6048 results for "canc*"

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• March 2019 data update for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  March 2019 data update for cord blood CD34+CD38-, CMP, GMP, MEP, monocyte, erythroid precursor, B cell and primary AML total blasts reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium. This dataset contains data for samples: CEMT0158 CEMT0159 CEMT0160 CEMT0161 CEMT0162 CEMT0163 CEMT0164 CEMT0165 CEMT0166 CEMT0167 CEMT0168 CEMT0169 CEMT0170 CEMT0171 CEMT0172 CEMT0189

  Dataset EGAD00001004950

• Multi-region sequencing of colon tumour biopsies and matched normals

  Dataset of adenoma and colon cancer multi-region sequencing. Publication: Nat Ecol Evol. 2018 Oct;2(10):1661-1672. doi: 10.1038/s41559-018-0642-z. Epub 2018 Aug 31.

  Dataset EGAD00001004966

• Whole exome sequencing of breast cancer samples pre- and post-neoadjuvant chemotherapy (NAC)

  Fastq files resulting from whole exome sequencing of trios of samples from 6 breast cancer patients: normal breast, pre-NAC biopsy and post-NAC surgical resection.

  Dataset EGAD00001004984

• Breast Cancer - immune clusters - RNA-seq

  In this study we use expression data from breast cancer tumors to define immune clusters in breast cancer. Immune clusters have gradual levels of immune infiltration. In the intermediate immune infiltration cluster, we found a worse prognosis which is independent of known clinicopathological features. We also found the immune clusters associated with treatment response. Further using gene expression data and deconvolution algorithms to dissect the immune contexture of the clusters.

  Dataset EGAD00001004985

• Whole Mitochondrial DNA sequencing of Gingivo-buccal Cancer : ICGC-India Project

  This dataset pertains to mitochondrial DNA amplicon sequencing of paired DNA samples from gingivo-buccal oral cancer patients. DNA was isolated from the tumor and blood tissues of 89 patients (178 samples). The sequencing libraries were prepared from whole mitochondrial amplicons using Nextera XT DNA Library Preparation Kit (Illumina) and sequenced in Illumina HiSeq platform. The uploaded BAM files are generated by aligning paired-end reads to the mitochondrial reference sequence (rCRS) using BWA-MEM.

  Dataset EGAD00001004987

• Colorectal cancer GWAS on the Spanish population

  Data on Affymetrix 6.0 arrays for Genome-Wide Association Study of colorectal cancer in the Spanish population. Additionally, geographical origin for each sample is provided, which constitutes the largest to-date Spanish genomic sample population

  Dataset EGAD00010001715

• Organoid Derivation Project WGS: Release 1

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005000

• Organoid Derivation Pilot: RNAseq

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients.

  Dataset EGAD00001005001

• MultiOMICS study of a pair of infant monozygotic twins with concordant B-cell ALL (WGS)

  B-cell acute lymphoblastic leukemia (B-cell ALL) is the most common cancer in childhood. Studying identical twins with B-cell ALL provides a unique and tractable model for deciphering the developmental timing of pre- and post-natal mutations contributing to clonal evolution. To date, this has mainly focused on major cytogenetic subgroups of childhood B-cell ALL, including MLL fusions, ETV6-RUNX1, hyperdiploidy, and BCR-ABL1. However, formal demonstration of the prenatal origin and “backtracking” the natural history of the leukemia remains understudied in “B-other”/Normal Karyotype (NK) B-cell ALL. To characterize the genetic landscape of this particular leukemia subtype, we performed whole genome DNA- and B-cell receptor (BCR)- on a pair of 8-month-old monozygotic twins diagnosed with concordant “B-other”/NK B-cell ALL.

  Dataset EGAD00001005017

• MultiOMICS study of a pair of infant monozygotic twins with concordant B-cell ALL (WGBS)

  B-cell acute lymphoblastic leukemia (B-cell ALL) is the most common cancer in childhood. Studying identical twins with B-cell ALL provides a unique and tractable model for deciphering the developmental timing of pre- and post-natal mutations contributing to clonal evolution. To date, this has mainly focused on major cytogenetic subgroups of childhood B-cell ALL, including MLL fusions, ETV6-RUNX1, hyperdiploidy, and BCR-ABL1. However, formal demonstration of the prenatal origin and “backtracking” the natural history of the leukemia remains understudied in “B-other”/Normal Karyotype (NK) B-cell ALL. To characterize the epigenetic landscape of this particular leukemia subtype, we performed DNA bisulfite-sequencing on a pair of 8-month-old monozygotic twins diagnosed with concordant “B-other”/NK B-cell ALL.

  Dataset EGAD00001005018

• Clonality of circulating tumor cells in breast cancer brain metastases patients

  The incidence of brain metastases in breast cancer (BCBM) patients is increasing. These patients have a very poor prognosis and therefore identification of blood-based biomarkers, such as circulating tumor cells (CTCs) and understanding the genomic heterogeneity could help to personalize treatment options. In this study, DNA from individual CTCs as well as corresponding primary tumors and brain metastases were analyzed by next generation sequencing (NGS) in order to evaluate copy number aberrations and single nucleotide variations (SNVs).

  Dataset EGAD00001005020

• A practical guide for mutational signature analysis in hematological malignancies

  Analysis of mutational signatures is becoming routine in cancer genomics, with implications for pathogenesis, classification, prognosis, and even treatment decisions. However, the field lacks a consensus on analysis and result interpretation. Using whole-genome sequencing of multiple myeloma (MM), chronic lymphocytic leukemia (CLL) and acute myeloid leukemia, we compare the performance of public signature analysis tools. We describe caveats and pitfalls of de novo signature extraction and fitting approaches, reporting on common inaccuracies: erroneous signature assignment, identification of localized hyper-mutational processes, overcalling of signatures. We provide reproducible solutions to solve these issues and use orthogonal approaches to validate our results. We show how a comprehensive mutational signature analysis may provide relevant biological insights, reporting evidence of c-AID activity among unmutated CLL cases or the absence of BRCA1/BRCA2-mediated homologous recombination deficiency in a MM cohort. Finally, we propose a general analysis framework to ensure production of accurate and reproducible mutational signature data.

  Dataset EGAD00001005028

• Genomic Signatures Reveal DNA Damage Response Deficiency in Brain Metastases of Colorectal Cancer

  Brain metastasis (BM) of colorectal cancer (CRC) is rare but lethal and lacks effective therapies or a good understanding of its genomic landscapes. We conduct an analysis of whole-exome sequencing (WES, Illumina HiSeq 2500 sequencing platform) on 11 patient-matched BMs, primary CRC tumours, and adjacent normal tissues; and whole-genome sequencing (WGS, Illumina HiSeq X Ten platform) on 8 patient-matched BMs, primary CRC tumors, and adjacent normal tissues to uncover the whole-genome mutational landscape of colorectal cancer with brain metastasis.

  Dataset EGAD00001005030

• Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small Cell Lung Cancer

  Tumor mutational burden (TMB) has emerged as a predictive biomarker of response to immune checkpoint inhibitors. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. Here we evaluate bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples. In CheckMate 026, TMB was retrospectively assessed in 312 patients with non-small cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne CDx™ assay. TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman’s r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. Parameter variation can therefore impact TMB calculations, highlighting the need for standardization. Encouragingly, WES and FoundationOne CDx outputs were highly correlated (Spearman’s r = 0.90) and differences could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms and centers is achievable.

  Dataset EGAD00001005035

• Comprehensive single cell study of lung adenocarcinoma from early to metastatic stages

  We performed single cell RNA sequencing (scRNA-seq) for 208,506 cells derived from 58 lung adenocarcinomas from 44 patients, which covers primary tumour, lymph node and brain metastases, and pleural effusion in addition to normal lung tissues and lymph nodes. The extensive single cell profiles depicted a complex cellular atlas and dynamics during lung adenocarcinoma progression which includes cancer, stromal, and immune cells in the surrounding tumor microenvironments.

  Dataset EGAD00001005054

• Molecular Profiling Reveals Unique Immune and Metabolic Features of Melanoma Brain Metastases. Cancer Discovery doi: 10.1158/2159-8290. PMID: 30787016 PMCID: PMC6497554

  The BAM files for WES and RNA seq used in the article "Molecular Profiling Reveals Unique Immune and Metabolic Features of Melanoma Brain Metastases." on cancer Discovery 2019. PMID: 30787016 PMCID: PMC6497554. Authors : Grant M Fischer, ..., Michael A Davies.

  Dataset EGAD00001005046

• The identification of genetic vulnerabilities in head and neck cancers for the development of novel treatments. (2019-06-10)

  Exome Sequencing in a set of Asian Head and Neck cancer cell lines, to identify mutations that can be used to genomically classify the cell lines. . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005082

• GBM cancer stem cell lines, RNA-seq and WGS data

  This dataset contains 3 GBM stem cell samples profiled by RNA-seq. Two of those samples have been profiled with WGS as well (with matched blood WGS data available)

  Dataset EGAD00001005077

• May 2019 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  May 2019 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001005060

• Combination Therapies for Personalised Cancer Medicine in drug resistant EGFR mutant lung cancer (2019-06-10)

  This study involves mutagenizing a range of different cell lines with ENU to identify those mutations which engender resistance to targeted treatment. . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005080

• Combination Therapies for Personalised Cancer Medicine in 11-18 (2019-06-10)

  This study involves mutagenizing 11-18 with ENU to identify those mutations which engender resistance to targeted treatment. . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005081

• Cancer and germline exomes, and cancer RNA-seq consisiting of FASTQ paired-end reads from melanoma and lung cancer samples

  Cancer and germline exomes, and cancer RNA-seq consisiting of FASTQ paired-end reads from melanoma and lung cancer samples

  Dataset EGAD00001005097

• Illumina TSO500 RNA Dataset for Manuscript titled: Evaluation of Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA) Samples from Advanced Non-Small Cell Lung Cancer for Whole Genome, Whole Exome and Comprehensive Panel Sequencing

  Illumina TSO500 RNA Dataset for Manuscript titled: Evaluation of Endobronchial Ultrasound-Guided Transbronchial Needle Aspiration (EBUS-TBNA) Samples from Advanced Non-Small Cell Lung Cancer for Whole Genome, Whole Exome and Comprehensive Panel Sequencing

  Dataset EGAD00001015397

• Combined MEK and BCL-2/XL inhibition is effective in high-grade serous ovarian cancer patient-derived xenograft models and BIM levels are predictive of responsiveness

  Most patients with late stage high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy but inevitably relapse and develop resistance, highlighting the need for novel therapies to improve patient outcomes. The MEK/ERK pathway is activated in a large subset of HGSOC, thus making it an attractive therapeutic target. Here, we systematically evaluated the extent of MEK/ERK pathway activation and efficacy of pathway inhibition in a large panel of well-annotated HGSOC patient-derived xenograft (PDX) models. The vast majority of models were nonresponsive to the MEK inhibitor cobimetinib (GDC-0973) despite effective pathway inhibition. Proteomic analyses of adaptive responses to GDC-0973 revealed that GDC-0973 upregulated the pro-apoptotic protein BIM, thus priming the cells for apoptosis regulated by BCL2-family proteins. Indeed, combination of both MEK inhibitor and dual BCL-2/XL inhibitor (ABT-263) significantly reduced cell number, increased cell death and displayed synergy in vitro in most models. In vivo, the GDC-0973 and ABT-263 combination was well tolerated and resulted in greater tumor growth inhibition than single agents. Detailed proteomic and correlation analyses identified two subsets of responsive models – those with high BIM at baseline that was increased with MEK inhibition and those with low basal Bim and high pERK levels. Models with low BIM and low pERK were non-responsive. Our findings demonstrate that combined MEK and BCL-2/XL inhibition has therapeutic activity in HGSOC models and provide a mechanistic rationale for clinical evaluation of this drug combination as well as the assessment of the extent to which BIM and/or pERK levels predict drug combination effectiveness in chemoresistant HGSOC.

  Dataset EGAD00001005111

• Long-read WGS of three cancer cell lines

  Long-read WGS of three cancer cell lines

  Dataset EGAD00001005184

• 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

• Tobacco exposure and somatic mutations in normal human bronchial epithelium

  That tobacco smoking causes lung cancer is well-established, but we lack quantitative understanding of its effects on genomes of normal bronchial epithelium. We sequenced whole genomes of 632 colonies derived from single bronchial epithelial cells across 16 subjects. Tobacco smoking is the major influence on mutation burden, adding 1000-10,000+ mutations/cell, massively increasing both between-subject and within-subject variance, and generating several distinct signatures of substitutions and indels. A population of cells in subjects with smoking history had mutation burdens equivalent to that expected for never-smokers: these cells lacked tobacco-specific mutational signatures, were four-fold more frequent in ex-smokers than current smokers, and had significantly longer telomeres than their more mutated counterparts. Driver mutations increased in frequency with age, affecting 4-14% of cells in middle-aged never-smokers. In current smokers, ≥25% of cells carried driver mutations and 0-6% cells had 2 or even 3 drivers. Thus, tobacco smoking increases mutation burden, cell-to-cell heterogeneity and driver mutations, but quitting promotes replenishment of bronchial epithelium from mitotically quiescent cells that have avoided tobacco mutagenesis.

  Dataset EGAD00001005193

• Single cell RNA sequencing of colorectal cancer

  To understand intrinsic cancer cell signatures and the surrounding microenvironemt and their interactions, we performed single-cell RNA sequencing on 63,689 cells from 23 patients with 23 primary colorectal cancer and 10 matched normal mucosa samples. Analyzing of primary colorectal cancer and normal mucosa samples show a comprehensive cellular landscape of colon cancer, which is a valuable resource for the development of therapeutic strategies.

  Dataset EGAD00001005198

• IBD-dysplasia

  This data set concerns DNA copy number alterations and mutation data from 30 IBD-associated dysplastic lesions and 13 IBD-associated cancers. DNA was isolated from formalin-fixed, paraffin-embedded material. Whole-genome shallow seq and Truseq amplicon cancer panel (Illumina) were used for detection of DNA copy number alterations and gene mutations, respectively.

  Dataset EGAD00001005196

• Matched WXS of 147 lung cancer patients treated with immunotherapy

  This data includes whole exome sequencing of matched normal-tumor samples of patients who have received immunotherapy. '-1' refers to matched normal sample and '-2' refers to matched tumor sample.

  Dataset EGAD00001005211

• The mutational landscape of normal human endometrial epithelium - Additional Samples

  All normal somatic cells are thought to acquire mutations but understanding of the rates, patterns, causes and consequences of somatic mutation in normal cells is limited. Uterine endometrium adopts multiple physiological states over a lifetime and is lined by a gland-forming epithelium. Whole genome sequencing of normal endometrial glands from women aged 19 to 81 years showed them to be clonal cell populations derived from recent common ancestors, with total mutation burdens that increase with age at ~29 base substitutions/year and which are many-fold lower than endometrial cancers. Normal endometrial glands frequently carry driver mutations in cancer genes. Driver mutation burdens increase with age and correlate negatively with parity. Phylogenetic trees of normal endometrial glands constructed using whole genome sequences indicated that clones with drivers often originate during the first decades and spread to colonise the endometrial epithelial lining. The results show that driver mutation landscapes differ between normal cell types, perhaps shaped by differences in normal tissue physiology, and suggest that the procession of neoplastic changes leading to endometrial cancer is initiated early in life.

  Dataset EGAD00001005214

• RNA sequencing of 31 pancreatic cancer organiods

  RNA sequencing of 31 pancreatic cancer organiods

  Dataset EGAD00001005217

• Latency and interval therapy affect the evolution in metastatic colorectal cancer

  The cohort of 15 patients included ten patients with available tissue from the primary tumor and ≥1 metastatic site, four patients with pairs of metastases only, and one patient with an anastomotic recurrence five years after initial resection of the primary tumor

  Dataset EGAD00001005226

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

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2019-08-07.

  Dataset EGAD00001005234

• Next gen seq of eye cancers (2019-08-14)

  Retinoblastoma (RB), the commonest eye cancer in children was the first cancer for which a genetic cause was identified: the Rb1 gene is a tumour suppressor gene that is mutated in RB. The Rb1 gene defect alone does not predict the clinical outcome. We propose to study other possible mechanisms: 1. Stepwise further mutations occur in RB, increasing its carcinogenesis. We will sequence the whole genome in RB tissue, and relate the different genes expressed to the treatments used. 2. Extracellular matric proteins contribute to a tumour permissive environment for RB to continue to grow. This includes Samll Leucine Rich Proteoglycans (SLRP), a family of 15 secreted extracellular matrix proteins involved in eye development. 3. Cancer stem cells (CSC), a subpopulation of treatment resistant cells, drive RB tumours, and whether these stem cells can be manipulated for new therapies. The aim of this study is to assist finding targeted diagnostic techniques and treatments for RB. . This dataset contains all the data available for this study on 2019-08-14.

  Dataset EGAD00001005251

• Exome and Transcriptome for gynecologic cancer gene-drug analysis

  Total of 180 gynecologic tumor specimens were subjected for targeted-exome and/or whole-transcriptome sequencing.

  Dataset EGAD00001005331

• Whole genome sequencing

  The diversity and heterogeneity within high-grade serous ovarian cancer (HGSC) is not well understood. We performed whole genome sequencing on primary and metastatic sites from highly clinically annotated HGSC samples. Samples were obtained pre-treatment based on a laparoscopic triage algorithm from patients who underwent R0 tumor debulking, or received neoadjuvant chemotherapy (NACT) with excellent or poor response.

  Dataset EGAD00001005240

• Predictor_ChemoNEAR_TNBC (2019-08-14)

  In this study a collection of core biopsies from breast cancer patients receiving neoadjuvant chemotherapy as part of the ChemoNEAR trial will be investigated. The study is intended to detect early acquired resistance in women with diagnosed breast carcinoma. Samples will undergo whole genome sequencing and analysis, including use of HR Predict. . This dataset contains all the data available for this study on 2019-08-14.

  Dataset EGAD00001005255

• Genomic variants in 121 genes associated with ovarian cancer in cancer tissue and derived organoids

  High-grade serous ovarian cancer (HGSOC) likely originates from the fallopian tube (FT) epithelium, but advanced stages are mostly found outside the FT. We used ex-vivo cultures of HGSOC and knock-out of tumor suppressors in FT organoids to study changes in epithelial cells and niche requirements for normal and transformed FT cells. We found that transformed cells require BMP signaling and are growth arrested in Wnt rich medium. A SureSelectXT Automation Custom Capture Library (Agilent) target enrichment panel was designed. The enrichment panel comprised all coding exons of 121 genes associated with ovarian cancer. Capture was performed according to the manufacturer’s instructions using an NGS Workstation Option B (Agilent) for automated library preparation starting with 3 μg DNA per sample. Sequencing was performed on a Illumina Hiseq 2500 system gnerating 2x100bp paired end reads and a target coverage of >200 per sample. Sequence reads were mapped to the haploid human reference genome (hg19) using BWA. Variants where called with FreeBayes v1.1.

  Dataset EGAD00001005279

• Deep multi-region WGS of lung cancer tumours

  Deep WGS (germline and 2-5 tumour regions) was performed on 20 patients: 13 lung adenocarcinoma, 5 squamous cell carcinoma, 2 small-cell lung cancers. A total of 68 BAM files are provided, where tumours were sequenced to 60x or 150x depth.

  Dataset EGAD00001005287

• LINE luminal breast cancer Neoadjuvant Chemotherapy Study (2019-08-28)

  A targeted gene screen of 365 known cancer genes in luminal breast cancer samples pre-chemotherapy and at resection post-chemotherapy to evalaute clonal expansion of chemotherapy cancer cells. . This dataset contains all the data available for this study on 2019-08-28.

  Dataset EGAD00001005297

• Patient-derived lung cancer organoids

  Patient-derived lung cancer organoids cram files : targeted seq 13 samples, whole exome seq 12 samples mutation profiles of PDO and matched tissue : aggregated vcf 1 file details : https://www.nature.com/articles/s41467-019-11867-6

  Dataset EGAD00001005317

• RNA-Seq of kidney cancer organoid biobank

  Dataset EGAD00001005318

• WGS of kidney cancer organoid biobank

  Dataset EGAD00001005319

• August 2019 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  August 2019 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001005335

• The dataset for Genome-wide cell-free DNA fragmentation in patients with cancer

  The dataset for Genome-wide cell-free DNA fragmentation in patients with cancer includes 538 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 EGAD00001005339

• Whole genome sequencing of cfDNA

  random whole-genome shotgun sequencing of cfDNA in control samples (NPH*) and late-stage cancer samples. First letter denotes primary cancer tissue (C: Colon, B: Breast, P: Prostate)

  Dataset EGAD00001005343

• Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class I neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment

  Whole exome sequencing data from a series of 5 patient derived organoids (PDOs) established from metastatic colorectal cancers (CRCs).

  Dataset EGAD00001005357

• Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data

  In this study, we performed systematic comparative analysis of seven widely-used SNV-calling methods, including SAMtools, the GATK Best Practices pipeline, CTAT, FreeBayes, MuTect2, Strelka2 and VarScan2, on both simulated and real single-cell RNA-seq datasets. We generated SMART-seq2 data for 70 CD45- single cells, which were derived from two colorectal cancer patients (P0411 and P0413). The average sequencing depths of these cells were 1.4 million reads per cell. We also generated tumor and adjacent normal bulk WES data, as well as tumor bulk RNA-seq data for these patients.

  Dataset EGAD00001005373

• Whole genome sequencing of osteosarcoma from patients seen at UT MD Anderson Cancer Center.

  Whole genome sequencing of 35 osteosarcoma patients (primary, relapsed, and metastatic) with matched normals. Tumors were sequenced at target 60X and matched normals at target 30X.

  Dataset EGAD00001005389

• WGS of human colorectal cancer organoid exposed to genotoxic pks+ E. coli

  Organoid cultures were exposed to two different E.Coli strains and a dye control with three biological duplicates. Their original culture was harvested as a control. In total 10 organoid cultures were whole-genome sequenced using the Novaseq6000 platforms. The data is deposited as .bam format.

  Dataset EGAD00001005416

• Single cells of colorectal cancer organoids

  Dataset contains 854 single cell sequenced colorectal cancer organoids.

  Dataset EGAD00001005422

• Cancer Alliance RNA-Seq total RNA

  Total RNA transcriptome profiling by high-throughput for individualized cancer interpretation

  Dataset EGAD00001006235

• Plasma methylome of metastatic prostate cancer

  The dataset contains plasma DNA methylation data derived from metastatic prostate cancer patients.

  Dataset EGAD00001005433

• Sharc-ovarian

  Low coverage nanopore sequencing of ovarian cancer tumors

  Dataset EGAD00001005472

• Peripheral clonal expansion of T cells (scRNA-seq)

  Single-cell RNA-seq of tumor-infiltrating lymphocytes from 14 cancer patients before treatment, taken from tumor, normal adjacent tissue, and peripheral blood. Dataset consists of paired-end FASTQ files, including replicate libraries and runs.

  Dataset EGAD00001005464

• Peripheral clonal expansion of T cells (scTCR-seq)

  Single-cell TCR-seq of tumor-infiltrating lymphocytes from 14 cancer patients before treatment, taken from tumor, normal adjacent tissue, and peripheral blood. Dataset consists of paired-end FASTQ files, including replicate libraries and runs.

  Dataset EGAD00001005465

• Cancer Alliance Exome

  Exome sequencing for individualized cancer interpretation

  Dataset EGAD00001006236

• Molecular Profiles of BRCA1-Associated Ovarian Cancer Treated by Platinum-Based Therapy

  Whole-exome sequencing data from Illumina NextSeq 500. It consists of 88 paired-end FASTQ files from 44 primary, residual, relapsed tumors and normal samples from the blood.

  Dataset EGAD00001005470

• Targeted and exome sequencing data from 24 prostate cancer patients with somatic hypermutation

  This dataset contains all available targeted and exome sequencing paired fastq files from our study, "Identification of hypermutation and defective mismatch repair in ctDNA from metastatic prostate cancer". Patient identifiers are denoted by the first three characters of the sample aliases (e.g. "P01"), and additional information is appended to reflect the panel used (targeted 73 gene panel: "PC", or whole-exome panel: "WXS"), and whether the sample represents cell-free DNA ("cfdna") or paired white-blood cell control ("wbc"). Several patients have multiple serial collections available, and these are denoted by the characters "C1, C2, C3," etc. All samples were sequenced using Illumina technology.

  Dataset EGAD00001005474

• Single cell RNAseq of PBMC from bladder cancer patients

  This dataset contains single cell RNA sequencing data of PBMC samples from 10 bladder cancer patients. cDNAs and single cell RNA libraries were prepared following manufacturer’s user guide (10x Genomics). Each library was sequenced in HiSeq4000 (Illumina) to achieve ~300 million reads following manufacturer’s sequencing specification.

  Dataset EGAD00001005481

• Reconstruction of complex rearrangement patterns causing the initiation of clear cell renal cell carcinoma.

  The genomic hallmark of clear cell renal cell carcinoma is the loss of the short arm of chromosome three. This appears to be the earliest genomic event in the formation of these cancers. Often chromosome 3 is lost at the same time as part of chromosome 5 is duplicated via an unbalanced translocation, often with features consistent with focal chromothripsis. In this study, we sought to reconstruct the chromothriptic event that underlies the initiation of kidney cancer. We used long read sequencing (promethION, Oxford Nanopore Technologies) of patient tumour-derived DNA to elucidate how a single cell division error can generate cancer genome complexity.

  Dataset EGAD00001005495

• UCSF Pediatric Bithalamic Glioma Genome Project

  Targeted next-generation sequencing of 13 pediatric bithalamic diffuse gliomas. BAM files of targeted next-generation DNA sequencing data of 13 pediatric gliomas, with multi-region sequencing data from 2 of these cases (17 total tumor samples). Genomic DNA was extracted from formalin-fixed, paraffin-embedded blocks of tumor tissue from using the QIAamp DNA FFPE Tissue Kit (Qiagen). Capture-based next-generation DNA sequencing was performed at the University of California, San Francisco Clinical Cancer Genomics Laboratory, using an assay that targets all coding exons of 480 cancer-related genes, select introns of 47 genes, and TERT promoter with a total sequencing footprint of 2.8 Mb (UCSF500 Cancer Panel). Sequencing libraries were prepared from genomic DNA, and target enrichment was performed by hybrid capture using a custom oligonucleotide library (Nimblegen SeqCap EZ Choice). Captured libraries were sequenced as paired-end 100 bp reads on an Illumina HiSeq 2500 instrument. Duplicate sequencing reads were removed computationally to allow for accurate allele frequency determination and copy number calling.

  Dataset EGAD00001005499

• Mapping the breast cancer metastatic cascade onto circulating tumour DNA using genetic and epigenetic clonal tracking

  Dataset EGAD00001005507

• Sequencing files for "A Unifying Paradigm for Transcriptional Heterogeneity and Squamous Features in Pancreatic Ductal Adenocarcinoma."

  Files from DNA and RNA sequencing from primary tumors and metastases from pancreatic cancer patients along with matched normal tissues.

  Dataset EGAD00001005519

• Bam files for a metastatic bladder cancer patient with BAP1 variants

  This dataset consists of three bam files (two cell-free DNA and one germline DNA) from a metastatic bladder cancer patient with BAP1 variants. Bam files were generated from targeted Illumina sequencing data.

  Dataset EGAD00001005520

• Heterogeneous metabolic signatures are linked to cancer cell differentiation in colorectal cancer

  Colorectal cancer (CRC) is characterized by functional intratumor heterogeneity that shares many similarites with the hierarchical organization of the normal intestinal epithelium. In order to relate transcriptional subtypes to functional tumor cell heterogeneity we applied scRNA-seq to 12 patient-derived CRC spheroid cultures. We identified shared expression programs that relate to intestinal lineages and revealed metabolic signatures that are linked to cancer cell differentiation. In addition, we validated and complemented sequencing results by quantitative microscopy using live-dyes and multiplexed RNA fluorescence in situ hybridization, thereby revealing metabolic compartmentalization and potential cell-cell interactions. Finally, we demonstrate functional differences between metabolically distinct lineage subtypes that might have strong implications for future treatment strategies of CRC.

  Dataset EGAD00001005524

• Ovarian cancer organoid biobank - followup

  WGS of more samples in the ovarian cancer organoid biobank dataset.

  Dataset EGAD00001005707

• MDA whole exome sequencing data for bladder cancer

  46 BAM files from 23 urothelial bladder cancer patients on an immunotherapy clinical trial. PBMC normal samples and solid tumor samples are paired. Alignment was done by BWA with reference genome hg19.

  Dataset EGAD00001005712

• Neoantigen study samples for prostate cancer

  Whole exome sequencing and RNA sequencing data from 30 patients with prostate cancer.

  Dataset EGAD00001005713

• DESIGN-NKI RNA-seq

  RNA-Seq data of 36 HPV-negative HNSCC specimens from patients treated at The Netherlands Cancer Institute, Amsterdam. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for polyA mRNA sequencing.

  Dataset EGAD00001005715

• DESIGN-NKI low coverage WGS

  Low-coverage whole genome sequencing data of 37 HPV-negative HNSCC specimens from patients treated at The Netherlands Cancer Institute. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for WGS to a depth of approx. 0.5X.

  Dataset EGAD00001005718

• NKI-MET HNSCC RNA-Seq

  RNA-Seq data of 8 HNSCC specimens from patients diagnosed with metastatic disease at The Netherlands Cancer Institute, Amsterdam. Primary HNSCC biopsy samples obtained prior to treatment were used for polyA mRNA sequencing.

  Dataset EGAD00001005720

• ART-NKI II HNSCC RNA-Seq

  RNA-Seq data of 25 HNSCC specimens from patients treated at The Netherlands Cancer Institute, Amsterdam and enrolled in the ARTFORCE trial. HNSCC biopsy samples obtained prior to treatment (chemo-radiotherapy) were used for polyA mRNA sequencing.

  Dataset EGAD00001005721

• DESIGN II HNSCC RNA-Seq

  RNA-Seq data of 28 HPV-negative HNSCC specimens from patients treated at the Netherlands Cancer Institute (Amsterdam), VU Medical Centre (Amsterdam) or MAASTRO Clinic (Maastricht) in The Netherlands. HNSCC biopsy samples were obtained prior to treatment (chemo-radiotherapy) for the prospective study within the DESIGN project and used for polyA mRNA sequencing.

  Dataset EGAD00001005722

• Illumina short-reads and 10X Genomics linked-reads sequencing data for MCF7 and a primary breast tumor sample

  This dataset contains whole genome sequencing data from Illumina short-reads sequencing (2X150bp) and 10X Genomics linked-reads sequencing. Both the sequencing technologies were used to sequence MCF7 cell line and a primary breast triple-negative cancer sample. The fastq of paired-end reads for both the samples sequenced with both the technologies is available.

  Dataset EGAD00001005724

• WES of blood and patient-matched cecum tumor tissue samples, Panel NGS of patient-matched cfDNA

  WES using IDT xGen Research Exome on Illumina NovaSeq 2x150bp: Normal sample (buffy coat), cecum tumor biopsy at diagnosis, ileocecal valve region tumor sample at week 19, pericolonic metastasis at week 19, lymph node metastasis at week 19, peritoneal metastasis at week 19. Week 19 samples from hemicolectomy. Deep coverage cfDNA NGS using PanCeq pan-cancer panel on NextSeq 2x150bp: week 2 and week 10.

  Dataset EGAD00001005737

• Whole exome sequencing of advanced gastric cancer

  To define the cellular characteristics of malignant ascites of advanced gastric cancer patients and search for therapeutic strategies, we obtained 5 malignant ascites and 1 cerebrospinal fluid from five patients with gastric cancer. We analyzed single-cell RNA-seq data of 180 cells from 4 malignant ascites and 1 cerebrospinal fluid metastasis using Fluidigm® C1™ System. Whole exome sequencing data was also generated from blood or tumor tissue.

  Dataset EGAD00001005740

• Whole-genome sequencing data for inflammatory breast cancer patients

  This study reports the whole-genome sequencing data for 20 inflammatory breast cancer patients, each of whom has one normal blood sample and one breast tumor sample. Overall, there are 40 files included in this study, in the format of BAM.

  Dataset EGAD00001005749

• Dataset for white blood cell and cell-free DNA analyses for detection of residual disease in gastric cancew

  The dataset for white blood cell and cell-free DNA analyses for detection of residual disease in gastric cancer includes 169 bam files from targeted deep sequencing on the Illumina HiSeq2500. The samples analyzed include genomic DNA from white blood cells and cell-free DNA from longitudinal blood collections of patients with gastric cancer.

  Dataset EGAD00001005750

• Colon Cancer Organoid Cultures and Tumors RNASeq Data

  Four micrograms of total RNA was used for cDNA library construction using the KAPA Stranded mRNA-Seq Kit (KR0960-v3.15), following manufacturer's protocol. The adaptor-ligated libraries were enriched by 6 cycles of polymerase chain reaction (PCR). Libraries were sequenced using the Novaseq 6000 with paired end 151bp reads.

  Dataset EGAD00001005753

• Colon Cancer Organoid Cultures and Tumors Whole Exome Sequencing Data

  Five hundred fifty nanograms of genomic DNA were input for library preparation after fragmentation by Covaris S2, following the KAPA Hyper Prep Kit (KR0961-V1.14) protocols, with selection for a library size range of 250-450 bp. Three hundred nanograms per library DNA each from 12 samples were normalized and combined into a single pool for exome capture using the xGen Lockdown Probes and Reagents based on their standard protocols

  Dataset EGAD00001005754

• 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 EGAD00001005841

• Colon Cancer Organoid Cultures and Tumors Whole Genome Sequencing Data

  Five hundred nanograms of genomic DNA was fragmented by Covaris S2, the fragmented DNAs were performed end-repair, A-tailing at the 3 prime end, adaptors ligation with an IDT dual-indexed UMI adaptor system at the terminal ends. The adapter ligated library with size range 300-750bp were selected by dual-SPRI method. Twenty percent of the size selected PCR-free libraries were enriched by 5 PCR cycles prior to library size assessment by Bioanalyzer Fragment Analyzer. The PCR-free libraries were quantified by qPCR.The PCR-free libraries were denatured and diluted to optimal concentration. Illumina NovaSeq 6000 was used for Pair-End 151bp sequencing.

  Dataset EGAD00001005759

• Hyperpolarized 13C MRI in breast cancer

  Transcriptomics for samples obtained from six patients (MBR01, MBR03, MBR05, MBR07, MBR10, MBR11)

  Dataset EGAD00001005760

• cfDNA analysis reveals relation of POLR1D amplification to bevacizumab resistance in colorectal cancer patients

  Bevacizumab is an approved anti-angiogenic drug for patients with metastasized colorectal cancer (mCRC) targeting VEGF. The survival benefit of anti-VEGF therapy in mCRC patients is limited to a few months and acquired resistance mechanisms are greatly unknown. Using plasma DNA, we studied the evolution of tumor genomes in a cohort of patients with mCRC (n=150) and observed a recurrent focal amplification (8.7% of cases) on chromosome 13q12.2. Analysis of TCGA data (n=619) suggested an association with later stages, which we confirmed by longitudinal plasma analyses. We defined the minimally amplified region and studied the mechanistic consequences of copy number gain of the involved genes. The amplification of one gene, POLR1D, impacted cell proliferation, resulting in upregulation of VEGFA, an important regulator of angiogenesis which has been implicated in the resistance to bevacizumab. In several patients, we observed the emergence of this 13q12.2 amplicon under bevacizumab treatment, which was invariably associated with evolution of therapy resistance. Hence, we describe a novel resistance mechanism against a widely applied treatment in mCRC patients which will impact clinical management .

  Dataset EGAD00001005761

• Single cell PDAC samples

  h5 files from 15 single cell PDAC samples described in "Transcription phenotypes of pancreatic cancer are driven by genomic events events during tumour evolution"

  Dataset EGAD00010001811

• 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

• Exome and Transcriptome for gastric cancer gene-drug analysis

  Newly generated 52 gastric tumor specimens were subjected for targeted-exome and/or whole-transcriptome sequencing

  Dataset EGAD00001005766

• Multi-region sequencing of a RET fusion positive cancer patient

  Deep WGS sequencing (160x) of 2 different sites of disease of a patient with a RET fusion positive cancer. Amplicon sequencing of 19 other sites of the same patients for the RET fusion.

  Dataset EGAD00001005776

• Sequencing files for "The Evolutionary Origins of Recurrent Pancreatic Cancer."

  Files from DNA sequencing from primary tumors and metastases from pancreatic cancer patients along with matched normal tissues. Sequencing files include those derived from whole exome sequencing as well as MSK-IMPACT sequencing.

  Dataset EGAD00001005779

• sWGS of non-cancer controls

  Paired end shallow whole genome sequencing (sWGS) data of cell-free DNA from plasma from self-reporting healthy individuals (MonRec Cohort)

  Dataset EGAD00001005805

• Exploiting evolutionary steering in cancer therapy

  This dataset contains 9 bam files of exome sequencing for an experiment of evolved resistance. Here a barcoded cell line (HCC827 - POT) has been treated under high concentrations of gefitinib (GEF) and trametinib (TRM) until resistance has evolved, as well as under control conditions (DMSO). The dataset contains exome sequencing of confluent cells for three replicates for each anti-cancer drug as well as two replicates of growth under DMSO conditions. The original barcoded cell line (POT) was also exome sequenced and is included in the cohort. Sequencing was performed on the Illumina NovaSeq platform.

  Dataset EGAD00001005782

• The identification of genetic vulnerabilities in head and neck cancers for the development of novel therapies (2020-01-15)

  CRISPR/Cas9 lethality screens in a set of Asian head and neck cancer cell lines to identify novel targets. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005784

• Somatic mutation and clonal evolution in premalignant lung disease - WGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulations of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from the lung tissue of individuals with a variety of lung diseases (COPD, UIP, IPF, Emphysema, pulmonary hypertension). This will allow us to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. Smoking is a large risk factor for developing many of these lung diseases so we are particularly keen to determining whether there is evidence of a smoking signature in these patients. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005786

• Somatic mutation and clonal evolution normal breast tissue TGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >70 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumour development. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005787

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: pancreas_LCM (2020-01-15)

  Samples prepared by LCM - 5 cases for pilot study. Bulk DNA not available. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005789

• Targeted sequencing data of breast tumors with germline BRCA1/2 mutations

  This dataset contains targeted sequencing of breast tumors with germline BRCA1/2 mutations (n = 30) and those without. Breast cancer related genes (n = 115) have been captured and sequenced.

  Dataset EGAD00001005802

• Anaplastic Thyroid Cancer aligned whole exome sequence data

  Whole exome sequence data in fastq format was aligned to the GRCH38 reference genome. Aligned sequence was preprocessed with GATK for Indel Realignment and Base Quality Score Recalibration. Duplicates were marked with Picard Mark Duplicates. Aligned sequence is in bam format. Details of the alignment can be found in he bam header. Tumour samples were classified as Anaplastic Thyroid, Poorly-differentiated or well-differentiated cancers.

  Dataset EGAD00001005791

• NanoString gene expression of PBMC from bladder cancer and RCC patients

  This dataset contains NanoString gene expression of PBMC from patients from IMvigor210, IMvigor211 and IMmotion150 cohorts

  Dataset EGAD00001005976