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• Mechanisms of Extreme Genomic Instability at Large Transcribed Genes

  Inhibiting DNA replication leads to copy number variant (CNV) formation throughout the genome, especially at chromosome fragile sites (CFSs). We previously showed that these hotspots for genome instability reside in late-replicating domains associated with large transcribed genes. In this study, we compared aphidicolin (APH)-induced CNV and CFS frequency between wild-type cells and isogenic cells in which FHIT gene transcription was ablated. We further examined the impact of altering RNase H1 expression on CNV or CFS induction frequency and analyzed R-loop formation genome-wide in a human fibroblast cell line. Data sets include Bru-seq nascent RNA transcription, whole genome sequencing for replication timing, SNP microarray analysis, and DRIP-seq. Results suggest that large gene transcription is a determining factor in replication stress-induced genomic instability and that CFSs mainly result from transcription-dependent passage of unreplicated DNA into mitosis with low R-loop levels observed at these loci.

  Study phs002066

• Impact of Genetic Polymorphisms on Human Immune Cell Gene Expression

  While many genetic variants have been associated with risk for human diseases, how these variants affect gene expression in various cell types remains largely unknown. To address this gap, the DICE [Database of Immune Cell Expression, Expression quantitative trait loci (eQTLs) and Epigenomics] project was established. Considering all human immune cell types and conditions studied, we identified cis-eQTLs for a total of 12,254 unique genes, which represent 61% of all protein-coding genes expressed in these cell types. Strikingly, a large fraction (41%) of these genes showed a strong cis-association with genotype only in a single cell type. We also found that biological sex is associated with major differences in immune cell gene expression in a highly cell-specific manner. These datasets will help reveal the effects of disease risk-associated genetic polymorphisms on specific immune cell types, providing mechanistic insights into how they might influence pathogenesis.

  Study phs001703

• METABRIC

  Solid tumors are complex tissues composed of a mixture of cancer and normal cells, which complicates the interpretation of their molecular profiles. Normal cell contamination can dilute cancer cell information and tissue architecture is generally not reflected in molecular assays. To address these challenges, we developed a computational approach based on standard Haematoxylin and Eosin-stained sections and demonstrated its power in a discovery cohort of 323 breast tumors and an independent validation cohort of 241 tumors. First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.First, to deconvolute cellular heterogeneity and detect subtle genomic aberrations, we introduced an algorithm based on tumor cellularity to increase the comparability of copy-number profiles between samples. Second, we demonstrated that a predictor for survival integrating image-based and molecular features significantly outperforms classifiers based on single data types. Third, we described and validated a novel, independent prognostic factor based on quantitative analysis of spatial patterns between stromal cells, which are not detectable by molecular assays. Our quantitative methods refine and complement molecular assays of tumor samples and could benefit all large-scale cancer studies.

  Study EGAS00000000098

• WES IDC samples

  PDX samples of breast cancer IDC mouse models created from patient tumor material of the NKI. Genomescan prepared the samples according to the procedure for Hybridization Capture using the Agilent SureSelectXT Human All Exon V7 kit. The prepared libraries were sequenced with Illumina sequencing technology. The samples are in fastq format and consist of the following PDX samples: IDC001, IDC125, IDC147, IDC200, IDC233, IDC244, IDC251, IDC272, IDC278, IDC281 and ILC120.

  Dataset EGAD50000001148

• CRISPR iPSC methods paper

  This submission is of the sequencing data used in the CRISPR iPSC methods paper. Specifically it is 3 fastq files that each represent a replicate of an experiment to transduce the Toronto KnockOut CRISPR Library - Version 3 (TKOv3) into inferred pluripotent stem cell (iPSC) derived macrophages. The sequencing is of the guide RNAs from the TKOv3 having been extracted from the transduced iPSC derived macrophages.

  Dataset EGAD00001007020

• scRNA-seq of HSPC treated with gemcitabine and carbplatin

  The study used cryopreserved hematopoietic stem and progenitor cells (HSPCs) harvested from one patient with chronic myelogenous leukemia (CML). The cells were thawed and acclimatized for 48 hours in StemMACS HSC Expansion Media XF (Miltenyi Biotec) supplemented with StemMACS HSC Expansion Cocktail (Miltenyi Biotec) at 37°C in a humidified atmosphere containing 5% CO2. 4 samples of 2000000 HSPCs in 2 ml media each were then prepared and treated for 24 hours: carboplatin-high: 150 µg/ml carboplatin carboplatin-low: 18.75 µg/ml carboplatin gemcitabine: 25 ng/ml gemcitabine control: no drug Single-cells were subsequently extracted using the ddSEQ™ Single-Cell Isolator (Bio-Rad) and later sequenced using the SureCell™ Whole Transcriptome Analysis 3' Library Prep Kit (Illumina) on the NextSeq 500 System (Illumina) using the NextSeq 500/550 High Output Kit v2.5 150 Cycles (Illumina) all following the manufacturer's instructions. FASTQ-files were then processed following the ddSeeker (Romagnoli, D., et al., BMC Genomics 2018, doi:10.1186/s12864-018-5249-x) and Drop-seq (Macosko, E.Z., et al., Cell 2015, doi:10.1016/j.cell.2015.05.002) protocols for processing scRNA-seq data to yield the final digital gene expression for each cell.

  Study EGAS00001004381

• Genomic analysis of HPV positive versus HPV negative esophageal adenocarcinoma

  High-risk human papillomavirus (hr-HPV) has been implicated in a subset of patients with esophageal adenocarcinoma (EAC). We therefore hypothesized that HPV associated EAC may have distinct genomic aberrations compared with viral negative esophageal cancer as shown in Head and Neck cancer and cervical cancer. Whole exome sequencing (WES) was performed to explore the mutational landscape and potential molecular signature of HPV positive versus HPV negative EAC, which includes four hr-HPV-positive and 8 HPV-negative treatment naive fresh-frozen EAC tissue specimens and matched normal tissues.

  Study EGAS00001001340

• Genomic Landscape of Apical Periodontitis

  We undertook the first genome-wide association (GWAS) study of apical periodontitis, enrolling 932 patients with deep carious lesions at UTHealth School of Dentistry and the University of Pittsburgh School of Dental Medicine. We used a case-control design to compare those with apical periodontitis to those without. Genotype data was generated for all participants and imputed using the TOPMed reference panel. We performed a single-variant association testing, a subphenotype analysis, and a genetically regulated expression association analysis, and generated a polygenic risk score and performed phenome-wide association analysis. We identified eight near-genome-wide significant loci for our primary outcome, eight genes where genetically regulated expression was associated with apical periodontitis status, and 33 phecodes that were associated with the generated polygenic risk score. We also replicated three loci previously reported in the literature. Genotype, phenotype, and results from the primary association analysis will be available via dbGaP.

  Study phs003252

• Uterine_Atlas_Endometriosis

  Uterine Atlas Endometriosis Endometriosis is a chronic inflammatory disease driven by oestrogen, affecting around 10% of women of child-bearing again world-wide. It is described by the presence of endometrium-like tissue outside of the uterus (ectopic endometriosis tissue) and debilitating chronic pain. There is a lack of understanding of endometriosis pathogenesis, as well as cellular composition of both the eutopic endometrium and ectopic endometriosis tissue of endometriosis patients.There are therefore two main aims for this study. Using single cell genomics and spatial methods we aim to: I) characterise endometrial cellular census in women with and without endometriosis II) characterise cellular census of ectopic endometriosis tissue Furthermore, we aim to establish endometrial organoids and endometriosis organoids from the samples collected and characterise these using single cell genomics and spatial methods. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Study EGAS00001004725

• Chromosomal copy number heterogeneity predicts survival rates across cancers

  Survival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis1,2. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing3. Here, we introduce a scalable measure of chromosomal copy number heterogeneity that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 35 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.

  Study EGAS00001004702