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• WES_dataset3

  Six EATLs and three MEITLs (among which 5 had only tumoral tissue available) were sequenced using the xgen research panels v2.0 from IDT DNA (Newark, New Jersey, USA).

  Dataset EGAD50000001618

• WES_dataset2

  Twenty-six EATLs and 22 additional cases of MEITLs (among which 5 had only tumoral tissue available) were sequenced using the xgen research panels v1.0 and v2.0 from IDT DNA (Newark, New Jersey, USA).

  Dataset EGAD50000001621

• WGS Data from 42 Multi-Region Sampled IPMN-PDACs and 12 Matched Normal Samples

  This dataset contains whole genome sequencing (WGS) data from 42 IPMN-PDAC and 12 normal samples, with tumours collected from multiple regions within each tumour to capture intra-tumour heterogeneity. Tumour and matched normal samples were sequenced to study somatic mutations, structural variants, copy number alterations, mutational signatures and clonal evolution during IPMN-PDAC progression. The sequencing was performed using Illumina NovaSeq instrument with 150 bp paired-end reads.

  Dataset EGAD50000001687

• ctDNA raw data (BAMs)

  Raw data of ctDNA profiling using the PredicineWES+ or PredicineBEACON assay in patients enrolled in divarasib phase I GO42144 study.

  Dataset EGAD50000000530

• Comparison clinical recommendation MASTER and panel sequencing: RNA data

  This dataset contains 19 case/control samples of RNA sequencing data. Sequencing was performed on Illumina HiSeq 4000 and NovaSeq 6000 using TruSeq Stranded mRNA Kit. The sequencing was always paired.

  Dataset EGAD50000000625

• RNA-Sequencing of cervical cancers

  We used RNA sequencing of 81 cervical cancer specimens to developed an immune-based gene expression signature to predict distant metastasis in cervical cancer patients treated with RT/cisplatin. Our 55-gene risk score, validated across independent cohorts, was strongly linked to higher rates of metastasis and lower survival. The score also correlated with higher copy-number alteration and a less immune-responsive tumor microenvironment, indicating its potential in identifying high-risk patients and informing targeted therapies.

  Dataset EGAD50000000120

• Single-cell RNA-seq of immune cell subsets isolated from human tumors (Kaptein et al., 2024)

  Single-cell RNA-seq profiling of effector (KLRG1+ PD1-), transitional (KLRG1+ PD1-intermediate), dysfunctional (CD39+ PD1+) and memory (IL7Rα+) CD8 T cells isolated from 4 human tumors (1 melanoma, 1 renal carcinoma and 2 ovarian carcinoma). Respective cell populations were identified and isolated using FACS. FASTQ files contain gene expression data, feature barcode antibody capture or TCR data on individual CD8 T cell subsets across the 4 different samples.

  Dataset EGAD50000000376

• OAK GO28915 Source Table

  Source data of clinical study data corresponding to figures reported in the paper titled: Anti-TIGIT antibody improves PD-L1 blockade through myeloid and Treg cells. PMID: 38418879 DOI: 10.1038/s41586-024-07121-9

  Dataset EGAD50000000368

• GO30103 Source Tables

  Source data of clinical study data corresponding to figures reported in the paper titled: Anti-TIGIT antibody improves PD-L1 blockade through myeloid and Treg cells. PMID: 38418879 DOI: 10.1038/s41586-024-07121-9

  Dataset EGAD50000000367

• Cityscape Source Tables

  Source data of clinical study data corresponding to figures reported in the paper titled: Anti-TIGIT antibody improves PD-L1 blockade through myeloid and Treg cells. PMID: 38418879 DOI: 10.1038/s41586-024-07121-9

  Dataset EGAD50000000366

• RNAseq and ATACseq data, derived from HGSC cell lines pre- and post- treatment with an epigenetic compound

  These data consist of transcriptome and chromatin accessibility data (RNAseq and ATACseq respectively) derived from five High-Grade Serous Ovarian Carcinoma (HGSC) cell lines. HGSC lines included PEO1, PEO4, PEA2, OVCAR5 and OVCAR8. Samples were taken pre- and post-treatment with a novel epigenetic compound, HKMTi-1-005, which targets the activity of two histone methyltransferases, EZH2 and G9a.

  Dataset EGAD50000000064

• Single cell sequencing on whole bone marrow of NBM and AML

  To generate a cellular taxonomy of the human NBM and AML, representing both rare hematopoietic stem/progenitor cells (HSPCs) and stromal niche populations, allowing assessment of their cellular diversity and predicted intercellular signaling, we performed single cell RNA sequencing (scRNAseq) on viably frozen bone marrow (BM) aspirates from four healthy donors and 6 NPM1+ AML patients at diagnosis

  Dataset EGAD00001011057

• Comprehensive single-cell genome analysis at nucleotide resolution using the PTA Analysis Toolbox

  Whole genome sequencing data of 57 single (PTA), clonally expanded and bulk human cells. Cells were obtained from bone marrow samples of patients with Fanconi Anemia (PMCFANCNN) or pediatric AML (PBNNNNN), from a clonal intestinal organoid line (STE0072/D-ORGWTNISL), from human cord blood (PMCCB15) and from a human lymphoblastoid cell line (PMCAHH1). WGS libraries were sequenced to ~15-30x genomic coverage (paired-end) on an Illumina Novaseq.

  Dataset EGAD00001011126

• Low-coverage whole genome methylation sequencing of cell-free DNA from healthy volunteers and allograft transplant recipients

  Low-coverage whole genome methylation sequencing of cell-free DNA (cfDNA) from healthy volunteers (n=2) and allograft transplant recipients (n=11). The cfDNA was extracted from urine and plasma and sequenced using both a single- and double-strand library preparation method (n=15 and n=18).

  Dataset EGAD00001010937

• Genome and transcriptome sequence data from a metastatic pancreatic adenocarcinoma patient

  Genome and transcriptome sequence data from a metastatic pancreatic adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001010316

• Genome and transcriptome sequence data from a pancreatic ductal adenocarcinoma patient

  Genome and transcriptome sequence data from a pancreatic ductal adenocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001010317

• Single-cell transcriptome landscape of developing fetal gonads defines somatic cell lineage specification in humans

  Single-cell RNA-seq dataset recovering the gene expression programs of around 128,000 single cells derived from 17 fetal testes and 16 fetal ovaries from 5 to 12 postconceptional weeks.

  Dataset EGAD00001009811

• A living biobank of patient-derived ductal carcinoma in situ Mouse-INtraDuctal xenografts identifies factors associated with risk of invasive progression

  Create a living biobank of patient-derived ductal carcinoma in situ (DCIS) Mouse-INtraDuctal (MIND) xenografts to find factors explaining invasive growth. Samples exist of both primary and pdx samples. Invasive growth was scored in the pdx.

  Dataset EGAD00001009336

• DS-ALL RNA-Seq

  RNA-Seq was performed on 249 DS-ALL samples. Library preparation was carried out using TruSeq Stranded Total RNA Library Prep Kit. The libraries were sequenced on a NovaSeq platform with read length of 2×101.

  Dataset EGAD00001009403

• Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models enables specific monitoring of tumour-derived extracellular RNA

  Total RNA sequencing (SMARTer Stranded Total RNA-Seq Kit v2) data of extracellular RNA (exRNA) from liquid biopsies of a BRC0004PR PDX and SK-N-BE(2C) CDX mouse model, and total RNA sequencing profiles of the matching PDX tumors.

  Dataset EGAD00001008318

• Genome and transcriptome sequence data from a poorly differentiated chordoma of C1-C2 spine patient

  Genome and transcriptome sequence data from a poorly differentiated chordoma of C1-C2 spine patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001008012

• Single-cell whole-genome sequencing of matched primary GBM tumours and patient-derived organoids

  Single-cell whole-genome sequencing of primary GBM tumours and matched patient-derived organoids. Obtained using the 10X Genomics single-cell CNV solution. Samples from two spatially distinct regions of five tumours from three patients (three primary, two recurrent).

  Dataset EGAD00001007937

• Low coverage WGS of plasma cfDNA

  This dataset includes FASTQ files of low coverage whole genome sequencing of cell free DNA from plasma samples. The samples include 271 plasma samples of patients with an adnexal mass and 125 plasma samples of healthy individuals.

  Dataset EGAD00001007748

• WES_SCLC_MDACC

  To identify genomic drivers present in limited-stage small cell lung cancer (LS-SCLC); To determine the overall tumor mutational burden in LS-SCLC; To determine genomic intratumor heterogeneity (ITH) in LS-SCLC.

  Dataset EGAD00001007004

• Profiling of H3K27ac landscape in five immune cell types from rheumatoid arthritis patients and healthy controls

  We collected peripheral blood mononuclear cells (PBMC) from 6 RA patients and 4 healthy controls, as well as synovial fluid (SF) from the same RA patients. We then sorted B cells, CD4+ and CD8+ T cells, regulatory T cells and monocytes using flow cytometry and profiled regions marked with H3K27ac using CUT&Tag.

  Dataset EGAD00001007003

• cfDNA

  The mutational status of 112 recurrently mutated genes in B-cell lymphoma was examined by targeted next-generation sequencing (NGS). Libraries were performed with 15-30 ng of cfDNA obtained from plasma using molecular-barcoded library adapters (ThruPLEX Tag-seq kit; Takara) coupled with a custom hybridization capture based method (SureSelect XT Target Enrichment System Capture strategy, Agilent Technologies Inc.) and sequenced in a MiSeq instrument (Illumina, 2x150bp).

  Dataset EGAD00001006566

• Single cell transcriptomes of in vitro differentiated hepatocyte-like cells in comparison to primary human hepatocytes

  Fastq files of deeply sequenced single cell RNA-seq data (Smartseq2, approx. 2 million reads / sample) of hepatocyte-like cells (HLC) and primary human hepatocytes (PHH). The dataset comprises data from two different in vitro differentiation protocols: Cellartis (Takara Bio, "CEL", n = 3) and as described by Wang et al. (PMID: 28287600, "HAY", n = 1), as well as PHH from 3 donors. Each replicate comprises 96 single cells.

  Dataset EGAD00001005946

• Chromatin accessibility analysis of hepatocyte-like cell in vitro differentiation from iPSC in comparison to primary human hepatocytes

  Fastq files of ATAC-seq data of induced pluripotent stem cells (iPSC), definitive endoderm (DE), hepatocyte-like cells (HLC) and primary human hepatocytes (PHH). The dataset comprises data from two different in vitro differentiation protocols: Cellartis (Takara Bio, "CEL", n = 4) and as described by Wang et al. (PMID: 28287600, "HAY", n = 1), as well as from 3 PHH donors.

  Dataset EGAD00001005934

• Next Generation Sequencing Characterization of Tregs in Human Peripheral Blood during Autoimmunity

  Tregs were sorted as CD4+CD25+CD127- cells from peripheral blood of 14 healthy individuals, 8 patients with mild/severe rheumatoid arthritis, 1 patient with systemic lupus erythematosus/rheumatoid arthritis, 2 patients with ulcerative colitis and 2 patients with Chrohn's disease. RNA was extracted and polyA libraries were prepared using the Illumina Truseq sample preparation kit v.2. Single-end 75bp sequencing was performed on NextSeq500.

  Dataset EGAD00001005446

• A Protein Neddylation Inhibitor MLN4924 Suppresses Patient-Derived Glioblastoma Cells via Inhibition of ERK and AKT Signaling

  To identify what factors cause a different reactivity to MLN4924, 15 cells were categorized into high, intermediate, and low MLN4924 resistance groups based on the half-maximal inhibitory concentration (IC50) of MLN4924. PDC1, PCD2, PDC3, PDC4, and PDC5 showed high MLN4924 sensitivity, whereas PDC12, PDC13, PDC14, and PDC15 showed low MLN4924 sensitivity. Whole-transcriptome sequencing of these 9 patient-derived glioblastoma stem cells was performed.

  Dataset EGAD00001005709

• Single cell RNA sequencing of colorectal cancer with diverse tissue dissociation protocols or fresh vs. frozen cell preparations.

  To evaluate 3 different tissue dissociation protocols or fresh vs. frozen cell preparations, we performed single-cell RNA sequencing on cancer or distant normal tissue dissociates from 2 colorectal cancer patients. Total 18,409 cells from 10 sample preparations were analyzed (5 primary colorectal cancer and 5 matched normal mucosa). The results suggest highly consistent cellular proportions were recovered with different sample preparation methods.

  Dataset EGAD00001005191

• Trimmed bam-files from whole genome sequencing data from plasma DNA

  Single-end sequencing data (trimmed to 60bp) of 104 plasma samples from donors without tumors (male=50; female=54) were merged and used to establish coverage profiles around the TSS and to establish a gene expression prediction algorithm. Dataset includes merged alignements of low coverage whole genome sequencing from plasma DNA from 50 male, 54 female non-cancer donors. Furthermore, 2 patients with metastasized breast cancer were sequenced on a NextSeq with higher depth.

  Dataset EGAD00001002254

• Genome and transcriptome sequence data from an atypical teratoid rhabdoid tumor patient

  Genome and transcriptome sequence data from an atypical teratoid rhabdoid tumor patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001655

• Genome and transcriptome sequence data from an atypical chronic lymphocytic leukemia patient

  Genome and transcriptome sequence data from an atypical chronic lymphocytic leukemia patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001656

• Genome and transcriptome sequence data from a parotid gland cancer patient

  Genome and transcriptome sequence data from a parotid gland cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001657

• Genome and transcriptome sequence data from an odontogenic ghost cell carcinoma patient

  Genome and transcriptome sequence data from an odontogenic ghost cell carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001658

• Genome and transcriptome sequence data from an appendix cancer patient

  Genome and transcriptome sequence data from an appendix cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001309

• Genome and transcriptome sequence data from a peritoneal mesothelioma patient

  Genome and transcriptome sequence data from a peritoneal mesothelioma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001310

• Genome and transcriptome sequence data from a peritoneal mesothelioma patient

  Genome and transcriptome sequence data from a peritoneal mesothelioma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001001311

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0020_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0020_002 for Triple negative breast cancer patient-derived xenograft SA604X8XB02164

  Dataset EGAD00001011254

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0019_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0019_001 for Triple negative breast cancer patient-derived xenograft SA604X7XB02089

  Dataset EGAD00001011253

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0015_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0015_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011252

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0014_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0014_001 for Triple negative breast cancer patient-derived xenograft SA604X6XB01979

  Dataset EGAD00001011251

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0164_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0164_002 for Triple negative breast cancer sample SA530X3XB03295

  Dataset EGAD00001011250

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_002 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03631

  Dataset EGAD00001011249

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0162_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0162_001 for Triple negative breast cancer sample SA1035X8XB03420

  Dataset EGAD00001011248

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0151_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0151_001 for Triple negative breast cancer patient-derived xenograft SA1035X8XB03425

  Dataset EGAD00001011247

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0162_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0162_002 for Triple negative breast cancer sample SA1035X7XB03502

  Dataset EGAD00001011246

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_002 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03340

  Dataset EGAD00001011245

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0145_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0145_001 for Triple negative breast cancer patient-derived xenograft SA1035X7XB03338

  Dataset EGAD00001011244