Whole genome sequencing delineates regulatory, copy number, and cryptic splice variants in early onset cardiomyopathy

Cardiomyopathy (CMP) is a heritable disorder. Over 50% of cases are gene-elusive on clinical gene panel testing. The contribution of variants in non-coding DNA elements that result in cryptic splicing and regulate gene expression has not been explored. We analyzed whole-genome sequencing (WGS) data in a discovery cohort of 209 pediatric CMP patients and 1953 independent replication genomes and exomes. We searched for protein-coding variants, and non-coding variants predicted to affect the function or expression of genes. Thirty-nine percent of cases harbored pathogenic coding variants in known CMP genes, and 5% harbored high-risk loss-of-function (LoF) variants in additional candidate CMP genes. Fifteen percent harbored high-risk regulatory variants in promoters and enhancers of CMP genes (odds ratio 2.25, p = 6.70 × 10−7 versus controls). Genes involved in α-dystroglycan glycosylation (FKTN, DTNA) and desmosomal signaling (DSC2, DSG2) were most highly enriched for regulatory variants (odds ratio 6.7–58.1). Functional effects were confirmed in patient myocardium and reporter assays in human cardiomyocytes, and in zebrafish CRISPR knockouts. We provide strong evidence for the genomic contribution of functionally active variants in new genes and in regulatory elements of known CMP genes to early onset CMP.This project was supported by the Ted Rogers Centre for Heart Research (SM, JE), the Canadian Institutes of Health Research (PJT 175034) (SM, JE) and by the Canadian Institutes of Health Research (ENP 161429), under the frame of ERA PerMed (SM). SM holds the Heart and Stroke Foundation of Canada & Robert M Freedom Chair in Cardiovascular Science. SWS holds the GlaxoSmithKline Endowed Chair in Genome Sciences at the Hospital for Sick Children and the University of Toronto. PGM holds a Canada Research Chair Tier 2 in Non-coding Disease Mechanisms. PGM acknowledges the support of the Government of Canada’s New Frontiers in Research Fund (NFRF), [NFRFE-2018-01305]. EO holds the Bitove Family Professorship of Adult Congenital Heart Disease. MM holds a Ramon y Cajal grant from the Spanish Ministry of Science and Innovation (RYC-2017-22249). WO is supported by funding from Fundació La Marató (321/C/2019). JB is funded by a Frans Van de Werf fellowship for clinical cardiovascular research, and by a senior clinical investigator fellowship of the FWO Flanders. KM was a National Science Foundation Graduate Research Fellow under grant no. DGE1144152 during the majority of the project. CS is the recipient of a National Health and Medical Research Council (NHMRC) Practitioner Fellowship (1154992). JI is the recipient of an NHMRC Career Development Fellowship (1162929). RDB is the recipient of a New South Wales Health Cardiovascular Disease Senior Scientist Grant. PSD is supported by the DBT/Wellcome Trust- Indian Alliance. We acknowledge the Labatt Family Heart Centre Biobank at the Hospital for Sick Children for access to DNA samples, and The Centre for Applied Genomics at the Hospital for Sick Children for performing WGS. We thank Xiucheng Cui and Emanuela Pannia for performing the zebrafish experiments at the SickKids Zebrafish Genetics and Disease Models Core (CRISPR-Cas9 and gRNA syntheses, zebrafish embryo microinjections, gRNA PCR validation, qRT-PCR, cardiac imaging). This research was made possible through access to the data and findings generated by the 100,000 Genomes Project. The 100,000 Genomes Project is managed by Genomics England Limited (a wholly owned company of the Department of Health and Social Care). The 100,000 Genomes Project is funded by the National Institute for Health Research and NHS England. The Wellcome Trust, Cancer Research UK and the Medical Research Council have also funded research infrastructure. The 100,000 Genomes Project uses data provided by patients and collected by the National Health Service as part of their care and support. We thank members of the ICGC/PCAWG working groups for generating the variant calls used in our case-control burden analyses.Peer Reviewed"Article signat per 38 autors/es: Robert Lesurf, Abdelrahman Said, Oyediran Akinrinade, Jeroen Breckpot, Kathleen Delfosse, Ting Liu, Roderick Yao, Gabrielle Persad, Fintan McKenna, Ramil R. Noche, Winona Oliveros, Kaia Mattioli, Shreya Shah, Anastasia Miron, Qian Yang, Guoliang Meng, Michelle Chan Seng Yue, Wilson W. L. Sung, Bhooma Thiruvahindrapuram, Jane Lougheed, Erwin Oechslin, Tapas Mondal, Lynn Bergin, John Smythe, Shashank Jayappa, Vinay J. Rao, Jayaprakash Shenthar, Perundurai S. Dhandapany, Christopher Semsarian, Robert G. Weintraub, Richard D. Bagnall, Jodie Ingles, Genomics England Research Consortium, Marta Melé, Philipp G. Maass, James Ellis, Stephen W. Scherer & Seema Mital"Postprint (published version

ORegAnno 3.0: A community-driven resource for curated regulatory annotation

The Open Regulatory Annotation database (ORegAnno) is a resource for curated regulatory annotation. It contains information about regulatory regions, transcription factor binding sites, RNA binding sites, regulatory variants, haplotypes, and other regulatory elements. ORegAnno differentiates itself from other regulatory resources by facilitating crowd-sourced interpretation and annotation of regulatory observations from the literature and highly curated resources. It contains a comprehensive annotation scheme that aims to describe both the elements and outcomes of regulatory events. Moreover, ORegAnno assembles these disparate data sources and annotations into a single, high quality catalogue of curated regulatory information. The current release is an update of the database previously featured in the NAR Database Issue, and now contains 1 948 307 records, across 18 species, with a combined coverage of 334 215 080 bp. Complete records, annotation, and other associated data are available for browsing and download at http://www.oreganno.org/

Molecular pathway analysis of mouse models for breast cancer

Human breast cancer is an extremely heterogeneous disease, consisting of a number of different subtypes with varying levels of aggressiveness reflected by distinct, but largely undefined, molecular profiles. Here we have analyzed several novel mouse models for breast cancer in the context of the human subtypes, and have shown parallels between the mice and humans at numerous biologically relevant levels. In addition, we have developed a statistical framework to help elucidate the individual molecular components that are at play across a panel of human breast or murine mammary tumors. Our results indicate that, while no mouse model captures all aspects of the human disease, they each contain components that are shared by a subset of human breast tumors. Furthermore, our statistical framework provides numerous advantages over previous methodologies, in helping to reveal the individual molecular pathways that make up the biology of the tumors.Le cancer du sein est connue pour être une maladie très hétérogène, composé d'un nombre de différents sous-types avec différents niveaux de l'agressivité et distinctes, mais indéfini, profils moléculaires. Ici, nous avons analysé plusieurs nouveaux modèles de souris pour le cancer du sein, dans le cadre des sous-types, et nous avons trouver des parallèles à un certain nombre de niveaux pertinents biologiques. En outre, nous avons développé une méthodologie statistique pour aider à élucider les différents composants moléculaires qui sont à jouer dans un groupe de tumours de sein d'humains ou mammaires murins. Nos résultats indiquent que, même si aucun modèle de souris capte tous les aspects de la maladie chez l'homme, chacun contiennent des composants qui sont partagées par un sous-ensemble de tumeurs mammaires humaines. En outre, notre outil statistique offre de nombreux avantages par rapport aux précédentes méthodes, pour aider à révéler les voies moléculaires qui composent la biologie des tumeurs

Stratified informatics analysis for breast cancer: types, subtypes, and models of the disease

Over the past decade, genomic technologies have promised to revolutionize breast cancer research with better predictions of disease progression and patient prognosis through the identification of novel molecular targets. These technologies have led to the discovery of numerous different genomic subtyping schemes, each purported to offer relevant information beyond traditional clinical features. Yet it remains unclear what these schemes have contributed to our understanding of breast cancer biology and the reasons for disease recurrence. To address this issue, we have built a novel framework termed Breast Signature Analysis Tool (BreSAT), along with a companion collection of breast cancer datasets, highly annotated signatures, statistics, and visualizations, designed for accurate and ease-of-use application in breast cancer. This framework represents a new way of cataloguing tumors according to their biological properties, as opposed to broad gene expression profiles. We have applied BreSAT and its associated catalogue of signatures to thousands of breast tumor samples, and identify that tumor properties associated with recurrence are confounded by association with other clinicopathological variables such as estrogen receptor status and the genomic subtype. In addition to identifying properties associated with recurrence in breast cancer, we have sought to discover the molecular markers that drive different tumor phenotypes. We identified that expression of the oncogene MET in mouse mammary glands leads to the generation of tumors with characteristics of human triple-negative breast cancer. Additionally, synergy between MET and loss of p53 in similar mouse model leads the development of tumors a claudin-low phenotype, that arise with a higher penetrance and lower latency. Moreover, MET activity is required for maintenance of the claudin-low morphological phenotype and metastatic capacity of cell lines, suggesting that MET may represent an avenue for targeted therapeutics in human patients with claudin-low breast tumors. Finally, we have discovered that molecular features of breast cancer progression from a non-invasive to an invasive state are confounded by tumor subtype. To find more accurate markers of disease progression, we identified tumor properties that differentiate non-invasive tumors from invasive ones within each subtype. We observed that there is little overlap, suggesting that distinct properties drive tumor progression in different subtypes. Furthermore, we were able to identify a small number of non-invasive breast tumors with molecular features that make them more likely to progress. Together, these discoveries are leading to a more comprehensive understanding of the molecular features that drive breast cancer biology, disease progression, and patient outcome.Au cours de la dernière décennie, les technologies génomiques ont promis de révolutionner la recherche sur le cancer du sein, avec de meilleures prévisions de progression de la maladie et le pronostic du patient, grâce à l'identification de nouvelles cibles moléculaires. Ces technologies ont conduit à la découverte de nombreux différents systèmes de sous-typage génomique, chaque censé fournir les informations pertinentes au-delà des caractéristiques cliniques traditionnels. Pourtant, on ne sait pas ce que ces programmes ont contribué à notre compréhension de la biologie du cancer du sein et les raisons de récidive de la maladie.Pour résoudre ce problème, nous avons construit un cadre nouveau appelé Breast Signature Analysis Tool (BreSAT), avec une collection d'ensemble de données sur le cancer du sein, des signatures annotés, des statistiques et des visualisations, conçu pour l'application et d'utilisation dans le cancer du sein. Ce cadre représente un nouveau mode de catalogage des tumeurs en fonction de leurs propriétés biologiques, par opposition à des profils d'expression génique larges. Nous avons appliqué BreSAT et notre catalogue de signatures à des milliers d'échantillons de tumeurs du sein, et d'identifier que les propriétés tumorales associés à la récidive sont confondus par l'association avec d'autres variables clinico-pathologiques tels que le statut de récepteur d'oestrogène et le sous-type génomique.En plus d'identifier les propriétés associées à une récidive du cancer du sein, nous avons cherché à découvrir les marqueurs moléculaires qui conduisent phénotypes tumoraux différents. Nous avons déterminé que l'expression de l'oncogène MET dans les glandes mammaires de souris conduit à la génération de tumeurs avec des caractéristiques du cancer du sein humaine triple négatif. De plus, la synergie entre MET et la perte de p53 dans un modèle de souris similaire conduit le développement de tumeurs avec un phénotype de claudine-bas, qui se posent avec une pénétrance élevée et une latence plus faible. En outre, l'activité MET est nécessaire pour le maintien du phénotype morphologique claudine-bas et la capacité métastatique de lignées cellulaires, suggérant que ce statut peut représenter une avenue pour les thérapies ciblées chez des patients humains atteints de tumeurs mammaires claudine-bas.Enfin, nous avons découvert que les caractéristiques moléculaires de la progression du cancer du sein à partir d'un non-invasive d'un état invasive sont brouillées par le sous-type de tumeur. Pour trouver des marqueurs plus précis de la progression de la maladie, nous avons identifié des propriétés tumorales qui différencient des tumeurs non invasives de ceux envahissantes au sein de chaque sous-type. Nous avons observé qu'il ya peu de chevauchement, ce qui suggère que des propriétés distinctes voiture progression tumorale chez les différents sous-types. En outre, nous avons pu identifier un petit nombre de tumeurs du sein non invasif avec des caractéristiques moléculaires qui les rendent plus susceptibles d'évoluer. Ensemble, ces découvertes mènent à une compréhension plus complète des caractéristiques moléculaires qui conduisent la biologie du cancer du sein, la progression de la maladie, et les résultats des patients

Autocrine Activation of the Wnt/β-Catenin Pathway by CUX1 and GLIS1 in Breast Cancers

Autocrine activation of the Wnt/β-catenin pathway occurs in several cancers, notably in breast tumors, and is associated with higher expression of various Wnt ligands. Using various inhibitors of the FZD/LRP receptor complex, we demonstrate that some adenosquamous carcinomas that develop in MMTV-CUX1 transgenic mice represent a model for autocrine activation of the Wnt/β-catenin pathway. By comparing expression profiles of laser-capture microdissected mammary tumors, we identify Glis1 as a transcription factor that is highly expressed in the subset of tumors with elevated Wnt gene expression. Analysis of human cancer datasets confirms that elevated WNT gene expression is associated with high levels of CUX1 and GLIS1 and correlates with genes of the epithelial-to-mesenchymal transition (EMT) signature: VIM, SNAI1 and TWIST1 are elevated whereas CDH1 and OCLN are decreased. Co-expression experiments demonstrate that CUX1 and GLIS1 cooperate to stimulate TCF/β-catenin transcriptional activity and to enhance cell migration and invasion. Altogether, these results provide additional evidence for the role of GLIS1 in reprogramming gene expression and suggest a hierarchical model for transcriptional regulation of the Wnt/β-catenin pathway and the epithelial-to-mesenchymal transition

Molecular Features of Subtype-Specific Progression from Ductal Carcinoma In Situ to Invasive Breast Cancer

Breast cancer consists of at least five main molecular “intrinsic” subtypes that are reflected in both pre-invasive and invasive disease. Although previous studies have suggested that many of the molecular features of invasive breast cancer are established early, it is unclear what mechanisms drive progression and whether the mechanisms of progression are dependent or independent of subtype. We have generated mRNA, miRNA, and DNA copy-number profiles from a total of 59 in situ lesions and 85 invasive tumors in order to comprehensively identify those genes, signaling pathways, processes, and cell types that are involved in breast cancer progression. Our work provides evidence that there are molecular features associated with disease progression that are unique to the intrinsic subtypes. We additionally establish subtype-specific signatures that are able to identify a small proportion of pre-invasive tumors with expression profiles that resemble invasive carcinoma, indicating a higher likelihood of future disease progression

The telomere length landscape of prostate cancer

Despite the known role of telomere length in cancer, its association with genomic features remains unclear. Here, the authors integrate telomere length, genomics, transcriptomics and proteomics in localized prostate cancer and reveal links between telomere maintenance, disease drivers and clinical outcomes