10158 results for "cancer rna-seq"

in 47.59 milliseconds.

• Reconstitution of Human Brain Cell Diversity in Organoids via Four Protocols [time-course bulkRNAseq]

  Time-course bulk-RNA sequencing of brain organoids grown in four distinct growth protocols designed to generate dorsal (D) and ventral (V) forebrain, midbrain (M) and striatum (S) tissue at days 13 (for dorsal and ventral)/16 (for midbrain and striatum), 25, 40, 80 and 120. Per per cell line-protocol-timepoint combination three organoids (biological replicates) were grown. For reference, also three samples of the pluripotent stem cells across cell lines, not yet exposed to any protocol, were sequenced (day 0, pluripotent).

  Study EGAS50000000663

• Cancer and germline exomes consisting of FASTQ paired-end reads from melanoma and lung cancer samples

  Efforts to precisely identify tumor human leukocyte antigen presented peptides (HLAp) capable of mediating T cell based tumor rejection still face important challenges. Recent reports suggest that non-canonical cancer HLAp could be immunogenic but their identification requires highly sensitive and accurate mass-spectrometry (MS)-based proteogenomics approaches. Here, we present a MS-based analytical pipeline that can precisely characterize the non-canonical HLAp repertoire, incorporating whole exome sequencing, bulk and single cell transcriptomics, ribosome profiling, and a combination of two MS/MS search tools. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLAp. Albeit often at low levels and in distinct subpopulations of cells, numerous non-canonical HLAp are shared across tumors. This analytical platform holds great promise for the discovery of novel cancer antigens for cancer immunotherapy.

  Study EGAS00001003723

• Human breast transcriptome analysis

  Obesity is a risk factor for the development of post-menopausal breast cancer and is associated with a worse breast cancer prognosis. Here we carried out transcriptome profiling on nontumorous breast tissue to define the molecular changes that occur related to excess body fat and adipose inflammation. A second goal was to determine if blood biomarkers reflect molecular changes in the breast. Increases in a variety of pathways linked to tumor development and progression were found. Moreover, increased expression of several drug targets including aromatase, the rate-limiting enzyme for estrogen biosynthesis, and PD-1 was observed. Various blood biomarkers that are associated with either elevated risk of breast cancer or worse prognosis were altered and correlated with molecular changes in the breast. Collectively, this study helps to explain both the link between obesity and breast cancer and the utility of blood biomarkers for determining risk and prognosis.

  Study EGAS00001004665

• National Human Genome Research Institute Tumor Sequencing Project (TSP) - Lung Adenocarcinoma

  The Tumor Sequencing Project (TSP) Consortium is a collaboration among participants at the Baylor College of Medicine Human Genome Sequencing Center, the Broad Institute Genome Sequencing Platform, the Dana Farber Cancer Institute, the Memorial Sloan-Kettering Cancer Center, the Genome Sequencing Center and Siteman Cancer Center at Washington University, the M.D. Anderson Cancer Center and the University of Michigan Medical Center. The TSP Part A will pilot approaches to large-scale identification of genomic changes in tumors by sequencing the exonic regions of 623 genes in 188 specimens of adenocarcinoma of the lung, as well as using high density SNP genotyping arrays for high resolution identification of changes in chromosomal copy number. The TSP Part B will pilot approaches to tumor characterization of lung adenocarcinoma samples using next-generation sequencing technologies and benchmark those results against Part A data generated with ABI3730 instruments.

  Study phs000144

• Glioma International Case Control Study (GICC)

  The main goals of the GICC Study were: 1) to identify novel genetic risk variants for glioma, as well as validate variants implicated by previous genome-wide association studies of glioma; and 2) to explore biologically relevant gene-gene and gene-environment interactions in glioma susceptibility. The GICC Study includes participants from the following centers: Brigham and Women's Hospital (Boston, Massachusetts), Case Western Reserve University (Cleveland, Ohio), Columbia University (New York, New York), the Danish Cancer Society Research Centre (Copenhagen, Denmark), Duke University (Durham, North Carolina), the University of Texas MD Anderson Cancer Center (Houston, Texas), Memorial Sloan Kettering Cancer Center (New York, New York), the Mayo Clinic (Rochester, Minnesota), NorthShore HealthSystem (Chicago, Illinois), Umea University (Umea, Sweden), the University of California, San Francisco (San Francisco, California), the University of Southern California (Los Angeles, California), and the Institute of Cancer Research (London, United Kingdom).

  Study phs001319

• Methylation Profiles of Cell-Free DNA Using Nanopore Sequencing

  Epigenetic characterization of cell-free DNA (cfDNA) is an emerging approach for detecting and characterizing diseases such as cancer. We developed a strategy using nanopore-based single-molecule sequencing to measure cfDNA methylomes. This approach generated up to hundreds of millions of reads for a single cfDNA sample from cancer patients, an order of magnitude improvement over existing nanopore sequencing methods. We developed a single-molecule classifier to determine whether individual reads originated from a tumor or immune cells. Leveraging methylomes of matched tumors and immune cells, we characterized cfDNA methylomes of cancer patients for longitudinal monitoring during treatment.Control subjects consist of healthy individuals without a positive diagnosis of cancer. cfDNA was obtained through purification of plasma from blood draws. Genomic DNA (gDNA) was derived from extraction of primary tumor tissue or from leukocytes from blood draws.

  Study phs002950

• Single-cell RNA sequencing of bronchoalveolar lavages from COVID-19 patients

  Discriminating mild-moderate from critical COVID-19 disease by innate and adaptive immune single-cell profiling of bronchoalveolar lavages.

  Study EGAS00001004717

• A novel subset of human CD33+ haematopoietic stem cells characterized at single-cell resolution

  This dataset comprises single-cell RNA sequencing of the human Lin-CD34+38-45RA-90+49f+ phenotype isolated from 2 normal cord donors. Library preparation was performed following a modified CEL-Seq2 protocol.

  Dataset EGAD00001003955

• Vγ9Vδ2-T cells in chronic lymphocytic leukemia patients and healthy controls

  RNA sequencing data from Vγ9Vδ2-T cells from chronic lymphocytic leukemia patients and age-mnatched healthy controls. Matched Vγ9Vδ2-T cell samples before and after expansion with autologous monocyte-derived dendritic cells for each donor are included.

  Dataset EGAD00001004325

• Genomic Landscape of Radiation Induced Meningiomas

  EGAS00001002317 - Whole exome sequencing of data of 18 RIMs with matched bloods. Median depth of 112x (range of 110-120). Performed on Illumina HiSeq Platform. EGAS00001002318 - RNA sequencing data of 18 RIMs on the Illumina HiSeq Platform.

  Dataset EGAD00001004358

• Transcriptomic origins of mpMRI visibility

  We profiled the transcriptomes (RNA-sequencing) of 40 clinically significant invisible and visible tumors, all with ISUP Grade 2 disease and treated by radical prostatectomy. Twenty tumors were mpMRI invisible (PI-RADSv2: 1-2), while 20 tumors were visible (PI-RADsv2: 5).

  Dataset EGAD00001004397

• Total RNAseq in the sporadic ALS and healthy control motor cortex

  The motor cortex is the earliest affected brain region in ALS. This dataset contains total RNA sequencing (stranded, 2x101bp) data derived from the motor cortex of 11 sporadic ALS patients and 8 healthy controls.

  Dataset EGAD00001006022

• Potent neutralizing antibodies against SARS-CoV-2

  The COVID-19 pandemic urgently needs therapeutic and prophylactic interventions. Here we report the rapid identification of SARS-CoV-2 neutralizing antibodies by high-throughput single-cell RNA and VDJ sequencing of antigen-enriched B cells from 60 convalescent patients.

  Dataset EGAD00001006130

• MPNST_Data

  WES from 51 cases initially diagnosed as Malignant Nerve Sheath Tumours (MPNST) and RNA sequencing data from 10 MPNST cases. Find more information in article: Lyskjær et al, 2020, J Pathol, "H3K27me3 expression and methylation status in histological variants of malignant peripheral nerve sheath tumours".

  Dataset EGAD00001006253

• Single nucleus and in situ RNA sequencing reveals cell topographies in the human pancreas

  To investigate the cellular composition of the human pancreas, we performed single-nucleus sequencing from snap frozen biopsies of pancreata from adult, neonatal and diseased (chronic pancreatitis) human donors.

  Dataset EGAD00001006396

• Inherited CD28 deficiency in otherwise healthy patients with disseminated warts and giant horns

  this dataset corresponds to 3 patient of HPV-driven warts single cell RNA data generated through the 10X genomics platform and aligned on GRCh38 reference using the cell ranger tools.

  Dataset EGAD00001006644

• ACC RNASeq data

  Samples prepared using TruSeq Stranded Total RNA library kit. Samples sequenced on a HiSeq 2000.

  Dataset EGAD00001008192

• Sequencing data for oesophageal and related samples - Ng, Contino et al (RNA)

  Data supporting: "Interplay of processes shapes structural variations undergoing selection in oesophageal adenocarcinoma" Ng, Contino et al. RNAseq (BAM files) 214 oesophageal adenocarcinoma samples

  Dataset EGAD00001007809

• Sequencing data for oesophageal and related samples - Alex Frankell et al (RNA)

  Data supporting: "The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic." Frankell et al. RNAseq (BAM files) 116 tumours

  Dataset EGAD00001004423

• RNAseq of SCCOHT patient tumors

  This data set contains BAM files of the RNAseq analysis for three SCCOHT patient tumors. Total mRNA was isolated from fresh frozen tumor samples. RNA sequencing was performed using Illumina HiSeq 4000, paired end 150 bp.

  Dataset EGAD00001007829

• Single cell study of infant leukemias

  We have performed single cell RNA-sequencing for infant and childhood B-cell acute lymphoblastic leukemias as well as infant acute myeloid leukemias at diagnosis. The sequencing was performed with 10X Chromium single cell 3’ and 5’ chemistry.

  Dataset EGAD00001007853

• RNAseq of preneoplasia lung adenocarcinoma

  RNA sequencing of 168 pulmonary samples including lung preneoplasia atypical adenomatous hyperplasia (AAH, N=38), adenocarcinoma in situ (AIS, N=22), minimally invasive adenocarcinoma (MIA, N=19) and invasive lung adenocarcinoma (ADC, N=38) and adjacent lung tissues (Normal, N=62).

  Dataset EGAD50000000395

• Single-cell RNA sequencing data of patients with T follicular helper cell lymphomas

  This dataset contains single-cell count data generated by the Cellranger pipeline (v3.1.0, 10X Genomics) from 9 LN and 16 PB samples of 14 patients with T follicular helper cell lymphomas (TFHLs) as well as 7 homeostatic LN (HLN) samples.

  Dataset EGAD50000000402

• Pregnancy-associated melanoma

  The aim of this project is to assess differences in intratumoral immune composition in pregnant melanoma patients versus non pregnant melanoma controls. Samples (N=25) were obtained from a local patient database. From archived FFPE we isolated RNA and performed NGS and bio-informatica data-analysis.

  Dataset EGAD50000000706

• RNAseq of samples from CLL patients treated with ibrutinib in vivo

  The dataset contains RNA sequencing data from 22 paired samples of 11 patients before and during ibrutinib therapy (median time on therapy 2 weeks; range 1-12 weeks). The sequencing was performed in two batches.

  Dataset EGAD50000000879

• Somatic_mutation_and_clonal_evolution_in_the_human_bladder_WES_NOVASEQ

  n this study, we aim to characterise the landscape of mutation and clonal selection in the human bladder. The study includes targeted sequencing of laser-dissected microbiopsies from the bladder. The samples utilised in this study will include urothelium from transplant donors with no history of bladder cancer and cystectomy specimens from patients with bladder cancer.

  Study EGAS00001003456

• Genomic characterization of metastatic breast cancers

  While large efforts have been done to characterize early breast cancer, little is known about the genomic landscape of metastatic breast cancer. In the present study, we performed whole exome sequencing of 629 metastatic breast cancers, in order to identify new candidate targets and better stratify patients eligible for innovative therapies.

  Study EGAS00001003290

• Tracing early predictors of glioma evolution under therapy

  Clonal evolution drives cancer progression and therapeutic resistance. Recent studies revealed divergent longitudinal trajectories in gliomas, but early molecular traits steering post-treatment cancer evolution remain unclear. We analyzed sequencing data of 544 initial-recurrent adult diffuse glioma pairs to identify genomic and transcriptomic early predictors of tumor evolution in each molecular subtype.

  Study EGAS00001006894

• AVENIO Expanded ctDNA panel sequence alignments (BAMs)

  The AVENIO ctDNA Expanded Kit is a next-generation sequencing (NGS) liquid biopsy assay with a 77 gene panel (192 kb) containing genes in U.S. National Comprehensive Cancer Network (NCCN) Guidelines and emerging cancer biomarkers. This pan-cancer assay was applied to 100 plasma samples from patients with lung cancer undergoing treatment in the OSCILLATE trial. After 150 bp paired-end sequencing, reads were aligned to the human genome reference with the AVENIO Oncology Analysis Software. These files are the sorted non-deduplicated alignments generated by the analysis software used for subsequent variant, indel and CNV calling.

  Dataset EGAD50000000138

• cell-Free DNA Genomic Profiling and its Clinical Implementation in Advanced Prostate Cancer

  Sixty-eight patients with advanced prostate cancer in castration-resistant or castration-sensitive settings undergoing treatment at the University Hospital Basel or the St. Claraspital Basel (Switzerland) were selected for targeted parallel sequencing analysis on liquid biopsy (plasma cfDNA) and matched formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples.

  Study EGAS50000000234

• Angiopredict: predicting response for bevacizumab treatment

  In ANGIOPREDICT, academic cancer biologists and industry-based biotechnology researchers worked with clinicians to identify biomarkers to predict whether individual metastatic colorectal cancer patients will respond positively to bevacizumab containing therapy. In this retrospective study, copy number profiles were determined using whole genome shallow sequencing, and progression free survival and treatment data was collected.

  Study EGAS00001002724

• Long-term Survival Update and Extended RAS Mutational Analysis of the CAIRO2 Trial: Addition of Cetuximab to CAPOX/Bevacizumab in Metastatic Colorectal Cancer

  Here we present updated survival of the CAIRO2 trial and assessed whether the addition of anti-EGFR to anti-VEGF therapy could still be an effective treatment option for patients with extended RAS/BRAF wildtype and left-sided metastatic colorectal cancer (mCRC).

  Study EGAS50000000775

• IMPRESS: Improved methylation profiling using restriction enzymes and smMIP sequencing, combined with a new biomarker panel, creating a multi-cancer detection assay

  Background Despite the worldwide progress in cancer diagnostics, more sensitive diagnostic biomarkers are needed. The methylome has been extensively investigated in the last decades, but a low-cost, bisulfite-free detection method for multiplex analysis is still lacking. Methods We developed a methylation detection technique called IMPRESS, which combines methylation-sensitive restriction enzymes and single-molecule Molecular Inversion Probes. We used this technique for the development of a multi-cancer detection assay for eight of the most lethal cancer types worldwide. We selected 1791 CpG sites that can distinguish tumor from normal tissue based on DNA methylation. These sites were analysed with IMPRESS in 35 blood, 111 tumor and 114 normal samples. Finally, a classifier model was built. Results We present the successful development of IMPRESS and validated it with ddPCR. The final classifier model discriminating tumor from normal samples was built with 358 CpG target sites and reached a sensitivity of 0.95 and a specificity of 0.91. Moreover, we provide data that highlight IMPRESS’s potential for liquid biopsies. Conclusions We successfully created an innovative DNA methylation detection technique. By combining this method with a new multi-cancer biomarker panel, we developed a sensitive and specific multi-cancer assay, with potential use in liquid biopsies.

  Study EGAS50000000624

• WGS

  Whole-genome sequencing of pancreatic cancer organoids and matched germline controls.

  Study EGAS00001007211

• Breast Cancer Follow Up Series

  Breast Cancer Follow Up Series

  Dataset EGAD00001000066

• Cancer Single Cell Sequencing

  Cancer Single Cell Sequencing

  Dataset EGAD00001000067

• Lung Cancer Whole Genomes

  Lung Cancer Whole Genomes

  Dataset EGAD00001000144

• Elucidating the heterogeneity of immunotherapy response and immune-related toxicities by longitudinal ctDNA and immune cell compartment tracking in lung cancer

  Study EGAS00001007291

• CLL Cancer Whole Genome Sequencing

  CLL Cancer Whole Genome Sequencing

  Dataset EGAD00001000013

• PanCuRx Translational Research Initiative

  The goal of the PanCuRx TRI is to seek solutions to the high fatality rate of pancreatic adenocarcinoma (PDAC), the most common type of pancreatic cancer, by generating new knowledge about the genetics and biology of the disease, mechanisms of how tumours grow and tailored treatment options. PDAC is the fourth leading cause of cancer death in Canada and is expected to become the second-leading cause of cancer death within the next decade. The current five-year survival rate of eight per cent is the lowest of all epithelial cancers. There are major clinical challenges to treating the disease, including the fact that PDAC spreads to other parts of the body early so surgical removal of the tumour benefits few patients, that PDAC has proven relatively resistant to systemic therapies such as chemotherapy and that there has been limited benefit from the use of newer molecular targeted agents. The PanCuRx team has created the world’s first resource of tumour-purified PDAC whole genomes; more than 500 PDAC samples, with combined clinical annotation, have been successfully sequenced and analyzed at the Ontario Institute for Cancer research in Toronto. The resulting clinical genomic resource created from this data has led to multiple new findings that have established a framework to better understand PDAC biology and lays the foundation for new genomic approaches to better understanding the disease. The team also launched a clinical trial called COMPASS (Comprehensive Molecular Characterization of Advanced Ductal Pancreas Adenocarcinoma for Better Treatment Selection: A Prospective Study), which is recruiting patients with advanced PDAC (both locally advanced and metastatic) at The Princess Margaret Cancer Centre (PM) in Toronto. The study uses the molecular results from individual patient tumours to guide selection of better second line treatment on a case-by-case basis. The primary objective of the initial study was to assess the feasibility of collecting tumour materials from patients with advanced PDAC and delivering results within 56 days of biopsy. Having achieved this objective, the trial has expanded to other cancer centres across Canada. Pancreatic cancer is one of the most deadly forms of cancer and outcomes have not improved in four decades. The PanCuRx initiative is providing a better understanding of the disease and actively applying new knowledge to cancer patients through clinical trials. This knowledge will help to improve quality of life and prolong survival for pancreatic cancer patients worldwide.

  Study EGAS00001002543

• Whole Genome Scan for Pancreatic Cancer Risk in the Pancreatic Cancer Cohort Consortium and Pancreatic Cancer Case-Control Consortium (PanScan)

  Within the framework of the NCI-sponsored Cohort Consortium, investigators from 12 prospective epidemiologic cohorts formed the Pancreatic Cancer Cohort Consortium in 2006. This study, also known as "PanScan", is funded by the National Cancer Institute (NCI) and involves conducting a genome-wide association study (GWAS) of common genetic variants to identify markers of susceptibility to pancreatic cancer. In 2007, the study was expanded to include 8 case-control studies. The study team includes scientists from the cohorts comprising the Consortium, the NCI and the Pancreatic Cancer Case Control Consortium (PanC4). PanScan I and II were conducted in 12 cohort studies and 8 case-control studies, leading to the discovery of four novel regions in the genome associated with risk for pancreatic adenocarcinoma. The third phase of PanScan (PanScan III) was conducted using recently identified incident pancreatic cancer cases drawn from fourteen cohorts from the cohort consortium, including nine prospective cohorts who participated in PanScan I, and five newly joined cohorts. The nine cohort studies that participated in PanScan I and had new genotyping of cases in PanScan III include ATBC, CPS-II, EPIC, HPFS, NHS, PHS, PLCO, SMWHS, and WHI; the five newly joined cohort studies include the Agricultural Health Study (AHS), the Multiethnic Cohort Study (MEC), the Melbourne Collaborative Cohort Study (MCCS), the Vitamins and Lifestyle Study (VITAL), and Selenium and Vitamin E Cancer Prevention Trial (SELECT). In addition to the cases from cohorts, we also included cases from the Gastrointestinal Cancer Clinic of Dana-Farber Cancer Institute Study (DFCI-GCC); from the University Hospital in Heidelberg, Germany, which is part of a larger European clinical case-control study (PANDoRA); and from clinic-based cases from eastern Spain (PANKRAS-II). The dbGaP datasets available include all subjects previously made available from PanScan I and II, plus 1,582 new incident pancreatic cancer cases of European descent from prospective cohorts, case-control studies or case series (genotyped as part of PanScan III). Also included are 61 pancreatic cancer cases and 67 control subjects from PanScan I as well as 173 pancreatic cancer cases from PanScan III of Asian ancestry from the Shanghai Men's and Women's Health Study (Supplemental Table 10, Wolpin et al. (Nat Genet, 2014)). The control population used in the analysis for the Wolpin et al. manuscript included cancer-free individuals from the prospective cohorts that contributed pancreatic cancer cases to PanScan III and controls from the Spanish Bladder Cancer SBC/EPICURO study that were previously genotyped using the OmniExpress, Omni 1M or Omni 2.5M SNP arrays. The data from these control subjects were posted to dbGaP under the GWAS in which they were initially genotyped and will not be made available in duplicate under this dbGaP study.The summary statistics for PanScan I-III were generated as detailed in Wolpin BM. et al., Genome-wide association study identifies multiple susceptibility loci for pancreatic cancer, Nature Genetics 2014; 46(9):994-1000 (https://www.nature.com/articles/ng.3052), and Klein, A. et al., Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer, Nature Communications, 2018;9(1):556 (https://www.nature.com/articles/s41467-018-02942-5). The dataset includes results from an association study of 5,117 individuals diagnosed with pancreatic ductal adenocarcinoma (PDAC) and 8,845 control individuals, or a total of 13,962 subjects of European ancestry (as compared to the genotype and phenotype information under this project that includes 9,437 individuals (PanScan I and II PDAC case and control individuals and PanScan III PDAC cases only). This is due to the fact that PanScan III “borrowed” GWAS data from control individuals genotyped separately from the PanScan GWAS project and are therefore not included as raw genotypes in phs000206.v5.p3. Association analysis was performed separately for PanScan I-II and PanScan III, followed by a meta-analysis of the two datasets. Results were filtered based on a minor allele frequency (MAF) < 0.01, an imputation INFO score < 0.3 and a heterogeneity P-value < 1x10-10 leaving a total of 9,758,390 variants. Columns in the summary statistics dataset are as follows: ID: variant rsIDChr: chromosome numberPosition: position in the chromosome, genome build GRCh37/hg19MarkerName: variant identifierAllele1: reference alleleAllele2: alternative alleleFreq1: allele frequency for allele2FreqSE: standard error of the allele frequencyMinFreq: the minimal allele frequency across studiesMaxFreq: the max allele frequency across studiesEffect: effect size for allele2StdErr: standard errorP-value: meta-analysis p-valueDirection: summary of effect direction for each studyHetISq: I^2 statistic which measures heterogeneity on scale of 0-100%HetChiSq: chi-squared statistic in simple test of heterogeneityHetDf: degrees of freedom for heterogeneity statisticHetPVal: P-value for heterogeneity statistic

  Study phs000206

• Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients

  Molecular cancer paper (https://doi.org/10.1186/s12943-021-01327-5): This dataset contain shallow whole-genome sequencing (sWGS) of plasma cell-free DNA from cancer patients and healthy subjects, obtained with both Nanopore and Illumina technology. A total of 6 cancer patients and 5 healthy subjects have been sequenced with Nanopore; 4 of the cancer patients have been also sequenced with Illumina. In addition, genomic DNA from white blood cells of one healthy subjects, genomic and 160bp DNA from HEK cells have been sequenced with Nanopore. Genome Biology paper: 3 additional healthy samples have been sequenced (HU), two different bioinformatic pipeline were applied. 2019: Fastqs from the molecular cancer paper were re-demultiplexed and adapter-trimmed (using guppy for multiplex samples, and porechop for singleplex) preserving 5' ends to allow fragmentomics analysis. HAC: All the samples were basecalled with the same updated High Accuracy model (the latest at the time of the analysis) and post-processed as the 2019 dataset. Raw FAST5 are currently available upon request, but will be uploaded soon.

  Dataset EGAD00001006888

• MSI-Exome-DAC Team Microsatellites Instability and Cancer, INSERM Hospital Saint-Antoine, Paris 12, France Carte d'identité des tumeurs, French league against cancer, Paris France

  Dac EGAC00001000665

• CLL cancer Sample Sequencing

  CLL cancer Sample Sequencing

  Dataset EGAD00001000004

• Various Cancer Fusion Gene Sequencing

  Various Cancer Fusion Gene Sequencing

  Dataset EGAD00001000005

• Genentech gallbladder cancer study - exome

  Genentech gallbladder cancer study - exome

  Dataset EGAD00001004853

• Methylation profiles in blood (breast cancer cases)

  Raw methylation data from blood samples in breast cancer cases.

  Dataset EGAD00010002470

• Circulating tumor DNA, pathological and immunologic responses to neoadjuvant nivolumab or nivolumab plus relatlimab and chemoradiotherapy in resectable esophageal/gastroesophageal junction cancer

  Study EGAS00001007299

• Testing_the_feasibility_of_genome_scale_sequencing_in_routinely_collected_FFPE_cancer_specimens_versus_matched_fresh_frozen_samples

  Testing the feasibility of genome-scale sequencing in routinely collected formalin-fixed paraffin-embedded (FFPE) cancer specimens versus matched fresh-frozen samples using whole exome and targeted exome capture prior to Illumina sequencing.

  Study EGAS00001000173

• Genome and transcriptome sequence data from a non small cell lung cancer patient

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

  Dataset EGAD00001001963

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

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

  Dataset EGAD00001001964

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

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

  Dataset EGAD00001001969

• Genome and transcriptome sequence data from a melanoma skin cancer - squamous cell carcinoma patient

  Genome and transcriptome sequence data from a melanoma skin cancer - squamous cell carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001002018

• Genome and transcriptome sequence data from a GI primary (prev breast cancer) patient

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

  Dataset EGAD00001002979

• Genome and transcriptome sequence data from a low grade serous ovarian cancer patient

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

  Dataset EGAD00001003020

• Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient

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

  Dataset EGAD00001003054

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

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

  Dataset EGAD00001003639

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

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

  Dataset EGAD00001003617

• Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient

  Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003650

• Genome and transcriptome sequence data from a metastatic clear cell ovarian cancer patient

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

  Dataset EGAD00001003672

• Genome and transcriptome sequence data from a low grade serous ovarian cancer patient

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

  Dataset EGAD00001003680

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

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

  Dataset EGAD00001003682

• Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient

  Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003717

• Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient

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

  Dataset EGAD00001003713

• Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient

  Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004626

• Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient

  Genome and transcriptome sequence data from a metastatic non-small cell lung cancer patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004620

• Genome and transcriptome sequence data from a low-grade serous ovarian cancer patient

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

  Dataset EGAD00001004697

• Genome and transcriptome sequence data from a high grade serous ovarian cancer patient

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

  Dataset EGAD00001004638

• Genome and transcriptome sequence data from a non-small cell lung cancer patient

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

  Dataset EGAD00001004672

• Genome and transcriptome sequence data from an adenocarcinoma of primary unknown cancer patient

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

  Dataset EGAD00001002577

• Genome and transcriptome sequence data from an adenocarcinoma of unknown primary cancer patient

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

  Dataset EGAD00001002606

• Genome and transcriptome sequence data from an adenocarcinoma of unknown primary cancer patient

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

  Dataset EGAD00001002611

• Sequencing of Plasma DNA from breast cancer patients

  Dataset contains plasma DNA whole genome sequencing on 4 breast cancer patients. It also includes matched germline and tumour whole genome sequencing data. Two benign cancer patients were also sequenced and their plasma DNA and matched germline whole genome sequencing data are included in the dataset. Samples were sequenced on Illumina HiSeqX Ten.

  Dataset EGAD00001006869

• Low-coverage whole-genome sequencing of cancer and healthy plasma circulating DNA

  Plasma samples from patients with melanoma (stage II/III/IV) and breast cancer (stage IV) as well as healthy individuals were subjected to low-coverage whole-genome sequencing (less than 10x average depth). This dataset contains raw fastq files from 39 breast cancer, 127 melanoma and 42 healthy control plasma samples.

  Dataset EGAD00001011817

• Target sequencing of ROS1-rearranged lung cancer patients

  We performed target capture sequencing of ROS1-rearranged non-small cell lung cancer patients to develop and examine the efficacy of new molecular targeted drug against ROS1 tyrosine kinase.

  Study JGAS000189

• Whole exome and transcriptome sequencing of biliary tract cancer

  To uncover the full repertoire of genetic and transcriptomic landscape of biliary tract cancer (BTC), we molecularly characterized BTC based on genetic and transcriptomic alteration profiles.

  Study EGAS00001000950

• Genome and transcriptome sequence data from a lung cancer patient

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

  Dataset EGAD00001004650

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001004711

• Genome and transcriptome sequence data from a lung cancer patient

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

  Dataset EGAD00001001961

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001002532

• Genome and transcriptome sequence data from a metastatic cervical cancer patient

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

  Dataset EGAD00001002561

• Genome and transcriptome sequence data from a metastatic prostate cancer patient

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

  Dataset EGAD00001002638

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

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

  Dataset EGAD00001005833

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001002997

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003003

• Genome and transcriptome sequence data from a metastatic gastric cancer patient

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

  Dataset EGAD00001003013

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003037

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003039

• Genome and transcriptome sequence data from a breast cancer patient

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

  Dataset EGAD00001003026

• Genome and transcriptome sequence data from a metastatic lung cancer patient

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

  Dataset EGAD00001003045

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003068

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003070

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003652

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003666

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003671

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003721

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001003732

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

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

  Dataset EGAD00001003740

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001004623

• Genome and transcriptome sequence data from a metastatic gastric cancer patient

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

  Dataset EGAD00001004625