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• LBC1921 fastq files

  297 members of the LBC1921 were sequenced using the Illumina HiSeq X platform. This dataset contains the fastq files.

  Dataset EGAD00001008775

• Pre-neoadjuvant treatment biopsy RNAseq breast cancer dataset

  This dataset contains RNAseq data of n=87 pre-treatment biopsies of triple negative and luminal- type breast cancer patients, all scheduled to receive neoadjuvant chemotherapy. Gene expression data is linked with treatment response and survival.

  Dataset EGAD00001008433

• Mixture of 4

  Mixture of 4 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008724

• Mixture of 3

  Mixture of 3 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008729

• Oropharyngeal Squamous Cell Carcinoma Mutanome

  This dataset comprises genetic variation data (as somatic indels and snvs VCFs) of 38 OPSCC tumors. WES was done using NextSeq 500 System running in 150 cycles (2x 75bp paired-end) mode. Sequence information was converted to FASTQ format using bcl2fastq v2.20.0.422. VCFs were generated using the Strelka package.

  Dataset EGAD00001009167

• Differential methylation positions

  This dataset includes an analyzed DMP file that provide the information about differential methylation positions based on Illumina Infinium MethylationEPIC BeadChip. All samples (5 lung cancer cases vs. 5 benign lung disease controls) were obtained from bronchial washings at the site of the lesion under bronchoscopy manipulation. Of the five lung cancer cases, 3 are adenocarcinoma and 2 are squamous carcinoma.

  Dataset EGAD00001010147

• RNA sequencing

  RNAseq of 45 high-grade serous ovarian cancer tumour samples. Libraries were generated using the NEB Ultra II Directional RNA library Prep kit with polyA enrichment. Libraries were sequenced as paired-end 50 or 100bp on an Illumina NextSeq or NovaSeq.

  Dataset EGAD00001009844

• Analyses of RNAseq data from NEN patient derived tumor organoids (PDTOs) and matched parental tumors

  The dataset includes RNA-seq expression R data, RNA-seq gene counts matrix, and RNA-seq gene FPKM matrix from 21 NEN tumors or metastases and matched PDTOs. The sequencing was done with either a Nextseq 2000 or a NovaSeq 6000 instrument.

  Dataset EGAD00001009994

• Molecular analysis of cancer genomes in children with Lynch syndrome: exploring causal associations WGS

  Molecular analysis of cancer genomes in children with Lynch syndrome: exploring causal associations WGS

  Dataset EGAD00001010042

• 340 human whole genome sequences from Angola and Mozambique

  CRAM files of 340 human genomes from Angola and Mozambique. Paired-end reads were generated in an Illumina-X Ten and were mapped against the human reference genome build hg19/GRCh37. More details about the sequencing and the samples in Tallman et al. 2023. Nature Communications.

  Dataset EGAD00001011992

• Geographic variation of mutagenic exposures in kidney cancer genomes – sequence data (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – sequence data (Mutographs)

  Dataset EGAD00001012102

• Single-Cell Mitochondrial Variant Profiling via TAMITO-seq

  This dataset focuses on mitochondrial variant detection using the TAMITO-seq protocol. A pre-designed mitochondrial panel (https://designer.missionbio.com/catalogpanels/Virtual-mtDNA) was spiked into the scTAMseq workflow targeting 200 CpGs and 167 genotyping loci. Data were generated from a peripheral blood sample of a healthy donor, donor X.2, multiplexed with data from another donor.

  Dataset EGAD00001015494

• Multi-Omic Investigation of Beckwith-Wiedemann Syndrome Wilms Tumor

  Wilms’ Tumor (WT), also known as nephroblastoma, is one of the most common malignant kidney tumors diagnosed in children. One of the most common WT predisposition syndromes is Beckwith-Wiedemann Syndrome (BWS, OMIM: 130650), which affects approximately 1/10,000 live births. In this study, we perform Whole Exome Sequencing (WES), DNA methylation array, and messenger RNA-Sequencing (mRNA-Seq), to examine the genomic, methylomic, and transcriptomic signature of BWS WT. We analyzed six tumor-normal paired samples, three BWS tumor only samples, and four non-BWS control samples to identify BWS WT drivers.

  Study phs002769

• Assessment of Complex Chromosomal Changes in De-Identified Cell Lines

  In this study, we obtained DNA from the Coriell Repository from individuals with complex chromosomal changes. The database ("Chromosome Abnormalities") of these de-identified samples contains 1,366 individuals as per https://www.coriell.org/1/NIGMS. We started with 16 individuals in this pilot study that we will hopefully expand out in a potential large NIH grant using information derived in this pilot study. De-identified samples were sequenced using internal funding from Washington University in St. Louis. A description of the sequencing we did on these individuals is provided in our medRxiv preprint at https://www.medrxiv.org/content/10.1101/2025.03.28.25324850v1.

  Study phs004000

• Whole genome sequencing data of paediatric KMT2A-rearranged acute lymphoblastic leukemia

  Pediatric KMT2A-rearranged leukemia is an aggressive subtype of leukemia commonly seen in infants (less tha 1 year old) and young children. It involves rearrangements of the KMT2A gene (formerly MLL) on chromosome 11q23, resulting in fusion genes that drive uncontrolled cell growth. This subtype is most often found in acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) and is associated with a poor prognosis. In infants, the 5-year event-free and overall survival rates are around 35–40%. Despite intensive chemotherapy, outcomes remain challenging, prompting ongoing research into targeted therapies and novel treatment strategies.

  Study EGAS50000001079

• Mind the gap: the relevance of the genome reference to resolve rare and pathogenic inversions

  Chromosomal inversions (INV) are particularly challenging to detect due to their copy-number neutral state and association with repetitive regions. Inversions represent about 1/20 of all balanced structural chromosome aberrations and can lead to disease by gene disruption or altering regulatory regions of dosage sensitive genes in cis. Short-read genome sequencing (srGS) can only resolve ~70% of cytogenetically visible inversions referred to clinical diagnostic laboratories, likely due to breakpoints in repetitive regions. Here we study twelve inversions by srGS (n=3) or long read genome sequencing (lrGS) (n=9)

  Study EGAS50000000436

• RNA sequencing data of pediatric KMT2A-rearranged acute lymphoblastic leukemia

  Pediatric KMT2A-rearranged leukemia is an aggressive subtype of leukemia commonly seen in infants (less tha 1 year old) and young children. It involves rearrangements of the KMT2A gene (formerly MLL) on chromosome 11q23, resulting in fusion genes that drive uncontrolled cell growth. This subtype is most often found in acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) and is associated with a poor prognosis. In infants, the 5-year event-free and overall survival rates are around 35–40%. Despite intensive chemotherapy, outcomes remain challenging, prompting ongoing research into targeted therapies and novel treatment strategies.

  Study EGAS50000001799

• Progression to AML is predictable and distinct from age related clonal hematopoiesis

  To examine the occurrence of somatic mutations prior to the development of acute myeloid leukemia, we undertook deep ECS (estimated limit of detection 1 mutation in 2500 cells) of 261 AML associated genes in 96 pre-AML cases and 420 age and gender-matched healthy controls of an average of 3853 unique DNA molecules covering each of the 1.19M bases spanning the target gene panel. The findings suggest that the development of clonal hematopoiesis and accumulation of somatic mutations is distinct and accelerated in pre-AML cases compared to healthy individuals

  Study EGAS00001002570

• Nala TAS-LRS PGx Study

  Sequencing was performed on the PromethION Flow Cell R10 (M version) using the P2 Solo Sequencer (MinION release 24.02.16). The library was divided into three portions and loaded in a tapered manner across three time points. At the first time point, the initial portion of the library was loaded, and sequencing began in whole-genome mode (without adaptive sampling), to monitor QC (N50 within the expected range). After one hour, adaptive sampling was activated. A custom BED file was used to define the target regions (N=326). Each region was extended by 20 kb upstream and downstream, and overlapping regions were merged into non-redundant intervals using bedtools43, covering a total of 1.3% of the human genome. At the second time point (20–24 hours after sequencing began, or when fewer than 2000 pores remained active), the flow cell was washed following ONT’s Flow Cell Wash Kit protocol, and the second portion of the library was loaded. At the third time point (40–48 hours), the second portion of the library was retrieved, and merged with the third portion. The flow cell was washed and reloaded with the mixed library. POD5 files were basecalled using Dorado v0.8.1 in super-accuracy (SUP) mode with the dna_r10.4.1_e8.2_400bps_SUP@v5.0.0. Reads from the initial 1 h Whole Genome Sequencing (WGS) and subsequent 72 h adaptive sampling were filtered for Q-scores >10. Passing reads were demultiplexed using Dorado’s demux function, then combined per sample and mapped to the GRCh38 reference genome using Minimap2 v2.22. Files were converted to CRAM using samtools.

  Dataset EGAD50000001609

• MutWP4: CRUK Grand Challenge Mutographs of Cancer: Gastric Organoids (2019-08-07)

  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. . This dataset contains all the data available for this study on 2019-08-07.

  Dataset EGAD00001005234

• A Tumour Organoid Biobank for Mapping Cancer Cell Vulnerabilities - TGS

  Targeted gene sequencing data from cancer patient-derived organoids and their matched tumour tissue samples. Part of a study to include genomic and transcriptomic sequencing data, along with CRISPR screening data from cancer patient-derived organoids and matched tumour tissues. Some organoids were developed as part of the Human Cancer Models Initiative (HCMI) in collaboration with Cancer Research UK (CRUK). The study includes organoids derived from colorectal, oesophageal, ovarian, pancreatic, and stomach cancer samples.

  Dataset EGAD00001015468

• A Tumour Organoid Biobank for Mapping Cancer Cell Vulnerabilities - RNA

  RNA sequencing data from cancer patient-derived organoids. Part of a study to include genomic and transcriptomic sequencing data, along with CRISPR screening data from cancer patient-derived organoids and matched tumour tissues. Some organoids were developed as part of the Human Cancer Models Initiative (HCMI) in collaboration with Cancer Research UK (CRUK). The study includes organoids derived from colorectal, oesophageal, ovarian, pancreatic, and stomach cancer samples.

  Dataset EGAD00001015470

• Genomics Define Malignant Transformation in Myeloma Precursor Conditions

  Multiple myeloma (MM) is consistently preceded by monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). While these precursor conditions are asymptomatic, they are not entirely benign and carry a lifelong risk of progression to MM. Unlike other cancers defined by pathology, malignant transformation from MGUS or SMM to MM has so far relied on demonstration of clinical end-organ damage as morphology and cytogenetics cannot reliably distinguish them. In this study, using genomic data from 374 patients with MGUS or SMM (277 training, 97 validation), to our knowledge, we demonstrate for the first time the ability to identify malignant transformation in MGUS and SMM. We introduce the concept of genomic MM and genomic MGUS to differentiate the subsets of MGUS and SMM that are biologically malignant with genomic features indistinguishable from MM from the subset that is premalignant and unlikely to progress to malignancy. Importantly, we find that most SMM has biological features of malignant transformation indistinguishable from MM. As expected, this subset that we consider having genomic MM is associated with a high risk of progression to MM although some patients remained progression-free beyond 5 years. Conversely, 60% of MGUS and 10% of SMM have no evidence of malignant transformation (genomic MGUS), with no progression during follow-up. Integration of genomic features with the 2/20/20 International Myeloma Working Group model significantly improved the prediction of progression among genomic MM. These findings support the use of genomic criteria to refine the classification and the risk stratification in myeloma precursor conditions.

  Dataset EGAD00001015768

• Single cell transcriptional consequences of leukaemogenic SETBP1 mutations

  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, we examined the transcriptional consequences of _SETBP1 gain-of-function variants in haematopoietic cells from natural variation.

  Dataset EGAD00001015829

• CIDR: The Role of Rare Coding Variation in Prostate Cancer in Men of African Ancestry - RESPOND Project 2

  In RESPOND Project 2, we seek to identify rare genetic factors that are associated with prostate cancer (PCa) risk and aggressiveness in men of African ancestry (AA). We will conduct exome sequencing of 15,000 prostate cancer cases and 5,000 controls from the RESPOND cohort and the African Ancestry Prostate Cancer Consortium (AAPC) with cases selected based on risk categories: high-risk (stage T3/T4 or Gleason 8+ or PSA>20 ng/ml), intermediate-risk (stage T2b/T2c or Gleason 7 or PSA 10-20 ng/ml) and low-risk disease (stage T1/T2a and Gleason ≤ 6 and PSA<10 ng/ml). We expect the findings from this Project to significantly advance knowledge of susceptibility to aggressive PCa and racial/ethnic disparities in PCa risk, and to guide the development of future preventive, early detection and prognostic measures for AA men. The first phase of the study will include exome sequence data for approximately 7,500 cases and 5,000 controls from AAPC. The second phase of the study will include exome data for 5,000 cases from RESPOND and AAPC.ACKNOWLEDGMENTS and CONTRIBUTING SITES Multiethnic Cohort (MEC): The MEC and the genotyping in this study were supported by National Institutes of Health (NIH) grants CA63464, CA54281, CA1326792, CA148085, and HG004726. Cancer incidence data for the MEC and Los Angeles Study of Aggressive Prostate Cancer (LAAPC) studies have been collected by the Los Angeles Cancer Surveillance Program of the University of Southern California with federal funds from the National Cancer Institute (NCI)/NIH/Department of Health and Human Services (DHHS) under Contract No. N01-PC-35139, and the California Department of Health Services as part of the state-wide cancer reporting program mandated by California Health and Safety Code Section 103885, and grant 1U58DP000807-3 from the Centers for Disease Control and Prevention (CDC). Ghana Prostate Study (GPS): The Ghana Prostate Study was funded by the Intramural Program of the NCI/NIH/DHHS under Contract No. HHSN261200800001E. Men of African Descent and Carcinoma of the Prostate (MADCaP): We thank all MADCaP study participants. This work is a product of the MADCaP network. This work was supported by NCI/NIH grant U01CA184374 to Timothy Rebbeck and National Institute of General Medical Sciences (NIGMS) MIRA grant R35GM133727 to Joseph Lachance. Additional funding includes a seed grant from the Integrated Cancer Research Center at Georgia Institute of Technology. UGANDA: The UGANDA study was supported by NIH grant R01CA165862. Southern Community Cohort (SCCS) is funded by NIH grant CA092447. SCCS sample preparation was conducted at the Epidemiology Biospecimen Core Lab that is supported in part by the Vanderbilt Ingram Cancer Center (CA68485). Data on SCCS cancer cases were provided by Alabama Statewide Cancer Registry, Kentucky Cancer Registry, Office of Cancer Surveillance at Tennessee Department of Health, Florida Cancer Data System, Central Cancer Registry at North Carolina Division of Public Health, Georgia Comprehensive Cancer Registry, Louisiana Tumor Registry, Mississippi Cancer Registry, South Carolina Central Cancer Registry, Virginia Cancer Registry at Virginia Department of Health, and Cancer Registry at Arkansas Department of Health. The Arkansas Central Cancer Registry is fully funded by a grant from National Program of Cancer Registries (NPCR)/Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the Mississippi Cancer Registry which participates in the NPCR/CDC. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. Karuprostate: The Karuprostate study was supported by the French National Health Directorate and by the Association pour la Recherche sur les Tumeurs de la Prostate. Séverine Ferdinand, Marc Romana. The North Carolina - Louisiana Prostate Cancer Project (PCaP) is carried out as a collaborative study supported by the Department of Defense contract DAMD 17-03-2-0052. The authors thank the staff, advisory committees and research subjects participating in the PCaP study for their important contributions. We would also like to acknowledge the UNC BioSpecimen Facility and the LSUHSC Pathology Lab for our DNA extractions, blood processing, storage and sample disbursement (https://genome.unc.edu/bsp). Gene-Environment Interaction in Prostate Study (GECAP) was supported by NIH grant ES011126. King County Prostate Cancer Study (KCPCS) was supported by NIH grants CA056678, CA082664, and CA092579, with additional support from the Fred Hutchinson Cancer Research Center. We thank the participants in these studies, and Ms. Suzanne Kolb for help with study management. The Los Angeles Study of Aggressive Prostate Cancer (LAAPC) was funded by grant 99-00524V-10258 from the Cancer Research Fund, under Interagency Agreement #97-12013 (University of California contract #98-00924V) with the Department of Health Services Cancer Research Program. Cancer incidence data for the MEC and LAAPC studies have been collected by the Los Angeles Cancer Surveillance Program of the University of Southern California with Federal funds from the NCI/NIH/DHHS under Contract No. N01-PC-35139, and the California Department of Health Services as part of the state-wide cancer reporting program mandated by California Health and Safety Code Section 103885, and grant 1U58DP000807-3 from the CDC. Prostate Cancer Studies at MD Anderson (MDA) was supported by grants CA68578, ES007784, DAMD W81XWH-07-1-0645, and CA140388.

  Study phs002637