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• Genome and transcriptome sequence data from a pancreatic adenocarcinoma patient

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

  Dataset EGAD00001010318

• Prostate cancer m6A epitranscriptome

  Using meRIP-sequencing, we profiled N-6 methyladenosine (m6A) in a cohort of 148 primary prostate cancer samples as part of the Canadian Prostate Cancer Genome Network project (CPC-GENE). Paired-end sequencing of 150 bp reads were mapped to GRCh38.p13 using annotations from gencode.v34.chr_patch_hapl_scaff.annotation.gtf. Peaks were called using MeTPeak, joint peaks were identified and IP reads were quantitated, normalized and adjusted using Input reads to obtain estimates of m6A abundance.

  Dataset EGAD00001009869

• Single-cell RNA-seq of patient-derived organoids across three generations

  Single-cell RNA-seq of first-, second-, and third-generation patient-derived organoids. Obtained using the 10X Genomics single-cell 3' expression solution (v3 chemistry). First- and second-generation PDOs from one patient and first-, second-, and third-generation PDOs from three additional patients.

  Dataset EGAD00001008431

• Stage-specific gene and transcript dynamics in human male germ cells

  To study global transcriptional dynamics during human spermatogenesis we sequenced total RNA of human testicular biopsies with 5 specific histological phenotypes: Sertoli cell-only (SCO, n=3), spermatogenic arrests at the spermatogonial (SPG, n=4), spermatocyte (SPC, n=3), and spermatid (SPD, n=3) level, as well as normal spermatogenesis (Normal, n=3).

  Dataset EGAD00001008652

• Targeted seqencing of serrated lesions of the colorectum

  Dataset consists of Oncomine Comprehensive Cancer Panel v3 sequencing of 16 tumor-normal mucosa pairs. Tumors include 8 sessile serrated lesions (SSL), 3 sessile serrated lesions with dysplasia (SSL/D), 2 traditional serrated adenomas (TSA) and 3 tubular adenoma s(TA).

  Dataset EGAD00001008191

• Single-nucleus Isoforms of Down Syndrome Brains (long-read)

  16 DS and Control brains samples were prepared using the 10X Genomics Single Cell 3' v3 kit. Pre-fragmented cDNA libraries were loaded onto a Pacific Biosciences Sequel II to sequence single-nucleus isoforms.

  Dataset EGAD00001008286

• Genome and transcriptome sequence data from an unspecified tissue chordoma patient

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

  Dataset EGAD00001008013

• NGS reads files of PSCCE

  The aligned bam file of next generation sequencing performed on PSCCE.

  Dataset EGAD00001007002

• Summary statistics of meta-analysis using two genome-wide association study of inflammatory bowel disease in Koreans.

  We combined samples from 1,469 inflammatory bowel disease (IBD) patients consisted of 896 Crohn’s disease (CD) and 573 ulcerative colitis (UC) and 4,041 controls used in our previously published GWAS with 1,726 additional IBD patients (725 CD and 1,001 UC) and 378 additional controls genotyped using the Asian Screening Array (ASA). We uploaded summary statistics of three meta-analyses in text files.

  Dataset EGAD00001006983

• Assessment of cannabidiol and Δ9-tetrahydrocannabiol in mouse models of medulloblastoma

  These data were used in the following publication: Andradas, C.; Byrne, J.; Kuchibhotla, M.; Ancliffe, M.; Jones, A.C.; Carline, B.; Hii, H.; Truong, A.; Storer, L.C.D.; Ritzmann, T.A.; et al. Assessment of Cannabidiol and delta9-Tetrahydrocannabiol in Mouse Models of Medulloblastoma and Ependymoma. Cancers 2021, 13, 330. https://doi.org/10.3390/cancers13020330 There are 4 paired-end RNA-seq samples from paediatric brain cancer cell lines.

  Dataset EGAD00001006923

• Whole genome sequencing of HSPCs and pAML

  Whole genome sequencing data (Illumina HiSeq and NovaSeq) of clonal cultures derived from pediatric human bone marrow-derived hematopoietic stem and multipotent progenitor cells (in total 44 samples from 10 donors) and bulk pediatric acute myeloid leukemia blasts (in total 6 samples from 6 patients) to study the mutation accumulation.

  Dataset EGAD00001006338

• AT-AML samples dataset

  Serial samples from one AT-AML patient as described in publication Goldgraben et al Pediatric Blood & Cancer 2020. Whole exome sequencing of a AT-'germline' blood sample, one bone marrow sample (at AML diagnosis) and 3 AML blood samples. Library preped using the Illumina Nextera Rapid Capture Exome Enrichment Kit, and sequenced as PE150 on HiSeq4000. Provided: 5 BAM files (GRCh37); 2 VCF analyses (germline and somatic)

  Dataset EGAD00001006090

• Genome and transcriptome sequence data from a metastatic rectal carcinoma patient

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

  Dataset EGAD00001005825

• Genome and transcriptome sequence data from an uveal melanoma patient

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

  Dataset EGAD00001005824

• Genome and transcriptome sequence data from a breast ductal carcinoma patient

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

  Dataset EGAD00001005823

• Genome and transcriptome sequence data from a pancreatic cancer patient

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

  Dataset EGAD00001005762

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

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

  Dataset EGAD00001005763

• BulkTCR-sequencing data from different human intestinal CD8 T cell subsets.

  5000 cells of each subset of CD8 T cells (CD103-KLRG1+, CD103-KLRG1- and CD103+ from LP and CD103+ IELs) were sorted into tubes. A modified SMART protocol was used in first-strand cDNA synthesis, and TCRalpha / TCRbeta genes were amplified in two rounds of semi-nested PCR reaction, following the method described in detail in Risnes et al., 2018.

  Dataset EGAD00001005049

• Genome and transcriptome sequence data from a metastatic rectal carcinoma patient

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

  Dataset EGAD00001004898

• Genome and transcriptome sequence data from a locally advanced breast cancer patient

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

  Dataset EGAD00001004897

• Whole exome sequencing and target gene sequencing of ESCC cases and healthy controls from Henan high-risk region

  In total, 186 FH+ ESCC cases were sequenced using whole-exome sequencing, then 1935 ESCC cases and 1186 geographically-matched healthy controls were sequenced using 7 Mb custom designed Roche SeqCap kit which targeted about 600 genes. The libraries were constructed and then sequenced in Illumina platform.

  Dataset EGAD00001004556

• Transcriptomic sequences of small intestinal Plasma cells from Celiac disease patients

  Transcriptomic sequences of small intestinal Plasma cell (PCs)s from Celiac disease patients. RNAseq data produced using Illumina paired-end (75bp) reads. Includes only raw data of sequences (fastq format). Samples from seven Celiac disease patients and four healthy controls. Samples from Celiac disease patients contain sub-groups of PCs that are either specific or not specific to autoantigen of the disease (TG2).

  Dataset EGAD00001004481

• WES analysis in identifying additive genetic factors that may contribute to the occurrence of moyamoya in neurofibromatosis type 1

  Five subjects from pedigree with co-occurrence of neurofibromatosis type 1 and moyamoya were sequenced in duplicate (0 and1). Kinship and phenotype: NF025, NF026 and NF027 were sibling all affected by neurofibromatosis type 1. NF026 also presented moyamoya. NF0262 and NF0263 were sibling both affected by neurofibromatosis type 1. NF0262 also presented moyamoya. NF026 and NF0262 were first cousins.

  Dataset EGAD00001004157

• The spatio-temporal evolution of lymph node spread in early breast cancer

  Set of multi-region sequenced breast cancer primary samples, lymph nodes and ctDNA. We collected samples from 11 breast cancer patients with lymph node involvement but no sign of distant metastasis. We performed a mix of whole-exome sequencing, targeted capture sequencing, and whole-genome sequencing of primary tumour samples and lymph nodes, as well as targeted capture sequencing of circulating tumour DNA.

  Dataset EGAD00001004122

• Bulk RNASeq on CD8+CD69+CD103+ and CD8+CD69+CD103- T cells form breast cancer.

  CD8+CD69+CD103+ and CD8+CD69+CD103- T cells were flow sorted from 1 primary triple negative breast cancer, 1 primary HER2 amplified breast cancer, and 1 triple negative liver metastasis. Prior to flow sorting, fresh tumor samples were digested to produce single cell homogenates on the day of surgery. Following RNA extraction, RNASeq was performed using polyA selection.

  Dataset EGAD00001003964

• Tumor-reactive heterotypic CD8 T cell clusters from clinical samples

  Emerging evidence suggests a correlation between CD8+ T cell-tumor cell proximity and anti-tumor immune response. However, it remains unknown whether these cells exist as functional clusters that can be isolated from clinical samples. By conventional and imaging flow cytometry, we show here that from 21/21 human melanoma metastases, we could isolate heterotypic clusters, comprising CD8+ T cells interacting with one or more tumor cells and/or antigen-presenting cells (APCs). Single-cell RNA-sequencing revealed that T cells from clusters were enriched for gene signatures associated with tumor reactivity and exhaustion. Clustered T cells exhibited increased TCR clonality indicative of expansion, while TCR-matched T cells showed more exhaustion and co-modulation when conjugated to APCs than to tumor cells. T cells that were expanded from clusters ex vivo exerted on average 9-fold increased killing activity towards autologous melanomas, which was accompanied by enhanced cytokine production. Upon adoptive cell transfer into mice, T cells from clusters showed improved patient-derived melanoma control, which was associated with increased T cell infiltration and activation. Together, these results demonstrate that tumor-reactive CD8+ T cells are enriched in functional clusters with tumor cells and/or APCs and that they can be isolated and expanded from clinical samples. Typically excluded by single cell gating in flow cytometry, these distinct heterotypic T cell clusters serve as a valuable source to decipher functional tumor-immune cell interactions, while they may also be therapeutically explored.

  Study EGAS50000000785

• LBC1936 fastq files

  1075 members of the LBC1936 were sequenced using the Illumina HiSeq X platform. This dataset contains the paired fastq files.

  Dataset EGAD00001008774

• WGS data of paediatric hyperdiploid B cell acute lymphoblastic leukemia (set3)

  The dataset includes FASTQ and BAM/CRAM files from diagnostic and matched remission (control) samples of eight hyperdiploid ALL patients (ALLT-364, ALLT-367, ALLT-368, ALLT-371, ALLT-377, ALLK-111, ALLT-382, ALLT-395). Sequencing libraries were made using PCR free-based WGS library preparation (PCR-), NEBNext® Ultra™ II DNA Library Prep Kit (Cat No. E7645). Both diagnostic (90x) and remission (30x) samples were sequenced using Illumina NovaSeq 6000.

  Dataset EGAD50000002427

• Oxford Nanopore Adaptive Sampling WGS

  Eight samples were analyzed on an Oxford Nanopore MinION flow-cell, and to evaluate multiplexing, four samples were run together on one flow-cell on the P2 Solo device. Using adaptive sampling, we targeted 10 regions of different structural variant types, including deletions, translocations and complex rearrangements. MinION sequencing resulted in between 14.1-18.3 Gb of data per flow cell, with mean autosomal on-target coverage of 28.4x and off-target read depth coverage of 5.3x.

  Dataset EGAD50000001821

• WGS data of paediatric ETV6::RUNX1 B cell acute lymphoblastic leukemia (set1)

  The dataset includes FASTQ and BAM/CRAM files from diagnostic and matched remission (control) samples of five ETV6::RUNX1 ALL patients (ALLT-380, ALLK-114, ALLT-388, ALLT-392, ALLT-394). Sequencing libraries were made using PCR free-based WGS library preparation (PCR-), using NEBNext® Ultra™ II DNA Library Prep Kit (Cat No. E7645). Both diagnostic (90x) and remission (30x) samples were sequenced using Illumina NovaSeq 6000.

  Dataset EGAD50000002285

• 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

• Indonesian Genome Diversity Project 3

  Low coverage whole genome sequences (1-3x) were collected to study human genetic history across Wallacea archipelago and West Papuan Region of Indonesia . We collected 254 newly sequenced genomes mostly from previously undocumented populations including 9 communities in Wallacea and 3 in West Papuan regions. Out of 254, there are 8 high coverage whole genome sequences were produce in the study (L4).

  Dataset EGAD50000000647

• 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

• 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

• BASIS miRNA data

  399 tumors profiled using Agilent miRNA microarrays (Product Number G4872A, design ID 046064). The arrays are based on miRBase release 19.0 and 2006 human miRNAs are represented. 150 ng total RNA was used as input.

  Dataset EGAD00010000917

• Neoantigen Immunogenicity Landscapes and Evolution of Tumor Ecosystems During Immunotherapy with Nivolumab

  We performed Whole Exome (WXS) and RNASeq sequencing on samples obtained from the same site before and during therapy from our prospective clinical trial (CA209-153, NCT02066636) of nivolumab in advanced Non-small cell lung cancer (NSCLC) patients that progressed on chemotherapy. There are 58 pre and 42 on therapy WXS samples and 24 pre and 12 on therapy RNASeq samples. All WXS tumor samples have matching normal samples.

  Dataset EGAD00001011302

• 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

• 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

• Molecular characterization of T-PLL by Next-Generation Sequencing

  Patients with T-cell prolymphocytic leukemia (T-PLL) were profiled with multiple OMICS approaches based on Next-Generation Sequencing (NGS). In total, data from RNA-Seq, Whole-Exome Sequencing, Whole-Genome Sequencing and amplicon panel analyses in 134 samples are available. All samples were processed as paired-end libraries on Illumina sequencing machines. The data are available as paired FastQ files.

  Dataset EGAD00001003825

• 5WGS and 35WES sample pairs belongs to COCA-CN

  There are 5WGS and 35WES sample pairs from the first affiliated hospital of kunming medical university, which belongs to ICGC projects COCA-CN.

  Dataset EGAD00001003456

• 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

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_004 for Triple negative breast cancer sample SA1035X6XB03209

  Dataset EGAD00001011243

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_002 for Triple negative breast cancer patient-derived xenograft SA1035X6XB03211

  Dataset EGAD00001011242

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_001 for Triple negative breast cancer sample SA1035X6XB03216

  Dataset EGAD00001011241

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0076_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0076_000 for Triple negative breast cancer sample SA1035X5XB03021

  Dataset EGAD00001011240

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0071_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0071_000 for Triple negative breast cancer sample SA1035X5XB03015

  Dataset EGAD00001011239

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_002 for Triple negative breast cancer sample SA1035X4XB02879

  Dataset EGAD00001011238

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0208_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0208_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03776

  Dataset EGAD00001011237

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_001 for Triple negative breast cancer sample SA535X10XB03693

  Dataset EGAD00001011236

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_002 for Triple negative breast cancer patient-derived xenograft SA535X10XB03696

  Dataset EGAD00001011235

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0195_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0195_001 for Triple negative breast cancer patient-derived xenograft SA535X8XB03664

  Dataset EGAD00001011234

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_001 for Triple negative breast cancer patient-derived xenograft SA535X9XB03616

  Dataset EGAD00001011233

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03617

  Dataset EGAD00001011232

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03434

  Dataset EGAD00001011231

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_002 for Triple negative breast cancer sample SA535X8XB03431

  Dataset EGAD00001011230

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03663

  Dataset EGAD00001011229

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_004 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011228

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_001 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011227

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03448

  Dataset EGAD00001011226