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• Genetics and Networks of Congenital Heart Defects

  Exome sequencing of families with Congenital Heart Defects of diverse sub-phenotypes. Comprises both parent-offspring trios for sporadic cases and multiplex families. Collaboration with David Brook, University of Nottingham. Funded by the British Heart Foundation.

  Dataset EGAD00001002251

• Whole genome sequencing generated from metastatic gliosarcoma patient samples

  This dataset contains whole genome sequencing data aligned to the b37 reference genome for 4 spatially and temporally distinct tumors from one patient with a matched normal blood sample.

  Dataset EGAD00001005745

• single-cell ATAC-seq

  Single-cell ATAC-seq data for 5 CLL samples (2 controls, 3 tumor) of the CancerEpiSys-PRECiSe project.

  Dataset EGAD00001005965

• Genome-wide data Iberian Roma

  VCF file with genome-wide data for 62 Iberian Roma samples.

  Dataset EGAD00001006358

• Whole exome sequencing datasets for Low-grade Serous Ovarian Carcinoma

  This dataset includes whole exome sequencing reads from 10 normal and 14 cell lines based on Agilent SureSelect XT Human All Exon v6. They are all 2*100bp reads sequenced using Illumina HiSeq4000.

  Dataset EGAD00001006440

• single cell RNAseq data of lung adenocarcinoma

  Single-cell RNA sequencing was performed for cells from five early-stage LUADs and fourteen multi-region normal lung tissues of defined spatial proximities from the tumors.

  Dataset EGAD00001006936

• Response labels from NABUCCO cohort 1 (NCT03387761)

  Data from NABUCCO cohort 1 (NCT03387761). This dataset includes the Response labels used for the analysis of the data. Details about the clinical definitions of Response can be found on the paper (https://doi.org/10.1038/s41591-020-1085-z)

  Dataset EGAD00001006853

• Bulk RNAseq fastq files

  RNAseq FASTq files from 15 samples from patients with ovarian cancer, 5 samples for each of the 3 tumor immune phenotypes (Infiltrated, Excluded and Desert).

  Dataset EGAD00001006975

• TPM matrix

  TPM matrices of counts from RNAseq data for IMvigor010 from bulk pre-treatment tumors.

  Dataset EGAD00001007653

• CBD-RAW-RNASEQ-MINI: Mini-bulk RNAseq data

  SmartSeq2 RNAseq data from 16 samples. For further information regarding this dataset, please contact Julian Knight and Alexander Mentzer at contact@combat.ox.ac.uk.

  Dataset EGAD00001007932

• RNA-seq

  This dataset contains the raw sequencing data (Runs) for all 10x Genomics single-cell RNA-seq Experiments.

  Dataset EGAD00001009823

• Raw data for "Spatial and temporal transcriptomics of medulloblastoma with chromothripsis identifies multiple genetic clones that resist to treatment and lead to relapse"

  FASTQ files from Illumina sequencing of 10x Visium experiments.

  Dataset EGAD00001010260

• Dataset for WES PCNSL

  WES sequencing of 23 samples from PCNSL tumor and blood control samples. Sequencing was performed on a NovaSeq 6000 and NextSeq 500 using Agilent SureSelectXT HS Human All Exon V7 and V8. Sequencing was always paired.

  Dataset EGAD00001010847

• DNA Double Strand Breaks in KMT2A-Rearranged AML patients

  DNA breakage at specific rearrangement-participating genes, which are more sensitive to a range of environmental and chemotherapeutic agents than other regions of the human genome, contributes to the generation of leukemia-causing rearrangements, and can be used as a predictor of susceptibility to cancer caused by gene rearrangements. As a direct proof-of-principle regarding the feasibility of employing fragile site breakage to identify and monitor high-risk populations to allow personalized treatments, genome-wide DNA break mapping/sequencing was used to determine whether preferential DNA breaks are generated at the major breakpoint regions of KMT2A in AML remission patients with previously diagnosed KMT2A rearrangement. We are submitting break mapping data from three patients with KMT2A rearrangements and three non-fusion patients, with and without etoposide treatments.

  Study phs002804

• Longitudinal Multi'omics of the Human Microbiome in Inflammatory Bowel Disease (IBDMDB)

  Inflammatory bowel diseases (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), affect millions of individuals worldwide. The Integrative Human Microbiome Project (HMP2) characterized a total of 132 CD, UC and non-IBD patients across a year to generate longitudinal molecular profiles of host and microbial activity during this time frame. A total of 2,965 stool, biopsy and blood specimens were produced and analyzed from this cohort. A comprehensive view of the gut microbiome's functional dysbiosis during IBD activity was derived from analyzed samples each providing a different window into disease activity. The study's resources, results and data are available through the Inflammatory Bowel Disease Multi'omics Database (IBDMDB) provide the most comprehensive description of host and microbial activities in IBD to date.

  Study phs001626

• Multimodal Mapping of the Immune Landscape in Human Pancreatic Cancer

  Pancreatic ductal adenocarcinoma (PDA) is characterized by an immune-suppressive tumor microenvironment that has rendered it largely refractory to immunotherapy. We used single-cell RNA sequencing on patient tumors and matched blood, and we uncovered a previously unappreciated, complex environment of immune-suppressive cellular interactions. These experiments reveal immune checkpoint expression heterogeneity on individual patient T cells, with corresponding ligands on neoplastic and myeloid cells. TIGIT was upregulated on tumor infiltrating CD8+ T cells and was the only checkpoint receptor consistently expressed by exhausted CD8+ T cells. Clinical annotation of the samples revealed that the exhausted signature is most prominent in patients with advanced stages of disease. Our findings suggest individualized combination therapy, involving anti-TIGIT therapies, may be necessary to extend the benefits of immunotherapy to pancreatic cancer patients.

  Study phs002071

• Single-Cell Sequencing Reveals Distinct Microenvironment Cell Types Associated with Response to High Dose Melphalan and Autologous Stem Cell Transplant in Multiple Myeloma

  The aim of this study is to understand the transcriptome of microenvironment cells in pre- and post- autologous stem cell transplantation (ASCT) samples between patients with complete response and partial response to transplant. Here, we studied immune cells from 40 multiple myeloma patients who have undergone ASCT. Samples were collected from 40 multiple myeloma patients before and after ASCT. Samples were sorted using magnetic activated cell sorting (MACS) and the non-tumor CD138- fraction underwent 3' single cell RNA-sequencing (scRNA-seq) or 5' scRNA-seq + 5' T cell receptor sequencing (TCRseq), to determine the scRNA transcriptome and TCR repertoire. This study includes 24 samples with 3' scRNA-seq, 56 samples with 5' scRNA-seq, and 56 samples with 5' TCRseq data.

  Study phs003219

• Ethiopia Genome Project (high coverage)

  The Ethiopian area stands among the most ancient ones ever occupied by human populations and their ancestors. Particularly, according to archaeological evidences, it is possible to trace back the presence of Hominids up to at least 3 million years ago. Furthermore, the present day human populations show a great cultural, linguistic and historic diversity which makes them essential candidate to investigate a considerable part of the African variability. Following the typing of 300 Ethiopian samples on Illumina Omni 1M (see Human Variability in Ethiopia project, previously approved by the Genotyping committee) we now have a clearer idea on which populations living in the area include the most of the diversity. This project therefore aims to sequence the whole genome of 300 individuals at low (4-8x) depth belonging to the six most representative populations of the Ethiopian area to produce a unique catalogue of variants peculiar of the North East Africa. Furthermore 6 samples (one from each population) will also be sequenced at high (30x) depth to ensure full coverage of the diversity spectrum. The retrieved variants will be of great help in evaluating the demographic dynamics of those populations as well as shedding light on the migrations out of Africa.

  Dataset EGAD00001000696

• Ex vivo RNA-seq in moderate COVID-19 monocytes

  This dataset was used to compare gene expression profiles of ex vivo isolated classical CD14+ monocytes from patients with moderate COVID-19 to those of healthy individuals. Blood samples were taken from patients with moderate COVID-19 admitted to hospitals in London (Hammersmith Hospital, Charing Cross Hospital, Saint Mary’s Hospital) 3-14 days after disease onset and 0-2 days after hospitalization and positive PCR, and before study treatment initiation. Moderate patients displayed mild or moderate COVID-19 pneumonia, defined as grade 3 or 4 WHO severity. Samples were collected from March 2020 to February 2021. Healthy donors were Imperial College staff with no prior diagnosis of or recent symptoms consistent with COVID-19, and where possible, were matched in age and sex distribution with COVID-19 patients. None of the participants of this study were COVID-19 vaccinated. Peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll Hypaque (GE Healthcare) gradient centrifugation <4 hours after blood collection. CD14+ monocytes were isolated using a positive selection magnetic sorting kit (StemCell Technologies, UK) from total PBMC and stimulated with vehicle, UV-inactivated SARS-CoV-2 (CoV-2). RNA was isolated using the RNeasy Micro Plus Kit (QIAGEN) following the manufacturer’s guidelines. RNA-sequencing was performed by the Oxford Genomics Centre. PolyA-enriched strand- specific libraries were prepared using NEBNext Ultra II Directional RNA Library Prep Kits (Illumina). All samples were pooled together and 150bp PE reads were sequenced on a Novaseq 6000 system.

  Dataset EGAD00001009800

• CcRCC_metabolic_heterogeneity

  Heterogeneity is a hallmark of clear cell renal cell carcinoma (ccRCC). In this study, hyperpolarized [1-13C]pyruvate MRI (HP-13C-MRI) was used to probe metabolic heterogeneity non-invasively in 6 ccRCC patients from which 58 tumor and healthy tissues biopsies were acquired postoperatively. MRI parameters were correlated with 146 metabolite concentrations and the expression of 2523 metabolic genes across 34 metabolic pathways. Using metabologram projections and pathway consensus scoring, several metabolic pathways were correlated with the metabolic MRI, including glycolysis, pentose phosphate pathway, and the TCA cycle. A simple thresholding approach applied to the imaging metrics was sufficient to provide meaningful metabolic pathway information. We also show that tissue metabolic heterogeneity increases in regions with higher pyruvate-to-lactate conversion on imaging. These results provide validation of the biological relevance of HP-13C-MRI and support the possibility of its clinical use to improve disease characterization, stratification, targeting of biopsies, and providing novel methods to monitor treatment response.

  Dataset EGAD00001015780

• Development of cell lines and mouse models of bone and soft tissue sarcoma to establish novel treatment

  Malignant giant-cell tumors are extremely rare bone sarcomas that transform from conventional giant-cell tumors during long periods of treatment. We have applied the culture methods and succeeded in establishing patient-derived organoids (PDO) of rare sarcomas. This study aimed to investigate the genomic characteristics of our established novel organoids from human malignant giant-cell tumors. The developed tumors with xenograft were genomically analyzed to compare their characteristics with those of the original tumors. Genetic changes over time throughout treatment were analyzed, and the genomic status of the established organoid was confirmed. Next generation sequencing revealed that the established organoids lacked the H3-3A G34W mutation. The xenografted organoids of the malignant giant-cell tumor had phenotypes genetically similar to those of the original tumor. The established organoids were confirmed to be derived from human malignant giant-cell tumors. In summary, the present study demonstrated a novel organoid model of a malignant giant-cell tumor that was genetically confirmed to be a malignant transformed tumor. Our organoid model could be used to elucidate the molecular pathogenesis of a malignant giant-cell tumor and develop novel treatment modalities.

  Study JGAS000618

• We performed whole-exome sequencing of 20 samples (10 actinic keratosis and 10 cutaneous squamous cell carcinoma) to investigate a potential relationship between DNA methylation-based subtypes and genetic mutation patterns (Rodriguez-Paredes et al., Nat Commun 2017)

  Cutaneous squamous cell carcinoma (cSCC) is the second most common skin cancer type and arises from keratinocytes. Most cSCC progress from a UV-induced precancerous lesion termed actinic keratosis (AK). Despite various efforts to characterize these lesions molecularly, the etiology of AK and its progression to cSCC remain only partially understood. Here we have used Infinium MethylationEPIC BeadChips to interrogate the DNA methylation status of about 850.000 CpGs in epidermal preparations from healthy skin, AK and cSCC. Importantly, we found that the premalignant AK samples displayed classical features of cancer methylomes and were highly similar to cSCC methylomes. Further analysis identified typical features of stem cell methylomes, such as a reduced DNA methylation age, non-CpG methylation and stem cell-related keratin and enhancer methylation patterns. Interestingly, this signature was detected only in one half of the AK and cSCC samples, while the other half showed keratin and enhancer methylation patterns that were more closely related to the control epidermis. These findings suggest the existence of two distinct subclasses of AK and cSCC that originate from distinct keratinocyte differentiation stages.

  Study EGAS00001002670

• TRACERx Renal 100

  Evolutionary dynamics of clear cell renal cell carcinoma (ccRCC) have not been studied prospectively or across all disease stages. Through the multi-centre prospective cohort study, Renal TRACERx Renal, we analysed 1209 primary tumour regions from 101 patients with ccRCC. We detect up to 30 driver events in a single tumour, and show that subclonal diversification is associated with known clinical parameters. Defining driver event co-occurrence, mutual exclusivity and timing at clone level, we reveal deterministic patterns of clonal evolution and disease progression. We find that ccRCC can be grouped into seven distinct evolutionary subtypes, ranging from tumours characterised by early fixation of multiple mutational and copy number driver events that can disseminate widely and rapidly; to highly branched tumours with >10 subclonal drivers and extensive parallel evolution presenting with oligometastatic disease. We identify either heterogeneity or chromosome instability, or both, as determinants of patient outcome. Our insights reconcile the variable clinical behaviour of ccRCC and the benefit of surgical intervention in early and late-stage disease; and offer potential biomarker opportunities to maximise the benefit of both surgery and active surveillance.

  Study EGAS00001002793

• MutWP4__CRUK_Grand_Challenge_Mutographs_of_Cancer__Gastric_Organoids

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001003151

• MutWP4__CRUK_Grand_Challenge_Mutographs_of_Cancer__Pancreatic_Organoids

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Study EGAS00001003520