7342 results for "canc*"

in 38.21 milliseconds.

• UROMOL 2020 - RNA-seq data validation

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed total RNA-seq data from 47 patients used for validation. Sequencing of total RNA was performed using KAPA RNA HyperPrep Kit with RiboErase HMR (Roche). RNA input was 100 to 500 ng. The dataset is composed of 94 fastq files.

  Dataset EGAD00001006967

• RNAseq of resident memory T cells from human lung tumor

  This dataset includes the RNA sequencing of 14 samples. Samples are FACS sorted CD8+ T cells expressing or not the integrin CD103. The paired samples (TRM and non-TRM) were sorted from the tumor of 7 lung cancer patients.

  Dataset EGAD00001006812

• Colorectal cancer cells possess an equipotent capacity to enter a developmental pausing-like state to survive chemotherapy

  Cancer cells enter a reversible drug tolerant persister (DTP) state to evade death from both chemotherapies and targeted agents. It is increasingly appreciated that the DTP state is an important driver of therapy failure and tumor relapse. We combined cellular barcoding and mathematical modeling in patient-derived colorectal cancer xenograft models to identify and characterize the cancer cells capable of generating DTPs in response to standard-of-care chemotherapy. Barcode analysis revealed no loss in clonal complexity of tumors that entered the DTP state and recurred following treatment cessation. Our data fits a mathematical model in which all cancer cells, and not a small subpopulation, possess an equipotent capacity to enter the DTP state. Mechanistically, we determined that DTPs display remarkable transcriptional and functional similarities to diapause, a reversible state of suspended embryonic development triggered by unfavorable environmental conditions. Our study provides new insights into how cancer cells use a developmentally conserved mechanism to drive the DTP state pointing to novel therapeutic opportunities to target diapause-like DTPs.

  Dataset EGAD00001006849

• Comprehensive RNA repository of tissue and plasma from patients with esophageal cancer or precursor lesions

  A comprehensive RNA repository (both coding and non-coding) from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy esophageal and disease tissue sample were collected. This dataset includes both mRNA and small RNA sequencing data (fastq.gz files) of all tissue and plasma samples. In total,102 RNA-seq libraries from 51 samples (17 plasma and 34 tissue samples) were sequenced (plasma mRNA libraries were sequenced twice).

  Dataset EGAD00001006857

• Targeted sequencing of Human esophageal epithelium microbiopsies

  NOTCH1 mutant clones occupy the majority of normal human esophagus by middle age, but are comparatively rare in esophageal cancers, suggesting NOTCH1 mutations may promote clonal expansion but impede carcinogenesis. Here we test this hypothesis. Visualizing and sequencing NOTCH1 mutant clones in aging normal human esophagus, reveals frequent biallelic mutations that block NOTCH1 signaling.  In mouse esophagus, heterozygous Notch1 mutation confers a competitive advantage over wild type cells, an effect enhanced by loss of the second allele.  Notch1 loss alters transcription but has minimal effects on epithelial structure and cell dynamics. In a carcinogenesis model, Notch1 mutations were less prevalent in tumors than normal epithelium. Deletion of Notch1 reduced tumor growth, an effect recapitulated by anti-NOTCH1 antibody treatment.  We conclude that Notch1 mutations in normal epithelium are beneficial as wild type Notch1 promotes tumor expansion. NOTCH1 blockade has therapeutic potential in esophageal squamous tumors.

  Dataset EGAD00001006969

• Clinical phenotype data

  Clinical data corresponding to the patients with ovarian cancer studied using scRNA-seq and bulk RNA-seq. Variables include molecular subtype, predicted immune phenotype, reviewing pathologist comment, final immune phenotype, histology characterization, and tumor stage.

  Dataset EGAD00001006973

• scRNA-seq using 10X Genomics platform

  Matrices of counts from single-cell RNA-seq data for 15 samples from patients with ovarian cancer, 5 samples for each of the 3 tumor immune phenotypes (Infiltrated, Excluded and Desert). Dissociated cells from each tumor sample have been sorted to isolate live cells from the 3 compartments: tumor, immune and stromal. Each of the compartments has been analyzed separately by scRNAseq, excluding some desert tumors for which cells from the stromal and immune compartments have been pooled. Sequencing was performed using 10X Genomics Chromium Single Cell platform (v2 Chemistry).

  Dataset EGAD00001006974

• 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

• HER2-negative primary breast cancers WES

  Temporal HER2-negative breast cancers WES

  Dataset EGAD00001006980

• Breast Cancer TNBC Single-Cell RNA-Seq Dataset

  Single-cell RNA-Sequencing of five TNBC primary breast cancers from Wu et al. (2020) EMBO J study. Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq 500 platform.

  Dataset EGAD00001006981

• Single-cell RNA-seq data from metastatic ovarian cancer for quality control study

  This study includes treatment-naïve fresh tissue sample from 4 HGSOC patients.

  Dataset EGAD00001006984

• Single-cell gene expression data from CD8+ T cells from two Austrian COVID19 patients stimulated with wildtype and mutant SARS-Cov-2 peptides

  The data contains single-cell gene sequencing data (10x Genomics) from FACS-purified CD8 T lymphocytes from two Austrian patients. The cells were stimulated with one MHC class I peptides obtained from a common (wild type) variant and an emerging mutant variant of the SARS-Cov-2 virus. Then the samples were multiplexed using hashtag oligos. We provide the raw and aligned sequence data for: i. The single-cell experiments ii. The PCR-amplified samples for enrichment of the hashtag oligo multiplexing barcodes iii. The PCR-amplified samples for enrichment of the T Cell Receptor (TCR) VDJ region for immuno-profiling. The samples and libraries were processed and obtained in collaboration between St. Anna Children's Cancer Research Institute (CCRI), CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, and the Medical University of Vienna. The cell barcodes and processed data has been submitted to the GEO database with GEO accession GSE166651.

  Dataset EGAD00001006995

• 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

• RNA-sequencing of primary urothelial bladder cancer samples

  Paired-end RNA-sequencing of tumour tissue samples (n=85) from primary urothelial bladder cancer patients. Sequencing was performed using either HiSeq (n=27) or NextSeq (n=58) Illumina platforms. Of the 85 samples, 78 are Non-muscle invasive (NMIBC) and 7 are Muscle invasive (MIBC).

  Dataset EGAD00001007005

• hereditary BrEAst Case CONtrol study (BEACCON)

  The BEACCON study aimed to address the lack of power of previous studies to identify novel BC predisposition genes by performing extensive sequencing in 12,000 women (11,511 analysed following exclusions) and further enhancing power by using an ‘extreme phenotype’ design with enrichment of familial non-BRCA1 and BRCA2 cases, compared with a control population of older women with ongoing confirmation of cancer-free status at June 2019. Three-quarters of the 1303 candidate genes screened were selected based on empiric evidence from local (69 multi-case BC families) or international whole exome sequencing studies, and the remainder were included to provide detailed coverage of functional pathways with established associations with BC.

  Dataset EGAD00001007025

• Substantial somatic genomic variation and selection for BCOR mutations in human induced pluripotent stem cells

  Human Induced Pluripotent Stem Cells (hiPSC) are an established patient-specific model system where opportunities are emerging for cell-based therapies. We compared and contrasted hiPSCs derived from different tissues, skin and blood, in the same individual. We show extensive single-nucleotide mutagenesis in all hiPSC lines, although fibroblast-derived hiPSCs (F-hiPSCs) are particularly heavily mutagenized by ultraviolet (UV)-related damage. We utilized genome sequencing data on 454 F-hiPSCs and 44 blood-derived hiPSCs (B-hiPSCs) to gain further insights. Across 324 whole genome sequenced (WGS) F-hiPSCs derived by the Human Induced Pluripotent Stem Cell Initiative (HipSci), UV-related damage is present in ~72% of cell lines, sometimes causing substantial mutagenesis (range 0.25-15 per Mb). Furthermore, we find remarkable genomic heterogeneity between independent F-hiPSC clones derived from the same reprogramming process in the same donor, due to oligoclonal populations within fibroblasts. Combining WGS and exome-sequencing data of 452 HipSci F-hiPSCs, we identify 272 predicted pathogenic mutations in cancer-related genes, of which 21 genes were hit recurrently three or more times, involving 77 (17%) lines. Notably, 151 of 272 mutations were present in starting fibroblast populations suggesting that more than half of putative driver events in F-hiPSCs were acquired in vivo. In contrast, B-hiPSCs reprogrammed from erythroblasts show lower levels of genome-wide mutations (range 0.28-1.4 per Mb), no UV damage, but a strikingly high prevalence of acquired BCOR mutations in ~57% of lines, indicative of strong selection pressure. All hiPSCs had otherwise stable, diploid genomes on karyotypic pre-screening, highlighting how copy-number-based approaches do not have the required resolution to detect widespread nucleotide mutagenesis. This work strongly suggests that models for cell-based therapies require detailed nucleotide-resolution characterization prior to clinical application.

  Dataset EGAD00001007029

• Cryopreservation of human cancers conserves tumour heterogeneity for single-cell multi-omics analysis

  Single-cell RNA-Sequencing of three primary breast cancers, two primary prostate cancers, and a metastatic melanoma sample from Wu et al. (2021) Genome Medicine study. Each tumour was sequenced across different cryopreservation conditions including Fresh Tissue (FT), cryopreserved single-cell suspensions (CCS), cryopreserved solid tissue fragments (CT) and a cryopreserved after overnight cold storage (CO). Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq platform.

  Dataset EGAD00001007030

• Molecular profiling of tissue autopsies and ctDNA

  The dataset contains high-throughput sequencing data derived from a cancer autopsy series of 10 patients. As part of this study, whole-exome sequencing and RNA-seq were performed for spatially distinct tissue biopsies from the patients. In addition, plasma samples from the patients were sequenced using a custom panelt to profile ctDNA. There are 106 files containing whole-exome sequencing data, 107 files containing RNA-seq data, and 9 files containing plasma sequencing data.

  Dataset EGAD00001007040

• bladder cancer patient data

  This contains H3K27ac ChIP-seq, RNA-seq and HiC fastq files.

  Dataset EGAD00001007052

• Early breast cancer (WGS)

  Low-coverage whole genome sequencing of 29 early breast cancer samples.

  Dataset EGAD00001007056

• Early breast cancer (WES)

  Whole-exome sequencing of 30 early breast cancer samples.

  Dataset EGAD00001007057

• CITE-seq of early breast cancer

  CITE-seq of early breast cancer samples.

  Dataset EGAD00001007058

• Circulating cell-free DNA analyses for Patient Monitoring in Small Cell Lung Cancer

  This study consists of over 200 data files from cfDNA and germline DNA from 69 patients and 32 healthy normal volunteers discussed in this publication.

  Dataset EGAD00001007070

• Whole Exome Sequencing of Mixed Histology Lung Cancer

  We conducted whole-exome sequencing (WES) and microarray profiling on 19 micro-dissected tumor regions of different histologic subtypes from 9 patients with lung cancers of mixed histologic patterns including 6 LUAD, 6 LCNEC, 3 SCLC, 3 LUSC, and one poorly differentiated NSCLC-NOS.

  Dataset EGAD00001007076

• WGS of HSPC clones of healthy individual sequenced with Illumina NovaSeq 6000

  Dataset contains WGS sequecing data from clonally expanded hematopoietic stem cells from 7 individual pediatric cancer patients. Samples were taken before (DX - diagnosis) or Follow-up (DX2/REM/FU - Diagnosis 2, remission or follow-up, respectively). In addtion, cord blood clones (Designated CB) treated with X-ray radiation, Cisplatin, Maphosphamide, Vincristine and Doxorubicin and untreated cord blood hematopoietic stem/progenitor cells were have been whole-genome sequenced. (Abbreviations RAD, CISPL, MAPH, VINC, DOX and CTRL, respectively)

  Dataset EGAD00001007078

• WES data of colorectal cancer patients

  35 samples from individuals with colorectal tumors, exome sequencing

  Dataset EGAD00001007745

• WGS data of colorectal cancer patients

  36 samples from individuals with colorectal tumors, whole genome sequencing

  Dataset EGAD00001007746

• Sequencing data for oesophageal and related samples - OACs 496 release (WGS)

  Data from 496 OCCAMS (Oesophageal Cancer Clinical And Molecular Stratification) cases. WGS BAM files 496x oesophageal adenocarcinoma samples 496x normal samples

  Dataset EGAD00001007785

• Myeloid cancer sequencing data

  SmMIP libraries using DNA from patients diagnosed with myeloid malignancies were generated in replicates and were sequenced on the NovaSeq SP platform (Illumina)

  Dataset EGAD00001007791

• Genomic characterization of co-existing biliary tract intraepithelial neoplasia and carcinoma lesions reveals distinct evolutionary paths of gallbladder cancer

  Gallbladder carcinoma is the most common cancer of the biliary tract with dismal survival largely due to delayed diagnosis. Biliary tract intraepithelial neoplasia (BilIN) is the common benign tumor that is suspected to be precancerous lesions. However, the genetic and evolutionary relationships between BilIN and carcinoma remain unclear. Here we performed whole-exome sequencing of coexisting low-grade BilIN (adenoma), high-grade BilIN, and carcinoma lesions, and normal tissues from the same patients.

  Dataset EGAD00001007792

• The dataset for Detection and characterization of lung cancer using cell-free DNA fragmentomes

  The dataset for Detection and characterization of lung cancer using cell-free DNA fragmentomes includes 872 bam files from whole genome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include plasma samples from healthy individuals and patients with cancer.

  Dataset EGAD00001007796

• Acquired RAD51C promoter methylation loss causes PARP inhibitor resistance in high grade serous ovarian carcinoma

  Analysis of RAD51C promoter methylation using targeted bisulfite sequencing (amplicon sequencing) in ovarian cancer pre-clinical models and patient samples.

  Dataset EGAD00001007799

• PELICAN33 Phenomic Dataset

  To better understand variation in metastatic prostate cancer behaviour, we assembled and analyzed longitudinal clinical and autopsy records in 33 men. The dataset is contained in a self-explanatory Excel Workbook, with each patient identified as A1, A2, etc. as listed in the "Combined longitudinal clinical and autopsy phenomic assessment in lethal metastatic prostate cancer: recommendations for advancing precision medicine" publication in European Urology Open Science. Please see Jasu J, Tolonen T, Antonarakis ES, Beltran H, Halabi S, Eisenberger MA, Carducci MA, Loriot Y, Van der Eecken K, Lolkema M, Ryan CJ, Taavitsainen S, Gillessen S, Högnäs G, Talvitie T, Taylor RJ, Koskenalho A, Ost P, Murtola TJ, Rinta-Kiikka I, Tammela T, Auvinen A, Kujala P, Smith TJ, Kellokumpu-Lehtinen PL, Isaacs WB, Nykter M, Kesseli J, Bova GS. Combined Longitudinal Clinical and Autopsy Phenomic Assessment in Lethal Metastatic Prostate Cancer: Recommendations for Advancing Precision Medicine. Eur Urol Open Sci. 2021 Jul 2;30:47-62. doi: 10.1016/j.euros.2021.05.011. PMID: 34337548; PMCID: PMC8317817. for more details.

  Dataset EGAD00001007800

• WES melanoma biopsies (UV1-hTERT-mm)

  The dataset contains phenotypes of 14 melanoma biopsies sequenced in connection with the study UV1-hTERT-mm, where the thereapeutic cancer vaccine UV1 is combined with ipilimumab in treatment of melanoma patients.

  Dataset EGAD00001007648

• Non-coding mutations reveal cancer driver cistromes in luminal breast cancer

  A dataset of ER+PR+ breast tumor samples that were analyzed in order to identify mutation enrichment in cis-regulatory elements and cistrome. The repository includes sequencing data from 88 patients. 26/88 were sequenced using ATAC-seq

  Dataset EGAD00001007650

• Dataset-linking-WGS-via-README-for-EGAS00001004884

  This dataset contains 139 Tumor and Control WGS files for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

  Dataset EGAD00001007669

• RNA-seq dataset

  HTG EdgeSeq fastq files of bulk baseline tumors from IMbassdor250: a randomised phase 3 trial comparing atezolizumab with enzalutamide vs enzalutamide alone in patients with metastatic castration-resistant prostate cancer.

  Dataset EGAD00001007685

• Exploring the extracellular transcriptome in seminal plasma for non-invasive prostate cancer diagnosis

  mRNA capture sequencing data of 57 seminal plasma samples.

  Dataset EGAD00001007688

• Tumor transcriptome profiling from the OAK and POPLAR randomized clinical trials of atezolizumab in 2L NSCLC

  This dataset includes full tumor transcriptomes from 891 advanced NSCLC tumors. These data originate from pre-treatment samples from two large randomized clinical trials for second-line non-small cell lung cancer (POPLAR and OAK). The patients in these trials were treated with either the PD-L1 inhibitor atezolizumab or chemotherapy.

  Dataset EGAD00001007703

• ScRNA-seq of 6 human FL

  Follicular lymphoma (FL) is an indolent cancer of mature B-cells but carries increased risk of transformation to a more aggressive histology over time. We present here comprehensive profiling both tumor and immune compartments in 6 diagnostic FL biopsies by single-cell RNA sequencing. This confirmed results from 155 FL tumors characterized by mass cytometry (CyTOF) which revealed two distinct evolutionary trajectories with disparate risk of transformation and alternate biologies.

  Dataset EGAD00001007691

• Lowpass whole genome sequencing of single circulating tumor cells (CTCs) in ALK-rearranged non-small-cell lung cancer resistant to ALK inhibitors

  Lowpass whole genome sequencing of 43 single CTCs and one tumor biopsy

  Dataset EGAD00001007700

• The evolution of hematopoietic cells under cancer therapy

  WGS TAML samples with their remission controls obtained from bone marrow.

  Dataset EGAD00001007706

• ChIP-seq in colorectal cancer and paired adjacent normal mucosa

  This dataset includes ChIP-seq data for H3K27ac and H3K4me1 on 20 paired samples of colorectal cancer and adjacent normal mucosa. One tumor sample that failed QC is not available.

  Dataset EGAD00001007707

• Deep targeted DNA sequencing dataset for the study "Molecular characteristics in Burkitt lymphoma over age groups"

  We performed deep targeted DNA sequencing with a panel of 134 selected cancer-related genes previously identified to be recurrently mutated in BL/B-AL. A Nextera rapid capture custom kit was designed using the Illumina DesignStudio. For every gene, all regions in which previous mutations had been described were covered. For 40 of these genes, the entire coding sequence was covered. Targeted deep sequencing was performed on a MiSeq-Sequencer using MiSeq Reagent Kit v2 (300 cycle) with 24 samples per run. There are 396 samples in this targetedDNAseq-dataset with 298 tumors (288 patients and 10 cell lines) and 98 normals. There are 132 female, 262 male, and 2 unknown gender samples in this dataset.

  Dataset EGAD00001007708

• Single-cell profiling of neoantigen-specific T cells in lung cancers treated with neoadjuvant PD-1 blockade

  This dataset includes 406 samples from non small cell lung cancer patients treated with neoadjuvant anti-PD-1. The Single Cell 5’ V(D)J and 5’ DGE kits (10X Genomics) were used to capture immune repertoire information and gene expression from the same cell in an emulsion-based protocol at the single cell level. Libraries were generated and sequenced on an Illumina NovaSeq instrument using 2x150bp paired end sequencing.

  Dataset EGAD00001007728

• Three large nuclear families in which a single child per family was diagnosed with cancer

  We collected saliva samples from three nuclear families having 4, 5, and 7 children, respectively. One child in each nfamily had been diagnosed with a pediatric tumor, and neither parent had been diagnosed with cancer. Diagnoses included Wilms tumor, low-grade astrocytoma, and Burkitt’s lymphoma, respectively. We used whole-genome sequencing to profile normal cells from each family member and a linked-read technology.

  Dataset EGAD00001007709

• Whole exome sequencing of pdx models of 2 patients with metastatic colorectal cancer

  Paired fastq files ( 12 pairs, WES) of EGFR treated and untreated PDX models of mCRCs of 2 patiens sequenced on Illumina HiSeq 2000, the enrichment kit was Agilent SureSelect V5+UTRs.

  Dataset EGAD00001007713

• Phylogenetic reconstruction of adult blood cancer reveals early origins and lifelong evolution - WGS

  Mutations in cancer-associated genes drive tumour outgrowth. However, the timing of driver mutations and dynamics of clonal expansion that lead to human cancers are largely unknown. We used 580,133 somatic mutations from whole-genome sequencing of 1013 clonal haematopoietic colonies to reconstruct the phylogeny of haematopoiesis, from embryogenesis to clinical disease, in 12 patients with myeloproliferative neoplasms which are blood cancers more common in older age. JAK2V617F, the pathognomonic mutation driving the majority of these cancers, was acquired in utero or childhood, with upper estimates of age of acquisition from 33 weeks gestation to 10.8 years, in all 5 patients in whom JAK2V617F was either the only or the first driver event.  Driver mutations associated with age-related clonal haematopoiesis occurred prior to or following JAK2V617F,  as independent clonal expansions in JAK2V617F-mutated patients, and as large clonal expansions in JAK2V617F-unmutated patients . These mutations were also acquired in utero or childhood, with DNMT3A mutations occurring by 8 weeks of gestation to 7.6 years across 4 patients, and PPM1D mutation occurring by age 5.8yrs in a patient with MPN lacking phenotypic driver mutations. Sequential driver mutation acquisition was common, separated by decades across life, and often outcompeted ancestral clones. The mean latency between JAK2V617F acquisition and clinical presentation was 30 years (range 11-54 years). Rates of clonal expansion were inferred from phylogenetic trees and varied substantially (3% to 190% expansion/year), were affected by additional driver mutations, and were predictive of latency to clinical presentation. Driver mutations and rates of expansion would have been detectable in blood one to four decades before clinical presentation. This study reveals how driver mutation acquisition early in life with life-long growth and evolution underlie adult myeloproliferative neoplasms, providing opportunities for early detection and intervention and a new paradigm for cancer development.

  Dataset EGAD00001007714

• Phylogenetic reconstruction of adult blood cancer reveals early origins and lifelong evolution - TGS

  Mutations in cancer-associated genes drive tumour outgrowth. However, the timing of driver mutations and dynamics of clonal expansion that lead to human cancers are largely unknown. We used 580,133 somatic mutations from whole-genome sequencing of 1013 clonal haematopoietic colonies to reconstruct the phylogeny of haematopoiesis, from embryogenesis to clinical disease, in 12 patients with myeloproliferative neoplasms which are blood cancers more common in older age. JAK2V617F, the pathognomonic mutation driving the majority of these cancers, was acquired in utero or childhood, with upper estimates of age of acquisition from 33 weeks gestation to 10.8 years, in all 5 patients in whom JAK2V617F was either the only or the first driver event.  Driver mutations associated with age-related clonal haematopoiesis occurred prior to or following JAK2V617F,  as independent clonal expansions in JAK2V617F-mutated patients, and as large clonal expansions in JAK2V617F-unmutated patients . These mutations were also acquired in utero or childhood, with DNMT3A mutations occurring by 8 weeks of gestation to 7.6 years across 4 patients, and PPM1D mutation occurring by age 5.8yrs in a patient with MPN lacking phenotypic driver mutations. Sequential driver mutation acquisition was common, separated by decades across life, and often outcompeted ancestral clones. The mean latency between JAK2V617F acquisition and clinical presentation was 30 years (range 11-54 years). Rates of clonal expansion were inferred from phylogenetic trees and varied substantially (3% to 190% expansion/year), were affected by additional driver mutations, and were predictive of latency to clinical presentation. Driver mutations and rates of expansion would have been detectable in blood one to four decades before clinical presentation. This study reveals how driver mutation acquisition early in life with life-long growth and evolution underlie adult myeloproliferative neoplasms, providing opportunities for early detection and intervention and a new paradigm for cancer development.

  Dataset EGAD00001007715

• Prediction of HLA genotypes using NGS data

  High-precision human leukocyte antigen (HLA) genotyping is crucial for anti-cancer immunotherapy, but existing tools predicting HLA genotypes using next-generation sequencing (NGS) data are insufficiently accurate. We compared the availability and accuracy of eight HLA genotyping tools (OptiType, HLA-HD, PHLAT, seq2HLA, arcasHLA, HLAscan, HLA*LA, and Kourami) using 1,275 cases from the 1000 Genomes Project data and created a new HLA-genotyping algorithm combining tools. Then, we assessed the new algorithm’s performance in 39 in-house samples with normal whole-exome sequencing (WES) data and polymerase chain reaction–sequencing-based typing (PCR-SBT) results.

  Dataset EGAD00001007733

• Evolutionary trajectories and clonal migration underlying tumor progression and lymph node metastasis in resectable lung cancer

  In this prospective study, targeted deep sequencing was performed on a total of 160 primary tumors (474 regions) and 112 lymph nodes from 125 patients with stage I-III lung cancer (LuCaTH). Progressive evolution at clonal divergence scale was observed while specific driver events were positively selected for clonal sweeps during tumor development. Between-region genetic divergence (BRGD) of tumors were assessed and positively correlated with tumor differentiation. A machine learning algorithm was employed to evaluate clinicopathological and molecular parameters of primary tumors underlying lymph node metastasis. By analyzing clonal lineages and metastatic trajectories across multiple nodal stations, we unraveled a common sequential LNM seeding pattern but with divergent modes of clonal spread.

  Dataset EGAD00001007587

• RBWES

  Retinoblastoma is a rare childhood cancer of the retina. We studied retinoblastoma by Whole-Exome-Sequencing.

  Dataset EGAD00001007591

• Single cells of colorectal cancer organoids

  458 single cell samples of multiple colorectal cancer organoids

  Dataset EGAD00001007305

• Whole Genome sequencing of colorectal cancer patients (SG-BULK-4)

  Whole Genome sequencing of colorectal cancer patients (SG-BULK-4)

  Dataset EGAD00001008625

• Spatial transcriptome sequence data from prostate cancer needle biopsies

  Spatial transcriptome sequencing data from prostate cancer needle biopsies. Dataset contains biopsies from before and after androgen deprivation therapy of 3 patients with 8 biopsies per patient in total. 2 sections (replicates) are taken from each biopsy.

  Dataset EGAD00001008636

• Whole Genome sequencing of colorectal cancer patients (SG-BULK-5)

  Whole Genome sequencing of colorectal cancer patients (SG-BULK-5)

  Dataset EGAD00001008637

• EPICC: Evolutionary Predictions in Colorectal Cancer WGS-3

  WGS profiling bam files from colorectal carcinoma and adenoma.

  Dataset EGAD00001008642

• Spatial transcriptome sequence data from cross section of cancer containing prostates

  Spatial transcriptome sequence data from two tumour containing prostates. Entire cross section of organ divided into cubes to fit spatial transcriptomics arrays. The dataset contains paired-end sequences from 21 sections of 1k array sections and 9 sections of 10x Visium sections for patient 1 as well as 28 sections of 10x Visium sections for patient 2.

  Dataset EGAD00001008644

• sWGS of dried blood spots (cfDNA/ctDNA)

  This dataset contains paired-end fastq sequencing files (n=212) from shallow WGS of 106 dried blood spot (DBS) samples, containing 91 DBS collected from OV04 ovarian cancer PDX mice, 10 DBS collected from healthy non-tumour bearing NSG mice, and 5 DBS generated from whole blood samples from 4 OV04 ovarian cancer patients.

  Dataset EGAD00001008651

• Integrated single-cell profiling dissects cell-state-specific enhancer landscapes of human tumor infiltrating T cells.

  Despite extensive studies on the chromatin landscape of exhausted T cells, the transcriptional wiring underlying functional and dysfunctional states of human tumor infiltrating lymphocytes (TILs) is incompletely understood. Here, we identify tissue-specific and general gene-regulatory landscapes in the wide breadth of CD8+ TIL functional states covering four cancer entities using single-cell chromatin profiling. We map enhancer-promoter interactions in human TILs by integrating single-cell chromatin accessibility with single-cell RNA-seq data from tumor entity-matching samples and prioritize key elements by super enhancer analysis. Our analysis reveals a human core chromatin trajectory to TIL dysfunction and identifies key enhancers, transcriptional regulators, and deregulated target genes involved in this process. Finally, we validate enhancer regulation at loci encoding PD1, TCF1, and TIM3 by targeting non-coding regulatory elements with potent CRISPR activators and repressors. In summary, our study advances the understanding of molecular regulation of TIL (dys-)function and provides a framework for modulating immunotherapeutic relevant TIL genes via their enhancers.

  Dataset EGAD00001008662

• Lung cancer and non-cancerous plasma cfDNA samples

  The dataset contains 90 lung cancer and 5 non-cancerous lung lesion plasma cfDNA samples collected in EDTA blood collection tubes. Shallow WGS was performed on an Illumina Novaseq S4 PE150bp. Samples are provided as raw reads without any prior processing.

  Dataset EGAD00001008666

• The genomic VCF data of the Integrative proteogenomic characterization of early esophageal cancer project

  The genomic VCF data of the Integrative proteogenomic characterization of early esophageal cancer project ,this dataset contains 90 VCF files.

  Dataset EGAD00001008672

• cfDNA and CDX/PDX methylation profiling in Small Cell Lung Cancer

  This study contains methyl-binding domain sequencing and shallow whole genome sequencing from circulating free DNA (cfDNA) for 79 patients with small cell lung cancer (SCLC) and 78 non-cancer controls. We also sequence genomic DNA (both methyl-binding domain sequencing and shallow whole genome sequencing) from 30 circulating-tumour-cell derived explant models (CDXs, from 23 unique patients with SCLC), 20 patient derived explant models (PDXs, from 10 unique patients with SCLC) and 13 lung tissue samples.

  Dataset EGAD00001008673

• CITE-seq for peripheral blood samples of 5 breast cancer patients

  We have 5 breast cancer patients who received VEN therapy. We collected their peripheral blood samples before and after long-term Venetoclax treatment. We apply CITE-seq protocol to these samples. This collection contains the all CITE-seq data for patients.

  Dataset EGAD00001008751

• Novel method for efficient establishment, expansion and drug response profiling of high-grade serous ovarian cancer organoids

  HGSOC patient-derived organoids and their tissue of origin

  Dataset EGAD00001008753

• Genomics characterization of BRAF-V600E colorectal cancer patients treated with anti-BRAF/EGFR

  WES files (fastQ) from 19 germline DNA, 22 tumor DNA, 5 patient-derived xenograft (PDX) DNA, and 9 plasma cfDNA) samples from 28 CRC-BRAF-mutated patients collected at baseline to anti-BRAF/EGFR therapies.

  Dataset EGAD00001008755

• Salivary Gland Cancer TSO500 dataset

  DNA was extracted from archival tissue of 119 patients with various SGC subtypes and sequenced using a targeted NGS panel encompassing 523 cancer related genes (TruSight Oncology 500, TSO500). This dataset belongs to the publication entitled: 'Identification of fusion genes and targets for genetically matched therapies in a large cohort of salivary gland cancer patients'.

  Dataset EGAD00001008759

• Bulk RNAseq

  Bulk tumor RNAseq FASTQ files from 124 samples from patients with hormone sensitive or castration resistant prostate cancer.

  Dataset EGAD00001008760

• Raw count table

  Raw count matrix of the 124 bulk tumor RNAseq samples from patients with hormone sensitive or castration resistant prostate cancer.

  Dataset EGAD00001008762

• Clinical data

  Clinical data from this cohort of patients, including hormone sensitive or castration resistant prostate cancer, overall survival, NMF subtypes, tumor TMB, prior treatment status, PD-L1 IC and TC scores from SP142 and SP263 as well as percentage of CD8 IHC at tumor center.

  Dataset EGAD00001008761

• Germline WES data of pediatric cancer patients

  This dataset includes bam files (aligned to hg38) from the germline of pediatric cancer patients.

  Dataset EGAD00001008763

• APOBEC mutagenesis is a common process in normal human small intestine

  The single base substitution mutational signatures SBS2 and SBS13, likely caused by APOBEC cytosine deaminases, are common in many human cancer types. However, the stimulus activating APOBEC mutagenesis is unknown and understanding of when it occurs in the progression from normal to cancer cell is limited. Here, as part of a wider survey of human tissues, we whole genome sequenced 342 microdissected normal epithelial crypts from the small intestines of 39 individuals. SBS2/13 mutations were present in 17% normal small intestine crypts and were likely due to APOBEC3A activity. Localised clusters of SBS2/13 mutations (kataegis) were also commonly found. APOBEC mutation burdens were variable between individuals and between crypts from the same individual. Crypts with SBS2/13 often had immediate crypt neighbours without SBS2/13, suggesting that the underlying cause of SBS2/13 is cell-intrinsic rather than a widely distributed microenvironmental exposure, or needs to be permitted by cell-intrinsic conditions. APOBEC mutagenesis occurred throughout the human lifespan, including in young children, and was episodic with a small number of episodes occurring during the life history of a single cell. The results indicate that APOBEC mutagenesis is more common in the small intestine epithelium than in many other cell types, and is an episodic process in vivo initiated or permitted by cell intrinsic factors.

  Dataset EGAD00001008764

• Precise reconstruction of the tumor microenvironment using bulk RNA-seq and a unique machine learning-based algorithm trained on artificial transcriptomes

  Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. Here, we present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of > 9,400 tissue and blood sorted cell RNA profiles to accurately reconstruct the tumor microenvironment (TME). Bioinformatic correction for technical and biological variability, aberrant cancer cell expression inclusion, and the accurate quantification and normalization of transcript expression increased the stability and robustness of Kassandra. Performance was validated on over 4,000 H&E tissue slides and more than 1,000 normal and tumor tissues by comparison with cytometric, immunohistochemical or single-cell RNA-seq measurements. Kassandra accurately deconvolved stromal and immune elements of blood and tumors, revealing the role of the TME in tumor pathogenesis. Digital TME reconstruction revealed that the presence of PD1-positive CD8+ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

  Dataset EGAD00001008776

• Dataset for urologic_cancer-RNA

  Rare cancer sequencing data of 75 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008851

• Dataset for GIST-RNA

  Rare cancer sequencing data of 50 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008852

• Dataset for liposarcoma-RNA

  Rare cancer sequencing data of 40 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008854

• Dataset for colorectal_cancer-RNA

  Rare cancer sequencing data of 97 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008855

• Dataset for gynecologic_cancer-RNA

  Rare cancer sequencing data of 48 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008856

• Dataset for Ewing_sarcoma_PNET-WHOLE_GENOME

  Rare cancer sequencing data of 18 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008874

• Dataset for cancer_of_unknown_primary-RNA

  Rare cancer sequencing data of 58 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008857

• Dataset for melanoma-RNA

  Rare cancer sequencing data of 49 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008858

• Dataset for soft_tissue_tumor-RNA

  Rare cancer sequencing data of 243 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008859

• Dataset for hematopoietic_malignancy-RNA

  Rare cancer sequencing data of 47 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008860

• Dataset for head_and_neck_cancer-RNA

  Rare cancer sequencing data of 92 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008861

• Dataset for neuroendocrine_adrenal_tumor-RNA

  Rare cancer sequencing data of 145 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008862

• Dataset for hepatopancreaticobiliary_malignancy-RNA

  Rare cancer sequencing data of 119 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008863

• Dataset for bone_cancer-EXON

  Rare cancer sequencing data of 87 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008865

• Dataset for bone_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 48 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008866

• Dataset for breast_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 12 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008867

• Dataset for breast_cancer-EXON

  Rare cancer sequencing data of 94 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008868

• Dataset for cancer_of_unknown_primary-EXON

  Rare cancer sequencing data of 62 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008870

• Dataset for cancer_of_unknown_primary-WHOLE_GENOME

  Rare cancer sequencing data of 46 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008869

• Dataset for colorectal_cancer-EXON

  Rare cancer sequencing data of 119 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008871

• Dataset for colorectal_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 54 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008872

• Dataset for Ewing_sarcoma_PNET-EXON

  Rare cancer sequencing data of 30 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008873

• Dataset for GIST-EXON

  Rare cancer sequencing data of 64 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008875

• Dataset for GIST-WHOLE_GENOME

  Rare cancer sequencing data of 18 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008876

• Dataset for gynecologic_cancer-EXON

  Rare cancer sequencing data of 55 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008877

• Dataset for gynecologic_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 38 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008878

• Dataset for head_and_neck_cancer-WHOLE_GENOME

  Rare cancer sequencing data of 56 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008879