expression dysregulation in the brain has been associated with bipolar disorder through candidate gene and microarray expression studies, but questions remain about isoform-specific dysregulation and the role of noncoding RNAs whose importance in the brain has been suggested recently but not yet characterized for bipolar disorder.The authors used RNA sequencing, a powerful technique that captures the complexity of gene expression, in postmortem tissue from the anterior cingulate cortex from 13 bipolar disorder case subjects and 13 matched comparison subjects. Differential expression was computed, and a global pattern of downregulation was detected, with 10 transcripts significant at a false discovery rate ≤5%. Importantly, all 10 genes were also replicated in an independent RNA sequencing data set (N=61) from the anterior cingulate cortex.Among the most significant results were genes coding for class A G protein-coupled receptors: SSTR2 (somatostatin receptor 2), CHRM2 (cholinergic receptor, muscarinic 2), and RXFP1 (relaxin/insulin-like family peptide receptor 1). A gene ontology analysis of the entire set of differentially expressed genes pointed to an overrepresentation of genes involved in G protein-coupled receptor regulation. The top genes were followed up by querying the effect of treatment with mood stabilizers commonly prescribed in bipolar disorder, which showed that these drugs modulate expression of the candidate genes.By using RNA sequencing in the postmortem bipolar disorder brain, an interesting profile of G protein-coupled receptor dysregulation was identified, several new bipolar disorder genes were indicated, and the noncoding transcriptome in bipolar disorder was characterized. These findings have important implications with regard to fine-tuning our understanding of the bipolar disorder brain, as well as for identifying potential new drug target pathways.

The human genome is by far the largest genome to be sequenced, and its size and complexity present many challenges for sequence assembly. The International Human Genome Sequencing Consortium constructed a map of the whole genome to enable the selection of clones for sequencing and for the accurate assembly of the genome sequence. Here we report the construction of the whole-genome bacterial artificial chromosome (BAC) map and its integration with previous landmark maps and information from mapping efforts focused on specific chromosomal regions. We also describe the integration of sequence data with the map.

Diffuse large B-cell lymphoma (DLBCL) is a genetically heterogeneous cancer composed of at least 2 molecular subtypes that differ in gene expression and distribution of mutations. Recently, application of genome/exome sequencing and RNA-seq to DLBCL has revealed numerous genes that are recurrent targets of somatic point mutation in this disease. Here we provide a whole-genome-sequencing-based perspective of DLBCL mutational complexity by characterizing 40 de novo DLBCL cases and 13 DLBCL cell lines and combining these data with DNA copy number analysis and RNA-seq from an extended cohort of 96 cases. Our analysis identified widespread genomic rearrangements including evidence for chromothripsis as well as the presence of known and novel fusion transcripts. We uncovered new gene targets of recurrent somatic point mutations and genes that are targeted by focal somatic deletions in this disease. We highlight the recurrence of germinal center B-cell-restricted mutations affecting genes that encode the S1P receptor and 2 small GTPases (GNA13 and GNAI2) that together converge on regulation of B-cell homing. We further analyzed our data to approximate the relative temporal order in which some recurrent mutations were acquired and demonstrate that ongoing acquisition of mutations and intratumoral clonal heterogeneity are common features of DLBCL. This study further improves our understanding of the processes and pathways involved in lymphomagenesis, and some of the pathways mutated here may indicate new avenues for therapeutic intervention.

Purpose Our aim was to reliably identify patients with advanced-stage classical Hodgkin lymphoma (cHL) at increased risk of death by developing a robust predictor of overall survival (OS) using gene expression measured in routinely available formalin-fixed paraffin-embedded tissue (FFPET). Methods Expression levels of 259 genes, including those previously reported to be associated with outcome in cHL, were determined by digital expression profiling of pretreatment FFPET biopsies from 290 patients enrolled onto the E2496 Intergroup trial comparing doxorubicin, bleomycin, vinblastine, and dacarbazine (ABVD) and Stanford V regimens in locally extensive and advanced-stage cHL. A model for OS separating patients into low- and high-risk groups was produced using penalized Cox regression. The model was tested in an independent cohort of 78 patients enriched for treatment failure but otherwise similar to patients in a population-based registry of patients treated with ABVD. Weighted analysis methods generated unbiased estimates of predictor performance in the population-based registry. Results A 23-gene outcome predictor was generated. The model identified a population at increased risk of death in the validation cohort. There was a 29% absolute difference in 5-year OS between the high- and low-risk groups (63% v 92%, respectively; log-rank P < .001; hazard ratio, 6.7; 95% CI, 2.6 to 17.4). The predictor was superior to the International Prognostic Score and CD68 immunohistochemistry in multivariate analyses. Conclusion A gene expression–based predictor, developed in and applicable to routinely available FFPET biopsies, identifies patients with advanced-stage cHL at increased risk of death when treated with standard-intensity up-front regimens.

Recently, the landscape of single base mutations in diffuse large B-cell lymphoma (DLBCL) was described. Here we report the discovery of a gene fusion between TBL1XR1 and TP63, the only recurrent somatic novel gene fusion identified in our analysis of transcriptome data from 96 DLBCL cases. Based on this cohort and a further 157 DLBCL cases analyzed by FISH, the incidence in de novo germinal center B cell–like (GCB) DLBCL is 5% (6 of 115). The fusion appears exclusive to GCB and was not seen in 138 non-GCB cases examined (P = .008, Fisher exact test) but was present at low incidence in follicular lymphoma (1 of 81). In all 7 cases identified, the 3′ end of the fusion consists of exons 4 and onwards of TP63. The recurrence, subtype enrichment, and the remarkably conserved nature of the TP63 portion of the fusion suggest an important functional role in the lymphomas that harbor this event.

Mantle cell lymphoma (MCL), an aggressive subtype of non-Hodgkin lymphoma, is characterized by the hallmark translocation t(11;14)(q13;q32) and the resulting overexpression of cyclin D1 (CCND1). Our current knowledge of this disease encompasses frequent secondary cytogenetic aberrations and the recurrent mutation of a handful of genes, such as TP53, ATM, and CCND1. However, these findings insufficiently explain the biologic underpinnings of MCL. Here, we performed whole transcriptome sequencing on a discovery cohort of 18 primary tissue MCL samples and 2 cell lines. We found recurrent mutations in NOTCH1, a finding that we confirmed in an extension cohort of 108 clinical samples and 8 cell lines. In total, 12% of clinical samples and 20% of cell lines harbored somatic NOTCH1 coding sequence mutations that clustered in the PEST domain and predominantly consisted of truncating mutations or small frame-shifting indels. NOTCH1 mutations were associated with poor overall survival (P = .003). Furthermore, we showed that inhibition of the NOTCH pathway reduced proliferation and induced apoptosis in 2 MCL cell lines. In summary, we have identified recurrent NOTCH1 mutations that provide the preclinical rationale for therapeutic inhibition of the NOTCH pathway in a subset of patients with MCL.

Follicular lymphoma (FL) cases with a t(14;18)(q32;q21) and minimal or no additional karyotypic alterations, such as copy number gains and losses and/or chromosomal rearrangements, may exhibit pathologic features and a clinical behavior similar to those with more complex karyotypes. This study sought to investigate whether the copy-neutral loss of heterozygosity (cnLOH) profiles of these minimally evolved t(14;18)(q32;q21)-positive follicular lymphoma (MEV-FL) cases are similar to or different from the majority of FL cases with more karyotypic alterations. Affymetrix SNP 6.0 array analysis was applied to the tumor genomes of 23 MEV-FL biopsy samples to assess for the presence of cnLOH. These cases carried either a single or no chromosomal abnormality in addition to t(14;18)(q32;q21) as determined by karyotyping. We found that, although these MEV-FL cases had simple karyotypes, they showed very similar cnLOH profiles as compared to cytogenetically complex cases. The most frequent regions affected by cnLOH were 1p (17%), 6p (17%), 12q (13%) and 16p (13%). Our study suggests that cnLOH alterations may serve as important contributors to the pathological and clinical manifestations of FL.

Background: Secondary structure interactions within introns have been shown to be essential for efficient splicing of several yeast genes. The nature of these base-pairing interactions and their effect on splicing efficiency were most extensively studied in ribosomal protein gene RPS17B (previously known as RP51B). It was determined that complementary pairing between two sequence segments located downstream of the 5' splice site and upstream of the branchpoint sequence promotes efficient splicing of the RPS17B pre-mRNA, presumably by shortening the branchpoint distance. However, no attempts were made to compute a shortened, 'structural' branchpoint distance and thus the functional relationship between this distance and the splicing efficiency remains unknown. Results In this paper we use computational RNA secondary structure prediction to analyze the secondary structure of the RPS17B intron. We show that it is necessary to consider suboptimal structure predictions and to compute the structural branchpoint distances in order to explain previously published splicing efficiency results. Our study reveals that there is a tight correlation between this distance and splicing efficiency levels of intron mutants described in the literature. We experimentally test this correlation on additional RPS17B mutants and intron mutants within two other yeast genes. Conclusion The proposed model of secondary structure requirements for efficient splicing is the first attempt to specify the functional relationship between pre-mRNA secondary structure and splicing. Our findings provide further insights into the role of pre-mRNA secondary structure in gene splicing in yeast and also offer basis for improvement of computational methods for splice site identification and gene-finding.

Motivation: Despite constant improvements in prediction accuracy, gene-finding programs are still unable to provide automatic gene discovery with desired correctness. The current programs can identify up to 75% of exons correctly and less than 50% of predicted gene structures correspond to actual genes. New approaches to computational gene-finding are clearly needed. Results: In this paper we have explored the benefits of combining predictions from already existing gene prediction programs. We have introduced three novel methods for combining predictions from programs Genscan and HMMgene. The methods primarily aim to improve exon level accuracy of gene-finding by identifying more probable exon boundaries and by eliminating false positive exon predictions. This approach results in improved accuracy at both the nucleotide and exon level, especially the latter, where the average improvement on the newly assembled dataset is 7.9% compared to the best result obtained by Genscan and HMMgene. When tested on a long genomic multi-gene sequence, our method that maintains reading frame consistency improved nucleotide level specificity by 21.0% and exon level specificity by 32.5% compared to the best result obtained by either of the two programs individually. Availability: The scripts implementing our methods are available from http://www.cs.ubc.ca/labs/beta/genefinding/ Contact: rogic@cse.ucsc.edu * To whom correspondence should be addressed.

We present an independent comparative analysis of seven recently developed gene-finding programs: FGENES,GeneMark.hmm, Genie, Genscan,HMMgene, Morgan, and MZEF. For evaluation purposes we developed a new, thoroughly filtered, and biologically validated dataset of mammalian genomic sequences that does not overlap with the training sets of the programs analyzed. Our analysis shows that the new generation of programs has substantially better results than the programs analyzed in previous studies. The accuracy of the programs was also examined as a function of various sequence and prediction features, such as G + C content of the sequence, length and type of exons, signal type, and score of the exon prediction. This approach pinpoints the strengths and weaknesses of each individual program as well as those of computational gene-finding in general. The dataset used in this analysis (HMR195) as well as the tables with the complete results are available athttp://www.cs.ubc.ca/∼rogic/evaluation/.

Abstract 295 Introduction: Diffuse large B-cell lymphoma (DLBCL) is the most common type of aggressive non-Hodgkin lymphoma (NHL), accounting for approximately 30–40% of all new lymphoma cases. While standard therapy using rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) has significantly increased the survival of DLBCL patients, approximately one third of DLBCL patients still remain unresponsive to or relapse after standard treatment. Further investigation into the genomic architecture of DLBCL will contribute to elucidating the causes of the poor outcomes in this subgroup of patients. While the copy number and the gene expression profiles of DLBCL specimens have been well described as separate analyses, a large-scale high resolution integration of both orthologous measurements has yet to be reported. The integration of these two data types in a clinically well-annotated cohort of DLBCL is crucial as it can potentially distinguish driver from passenger genomic aberrations and reveal associations with clinical outcome. Methods and Patients: Affymetrix SNP 6.0 microarrays were used to ascertain the copy number profiles in 151 pretreatment biopsies of DLBCL that were representative of the population of DLBCL patients treated at the British Columbia Cancer Agency. Clinical outcome data were available for all 151 patients with 142 patients receiving R-CHOP or R-CHOP-like treatment. Matching RNA-seq libraries were used to quantitate the gene expression levels in 91 samples. The SNP 6.0 pre-processing method cRMAv2 was used to generate raw probe intensities that were then normalized to 1258 HapMap3 SNP 6.0 arrays. Copy number state calls were predicted using HMM-Dosage. RNA-seq data were aligned using the split-read aware aligner GSNAP and gene expression values were generated using the metric reads per kilobase of transcript per million mapped reads. DriverNet analyses were utilized to predict functionally relevant driver genes and outcome correlations in R-CHOP treated patients were performed using Cox regression and the log-rank test. Results: The copy number landscape derived from the SNP 6.0 microarrays revealed previously reported large scale chromosomal deletions in chromosome 6p and amplifications in chromosomes 3, 7 and 18. By integrating the gene expression with copy number data, we found that gene copy number was correlated with its own gene expression (classified as being cis-correlated) in 23.5% of genes. In addition, we investigated copy number aberrations which were highly correlated with gene expression across the genome (classified as trans-correlated). This analysis revealed aberration hotspots in genomic locations 3q26-q28 (TBL1XR1, BCL6, TP63), 17p12 (NCOR1, MAP2K4), 18q11.1-q11.2 (RBBP8) and 22q11.21 (BID, IL17RA) suggesting that these hotspots regulate important pathways that may contribute to the pathogenesis of DLBCL. We identified previously reported focal amplifications (e.g. REL) and deletions (e.g. B2M, CDKN2A). Moreover, we identified novel focal deletions, including homozygous deletions, in chromatin modifying genes: LCOR (7.9%), RCOR1 (9.9%), and NCOR1 (17.9%), all of which were cis-correlated and were validated using fluorescence in situ hybridization. DriverNet analyses identified RCOR1 deletions as one of the main driver alterations. RCOR1 deletions were also found to be associated with progression-free survival (5-year progression-free survival: deleted 40% vs. non-deleted 75%, p=0.0188). Discussion: Our systematic integration of SNP 6.0 and RNA-seq data confirmed findings of previous studies and also revealed novel genomic aberration hotspots and highly focal and frequent deletions in chromatin modifying genes. Results derived from our large-scale high resolution data set indicate the feasibility and efficacy of integrative genomic analyses in revealing novel and pathogenetically relevant genomic aberrations in lymphoid cancers. The discovery of the association of RCOR1 deletions with progression-free survival suggests that RCOR1 deletions could be used as a prognostic marker and might indicate a molecular phenotype that can be targeted by novel therapeutic agents in DLBCL. Disclosures: No relevant conflicts of interest to declare.

Abstract 437 INTRODUCTION: Escape from immune surveillance has been proposed to play an important role in cancer and in particular in the pathogenesis of a subgroup of lymphomas. Using massively parallel sequencing, we have recently identified recurrent rearrangements of the CIITA gene in classical Hodgkin lymphoma (HL) and primary mediastinal B cell lymphoma (PMBCL) (Steidl et al, Nature 2011). Specifically, a gene fusion in the HL cell line KM-H2, involving the genes CIITA and FLJ27352, suppressed expression of CIITA-regulated MHC class II genes in a dominant-negative manner. Therefore, we sought to further investigate if gene alterations targeting the CIITA locus are recurrent somatic events in HL and PMBCL that lead to loss of gene function and thereby reduce immunogenicity of the malignant cells. PATIENTS AND METHODS: To characterize CIITA gene alterations, we comprehensively studied the cell lines DEV (nodular lymphocyte predominance HL-derived) and KARPAS1106P (PMBCL-derived) by whole-transcriptome paired-end sequencing (RNA-seq) and single nucleotide polymorphism arrays (Affymetrix SNP 6.0). To identify single nucleotide mutations in the CIITA coding sequence we studied additional 7 mediastinal biopsy specimens of PMBCL by RNA-seq. Fluorescence in-situ hybridization (FISH) was used to determine and validate structural and copy number alterations of the CIITA locus. 23 samples of PMBCL, 3 PMBCL-derived cell lines and 9 HL cell lines were studied for mutations and small deletions in CIITA intron 1 by long-range PCR and direct sequencing. CIITA and HLA-DR expression levels were determined by quantitative reverse transcriptase PCR. RESULTS: We identified chromosomal rearrangements affecting both alleles of CIITA in DEV cells resulting in expression of CIITA-PDL2, CIITA-SOCS1 and SOCS1-CIITA fusion transcripts. In KARPAS1106P cells chromosome 16 deletions result in the loss of one entire CIITA allele and partial loss of the other allele. As a consequence, in both cell lines wildtype CIITA and HLA-DR expression was undetectable. Genomic breakpoints within the CIITA rearrangements, together with previously identified translocations, defined a 1.6Kb breakpoint cluster region in CIITA Intron 1. Further analysis of this breakpoint cluster region revealed small intronic deletions in 10 of 23 (43%) PMBCL cases while no such deletions were detected in 18 diffuse large B cell lymphoma and 15 reactive lymph node samples. Furthermore, sequencing revealed multiple deletions ranging from 1–1948 bp and a high incidence of single nucleotide mutations in the breakpoint cluster region of the deleted alleles. The base pair changes were enriched for C to T and G to A transitions over other transitions and transversions, indicative of a somatic hypermutation process. We found significantly lower CIITA expression in cases with CIITA rearrangement and/or small intronic deletions compared to cases without CIITA alterations (p=0.044). RNAseq revealed three cases with non-synonymous coding-sequence mutations including a truncating mutation in exon 4. These changes were somatic in 3 cases with available matching normal DNA. DISCUSSION:CIITA is the target of multiple genomic hits including chromosomal translocation, deletion and coding sequence mutations in HL and PMBCL. In two HL and PMBCL cell lines this leads to complete gene inactivation and loss of HLA class II expression. These data characterize CIITA alterations as a recurrent underlying genomic event of the previously described immunophenotype of reduced HLA class II expression in a subset of PMBCL cases. Our data show that an immune escape mechanism utilized by tumor cells can be directly linked to somatically acquired alterations in cancer genomes. Disclosures: Siebert: Abbott/Vysis: .

Abstract 436 Background: Mantle cell lymphoma (MCL) is an aggressive subtype of non-Hodgkin's lymphoma that is characterized by the hallmark t(11;14)(q13;q32) translocation, as well as a high number of secondary chromosomal alterations. Further, a small number of genes such as TP53, ATM and CCND1 have been reported to be recurrently mutated in MCL, but do not fully explain the biology and do not adequately account for the wide spectrum of clinical manifestations, response to treatment and prognosis. The aim of this study was to discover new somatic mutations that could contribute to our understanding of the pathogenesis of MCL. Methods: In our discovery cohort, we sequenced the transcriptomes of 18 clinical samples (11 diagnostic and 7 progression biopsies) and 2 mantle cell lymphoma-derived cell lines (Mino and Jeko-1). For this purpose, whole transcriptome shotgun sequencing was performed on RNA extracted from fresh frozen tissue. We assembled an extension cohort of 103 diagnostic patient samples and 4 additional cell lines (Rec-1, Z-138, Maver-1, JVM-2), and performed Sanger sequencing of NOTCH1 exons 26, 27 and 34 on genomic DNA. We further exposed the 6 cell lines to 1 μM of the γ-secretase inhibitor XXI (compound E) for 7 days and measured cellular proliferation with an EdU incorporation assay. Survival analysis was carried out in the 113 patients with diagnostic biopsies and available outcome data. Results: NOTCH1 mutations were found in 14 out of 121 patient samples (11.6%) and in 2 out of 6 cell lines, Mino and Rec-1 (33.3%). The majority of these mutations (12 out of 14) lie in exon 34 that encodes the PEST domain of NOTCH1 and consist of either small frameshift-causing indels (10 cases) or nonsense mutations (2 cases). These mutations are predicted to cause truncations of the C-terminal PEST domain. To gain further insight into functional relevance, we treated 6 cell lines with compound E, an inhibitor of the γ-secretase complex that plays a critical role in the release of the intracellular domain of NOTCH1 after ligand-induced activation. In Rec-1, that harbours a NOTCH1 mutation, we observed a significant decrease in proliferation (mean percentage of cells in culture incorporating EdU decreasing from 47.5% to 1.4%, p Discussion: We have identified recurrent mutations in NOTCH1 in a subset of patients with MCL (11.6%). The frequency and the pattern of mutations are strikingly similar to what has recently been reported in chronic lymphocytic leukemia, the other major CD5 positive B-cell malignancy (Nature, 2011 Jun 5, 475:101–105 and J Exp Med, 2011 Jul 4, 208:1389–1401). NOTCH1 mutations are associated with adverse prognosis as evidenced by shortened overall survival. This latter finding, however, should ideally be validated in a larger and uniformly treated cohort. Finally, the sensitivity of the Rec-1 cell line to compound E suggests that NOTCH1 mutations could serve as the target for tailored therapy in mantle cell lymphoma. Disclosures: Sehn: Roche/Genentech: Consultancy, Honoraria, Research Funding. Connors:Roche: Research Funding.

Abstract 430 Introduction: 25–30% of patients with advanced stage classical Hodgkin lymphoma (cHL) develop progressive disease despite treatment with ABVD. While more intensive upfront treatments, such as BEACOPP, result in a higher failure free survival, this comes at the expense of increased toxicity and morbidity. 10–15% of patients with advanced stage cHL will ultimately succumb to the disease, with similar outcomes using different upfront regimens likely reflecting the planned use of second-line high dose therapy. The International Prognostic Factors Project Score, a prognostic tool developed using freedom from progression of disease as the outcome, has been used in clinical practice and has guided clinical trials. We hypothesized that biological factors, as measured by gene expression profiling in pretreatment formalin fixed paraffin embedded tissue (FFPET) samples, might provide a robust predictor of overall survival (OS). Method and Patients: NanoString technology was used to quantitate 261 mRNA species in diagnostic FFPET samples from patients in the Intergroup E2496 trial, which compares ABVD with Stanford V chemotherapy in locally advanced and advanced stage cHL. The 261 genes included individual genes and genes that represent cell and cellular process signatures that have been associated with outcome in cHL. Total RNA was extracted from whole section scrolls from the 309 FFPET blocks available. A penalized binary logistic regression model with leave-one-out cross validation was used to produce a parsimonious predictive model for OS. A threshold was selected that maximized the Chi square of the Log Rank (Mantel-Cox) test between the identified low and high risk groups. An independent cohort of patients with advanced stage cHL enriched for treatment failure, consisting of 59 patients from British Columbia treated with ABVD and 71 patients from Portugal treated with Stanford V, was used to test the generated model and threshold. Results: Gene expression of adequate quality was produced in 293 of the 309 samples (95%). At a median follow up of 5.3 years, 36 (12%) of the 293 patients had died. 51 genes were differentially expressed (t-test p < 0.05) with respect to OS, including genes that are part of macrophage, cytotoxic/NK cell and apoptosis signatures. A predictive model for OS was produced with an area under the curve (AUC) of the receiver operating characteristic (ROC) curve of 0.73. The predictor identified a high-risk group, comprising 39% of the cohort, with a 77% estimated 5-year OS compared with 96% in the low risk group (Figure 1A, log-rank p < 0.0001). The predictor was validated in the independent cohort giving an AUC of the ROC curve of 0.73 and an estimated 5-year OS of 71% in the high-risk group compared with 91% in the low risk group (Figure 1B, log-rank p = 0.006). Discussion: We have produced a parsimonious gene expression-based predictor of OS in cHL, developed in, and applicable to, widely available FFPET using NanoString technology. The predictor identifies, at diagnosis, a clinically meaningful proportion of patients at significantly higher risk of death when treated with ABVD or Stanford V. Further evaluation of the gene expression signature with alternative treatments, ideally in the context of clinical trials, is needed to understand the impact of more intensive cytotoxic therapies versus novel targeted approaches. Disclosures: Horning: Genentech: Employment, Equity Ownership. Connors:Seattle Genetics: Consultancy, Research Funding.

Abstract 2005 Background: Concurrent translocations in MYC and BCL2 determined by fluorescence in-situ hybridization (FISH), have been associated with a poor outcome in diffuse large B cell lymphoma (DLBCL) patients treated with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone (R-CHOP). However, unlike immunohistochemistry (IHC), FISH is expensive and is not routinely available in all clinical laboratories. The aim of this study was to determine if MYC protein expression or expression of the proliferation marker Ki-67 by IHC could be used to identify samples that harbour MYC translocations. Methods: DLBCL samples, diagnosed by an expert panel of hematopathologists according to the WHO criteria of 2008 and derived from 167 patients treated with R-CHOP, have been subjected to gene expression profiling (GEP) and FISH using commercial break-apart probes for BCL2 and MYC. MYC mRNA expression was determined using log2 normalized expression values of probe set 202431_s_at. The presence of MYC, BCL2 and Ki-67 protein expression was determined by IHC using commercially available antibodies (clone Y69 (Epitomics), clone 124 (Dako) and clone Ki-67 (Dako) respectively). MYC protein is not normally expressed in germinal centers (tonsils, negative control), thus the % of tumour cells staining for MYC was noted in each case. Thresholds for BCL2 protein (50%) and MYC mRNA (>9.4) were determined using the statistical software X-tile which determines the optimal threshold based on its association with clinical outcome. For MYC protein expression, no threshold was significant by X-tile thus a 40% threshold was used based on the bimodal distribution of the data, with a through occurring at 40%. Protein expression for MYC and Ki-67 was correlated to the presence of MYC translocations, MYC mRNA expression and outcome including overall survival (OS) and progression free survival (PFS). Results: Over-expression of MYC was present in 56/167 (34%) of DLBCL samples (18/167 translocations, 19/167 high mRNA and 47/167 high protein expression) and was not specific to the germinal center B cell (GCB) or activated B cell (ABC) molecular subtype. MYC protein expression (in ≥40% of cells) captured 13/18 MYC translocations and in 4/5 remaining cases, MYC staining was present in 20–39% of cells. MYC protein expression, alone, was not associated with OS but the presence of a MYC translocation was associated with an inferior OS in the ABC subtype only. BCL2 protein expression was also associated with an inferior OS in this cohort (p=0.002). Concurrent expression of MYC and BCL2 protein by IHC was associated with a markedly inferior OS compared to MYC protein- or MYC protein+/BCL2 protein- (median OS of 2 years, 7 years and > 7 years respectively, p90%) identified only 5/18 cases with MYC translocations and was not associated with outcome alone or in combination with BCL2. Conclusions: MYC deregulation in DLBCL is more common than previously reported (34%) and can occur in the absence of a MYC translocation. MYC and BCL2 protein expression could be easily determined by routine IHC in most clinical laboratories and should be prospectively tested as potential predictive biomarkers in DLBCL patients treated with R-CHOP. Disclosures: Grogan: Ventana Roche: Employment, Equity Ownership.