11769 results for "cancer rna-seq"

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• The patterns and dynamics of genomic instability in metastatic pancreatic cancer

  Pancreatic cancer is an aggressive malignancy with a five-year mortality of 97–98%, usually due to widespread metastatic disease. Previous studies indicate that this disease has a complex genomic landscape, with frequent copy number changes and point mutations, but genomic rearrangements have not been characterized in detail. Despite the clinical importance of metastasis, there remain fundamental questions about the clonal structures of metastatic tumours, including phylogenetic relationships among metastases, the scale of ongoing parallel evolution in metastatic and primary sites, and how the tumour disseminates. Here we harness advances in DNA sequencing to annotate genomic rearrangements in 13 patients with pancreatic cancer and explore clonal relationships among metastases. We find that pancreatic cancer acquires rearrangements indicative of telomere dysfunction and abnormal cell-cycle control, namely dysregulated G1-to-S-phase transition with intact G2–M checkpoint. These initiate amplification of cancer genes and occur predominantly in early cancer development rather than the later stages of the disease. Genomic instability frequently persists after cancer dissemination, resulting in ongoing, parallel and even convergent evolution among different metastases. We find evidence that there is genetic heterogeneity among metastasis-initiating cells, that seeding metastasis may require driver mutations beyond those required for primary tumours, and that phylogenetic trees across metastases show organ-specific branches. These data attest to the richness of genetic variation in cancer, brought about by the tandem forces of genomic instability and evolutionary selection.

  Study EGAS00000000064

• Machine learning to detect the SINEs of cancer

  We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA (cfDNA) through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a novel machine-learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to samples from 5108 individuals, 2037 with cancer and the remainder without cancer. Samples from cancer patients and controls were pre-specified into four cohorts used for: 1) model training, 2) analyte integration and threshold determination, 3) validation, and 4) reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the Validation Cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and 8 common protein biomarkers detected 51% of 1167 cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature – the global reduction of AluS sub-family elements in the circulating DNA of cancer patients. We confirmed this reduction through the analysis of another independent dataset obtained with a very different approach (whole genome sequencing). The evaluation of Alu elements therefore has the potential to enhance the performance of several methods designed for the earlier detection of cancer.

  Study EGAS00001007169

• ATAC-Seq on OCIAML-22 Fractions

  The donor AML patient (unsorted) that was used to generate OCI-AML22 was expanded for 3-4 months, then sorted for the indicated fraction (CD34p: CD34+; CD34m: CD34-)

  Study EGAS00001006511

• Mixture of 2

  Mixture of 2 unrelated individuals sequenced by 10x as a scRNA-seq. The dataset was then processed by Cell Ranger and deconvoluted to yield each individuals genetic profile. The clustering of SNPs is submitted as the processed file. The Sequencing fastqs are submitted as unprocessed files.

  Dataset EGAD00001008726

• Human brain development single cell sequencing

  379 tissue samples from various parts of the developing human embryo brain were dissociated and single cells were collected and processed without bias for mRNA-seq using 10X chromium 3' protocol. Libraries were sequenced on Illumina NovaSeq and reads aligned against the human GRCh38 genome.

  Dataset EGAD00001006049

• HiC-sequencing in human monocyte differentiation

  This dataset consists of in situ HiC-seq data from human monocytes, monocyte-derived dendritic cells as well as monocyte-derived cells that were subjected to siRNA treatment targeting CTCF or RAD21. In total, the data set includes 42 samples.

  Dataset EGAD00001007955

• AML-PMP miRNA-Seq

  miRNA libraries of the AML-PMP project were sequenced on the Illumina HiSeq 2000 instrument, approximately 16 samples per HiSeq lane, to a median depth of 5.7 million reads per library.

  Dataset EGAD00001009772

• Low-pass whole-genome DNA sequencing of cohesin-mutated and wildtype adult AMLs

  Low-pass whole-genome DNA sequencing of cohesin-mutated (STAG2 or RAD21 mutations) and wildtype (CTRL-AML) adult AMLs generated generated from ultrasound-fragmented genomic DNA. Samples were only sequenced shallow (20-40 Mio reads) Used for digital karyotyping and ChIP-seq background/copy-nuber normalization/correction.

  Dataset EGAD00001011207

• Genomics of Prostate Cancer

  This dataset includes genomic data from patients with prostate cancer. Specimens include archival active surveillance biopsies, radical prostatectomy specimens selected based on evidence of PD-L1 expression and/or T lymphocyte infiltrates, and rapid autopsy material from patients deceased of prostate cancer. These data include low pass whole genome sequencing and targeted sequencing with matched normal.

  Study phs002398

• Identifying and Reducing Disparities in Patient-Reported Outcomes Among African American Prostate Cancer Survivors

  This research study aimed to identify disparities in prostate cancer symptom burden and their contributors among African American, Hispanic, and non-Hispanic White prostate cancer survivors. We collected patient-reported outcomes data, focusing on various aspects of the survivors' health and well-being, clinical data, demographics data, and germline genomic data.

  Study phs003745

• Pilot study towards the development of a Companion/Complimentary Diagnosis platform using liquid biopsy with cfDNA for immune checkpoint inhibitor therapy in advanced urothelial cancer

  Preliminary analysis was conducted to develop a cfDNA-based platform capable of predicting gene mutation profiles, TMB, gene expression profiles (molecular subtype diagnosis), and immunohistochemical findings (PD-L1 expression) using the primary lesion tissue of bladder cancer and upper urinary tract epithelial cancer, verifying the feasibility of such development.

  Study JGAS000714

• Target sequencing of 11 hereditary breast cancer genes in Japanese

  We performed a case control association study for variants in coding regions of 11 hereditary breast cancer genes in 7,051 female unselected breast cancer patients, 11,241 female controls, 53 male patients, and 12,490 male controls of Japanese ancestry. The 11 genes were ATM, BRCA1, BRCA2, CDH1, CHEK2, NBN, NF1, PALB2, PTEN, STK11, and TP53.

  Study JGAS000140

• Cell_Line_Sub_Clone_Rearrangement_Screen

  Genomic libraries (500 bps) will be generated from total genomic DNA cancer samples and subjected to paired end sequencing on the llumina GA. Paired reads will be mapped to build 37 of the human reference genome to facilitate the generation of genome wide copy number information, and the identification of novel rearranged cancer genes and gene fusions.

  Study EGAS00001000178

• Cancer sequencing for somatic variant calling

  This dataset includes WES and RNAseq for 11 patients with metastatic melanoma, lung, kidney, and stomach cancers, enrolled in phase I clinical trials of TIL ACT (NCT03475134 & NCT04643574). WES was performed on matched cancer and healthy tissues, whereas RNAseq was performed on cancer tissues, using Illumina HiSeq 2500/4000 and Illumina NextSeq 550 systems.

  Study EGAS00001007101

• Genomic landscape of inflammatory breast cancer by whole-genome sequencing

  Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer, characterized by a highly invasive and metastatic phenotype, but little is known about its genetic drivers. Here we report whole-genome sequencing (WGS) of 20 newly diagnosed IBC biopsies to characterize for the first time the entire genomic landscape of this disease.

  Study EGAS00001004117

• Whole-exome sequencing of paired tumour/blood of 58 T1 stage bladder cancer patients

  To understand the molecular determinants that underpin the clinical heterogeneity of non-muscle-invasive bladder cancer (NMIBC) we carried out whole-exome sequencing (WES) of stage T1 bladder tumours to identify prognostic biomarkers and novel therapeutic targets. The study includes paired tumour/blood samples of 58 T1 stage bladder cancer patients.

  Study EGAS00001005765

• Plasma whole genome sequencing from patients with stage IV colorectal cancer and microsatellite instability

  This study aims to explore genomic features of cancer in plasma WGS data from patients with advanced colorectal cancer. Cell-free DNA (cfDNA) was extracted from plasma using the QIAamp Circulating Nucleic Acid Kit. Libraries were prepared with 5 to 250 ng of cfDNA using the NEBNext DNA Library Prep Kit.

  Study EGAS00001006377

• The Genomic Landscape of Prostate Cancer Brain Metastases

  Study EGAS00001004557

• Cancer Research UK manchester Institute Cancer Biomarker Centre

  Dac EGAC00001001765

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__Oesophageal_adenocarcinoma

  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 (40X and 20X depth respectively). 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.

  Study EGAS00001003702

• Intratumoural heterogeneity and immune modulation in lung adenocarcinoma of female smokers and never smokers

  Lung cancer is still the leading cause of cancer death worldwide despite declining smoking prevalence in industrialised countries. Although lung cancer is highly associated with smoking status, a significant proportion of lung cancer cases develop in patients who never smoked, with an observable bias towards female never smokers. A better understanding of lung cancer heterogeneity and immune system involvement during tumour evolution and progression in never smokers is therefore highly warranted. We employed single nucleus transcriptomics of surgical lung adenocarcinoma (LADC) and normal lung tissue samples from patients with or without smoking history. Immune cells as well as fibroblasts and endothelial cells respond to tobacco smoke exposure by inducing a highly inflammatory state in normal lung tissue. In the presence of LADC, we identified differentially expressed transcriptional programmes in macrophages and cancer-associated fibroblasts, providing insight into how the niche favours tumour progression. Within tumours, we distinguished eight subpopulations of neoplastic cells in female smokers and never smokers. Through pseudotemporal ordering, we inferred a trajectory towards two differentiated tumour cell states implicated in cancer progression and invasiveness. A proliferating cell population sustaining tumour growth exhibits differential immune modulating signatures in both patient groups. Our results resolve cellular heterogeneity and immune interactions in LADC, with a special emphasis on female never smokers and implications for the design of therapeutic approaches.

  Study EGAS00001006331

• Identification of colorectal cancer susceptibility genes in Japanese

  We performed whole genome sequencing of 617 colorectal cancer patients registered in BioBank Japan to identify novel susceptibility genes.

  Study JGAS000699

• Identification of gastric cancer susceptibility genes in Japanese

  We performed whole genome sequencing of 256 gastric cancer patients registered in BioBank Japan to identify novel susceptibility genes.

  Study JGAS000698

• Identification of gastric cancer susceptibility genes in Japanese

  We performed whole genome sequencing of 2067 gastric cancer patients registered in BioBank Japan to identify novel susceptibility genes.

  Study JGAS000701

• ENCORE__New_Targets_for_Effective_Combination_Therapies_in_Tumors_with_Unmet_Medical_Need

  This study utilises 2G-CRISPR screens to identify effective target combinations in KRAS-mutant colorectal and triple-negative breast cancer cell lines.

  Study EGAS00001004775