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• Comparison of single/pooled CTCs and bulk exome sequencing

  Single cell technologies allow the interrogation of tumor heterogeneity, providing insights into tumor evolution and treatment resistance. To better understand whether circulating tumor cells (CTCs) could complement metastatic biopsies for tumor genomic profiling, we characterized 11 single CTCs and 10 pooled CTC samples at the mutational and copy number aberration (CNA) levels, and compared these results with matched synchronous tumor biopsies from 3 metastatic breast cancer patients with triple-negative (TNBC), HER2-positive and estrogen receptor-positive (ER+) tumors. Similar CNA profiles and the same patient-specific driver mutations were found in bulk tissue and CTCs for the HER2-positive and TNBC tumors, whereas different CNA profiles and driver mutations were identified for the ER+ tumor, which presented two distinct clones in CTCs defined by mutations in ESR1 Y537N and TP53, respectively. Furthermore, de novo mutational signatures derived from CTCs described patient-specific biological processes. These data suggest that tumor tissue and CTCs provide complementary clinically relevant information to map tumor heterogeneity and tumor evolution.

  Dataset EGAD00001009052

• Transcriptional consequences of SETBP1 variants in developmental disorders and malignancies

  The SETBP1 gene encodes a DNA-binding nuclear protein that regulates gene expression across multiple tissues. Precise control of SETBP1 dosage is essential for normal cellular function, as both loss- and gain-of-function variants of SETBP1 can lead to severe phenotypic consequences. De novo germline gain-of-function point mutations that prolong SETBP1 half-life result in Schinzel-Giedion syndrome (SGS), an ultra-rare and severe congenital disorder associated with extensive developmental abnormalities and health complications. These SGS-associated variants cluster within a hotspot in the SKI-homology domain of SETBP1, overlapping with somatic mutations recurrently observed in myeloid leukaemia with poor prognosis. Thus far, the precise mechanisms by which these SETBP1 mutations drive disease remain largely unclear. Here, through single-cell and single-nucleus mRNA sequencing of a rare collection of primary patient samples with SGS and myeloid leukaemia, we examined the transcriptional consequences of SETBP1 gain-of-function variants in haematopoietic cells, comparing their impacts when acquired in the germline and somatically in malignancies.

  Dataset EGAD00001015482

• A practical guide for mutational signature analysis in hematological malignancies

  Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data.

  Dataset EGAD00001005028

• Genome-wide association study for Bladder Cancer Risk

  This study funded by the National Cancer Institute (NCI) involves conducting a genome-wide association study of common genetic variants to identify markers of susceptibility to bladder cancer. This bladder GWAS has led to the discovery of three novel regions in the genome associated with bladder cancer risk. Cases were defined as individuals having histologically confirmed primary carcinoma of the urinary bladder, including carcinoma in situ (ICD-0-2 topography codes C67.0-C67.9 or ICD9 codes 188.1-188.9). Scan data were obtained from two case-control studies carried out in Spain and the United States (specifically, those in the Maine and Vermont components of the New England Bladder Cancer Study) and three prospective cohort studies in Finland and the United States (specifically Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study, Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial, and The American Cancer Society Cancer Prevention Study II Nutrition Cohort). We used data from 591,637 single nucleotide polymorphisms 3,532 affected individuals (cases) and 5,119 controls of European descent and replication including 8382 cases and 48275 controls from 16 studies. In a combined analysis, we identified three new regions associated with bladder cancer on chromosomes 22q13.1, 19q12 and 2q37.1: rs1014971, (P ≈ 8 x 10-12) maps to a non-genic region of chromosome 22q13.1, rs8102137 (P = 2 x 10-11) on maps to CCNE1 and rs11892031 (P = 1 x 10-7) maps to the UGT1A cluster on 2q37.1. We confirmed four previously identified genome-wide associations on chromosomes 3q28, 4p16.3, 8q24.21 and 8q24.3 (Rothman N et al., Nature Genetics, 2010, PMID: 20972438). Through meta-analysis with the MD Anderson Texas Bladder Cancer Study (TXBCS), we also identified a novel susceptibility locus that mapped to a region of 18q12.3, marked by rs7238033 (P = 8.7 x 10(-9); allelic odds ratio 1.20 with 95% CI: 1.13-1.28) and two highly correlated SNPs, rs10775480/rs10853535 (r(2)= 1.00; P = 8.9 x 10(-9); allelic odds ratio 1.16 with 95% CI: 1.10-1.22) (Garcia-Closas M et al, Human Molecular Genetics, 2011) For NCI-GWAS2, we performed genotyping on cases and controls for the New Hampshire component of the New England Bladder Cancer Study (NEBCS-NH). For the majority of new bladder cancer cases, we genotyped only cases from four case-control studies, the Los Angeles Bladder Cancer Study (LABCS), the French Center for Research on Prostate Diseases (CeRePP), the French Bladder Study (FBCS) and the Brescia Bladder Cancer Study (BBCS). We used existing control data from four cohort studies already genotyped and subjected to rigorous quality control metrics: the European Prospective Investigation Into Cancer and Nutrition Study (EPIC), Womens Health Initiative (WHI), Health Professionals Follow-up Study (HPFS), Nurses Health Study (NHS), which have been a part of Cancer Genetic Markers of Susceptibility (CGEMS). Meta-analysis of NCI-GWAS1, NCI-GWAS2 and a previously reported GWAS TXBCS-GWAS along with taqman replication, identified two new loci: rs10936599 on 3q26.2 (P = 4.53 x 10(-9)) and rs907611 on 11p15.5 (P = 4.11 x 10(-8)). Two notable loci were also identified that approached genome-wide statistical significance: rs6104690 on 20p12.2 (P = 7.13 x 10(-7)) and rs4510656 on 6p22.3 (P = 6.98 X 10(-7)); these require further studies for confirmation (Figueroa J et al, Human Molecular Genetics, 2013).

  Study phs000346

• Effect of ETS2 modulation on chromatin accessibility and enhancer activity in inflammatory (TPP) macrophages

  This dataset consists of H3K27ac ChIP-seq and ATAC-seq in primary human macrophages following modulation of ETS2 expression. There are three datasets: H3K27ac ChIP-seq in ETS2-edited and unedited inflammatory (TPP) macrophages, H3K27ac ChIP-seq in ETS2-overexpressing and control M0 macrophages, and ATAC-seq in ETS2-edited and unedited inflammatory macrophages. For editing experiments, monocytes were transfected with CRISPR-Cas9 ribonucleoproteins containing a gRNA that was validated to edit ETS2, or a non-targeting control gRNA. Inflammatory macrophage differentiation was performed using conditions that model chronic inflammation (TPP): 3 days GM-CSF (50ng/mL) followed by 3 days GM-CSF, TNFa (50ng/mL), PGE2 (1mg/mL), and Pam3CSK4 (1mg/mL). For overexpression experiments, controlled overexpression of ETS2 mRNA or control mRNA (an equivalent amount of mRNA encoding the reverse complement of ETS2 – thereby controlling for the quantity, length and purine/pyrimidine composition of the transfected RNA but with a transcript that would not be translated) was induced in resting, non-activated (M0) macrophages by transfecting 500ng mRNA. To minimise non-specific activation due to the transfected RNA, in vitro transcription was performed using co-transcriptional capping (to minimise uncapped products), and incorporating modified, minimally immunogenic nucleotides (replacing uridine with N1-methyl-pseudouridine and cytidine with methylcytidine). After 18 hours, transfected cells were were activated with low dose LPS and harvested 6 hours later. For H3K27ac ChIP-seq, cells were harvested, cross-linked, quenched, lysed, and sheared. Immunoprecipitation of histone-DNA complexes was performed overnight at 4C with rotation using an anti-H3K27ac antibody and the SimpleChIP Plus Sonication ChIP kit (Cell Signaling Technology). Following reverse cross-linking, 50ng of immunoprecipitated DNA or input DNA were used to prepare sequencing libraries using the iDeal Library Preparation kit (Diagenode), according to manufacturer instructions. 10 PCR cycles were used for the amplification step and size selection was not performed. The quality and molarity of all libraries was assessed using a BioAnalyzer 2100 (Agilent) and the libraries were sequenced on an Illumina NovaSeq 6000. Raw data are provided as raw and aligned sequencing reads from H3K27ac-bound DNA and the input chromatin. ATAC-seq in TPP macrophages was performed using the Omni-ATAC protocol (Corces et al 2017) with the following modifications: cell number was increased to 75,000 cells, cell lysis time was increased to 5 minutes; volume of Tn5 transposase in the transposition mixture was doubled; duration of the transposition step was extended to 40 minutes. Sequencing libraries were prepared by PCR with the NEBNext Ultra II Q5Master Mix (NEB) as described previously (Corces et al 2017). Amplified libraries were cleaned using AMPure XP beads (Beckman Coulter). Sequencing reads were trimmed using TrimGalore! (Phred score 24), filtered to remove reads < 20 bp and aligned to the human genome (hg19) using Burrows-Wheeler Aligner (BWA). Aligned reads were converted to BAM files, sorted and indexed using SAMtools (Li et al, 2009). PCR duplicates and unmapped reads were removed, and the resulting BAM files were re-sorted and indexed. Raw data are provided as 100 bp paired-end Illumina reads from n = 3 donors for each experiment. BAM files are also provided.

  Study EGAS50000000109

• Whole Genome Scan for Pancreatic Cancer Risk in the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case-Control Consortium (PanScan)

  Within the framework of the NCI-sponsored Cohort Consortium, investigators from 12 prospective epidemiologic cohorts formed the Pancreatic Cancer Cohort Consortium in 2006. This study, also known as "PanScan", is funded by the National Cancer Institute (NCI) and involves conducting a genome-wide association study (GWAS) of common genetic variants to identify markers of susceptibility to pancreatic cancer. In 2007, the study was expanded to include 8 case-control studies. The study team includes scientists from the cohorts comprising the Consortium, the NCI and the Pancreatic Cancer Case Control Consortium (PanC4). PanScan I and II were conducted in 12 cohort studies and 8 case-control studies, leading to the discovery of four novel regions in the genome associated with risk for pancreatic adenocarcinoma. The third phase of PanScan (PanScan III) was conducted using recently identified incident pancreatic cancer cases drawn from fourteen cohorts from the cohort consortium, including nine prospective cohorts who participated in PanScan I, and five newly joined cohorts. The nine cohort studies that participated in PanScan I and had new genotyping of cases in PanScan III include ATBC, CPS-II, EPIC, HPFS, NHS, PHS, PLCO, SMWHS, and WHI; the five newly joined cohort studies include the Agricultural Health Study (AHS), the Multiethnic Cohort Study (MEC), the Melbourne Collaborative Cohort Study (MCCS), the Vitamins and Lifestyle Study (VITAL), and Selenium and Vitamin E Cancer Prevention Trial (SELECT). In addition to the cases from cohorts, we also included cases from the Gastrointestinal Cancer Clinic of Dana-Farber Cancer Institute Study (DFCI-GCC); from the University Hospital in Heidelberg, Germany, which is part of a larger European clinical case-control study (PANDoRA); and from clinic-based cases from eastern Spain (PANKRAS-II). The dbGaP datasets available include all subjects previously made available from PanScan I and II, plus 1,582 new incident pancreatic cancer cases of European descent from prospective cohorts, case-control studies or case series (genotyped as part of PanScan III). Also included are 61 pancreatic cancer cases and 67 control subjects from PanScan I as well as 173 pancreatic cancer cases from PanScan III of Asian ancestry from the Shanghai Men's and Women's Health Study (Supplemental Table 10, Wolpin et al. (Nat Genet, 2014)). The control population used in the analysis for the Wolpin et al. manuscript included cancer-free individuals from the prospective cohorts that contributed pancreatic cancer cases to PanScan III and controls from the Spanish Bladder Cancer SBC/EPICURO study that were previously genotyped using the OmniExpress, Omni 1M or Omni 2.5M SNP arrays. The data from these control subjects were posted to dbGaP under the GWAS in which they were initially genotyped and will not be made available in duplicate under this dbGaP study.The summary statistics for PanScan I-III were generated as detailed in Wolpin BM. et al., Genome-wide association study identifies multiple susceptibility loci for pancreatic cancer, Nature Genetics 2014; 46(9):994-1000 (https://www.nature.com/articles/ng.3052), and Klein, A. et al., Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer, Nature Communications, 2018;9(1):556 (https://www.nature.com/articles/s41467-018-02942-5). The dataset includes results from an association study of 5,117 individuals diagnosed with pancreatic ductal adenocarcinoma (PDAC) and 8,845 control individuals, or a total of 13,962 subjects of European ancestry (as compared to the genotype and phenotype information under this project that includes 9,437 individuals (PanScan I and II PDAC case and control individuals and PanScan III PDAC cases only). This is due to the fact that PanScan III &#8220;borrowed&#8221; GWAS data from control individuals genotyped separately from the PanScan GWAS project and are therefore not included as raw genotypes in phs000206.v5.p3. Association analysis was performed separately for PanScan I-II and PanScan III, followed by a meta-analysis of the two datasets. Results were filtered based on a minor allele frequency (MAF) &lt; 0.01, an imputation INFO score &lt; 0.3 and a heterogeneity P-value &lt; 1x10-10 leaving a total of 9,758,390 variants. Columns in the summary statistics dataset are as follows: ID: variant rsIDChr: chromosome numberPosition: position in the chromosome, genome build GRCh37/hg19MarkerName: variant identifierAllele1: reference alleleAllele2: alternative alleleFreq1: allele frequency for allele2FreqSE: standard error of the allele frequencyMinFreq: the minimal allele frequency across studiesMaxFreq: the max allele frequency across studiesEffect: effect size for allele2StdErr: standard errorP-value: meta-analysis p-valueDirection: summary of effect direction for each studyHetISq: I^2 statistic which measures heterogeneity on scale of 0-100%HetChiSq: chi-squared statistic in simple test of heterogeneityHetDf: degrees of freedom for heterogeneity statisticHetPVal: P-value for heterogeneity statistic

  Study phs000206

• Type 2 Diabetes Genetic Exploration by Next-generation sequencing in multi-Ethnic Samples (T2D-GENES)

  The T2D-GENES/GoT2D 13K exome sequencing study includes ~13,000 samples, half T2D cases and half T2D controls, from five ancestries (~5K Europeans, ~2K each of African-American, East-Asian, South-Asian, and Hispanic). Samples underwent deep exome sequencing, with SNVs and INDEls called according to GATK best practices; variant sites were then filtered according to the GATK best practices, and then samples and variants underwent further filtering based on aggregate genotype quality as described in Fuchsberger et al. (e.g. low call rate, excess heterozygosity for samples, low call rate or coverage for variants). Please note that one of the samples in the T2D-GENES vcf does not have phenotype data.

  Dataset EGAD00001002246

• GoT2D: Genetics of Type 2 Diabetes, a study of the the genetic architecture of type 2 diabetes

  The GoT2D study includes ~2800 samples, half T2D cases and half T2D controls, of Northern European ancestry sequenced over 3 three technologies: deep whole exome sequencing, low-pass (4x) whole genome sequencing, and OMNI 2.5M genotyping. Samples were ascertained to be phenotypically "extreme" (e.g. leaner, younger cases and older, more obese controls). Genotypes (SNVs, INDELs, and SVs) were called separately for each technology and then integrated via genotype refinement into a single phased reference panel; samples and variants were then excluded based on QC procedures described in Fuchsberger et al. Please note that 2 of the samples in the GoT2D vcf do not have phenotype data.

  Dataset EGAD00001002247

• Use of Deep Sequencing to Dectect Clonal Mutations In Sun Exposed Skin Epidermis PART2

  This study aims to define the landscape of somatic mutations in sun exposed human skin by deep sequencing, analyse their frequency and use the data to infer the effect of mutations on proliferating cell behaviour. The frequency of each mutation will reflect the size of the clone of cells in the tissue sample. By analyzing small samples, clones with as few as 100 cells will be detectable. Allele frequency distributions for each mutation will be used to infer cell fate using published methods (Klein et al. 2010). This study will shed unprecedented light on the early clonal events that lead to the emergence of cancer.

  Dataset EGAD00001000825

• Use of Deep Sequencing to Dectect Clonal Mutations In Sun Exposed Skin Epidermis

  This study aims to define the landscape of somatic mutations in sun exposed human skin by deep sequencing, analyse their frequency and use the data to infer the effect of mutations on proliferating cell behaviour. The frequency of each mutation will reflect the size of the clone of cells in the tissue sample. By analyzing small samples, clones with as few as 100 cells will be detectable. Allele frequency distributions for each mutation will be used to infer cell fate using published methods (Klein et al. 2010). This study will shed unprecedented light on the early clonal events that lead to the emergence of cancer.

  Dataset EGAD00001001090