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• De_novo_mutations_in_cell_free_foetal_DNA__cffDNA_

  This project is to develop and validate a method to detect de novo mutations in a foetal genome through deep sequencing of cell-free DNA from the plasma of pregnant women.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 EGAS00001000322

• Exome sequencing of a Novel Primary T Cell Immunodeficiency Kindred (2019-08-19)

  Primary T cell immunodeficiency disorders have a heterogeneous genetic basis. This study will focus on one case characterised by severe T cell lymphopenia in the index case. We aim to sequence the complete exomes of this individual, her three unaffected siblings and parents in an effort to identify the causative genetic mutation responsible for this disorder. We will perform exome capture using Agilent SureSelect system, followed by sequencing on the HiSeq platform. Our study has the potential to uncover genes important for T cell development and novel therapeutic strategies to treat T cell immunodeficiencies. . This dataset contains all the data available for this study on 2019-08-19.

  Dataset EGAD00001005263

• Genomic and transcriptomic analysis on hepatocarcinogenesis after HCV eradication

  Little is known about the genomic and transcriptomic features of hepatocellular carcinoma developing after HCV eradication (SVR-HCC). In this study, we aimed to unveil the genomic and transcriptomic basis of hepatocarcinogenesis after SVR.

  Study JGAS000234

• RNA sequencing of high hyperdiploid and ETV6/RUNX1-positive pediatric acute lymphoblastic leukemia

  We performed RNA-sequencing of high hyperdiploid and ETV6/RUNX1-positive ALL cases by using the Human Ribo-Zero rRNA Removal Kit to investigate the gene expression in high hyperdiploid ALL.

  Study EGAS00001003079

• Targeted DNA sequencing on 37 Merkel Cell Carcinomas from New Zealand with known Merkel cell polyomavirus status

  To determine the genomic landscape of NZ virus-negative MCCs (VN-MCC), assess genomic evolution in metastases, and catalogue the distribution of TP53 and RB1 variants from international studies.

  Study EGAS00001006873

• Whole genome sequencing of adult glioblastoma nuclei

  Three primary adult glioblastoma specimens were dissociated and nuclei were extracted. A portion of the nuclei was used for single-cell ATAC seq and the remainder were submitted for whole genome sequencing, to provide orthogonal validation of copy number variations in the samples compared to single-cell ATAC seq. Samples were sequenced on the Illumina NovaSeq 6000 in paired-end mode.

  Dataset EGAD00001007651

• Gut microbiome modulates response to anti PD1 immunotherapy in metastatic melanoma patients

  There is a growing appreciation of the role of the microbiome in cancer, and evidence in pre-clinical models that the gut microbiome may modulate responses to immune checkpoint blockade though this has not been well-characterized in patients. We analyzed the oral (n=86)and gut (n=43) 16S microbiome in melanoma patients on PD-1 blockade. Significant differences were noted in the diversity and composition of the gut microbiome between responders and non-responders in patients with a fecal microbiome sample, with significantly higher alpha diversity and relative abundance of Ruminococcaceae bacteria) in R. Metagenomic studies (n=25) revealed functional differences in gut bacteria in R including enrichment of anabolic pathways. Immune profiling demonstrated enhanced systemic and anti-tumor immunity in patients with a favorable gut microbiome as well as in germ-free mice receiving fecal transplant from responding patients. Together, these data have important implications for treatment with immune checkpoint blockade in cancer. The fastq files associated with this dataset are stored at ENA under the following accession numbers: PRJEB22894 (Fecal 16S), PRJEB22874 (Oral 16S), PRJEB22895 (Murine 16S), PRJEB22893 (Fecal WGS).

  Study EGAS00001002698

• Integrative analysis of exome-seq, RNA-seq, ATAC-seq (bulk and single-cell), and Hi-C data generated from 3-D spatially mapped samples acquired during surgical resection from 10 patients diagnosed with IDH-WT glioblastoma

  Glioblastoma (GBM), the most common primary brain cancer in adults, remains incurable with no targeted therapies approved despite decades of investigation into its molecular landscape. Treatment failure is attributed to intratumoral heterogeneity in the GBM genome and epigenome, which foster tumor evolution and selection of resistant clones. However, tumor evolution and intratumoral heterogeneity remain poorly understood on the level of the whole tumor as most studies are based on single samples per patient and lack spatial context. Here, we have used 3-D neuronavigation during surgical resection for 10 primary IDH-WT GBM patients to collect 102 samples representing maximal tumor diversity, each mapped by 3-D spatial coordinates. We have applied a strategic set of genomic and epigenomic assays spanning multiple levels of resolution to discover, orthogonally validate, and functionally assess drivers of tumor evolution and intratumoral heterogeneity. These include extrachromosomal DNA amplifications, chromothripsis events, inversions, and translocations that disrupt both the GBM genome and epigenome while generating fusion transcripts and opportunities for therapeutic intervention. We define epigenomic programs that contribute to GBM evolution and intratumoral heterogeneity, revealing their 3-D spatial patterning within whole tumors and their cell type(s) of origin in single-cell data from the same tumor samples. Notably, we distinguish neuronal, glial, and immune programs aberrantly active in tumor cells from their counterparts in normal cells and discover NEUROD1, JUN/FOS, and NF1 transcription factors as key drivers of GBM evolution and growth. Collectively, these data provide unprecedented insight into GBM evolution and intratumoral heterogeneity from single-cell to whole-tumor resolution, redefining current understanding and providing a rich resource of targets for therapeutic investigation.

  Study EGAS00001006785

• Transcriptomics-driven classification of ALAL

  Acute leukemia of ambiguous lineage (ALAL) is a rare, poor-prognosis subtype of acute leukemia that cannot be assigned to a single hematopoietic lineage. Although ALAL patients are typically treated with acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL) regimens, optimal treatment choice is hindered by their lineage ambiguity. Therefore, we investigated the added value of transcriptomics for improving lineage assignment, currently based mainly on surface markers. First, we used an in-house pipeline to comprehensively detect genetic lesions in RNA-sequencing data (n=23) with a sensitivity >90%, as confirmed by targeted DNA sequencing. Second, we compared ALAL gene expression profiles (GEPs) with representative AML (n=145), B-ALL (n=223) and T-ALL (n=85) cases. In a principal component analysis, ALALs did not form a separate group, but clustered with AML, B-ALL or T-ALL. Accordingly, a machine learning classifier trained with GEPs of acute leukemias segregated 20/23 ALALs into myeloid-, B- or T-lymphoid leukemia. These 20 cases harbored genetic abnormalities consistent with the classifier-assigned leukemia. Furthermore, ALAL GEPs were deconvoluted with single-cell transcriptional profiles of normal hematopoietic cells using CIBERSORTx, revealing enrichment for signatures of lineages corresponding to the leukemic type predicted by our algorithm. The classifier was validated on an external ALAL cohort (n=24), assigning 75% of the patients to a lineage matching their immunophenotypic and methylation profiles. In conclusion, integrative analysis of RNA-sequencing data can accurately classify most ALAL cases. The pipeline and classifier developed in our study are valuable tools to improve ALAL diagnosis and guide therapeutic decisions.

  Study EGAS00001007967

• Whole-genome sequencing with complement C1s deficiency linked to systemic lupus erythematosus

  This dataset includes two FASTQ files (R1 and R2) from an Illumina germline nuclear DNA whole-genome sequencing run of a single sample. The sample corresponds to a 34-year-old male with a rare deficiency in complement component C1s, linked to systemic lupus erythematosus (SLE). Genomic analysis revealed two novel mutations in the C1S gene.

  Dataset EGAD50000000988