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• Genome and transcriptome sequence data from a unknown - likely pancreatobiliary / intrahepatic cholangiocarcinoma in liver patient

  Genome and transcriptome sequence data from a unknown - likely pancreatobiliary / intrahepatic cholangiocarcinoma in liver patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001010989

• Systemic mutagen exposures reported by normal kidney cell genomes - peripheral blood samples (NanoSeq)

  Lifestyle, environmental and other exposures to exogenous mutagens generate somatic mutations in normal human cells in vivo and increase cancer risk. However, the global repertoire of exogenous mutagen exposures is uncertain. Using single-molecule duplex sequencing of normal kidney (n=319) and blood (n=272) samples from 10 countries, we show that kidney proximal tubule cells exhibit higher mutation rates than most normal cell types despite low cell division rates. Compared to cells from kidney glomeruli, medulla, distal tubules, or peripheral blood, proximal tubule cells show marked enrichment of mutational signatures due to the exogenous carcinogenic mutagens, aristolochic acids, and of several signatures of unknown causes. The results suggest the existence of multiple, common, systemically circulated mutagens affecting human populations and indicate that the genomes of kidney proximal tubule cells report such exposures with high sensitivity.

  Dataset EGAD00001015824

• Systemic mutagen exposures reported by normal kidney cell genomes - bulk kidney samples (NanoSeq)

  Lifestyle, environmental and other exposures to exogenous mutagens generate somatic mutations in normal human cells in vivo and increase cancer risk. However, the global repertoire of exogenous mutagen exposures is uncertain. Using single-molecule duplex sequencing of normal kidney (n=319) and blood (n=272) samples from 10 countries, we show that kidney proximal tubule cells exhibit higher mutation rates than most normal cell types despite low cell division rates. Compared to cells from kidney glomeruli, medulla, distal tubules, or peripheral blood, proximal tubule cells show marked enrichment of mutational signatures due to the exogenous carcinogenic mutagens, aristolochic acids, and of several signatures of unknown causes. The results suggest the existence of multiple, common, systemically circulated mutagens affecting human populations and indicate that the genomes of kidney proximal tubule cells report such exposures with high sensitivity.

  Dataset EGAD00001015825

• Systemic mutagen exposures reported by normal kidney cell genomes - kidney cancer samples (whole-genome sequencing)

  Lifestyle, environmental and other exposures to exogenous mutagens generate somatic mutations in normal human cells in vivo and increase cancer risk. However, the global repertoire of exogenous mutagen exposures is uncertain. Using single-molecule duplex sequencing of normal kidney (n=319) and blood (n=272) samples from 10 countries, we show that kidney proximal tubule cells exhibit higher mutation rates than most normal cell types despite low cell division rates. Compared to cells from kidney glomeruli, medulla, distal tubules, or peripheral blood, proximal tubule cells show marked enrichment of mutational signatures due to the exogenous carcinogenic mutagens, aristolochic acids, and of several signatures of unknown causes. The results suggest the existence of multiple, common, systemically circulated mutagens affecting human populations and indicate that the genomes of kidney proximal tubule cells report such exposures with high sensitivity.

  Dataset EGAD00001015826

• Systemic mutagen exposures reported by normal kidney cell genomes - microdissected kidney samples (NanoSeq)

  Lifestyle, environmental and other exposures to exogenous mutagens generate somatic mutations in normal human cells in vivo and increase cancer risk. However, the global repertoire of exogenous mutagen exposures is uncertain. Using single-molecule duplex sequencing of normal kidney (n=319) and blood (n=272) samples from 10 countries, we show that kidney proximal tubule cells exhibit higher mutation rates than most normal cell types despite low cell division rates. Compared to cells from kidney glomeruli, medulla, distal tubules, or peripheral blood, proximal tubule cells show marked enrichment of mutational signatures due to the exogenous carcinogenic mutagens, aristolochic acids, and of several signatures of unknown causes. The results suggest the existence of multiple, common, systemically circulated mutagens affecting human populations and indicate that the genomes of kidney proximal tubule cells report such exposures with high sensitivity.

  Dataset EGAD00001015827

• Reference Epigenomes and Regulatory Landscape of CLL

  Analysis of the reference epigenomes and regulatory landscape of CLL as a whole and its major clinico-biological subtypes (with mutated and unmutated IGHV) in the light of the normal B-cell differentiation. We have extensively characterized the reference epigenomes of seven primary chronic lymphocytic leukemia samples (CLLs) with mutated (n=5) and unmutated IGHV (n=2) as well as several mature B-cell subpopulations (naive B cells from blood and tonsil, germinal center B cells, memory B cells and plasma cells from tonsil) using genome-wide maps of six histone marks (H3K4me3, H3K4me1, H3K27ac, H3K36me3, H3K9me3 and H3K27me3), DNA accessibility (ATAC-seq), DNA methylation (whole-genome bisulfite sequencing) and gene expression (RNA-seq). Furthermore, we have mapped the regulatory chromatin landscape of 100 additional CLL cases using chIP-seq of H3K27ac and ATAC-seq and linked these data to additional layers of information (whole-genome and/or whole-exome sequencing (WGS/WES), RNA-seq and DNA methylation microarrays) studied in the context of the International Cancer Genome Consortium (ICGC).

  Dataset EGAD00001004046

• Multi-omic single-cell profiling of peripheral blood immune cells from COVID-19 patients and controls.

  The COVID-19 pandemic, caused by SARS coronavirus 2 (SARS-CoV-2), has resulted in excess morbidity and mortality as well as economic decline. To characterise the systemic host immune response to SARS-CoV-2, we performed single-cell RNA-sequencing coupled with analysis of cell surface proteins, providing molecular profiling of over 800,000 peripheral blood mononuclear cells from a cohort of 130 patients with COVID-19. Our cohort, from three UK centres, spans the spectrum of clinical presentations and disease severities ranging from asymptomatic to critical. Three control groups were included: healthy volunteers, patients suffering from a non-COVID-19 severe respiratory illness and healthy individuals administered with intravenous lipopolysaccharide to model an acute inflammatory response. Full single cell transcriptomes are coupled with quantification of 188 cell surface proteins, and T and B lymphocyte antigen receptor repertoires.

  Study EGAS00001005465

• IYDP Indonesian Y chromosome Diversity Project

  Island Southeast Asia and Oceania host one of the world’s richest assemblages of human phenotypic, linguistic and cultural diversity. We compiled ~9.7 Mb of Y chromosome sequence from a diverse sample of over 380 men from this region, including 126 from Indonesian islands, first reported here. The granularity of this dataset allows us to fully resolve and date the regional Y chromosome phylogeny. This improved Y chromosome topology highlights localized events with important historical implications, including pre-Holocene contact between Mainland and Island Southeast Asia, ongoing interactions between Australia and the Papuan world, and a sustained period of diversification following the flooding of the ancient Sunda and Sahul continents as the insular landscape of today formed. The high-resolution phylogeny of the Y chromosome presented here thus enables a detailed exploration of past isolation, interaction and change in one of the world’s least understood regions.

  Study EGAS00001006028

• DNA methylation-based classification of sinonasal tumors [Proteomics data]

  The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent previously misclassified adenoid cystic carcinomas. The repository includes the raw mass spectrometry-based proteomics data

  Study EGAS00001006712

• DNA methylation-based classification of sinonasal tumors [DNA sequencing]

  The diagnosis of sinonasal tumors is challenging due to a heterogeneous spectrum of various differential diagnoses as well as poorly defined, disputed entities such as sinonasal undifferentiated carcinomas (SNUCs). In this study, we apply a machine learning algorithm based on DNA methylation patterns to classify sinonasal tumors with clinical-grade reliability. We further show that sinonasal tumors with SNUC morphology are not as undifferentiated as their current terminology suggests but rather reassigned to four distinct molecular classes defined by epigenetic, mutational and proteomic profiles. This includes two classes with neuroendocrine differentiation, characterized by IDH2 or SMARCA4/ARID1A mutations with an overall favorable clinical course, one class composed of highly aggressive SMARCB1-deficient carcinomas and another class with tumors that represent previously misclassified adenoid cystic carcinomas. This repository includes the results from DNA sequencing and mass spectrometry-based proteomics.

  Study EGAS00001006713