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• Transcriptomics for IMMU-SCCHN1 cohort

  The goal of IMMUcan is to understand how the immune system interacts with tumors and how therapeutic interventions influence this interaction. This cohort includes patients with confirmed locally recurrent and/or metastatic squamous cell carcinoma of the head and neck (SCCHN) who receive first-line treatment with immune checkpoint inhibitors (ICI) or ICI combined with chemotherapy. This dataset contains transcriptomics data processed with the STAR pipeline. Two sequencing kits were employed: Roche KAPA for high-quality RNA samples and Takara SMART-Seq Stranded for samples with limited RNA quantities.

  Dataset EGAD50000002206

• Evolution of the African pygmy phenotype

  The evolutionary history of the human ‘pygmy’ phenotype (small body size), a characteristic of African and Southeast Asian rainforest hunter-gatherers, is largely unknown. Here we use a genome-wide admixture mapping analysis to identify 16 genomic regions that are significantly associated with the pygmy phenotype in the Batwa, a rainforest hunter-gatherer population from Uganda (East Central Africa). The identified genomic regions have multiple attributes that provide supporting evidence of genuine association with the pygmy phenotype, including enrichments for SNPs previously associated with stature variation in Europeans and for genes with growth hormone receptor and regulation functions. To test adaptive evolutionary hypotheses we computed the haplotype-based iHS statistic and the level of population differentiation (FST) between the Batwa and their agricultural neighbors, the Bakiga, for each SNP. Both |iHS| and FST values were significantly higher for SNPs within the Batwa pygmy phenotype-associated regions than the remainder of the genome, a signature of polygenic adaptation. In contrast, when we expanded our analysis to include Baka rainforest hunter-gatherers from Cameroon and Gabon (West Central Africa) and Nzebi and Nzime neighboring agriculturalists, we did not observe elevated |iHS| or FST values in these genomic regions. Together, these results suggest adaptive and at least partially convergent origins of the pygmy phenotype even within Africa, supporting the hypothesis that small body size confers a selective advantage for tropical rainforest hunter-gatherers but raising questions about the antiquity of this behavior.

  Study EGAS00001000908

• Long-read whole genome sequencing as a tool for variant detection in inherited retinal dystrophies

  WGS sequencing using nanopore long reads sequencing. These data is used for the diagnosis of patients with inherited retinal dystrophies.

  Study EGAS50000000846

• Health Professionals Follow-Up Study

  Health Professionals Follow-up Study (HPFS) is a prospective cohort study of men designed to evaluate hypotheses about men’s health related to diet and lifestyle to the incidence of serious illnesses, such as cancer, heart disease, and other vascular diseases. It consisted of 51,529 U.S. male health professionals who were 40-75 years old at baseline in 1986 and completed a mailed six-page baseline questionnaire. Information on age, height and weight, ancestry, medications, disease history, physical activity, lifestyle factors, and diet were gathered, with follow-up questionnaires sent biennially to update information and record health outcomes with every four years to update their diet. This datafile includes responses to the 2016 questionnaire, which is available at: https://cdn1.sph.harvard.edu/wp-content/uploads/sites/2471/2017/09/16L.pdf

  Study phs002460

• 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 “borrowed” 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) < 0.01, an imputation INFO score < 0.3 and a heterogeneity P-value < 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

• Discovery and validation of an ancillary genomic test of malignancy for primary melanocytic tumors

  Background:Pathological diagnosis of melanocytic tumors can be difficult and prone to error. More accurate and reliable ancillary investigations than those currently available are required to improve diagnostic accuracy. Goals: (1) to generate a genomic model for distinguishing benign (nevi) from malignant (melanoma) melanocytic skin tumors, developed from a customized gene panel evaluated on a cohort of 250 nevi and melanomas. (2) To evaluate the performance of the genomic model in melanocytic tumors of uncertain diagnosis/behavior. Details: The cohort of 250 melanomas and naevi was split into a discovery set and a validation set. Our genomic model obtained an area under the curve of 95% in the discovery cohort and 96% for the validation cohort. Based on a Youden index threshold, the resulting classifier shows high specificity (>95% in the discovery and validation cohorts), with sensitivities >80% in the discovery and validation cohorts. Evaluation of the genomic model on a cohort of 110 melanocytic tumors identifies key driver mutations in agreement with the likely pathway of origin of the tumors. The probability of melanoma (p=0.02) from the genomic model was significantly associated with pathology-based estimates of malignancy for melanocytic tumors lacking pathway-defining genomic aberrations (conventional tumors). The genomic model identified 8/36 (22%) conventional tumors with unresolved malignancy to have a high probability of melanoma, with 7/8 of these cases showing negative/non-aberrant results with either fluorescent in-situ hybridization, PRAME and/or p16 immunostains. Furthermore, no evidence of malignant behavior based on the genomic model was seen in 4 conventional borderline tumors with more than 1 year follow-up. Conclusion: This genomic model can become a clinically useful ancillary tool that is highly specific for differentiating melanomas from nevi.

  Study EGAS50000000887

• Dynamics of multiple resistance mechanisms in plasma DNA and their clinical implications for NSCLC patients receiving EGFR-targeted therapies

  Background:The development of resistance mechanisms to targeted therapies is complicated by underlying tumour heterogeneity. When multiple resistance mechanisms co-exist, identifying the dominant driver(s) is critical for selection of treatment. Patients and methods:We studied the relative dynamics of multiple oncogenic drivers and resistance mechanisms in 50 patients with EGFR-mutant non-small cell lung cancer (NSCLC), during treatment with gefitinib and hydroxychloroquine (NCT00809237). We performed digital PCR and targeted deep sequencing of cell-free DNA from longitudinal plasma samples from all patients, and shallow whole genome sequencing of serial samples from three patients who underwent histological transformation to small-cell lung cancer (SCLC). ResultsEGFR activating mutations were assessed in tumour samples and accurately identified in plasma samples of 95% of patients (41/43). We identified additional mutations including EGFR T790M (31/50, 62%), TP53 (23/50, 46%), PIK3CA (7/50, 14%), and PTEN (4/50, 8%), and tracked their relative levels in plasma. Patients with both TP53 and EGFR mutations detected in plasma pre-treatment tended to have worse overall survival than those where only EGFR was detected. The resistance-conferring EGFR T790M mutation was identified in 62% of the patients who progressed. Changes in relative levels of T790M and activating mutations in EGFR corresponded to clinical responses in two patients who were given sequential treatments. Patients who progressed without T790M detected in plasma had worse PFS during TKI continuation, and developed alternative resistance mechanisms, that could be identified in plasma DNA, including SCLC-associated copy number changes and TP53 mutations, that tracked with progression and response to subsequent therapies. Conclusion:Longitudinal analysis of multiple oncogenic drivers in ctDNA may help identifying dominant mechanisms of resistance to EGFR-targeted therapies, and highlight the importance of monitoring genetic events that are not direct drug targets but provide non-invasive molecular information that may guide clinical management.

  Study EGAS00001002908

• Post_Mortem_Tissue_COVID19_RNA

  Single cell analysis of post mortem tissue samples from SARS-CoV2-infected patients We aim to directly identify the human cell types infected by SARS-CoV-2 and measure the cellular response to COVID19 infection across 20 different tissues from infected patient autopsies. We have examined the expression pattern of viral entry receptors across healthy human tissues to predict several candidate target cell types across the airway and heart. In addition, high prevalence of cardiac failure and abnormal renal function in COVID19 patients implicates heart and kidney involvement, but the pathogenesis of organ specific damage - whether via a direct cytopathic mechanism or an indirect inflammatory response - remains unknown . Currently, we lack confirmation of target cell types and cellular processes in infected tissues as autopsies are discouraged in most countries due to health and safety risks. Our collaborators Drs Michael Osborn and Brian Hanley (Imperial College) have outlined guidelines to perform post-mortem in COVID19 patients (Hanley et al., 2020) and have established a programme of autopsies for research to be performed in a high-risk facility at Westminster Public Mortuary. Here, we propose to identify infected cell types and aberrant molecular pathologies in this precious tissue resource using single cell and spatial genomics. We will prioritise three organ systems: the human airway, heart and the kidney. We will directly examine the cellular identities of SARS-CoV-2 infected cell types and identify the cellular responses to infection across these organs. This fundamental knowledge will help guide future treatment choices for COVID19. 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 EGAS00001004442

• Post_Mortem_Tissue_COVID19_spatial

  Single cell analysis of post mortem tissue samples from SARS-CoV2-infected patients We aim to directly identify the human cell types infected by SARS-CoV-2 and measure the cellular response to COVID19 infection across 20 different tissues from infected patient autopsies. We have examined the expression pattern of viral entry receptors across healthy human tissues to predict several candidate target cell types across the airway and heart. In addition, high prevalence of cardiac failure and abnormal renal function in COVID19 patients implicates heart and kidney involvement, but the pathogenesis of organ specific damage - whether via a direct cytopathic mechanism or an indirect inflammatory response - remains unknown . Currently, we lack confirmation of target cell types and cellular processes in infected tissues as autopsies are discouraged in most countries due to health and safety risks. Our collaborators Drs Michael Osborn and Brian Hanley (Imperial College) have outlined guidelines to perform post-mortem in COVID19 patients (Hanley et al., 2020) and have established a programme of autopsies for research to be performed in a high-risk facility at Westminster Public Mortuary. Here, we propose to identify infected cell types and aberrant molecular pathologies in this precious tissue resource using single cell and spatial genomics. We will prioritise three organ systems: the human airway, heart and the kidney. We will directly examine the cellular identities of SARS-CoV-2 infected cell types and identify the cellular responses to infection across these organs. This fundamental knowledge will help guide future treatment choices for COVID19. 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 EGAS00001004441

• Analysis of Donor Pancreata Defines the Transcriptomic Signature and Microenvironment of Early Neoplastic Pancreatic Lesions

  The adult healthy human pancreas has been poorly studied given lack of indication to obtain tissue from the pancreas in the absence of disease and rapid postmortem degradation. We obtained pancreata from brain dead donors thus avoiding any warm ischemia time. We found that neoplastic lesions occur frequently in healthy organs, in donors as young as in their 3rd decade of life. Using the 10X Genomics Platform, we performed single cell RNA sequencing on 7 donor pancreata, 6 of which we separately sequenced tissue from the pancreas head and tail, for a total of 11 sequencing runs. Of note, because one of our runs (containing sequencing from pancreas head and tail) did not pass qc, our final analysis in our study utilized 6 donor pancreata, 5 of which contained pancreas head and tail sequenced separately. This is a robust single-cell dataset of healthy, nonpathologic pancreas tissue and includes acinar, ductal, and non-epithelial cells (myeloid cells, fibroblasts, endothelial cells, lymphocytes).Briefly, the donor pancreas was dissected by a clinically-trained pancreatobiliary surgeon. The organ was dissected into head, body, and tail, with portions of each placed into DMEM media with 1% BSA/10µM Y27632. Tissue was minced into 1mm3 pieces, then digested with 1 mg/mL collagenase P for 20-30 min at 37°C with gentle agitation. Digested tissue was rinsed three times with DMEM/1% BSA/10µM Y27632, then filtered through a 40µm mesh. Resulting cells were submitted to the University of Michigan Advanced Genomics Core for single cell sequencing using the 10x Genomics Platform. FASTQ files of all sequencing runs along with clinical annotation of donors is available through this dbGaP submission.

  Study phs003229