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• McGill Epigenomics Mapping Centre

  The McGill Epigenomics Mapping Centre (EMC) and Data Coordination Centre (EDCC) were established in 2012 at the McGill University and Genome Quebec Innovation Centre (Montreal, Canada) to support large-scale human epigenome mapping for a broad spectrum of cell types and diseases. Previous epigenetic studies have provided profiles of specific chromatin marks in relation to basic biological processes, and future studies of molecular mechanisms must build upon these proofs-of-concept by integrating sequence-based variation with multiple levels of epigenetic and transcriptional regulation across the genome of human tissues and animal disease models. This project leverages the high-throughput sequencing infrastructure and expertise in genomics and transcriptomics at the McGill Innovation Centre to carry out this research. Controlled access to the data by collaborators and the greater scientific community is gained via a portal, which takes advantage of Compute Canada high-performance computing cluster resources to manage the large volume of data associated from the generation of reference epigenome maps. McGill University is one of two Canadian mapping and data coordination centres, the other based at the Michael Smith Genome Sciences Centre in Vancouver, British Columbia. The generation of comprehensive epigenome maps at McGill University is part of a larger international effort that is coordinated by the International Human Epigenome Consortium (IHEC), whose overall long-term objective is to determine the extent to which the epigenome shapes human populations over generations and in response to the environment. This project is funded under the Canadian Epigenetics, Environment, and Health Research Consortium (CEEHRC) by the Canadian Institutes of Health Research and by Genome Quebec, with additional support from Genome Canada. The computing and networking infrastructure, and part of the software development, are provided by Compute Canada and CANARIE.

  Study EGAS00001000995

• UK10K COHORT ALSPAC

  The UK10K project proposes a series of complementary genetic approaches to find new low-frequency/rare variants contributing to disease phenotypes. These will be based on obtaining the genome-wide sequence of 4000 samples from the TwinsUK and ALSPAC cohorts (at 6x sequence coverage), and the exome sequence (protein-coding regions and related conserved sequence) of 6000 samples selected for extreme phenotypes. Our studies will focus primarily on cardiovascular-related quantitative traits, obesity and related metabolic traits, neurodevelopmental disorders and a limited number of extreme clinical phenotypes that will provide proof-of-concept for future familial trait sequencing. We will directly analyse quantitative traits in the cohorts and the selected traits in the extreme samples, and also use imputation down to 0.1% allele frequency to extend the analyses to further sample sets with genome wide genotype data. In each case we will investigate indels and larger structural variants as well as SNPs, and use statistical methods that combine rare variants in a locus or pathway as well as single-variant approaches. The Avon Longitudinal Study of Parents and Children (ALSPAC) is a two-generation prospective study. Pregnant women living in one of three health districts in the former county of Avon with an expected delivery date between April 1991 and December 1992 were eligible to be enrolled in the study, and this formed the initial point of contact for the development of a large, family based resource. Information has been collected on the children and the mothers through retrieval of biological materials (e.g. antenatal blood samples, placentas), biological sampling (e.g. collection of cord blood, umbilical cord, milk teeth, hair, toenails, blood and urine), self-administered questionnaires, data extraction from medical notes, linkage to routine information systems and at repeat research clinics.

  Study EGAS00001000090

• Population Structure and Genetic Diversity in Argentinean populations

  Argentina was one of the last places peopled when humans entered the Americas. Its earliest archeological sites are dated from 13,000 years BP, and throughout its history the region has exhibited broad cultural diversity, involving both hunter-gatherer and agro-pastoral societies, different linguistic families, the Southern expansion of the Inca Empire, the arrival of the Spanish conquistadors and slave trade from Africa. More recently, Argentina has had the highest proportion of influx of transoceanic migration between 1850 and 1940 of any Latin American country. Furthermore, the geographical distribution of immigrants was oriented towards the Pampa region of central-eastern Argentina and the Northeast, so different patterns of mixed ancestry throughout the country are expected. In this present work we aim to describe the history and structure of the Argentinean population. Thus far, we have collected DNA samples from 2904 participants from 15 dispersed regions in Argentina, from the Andean populations in the Northwest to the river lands of the Northeast. Our preliminary data includes genome-scale Illumina Exome Array 250K genotypes, plus next-generation sequencing data from uniformly spaced genomic regions targeted with a genotype-by- sequencing strategy (which covers 1.5% of the genome), for 375 participants from 9 of the 15 populations. We will identify proportions and patterns of European, African and Indigenous American ancestry in the Argentinean cohort via local ancestry inference. The first series of analyses will focus on genomic tracts of Indigenous American ancestry for fine-scale examination of sub-continental structure by comparing to a panel of 493 samples from 52 Indigenous American populations from throughout the Americas. Furthermore, this work will serve as a test of different low-cost strategies (exome genotype array vs. genotype-by-sequencing) for accurate inference of population structure in extant populations.

  Study EGAS00001001663

• Copy number profiling of primary samples and cell lines of retinoblastoma

  While RB1 loss initiates retinoblastoma development, additional somatic copy number alterations (SCNAs) can drive tumor progression. Although SCNAs have been identified with good concordance between studies at a cytoband resolution, accurate identification of single genes for all recurrent SCNAs is still challenging. This study presents a comprehensive meta-analysis of genome-wide SCNAs integrated with gene expression profiling data, narrowing down the list of plausible retinoblastoma driver genes. We performed SCNA profiling of 45 primary retinoblastoma samples and 8 retinoblastoma cell lines by high-resolution microarrays. We combined our data with genomic, clinical and histopathological data of ten published genome-wide SCNA studies, which strongly enhanced the power of our analyses (N=310). Comprehensive recurrence analysis of SCNAs in all studies integrated with gene expression data allowed us to reduce candidate gene lists for 1q, 2p, 6p, 7q and 13q to a limited gene set. Besides the well-established driver genes RB1 (13q-loss) and MYCN (2p-gain) we identified CRB1 and NEK7 (1q-gain), SOX4 (6p-gain) and NUP205 (7q-gain) as novel retinoblastoma driver candidates. Depending on the sample subset and algorithms used, alternative candidates were identified including MIR181 (1q-gain) and DEK (6p gain). Remarkably, our study showed that copy number gains rarely exceeded change of one copy, even in pure tumor samples with 100% homozygosity at the RB1 locus (N=34), which is indicative for intra-tumor heterogeneity. In addition, profound between-tumor variability was observed that was associated with age at diagnosis and differentiation grades. Since focal alterations at commonly altered chromosome regions were rare except for 2p24.3 (MYCN), further functional validation of the oncogenic potential of the described candidate genes is now required. For further investigations, our study provides a refined and revised set of candidate retinoblastoma driver genes.

  Study EGAS00001001715

• Diagnostic utility of clinical genome reanalysis in rare pediatric disorders using long-read sequencing

  More than half of presumed genetic disease cases remain undiagnosed following exome and/or short-read genome sequencing (SR-GS). Long-read genome sequencing (LR-GS) has emerged as a promising next step for uncovering etiologies missed by current standard-of-care SR genetic testing. However, structural variant (SV) interpretation remains a major challenge in LR-GS clinical pipelines, owing to limitations in SV call reliability, population allele frequency estimation, and evaluation of functional impact. To further develop strategies for the effective clinical use of LR-GS, we performed LR-GS using PacBio HiFi technology on 20 families with developmental disorders of suspected genetic origin and prior negative or inconclusive genetic testing (including exome and/or genome sequencing). Analysis was performed using a comprehensive pipeline integrating phased assembly– and read-based variant calling with LR-GS population allele frequency filtering. Phenotype-driven variant prioritization using Human Phenotype Ontology (HPO) terms was performed using a combination of tertiary analysis solutions, including publicly (SvAnna) and commercially (Illumina Emedgene) available tools. Definitive diagnostic findings were identified in 1/20 (5%) families. Variants of suspicious diagnostic significance were identified in 6/20 (30%). All findings of diagnostic significance were clinically confirmed using the proband’s prior clinical SR sequencing results, demonstrating the value of reanalysis and highlighting differences in clinical interpretation. One case harbored a homozygous ~50 kb deletion of initial interest that was only detected with LR-GS; however, it was ruled inconclusive based on frequency data from LR-GS population databases, underscoring the necessity of population filtering for structural variant clinical interpretation. Collectively, the observed increase in diagnostic yield (by 35% including suspicious findings) in this exome-/SR-GS-negative clinical cohort was driven primarily by genome reanalysis rather than identification of variants unique to LR-GS data. Additional functional studies are necessary to elucidate the clinical relevance of suspicious findings.

  Study EGAS50000001464

• Characterization of genetic intratumor heterogeneity in colorectal cancer and matching patient-derived spheroid cultures.

  Patient-derived in vitro cultures of colorectal cancer (CRC) may help guide treatment strategies prior to patient treatment. However, most previous studies have been performed on a single biopsy per tumor. The purpose of this study was to analyze multiple spatially distinct biopsies from CRCs and see how well intratumor heterogeneity (ITH) was recapitulated in matching patient-derived spheroids. Three to five biopsies were collected from six CRC tumors. Each biopsy was split in two; one half was used for spheroid culturing, the other half was used for direct DNA and RNA purification. For two of the patients, lymph node metastases were analyzed. Somatic mutations were called from whole exome sequencing data. Each tumor contained multiple mutations shared across all biopsies and spheroids, including major CRC drivers such as APC, KRAS, and TP53. At the same time, all tumors exhibited ITH on both mutation and copy number level. The concordance between biopsies and spheroids ranged between 40-70% for coding mutations. For three of the patients the biopsy and spheroid from matching areas clustered together, meaning that the spheroid resembled the area of origin more than the other areas. However, all biopsies and spheroids contained private mutations. Thereby, multiple cultures from spatially distinct sites of the tumor increase the insight into the genetic profile of the entire tumor. RNA sequencing based molecular subtyping was stable and seemed independent of genetic ITH. In conclusion, all examined CRC tumors contained genetic ITH on mutation and copy number level. Spheroid cultures partly reflected this ITH, meaning that multiple cultures from distinct sites of the tumor improved the representation of the different genetic subclones in the primary tumor. This should be taken in to account when establishing patient-derived models for drug screening.

  Study EGAS00001002684

• Genomic Profiling of Pediatric Tumors by Cell Free DNA Sequencing

  BACKGROUND Analysis of cell-free DNA (cfDNA) from peripheral blood has been studied extensively for detection and monitoring therapy response in adult cancers, but utility in pediatric tumors is unclear. METHODS We studied 233 children with hematologic, solid and brain tumors. cfDNA was extracted from 1 mL of plasma collected at diagnosis. Circulating tumor DNA (ctDNA) in cfDNA was quantified and diverse classes of somatic genomic variants were detected using PeCan-Seq. PeCan-Seq findings were compared with matched tumor whole-genome and whole-exome sequencing data. Minimal residual disease (MRD) status was evaluated by cfDNA PeCan-Seq in 14 patients with B-cell acute lymphoblastic leukemia. RESULTS Plasma cfDNA yields at diagnosis were higher for hematologic malignancies than for solid and brain tumors. Patients with hematologic malignancy were 100% ctDNA-positive, the median ctDNA fraction in cfDNA was 0.77. We detected 97% variants that were present in tumors and covered by the capture panel, and 50 subclonal variants that were present in cfDNA only. Our approach enabled detection of sequence and structural variants in B-ALL ctDNA at varying levels of MRD. Nineteen of 38 patients with solid tumors were ctDNA-positive, with high prevalence in Ewing Sarcoma, liver tumors and osteosarcoma. One of 19 brain tumor patients was ct-DNA positive, indicating lack of utility for plasma-derived cfDNA in this disease. Notably, cfDNA analysis of a single plasma sample identified genomic features in ctDNA derived from B-ALL, concurrent primary neuroblastoma, and a later neuroblastoma relapse, indicating that genomic variations from multiple tumors could be detected simultaneously. CONCLUSIONS Pediatric patients with hematologic malignancy had an abundance of plasma ctDNA at diagnosis. Plasma ctDNA levels varied between different solid tumor types, more studies are needed to determine utility. Panel-based cfDNA sequencing could serve as a non-invasive approach for pediatric cancer diagnosis and disease monitoring.

  Study EGAS50000000072

• Identification of intronic sequences promoting natural skipping of ABCA4 exon 15 using long-read transcriptomics and midigene assays

  Stargardt disease is an inherited retinal disease that leads to progressive loss of vision, and is caused by biallelic variants in the ABCA4 gene. A significant proportion of ABCA4 variants of variable penetrance affect pre-mRNA splicing. Previously, we observed the exclusion of certain ABCA4 exons into the final transcript in the absence of any disease-causing variants. In this study, we explored this phenomenon that we coined natural exon skipping (NES) for the entire ABCA4 transcript, using targeted RT-PCR analysis as well as long-read transcriptome sequencing of control retina samples. For the most abundant NES event, which is skipping of ABCA4 exon 15, overlapping ABCA4 deletion constructs were employed to identify responsible cis-regulatory sequence motifs. Subsequently, antisense oligonucleotides (AONs) were designed and tested to reverse NES and restore fully-spliced ABCA4 mRNA. We identified intronic regions on each side of exon 15 that, when deleted, increased the inclusion of exon 15 in the final transcripts. Interestingly, these motifs appear to work in a synergistic manner. Further fine-mapping of these regions revealed several cis-regulatory motifs that may be responsible for inducing NES, on each side of the exon. AONs targeting these motifs unfortunately did not decrease the skipping of exon 15. In conclusion, we discovered an intriguing phenomenon that, in the absence of disease-causing variants, results in the skipping of one or more exons in the ABCA4 transcript. These NES events appear to be regulated by cis-regulatory motifs within the flanking introns. This work captured the extend of NES throughout ABCA4 transcript and the role of cis-regulatory motifs in pre-mRNA splicing.. The use of AONs targeting cis-regulatory motifs is an innovative strategy for splicing modulation, suitable for complementing future variant-specific splicing modulation therapies in ABCA4 retinopathies.

  Study EGAS50000000071

• Intra-tumor heterogeneity of localized lung adenocarcinomas defined by multi-region sequencing

  Background: Intra-tumor heterogeneity (ITH) may have profound impacts on biopsy strategy, characterization of actionable targets, treatment planning, and drug resistance. ITH of lung cancers has not been well studied. Methods: We performed multi-region whole exome sequencing (average sequencing depth 277x) on 48 tumor regions from 11 resected lung adenocarcinomas (8 stage I, 2 stage II and one stage III tumors) followed by deep sequencing validation (average sequencing depth 863x). Results: All tumors showed clear evidence of spatial ITH. On average, 76% of all mutations and 20/21 known cancer gene mutations were present in all regions of individual tumors (trunk mutations). Copy number analysis did not show substantial difference in large-scale chromosome aberrations. A canonical KRAS p.G12C mutation was detected in only 1 of 4 tumor regions (branch mutations) at exome depth but detected in all four tumor regions (trunk mutations) at increased sequencing depth. In addition, many heterogeneous branch mutations defined by exome sequencing were detectable in all regions of individual tumors with increasing sequencing depth. Significant differences in mutational spectrum were observed in 6 tumors indicating that specific mutational processes were likely operative at different times during development of these tumors. With a median follow up of 21 months post surgery, 3 patients have relapsed and all 3 patients presented with significantly larger fractions of subclonal mutations in their primary tumors. Conclusions: Although multi-region sampling is still required to fully assess ITH complexity, single biopsy analysis at appropriate depth might be sufficient to identify the majority of known cancer gene mutations in localized lung adenocarcinomas. Larger subclonal mutation fractions may be associated with increased likelihood of postsurgical relapse in patients with localized lung adenocarcinomas. Studies on larger cohort, ideally with comprehensive clinical annotation and repeat biopsy at relapse are needed to fully understand the clinical impact of ITH.

  Study EGAS00001000930

• Bulk-RNA Sequencing of high-grade pancreatic and non-pancreatic Neuroendocrine Neoplasms

  Therapeutic decisions in oncology depend on a precise pathological classification of individual neoplasms. Recent years have seen an intensification of research activities aimed at the extraction of clinically relevant information from patient-derived 'omics' data based on Machine-Learning models. However, a comprehensive training of Machine-Learning models requires sufficiently large numbers of training samples, which are usually not available for rare cancer types. The problem is worsened when individual tissues segregate into different cancer subtypes, as their discrimination would require even more training samples. Methods: Here, we report on a new data-augmentation technique to support the training of Machine-Learning models on ‘omics’ data from pancreatic neuroendocrine neoplasms (panNEN). PanNENs display all properties described above: Only about 2-3% of all pancreatic neoplasms are neuroendocrine and they fall into different subtypes with distinctly different prognosis, which makes the precise classification of such samples both difficult and important for therapy decisions. The approach reconstructs a given transcriptome based on healthy pancreatic cell type signatures and creates a Machine-Learning model that integrates the observed reconstruction error and predicted cell type proportions as features. Results: A benchmark of the deconvolution model predictions with the ground-truth found that the model could efficiently predict the sample grading, disease-related patient survival time, and differentiate between different subtypes in four panNEN and one mixed panNEN and non-pancreatic NENs dataset. We compared the predictive performance of the deconvolution-trained model to that of a model trained directly on the transcriptomic data, under inclusion of the Ki-67 classification gold standard biomarker, and found the performances to be comparable. Conclusion: Our approach serves as a data-augmentation technique to facilitate the training of Machine Learning models for rare cancer types via utilization of data from healthy origin. Additionally, the deconvolution results were clinically interpretable and further research can render the deconvolution approach an effective complementary asset for the clinical classification of neoplasms.

  Study EGAS00001004861