12321 results for "cancer rna-seq"

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• 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

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Colorectal_LCM (2020-02-20)

  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, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors 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 2020-02-20.

  Dataset EGAD00001005994

• 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

• 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

• 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

• Liquid-based genomic profiling in high-risk localized prostate cancer.

  In this study, we evaluated the feasibility and performance of tissue and liquid-based genomic profiling in patients with high-risk localized prostate cancer. Genomic DNA from 12 FFPE tissue samples and cell-free DNA from 27 plasma samples were analyzed using Illumina TruSight Oncology (TSO) 500 panels.

  Study EGAS50000001712

• 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

• 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

• ICGC Prostate Cancer Whole Genome Sequencing

  Whole Genome Sequencing Illumina HiSeq data from 20 men with prostate cancer. 20 samples were taken from primary tissue obtained at prostatectomy (target sequencing depth 50X) with matched blood control (target sequencing depth 30X). These were submitted for use in the ICGC Pan-Cancer Analysis of Whole Genomes project. Same raw data submitted in EGAD00001001116. As of September 2020, some of the studies using these data include: Wedge et al, Nature Genetics 2018 (PMID: 29662167) Pan-Cancer Analysis of Whole Genomes, Nature 2020 (PMID: 32025007)

  Dataset EGAD00001000892

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

  Study EGAS00001007291

• Liquid Biopsy High Grade Serous Ovarian Cancer WGS

  Liquid Biopsy High Grade Serous Ovarian Cancer WGS.

  Dataset EGAD50000001520

• WGS

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

  Study EGAS00001007211

• 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

• 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

• Construction of Thousands of Single Cell Genome Sequencing Libraries Using Combinatorial Indexing

  Single-cell genome sequencing has proven valuable for the detection of somatic variation, particularly in the context of tumor evolution. Current technologies suffer from high library construction costs which restrict the number of cells that can be assessed and thus impose limitations on the ability to measure heterogeneity within a tissue. Here, we present Single cell Combinatorial Indexed Sequencing (SCI-seq) as a means of simultaneously generating thousands of low-pass single cell libraries for somatic copy number variant detection. We constructed libraries for 16,698 single cells from a combination of cultured cell lines, primate frontal cortex tissue, and two human adenocarcinomas, including a detailed assessment of subclonal variation within a pancreatic tumor. "Reprinted from Vitak, S.A., et. al. 2017 with permission from Nature Methods."

  Study phs001268

• Ribosome profiling shows variable sensitivity to detect open reading frames for conventional and different types of cryptic T cell antigens

  The aim of the study was to explore the sensitivity of ribosomal profiling to detect ORFs for cryptic antigens targeted by T cells in immune responses. Ribosomal profiling was performed for six EBV-LCLs. Detection of conventional and cryptic MiHA-encoding ORFs was compared. The data showed that open reading frames (ORFs) for conventional MiHAs as well as cryptic MiHAs in upstream ORFs were frequently detected with similar sensitivity. However, only a limited number of MiHAs in in reading frames alternative to protein-coding sequences and no ORFs derived from long non-coding RNAs were detected. In conclusion, Ribo-seq for identification of antigen-encoding ORFs is useful to screen for cryptic antigens in upstream ORFs as potential targets for immunotherapy.

  Study EGAS50000000322

• XClone for analyzing somatic copy number alterations

  Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

  Study EGAS00001007854

• Epigenome mapping in purified cells of the digestive and urogenital organs: Participation in International Human Epigenome Consortium (IHEC) (ChIP-seq)

  The aim of this study is to participate in International Human Epigenome Consortium (IHEC, http://ihec-epigenomes.org/), through disclosure of quality epigenome profiles of various normal cell lineages and diseased cells purified from surgically resected materials of multiple Japanese people.

  Study JGAS000027

• Log2-CPM (voom)-normalised read-count matrix of the RNAseq Datasets of CLUSTER JIA pre-MTX cohort

  Samples were sequenced with NovaSeq6000 and the sequencing files were processed using the RSSnextflow workflow (https://gitlab.com/b8307038/rssnextflow). The raw read counts from all batches were combined. Analysis-ready batch-corrected read counts were generated using ComBat-seq. Read counts normalisation for DGE analysis was performed with limma-voom function (log2 CPM) as seen in the pipeline.

  Dataset EGAD50000002170

• Somatic L1 retrotransposition mapping in high-grade serous ovarian carcinoma using LDI-PCR/Nanopore-sequencing (part 2)

  Somatic L1 retrotransposition was mapped in high-grade serous carcinoma (HGSC) tumors using LDI-PCR/Nanopore-seq method. This method identifies somatic L1 insertions originating from two active L1 loci at chr22q12.1 and chrXp22.2. Multiple tumors from the same patient were utilised to understand L1-mediated intrapatient heterogeneity. Long-read data generated by LDI-Nanopore sequencing was aligned to the reference genome (GRCh38). This dataset contains the remaining file of this study.

  Dataset EGAD50000001757

• Transcriptome analysis of human iPSC-derived microglia in a novel human 3D cortical tissue model

  scRNA-seq analysis of human iPSC-derived microglia in a novel, human in vitro 3D cortical tissue model. Samples include three distinct conditions (each as biological duplicates): wild-type microglia extracted from cultures after 1 month (WT_1mo) and 3 months (WT_3mo) of culturing, as well as knock-in microglia extracted from cultures carrying three familial AD mutations in the APP gene (Swedish, Iberian, Arctic; introduced by CRISPR/Cas9-mediated knock-in) after 3 months of culturing (KI_3mo).

  Dataset EGAD50000000677

• Gene expression matrix for Smart-seq2 data of peripheral blood B cells

  The dataset contains scRNA-seq gene expression matrix (csv file) and metadata (csv file) after quality control filtering for data generated with the Smart-seq2 protocol. Transcript expression was quantified with Salmon v0.11.3 using cDNA sequences from GRCh38.94 and k-mer length 25, and was aggregated to gene level and transcript-length-corrected using tximport v1.8.0. The dataset comprises IgA+ transglutaminase 2-specific and other IgA+ B cells from the peripheral blood of 355 cells from two untreated celiac disease patients.

  Dataset EGAD50000000338