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• Whole genome sequencing in 1038 index cases reveals novel causative genes in pulmonary arterial hypertension

  Pulmonary arterial hypertension (PAH) is a rare disorder with a poor prognosis. Deleterious variation within genes encoding components of the transforming growth factor-ß pathway underlie the majority of heritable forms of PAH. Identifying the missing genetic contribution is challenging, even with genes of large effect size, since it likely involves mutations in genes confined to small numbers of PAH cases. In this study, we performed whole genome sequencing, comparing 1038 PAH index cases to 6385 subjects with other rare diseases. Rare variant analysis identified mutations in novel causal genes, namely ATP13A3, AQP1 and SOX17, and provided independent validation of a critical role for GDF2 in PAH. We detected mutations predicted to be disruptive of function in most, but not all, previously reported PAH genes. Taken together these findings provide new insights into the molecular basis of PAH, and support a central role for endothelial dysregulation in disease pathogenesis.

  Dataset EGAD00001003423

• Ovarian Cancer Organoid Biobank - RNASeq data

  Ovarian cancer (OC) is a heterogeneous disease usually diagnosed at a late stage. Experimental in vitro models that faithfully capture the hallmarks and tumor heterogeneity of OC are limited and hard to establish. We present a novel protocol that enables efficient derivation and long-term expansion of OC organoids. Utilizing this protocol, we have established 56 organoid lines from 32 patients, representing the spectrum of ovarian neoplasms, including non-malignant borderline tumors, as well as mucinous, clear-cell, endometrioid, low- and high-grade serous carcinomas. OC organoids recapitulate histological and genomic features of the pertinent lesion from which they were derived, illustrating intra- and inter-patient heterogeneity, and can be genetically modified. We show that OC organoids can be used for drug screening assays and capture different tumor subtype responses to the gold standard platinum-based chemotherapy, including acquisition of chemoresistance in recurrent disease. Finally, OC organoids can be xenografted, enabling in vivo drug sensitivity assays. Taken together, this demonstrates their potential application for research and personalized medicine.

  Dataset EGAD00001004509

• Sequencing component for the whole genome methylation analysis in PBMCs and cell subsets (pilot study)

  DNA methylation has been shown to play a major role in determining cellular phenotype by regulating gene expression. Moreover, dysregulation of differentially methylated genes has been implicated in disease pathogenesis of various conditions including cancer development as well as autoimmune diseases such as systemic Lupus erythematosus and rheumatoid arthritis. Evidence is rapidly accumulating for a role of DNA methylation in regulating immune responses in health and disease. However, the exact mechanisms remain unknown. The overall aim of the project is to investigate the role of epigenetic mechanisms in regulating immunity and their impact on autoimmune disease pathogenesis.The aim of this pilot study is to perform whole genome methylation analysis in peripheral blood mononuclear cells (PBMCs) and cell subsets (CD4, CD8, CD14, CD19, CD16 and whole PBMCs) obtained from 6 healthy volunteers. Whole genome methylation analysis will be performed using two methodological approaches, the Infinium Methylation Bead Array K450 (Illumina) and MeDIP-seq. mRNA expression arrays will also be performed in order to correlate DNA methylation with gene expression as well as genotyping on the Illumina OmniExpress chip

  Dataset EGAD00001000394

• Sequencing data for oesophageal and related samples - Xiaodun Li et al (WGS, RNA)

  Data supporting: "Esophageal adenocarcinoma organoid cultures recapitulate human disease heterogeneity and provide a model for clonality studies and precision therapeutics." Li et al. WGS (BAM files) RNAseq (BAM files) Tumours, organoids, normals

  Dataset EGAD00001004007

• The landscape of somatic mutation in normal colorectal epithelial cells

  The colorectal adenoma-carcinoma sequence has provided a paradigmatic framework for understanding the successive somatic genetic events and consequent clonal expansions leading to cancer. As for most cancer types, however, understanding of the earliest phases of colorectal neoplastic change, which may occur in morphologically normal tissue, is comparatively limited because of the difficulty of detecting somatic mutations in normal cells. Each colorectal crypt is a small clone of cells derived from a single recently-existing stem cell. Here, we sequenced hundreds of normal crypts from 42 individuals. Signatures of multiple mutational processes were revealed, some ubiquitous and continuous, others only found in some individuals, in some crypts or during some phases of the cell lineage from zygote to adult cell. Likely driver mutations were present in ~1% of normal colorectal crypts in middle-aged individuals, indicating that adenomas and carcinomas are rare outcomes of a pervasive process of neoplastic change across morphologically normal colorectal epithelium.

  Dataset EGAD00001004193

• FASTQ files of the small RNA-Seq dataset from the POPS cohort

  Placental biopsies were collected within 30 minutes of birth and flash frozen in RNAlater(ThermoFisher). For each biopsy,total placental RNA was extracted from approximately 5 mg of tissue using the “mirVana miRNA Isolation Kit” (Ambion) followed by DNase treatment (“DNA-free DNA Removal Kit”, Ambion). RNA quality was assessed with the Agilent Bioanalyzer and all the samples with RIN values ≥ 7.0 were used in the downstream experiments. Total RNA-libraries were prepared from 300-500ng of total placental RNA with the “TruSeq Stranded Total RNA Library Prep Kit with Ribo-Zero Human/Mouse/Rat”(Illumina). Small RNA-libraries were prepared from 150ng of total placental RNA with the “NEBNext Multiplex Small RNA Library Prep Kit for Illumina”(New England Biolabs)and concentrated using the “QIAquick PCR purification kit”(Qiagen). Paired libraries were combined and size selected using the Pippin Prep and 3% Agarose Gel Cassette with marker F (Sage Science),pooled and sequenced (single-end, 50bp) using a Single End V4 cluster kit and HiSeq4000 instrument.

  Dataset EGAD00001004860

• Analysis of the B cell receptor repertoire in six immune-mediated diseases

  Our understanding of the BCR repertoire in the context of immune-mediated diseases is incomplete, and defining this could provide new insights into pathogenesis and therapy. Here, we compared the BCR repertoire in systemic lupus erythematosus, anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis, Crohn’s disease, Behçet’s disease, eosinophilic granulomatosis with polyangiitis, and immunoglobulin A (IgA) vasculitis by analysing BCR clonality, use of immunoglobulin heavy-chain variable region (IGHV) genes and—in particular—isotype use. An increase in clonality in systemic lupus erythematosus and Crohn’s disease that was dominated by the IgA isotype, together with skewed use of the IGHV genes in these and other diseases, suggested a microbial contribution to pathogenesis. Different immunosuppressive treatments had specific and distinct effects on the repertoire; B cells that persisted after treatment with rituximab were predominately isotype-switched and clonally expanded, whereas the inverse was true for B cells that persisted after treatment with mycophenolate mofetil. Our comparative analysis of the BCR repertoire in immune- mediated disease reveals a complex B cell architecture, providing a platform for understanding pathological mechanisms and designing treatment strategies.

  Dataset EGAD00001005431

• MutWP5: CRUK Mutographs of Cancer: Lung: PD38234 (Targeted) (2020-01-29)

  Sequencing of LCM-derived microbiopsies from explanted lung from pulmonary fibrosis patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Targeted sequencing will be conducted on samples to identify drivers of interest and clonality of the samples, well-performing samples will be sent for subsequent whole-genome sequencing. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2020-01-29.

  Dataset EGAD00001005925

• Somatic mutation and clonal evolution in premalignant lung disease - WGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulations of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from the lung tissue of individuals with a variety of lung diseases (COPD, UIP, IPF, Emphysema, pulmonary hypertension). This will allow us to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. Smoking is a large risk factor for developing many of these lung diseases so we are particularly keen to determining whether there is evidence of a smoking signature in these patients. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005786

• RNA-seq study of a Princess Margaret Cancer Centre human acute myeloid leukemia patient cohort

  The PMCC AML RNAseq dataset consists of 81 AML patient samples (clinical data in Supplemental Table 11 of manuscript), processed in two batches. These patient samples are able to engraft in the NSG (NOD.Cg PrkdcscidIl2rgtm1Wjl /SzJ) mouse model. Five patients (90543, 598, 90240, 110484, 100500) were included in both batches. Viaably frozen material from the Leukemia Tissue Bank at Princess Margaret Cancer Centre/ University Health Network were thawed by dropwise addition of X-VIVO + 50% fetal calf serum supplemented with DNase (100μg/mL final concentration, Roche). RNA was extracted from bulk peripheral blood mononuclear cells (PBMC) using the RNeasy Micro Kit (Qiagen Inc.). A paired-end 76 base-pair flow-cell lane Illumina High seq 2000 yielded an average of 240 million sequence reads aligning to genome per sample at the Genome Sciences Centre, BC Cancer Agency for cohort 1. Cohort 2 was subjected to 125 bp, paired-end RNA-sequencing on the Illumina HiSeq 2500 with an average of 50 million reads/sample at the Centre for Applied Genomics, Sick Kids Hospital.

  Dataset EGAD00001006576

• A108735A

  Whole genome sequencing for single cells for library A108735A 714 cells; filetype=bam

  Dataset EGAD00001007593

• WGS data for manuscript titled: Multi-omic features of oesophageal adenocarcinoma in patients treated with preoperative neoadjuvant therapy

  WGS data for manuscript titled: Multi-omic features of oesophageal adenocarcinoma in patients treated with preoperative neoadjuvant therapy

  Dataset EGAD00001008646

• Targeted myeloid panel DNA-Sequencing Mutations Matrix Validation Cohort

  Profiling of co-mutations was done by targeted resequencing using the TruSight Myeloid assay (Illumina, Chesterford, UK) covering 54 genes recurrently mutated in AML: BCOR, BCORL1, CDKN2A, CEBPA, CUX1, DNMT3A, ETV6, EZH2, IKZF1, KDM6A, PHF6, RAD21, RUNX1, STAG2, ZRSR2, ABL1, ASXL1, ATRX, BRAF, CALR, CBL, CBLB, CBLC, CDKN2A, CSF3R, FBXW7, FLT3, GATA1, GATA2, GNAS, HRAS, IDH1, IDH2, JAK2, JAK3, KIT, KRAS, MLL, MPL, MYD88, NOTCH1, NPM1, NRAS, PDGFRA, PTEN, PTPN11, SETBP1, SF3B1, SMC1A, SMC3, SRSF2, TET2, TP53, U2AF1 and WT1. For each reaction, 50 ng of genomic DNA was used. Library preparation was done as recommended by the manufacturer (TruSight Myeloid Sequencing Panel Reference Guide 15054779 v02, Illumina). Samples were sequenced paired-end (150 bp PE) on NextSeq- (Illumina) or (300 bp PE) MiSeq-NGS platforms, with a median coverage of 3076 reads (range 824–30565). Sequence data alignment of demultiplexed FastQ files, variant calling and filtering was done using the Sequence Pilot software package (JSI medical systems GmbH, Ettenheim, Germany) with default settings and a 5% variant allele frequency (VAF) mutation calling cut-off. Human genome build HG19 was used as reference genome for mapping algorithms.

  Dataset EGAD00001008506

• Alignment BAM files and gene count files of the Illumina Sequencing Data (10 tumor samples)

  Reads were processed with the RNA-seq workflow 1.3.0 developed by the DKFZ Omics IT and Data Management Core Facility (https://github.com/DKFZ-ODCF/RNAseqWorkflow). First, FASTQ reads were aligned via two-pass alignment using STAR 2.5.3a. The STAR index was generated from the 1000 Genomes assembly and GENCODE Version 19 gene models with a sjdbOverhang of 200. Duplicate marking of the resultant main alignment file was done with sambamba 0.6.5. Gene-specific read counting was performed using featureCounts (from Subread 1.5.1) over exon features based on GENCODE Version 19 gene models. Both reads of a paired fragment were used for counting, and the quality threshold was set to 255, indicating that STAR found a unique alignment. Strand-specific counting was also used. For RPKM and TPM calculations, all genes on chromosomes X and Y, the mitochondrial genome, as well as rRNA and tRNA genes were omitted as they are likely to introduce library size estimation biases.

  Dataset EGAD00001008969

• Dataset for the Pearl study

  Biomarkers to identify patients without benefit from adding everolimus to endocrine treatment in metastatic breast cancer (MBC) are needed. We report the results of the Pearl trial conducted in five Belgian centers assessing 18F-FDG-PET/CT non-response (n=45) and ctDNA detection (n=46) after 14 days of exemestane-everolimus (EXE-EVE) to identify MBC patients who will not benefit. Metabolic non-response rate was 66.6%. Median PFS in non-responding patients (using as cut-off 25% for SUVmax decrease) was 3.1 months compared to 6.0 months in those showing response (HR: 0.77, 95% CI: 0.40-1.50, p=0.44). Difference was significant when using a “post-hoc” cut-off of 15% (PFS 2.2 months vs 6.4 months). ctDNA detection at D14 was associated with PFS: 2.1 months vs 5.0 months (HR-2.5, 95% CI: 1.3-5.0, p=0.012). Detection of ctDNA and/or the absence of 18F-FDG-PET/CT response after 14 days of EXE-EVE identifies patients with a low probability of benefiting from treatment. Independent validation is needed.

  Dataset EGAD00001007979

• 10x Genomics Single Cell Gene Expression for SA604X7XB02089

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA604 passage 7

  Dataset EGAD00001009119

• 10x Genomics Single Cell Gene Expression for SA1053EX1XB01608

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1053 passage 1

  Dataset EGAD00001009123

• 10x Genomics Single Cell Gene Expression for SA1188

  10x Genomics Single Cell Gene Expression for Telomerase immortalized breast epithelium cell line 184-hTert L9 116.66

  Dataset EGAD00001009114

• 10x Genomics Single Cell Gene Expression for SA609X6XB01899

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA609 passage 6

  Dataset EGAD00001009120

• 10x Genomics Single Cell Gene Expression for SA1049AX1XB01417

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1049 passage 1

  Dataset EGAD00001009121

• 10x Genomics Single Cell Gene Expression for SA1052DX1XB01519

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1052 passage 1

  Dataset EGAD00001009122

• 10x Genomics Single Cell Gene Expression for SA1051AX1XB01452

  10x Genomics Single Cell Gene Expression for High grade serous ovarian carcinoma patient-derived xenograft SA1051 passage 1

  Dataset EGAD00001009124

• 10x Genomics Single Cell Gene Expression for SA535X6XB03099

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 6

  Dataset EGAD00001009116

• 10x Genomics Single Cell Gene Expression for SA535X4XB05462

  10x Genomics Single Cell Gene Expression for Triple negative breast cancer patient-derived xenograft SA535 passage 4

  Dataset EGAD00001009115

• 10x Genomics Single Cell Gene Expression for SA1055

  10x Genomics Single Cell Gene Expression for Telomerase immortalized breast epithelium cell line 184-hTERT-22 L9 112.109

  Dataset EGAD00001009111