4756 results for "*oma"

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• ACT-Discover: identifying karyotype heterogeneity in pancreatic cancer evolution using ctDNA

  Background Liquid biopsies, and the dynamic tracking of somatic mutations within circulating tumour DNA (ctDNA) can provide insight into the dynamics of cancer evolution and the intra-tumour heterogeneity that fuels treatment resistance. However, identifying and tracking dynamic changes in somatic copy-number alterations (SCNAs), which have been associated with poor outcome and metastasis, using ctDNA is challenging. Pancreatic adenocarcinoma is a disease which has been considered to harbour early punctuated events in its evolution, leading to an early fitness peak, with minimal further subclonal evolution. Methods To interrogate the role of SCNAs in pancreatic adenocarcinoma cancer evolution, we applied whole-exome sequencing of 55 longitudinal cell-free DNA (cfDNA) samples taken from 24 patients (including 8 from whom a patient-derived xenograft (PDX) was derived) with metastatic disease prospectively recruited into a clinical trial. We developed a method, Aneuploidy in Circulating Tumour DNA (ACT-Discover), that leverages haplotype phasing of paired tumour biopsies or PDXs to identify SCNAs in cfDNA with greater sensitivity. Results SCNAs were observed within 28 of 47 evaluable cfDNA samples. Of these events, 30% could only be identified by harnessing the haplotype-aware approach leveraged in ACT-Discover. The exceptional purity of PDX tumours enabled near-complete phasing of genomic regions in allelic imbalance, highlighting an important auxiliary function of PDXs. Finally, although the classical model of pancreatic cancer evolution emphasises the importance of early, homogenous somatic events as a key requirement for cancer development, ACT-Discover identified substantial heterogeneity of SCNAs, including parallel focal and arm-level events, affecting different parental alleles within individual tumours. Indeed, ongoing acquisition of SCNAs was identified within tumours throughout the disease course, including within an untreated metastatic tumour. Conclusions This work demonstrates the power of haplotype phasing to study genomic variation in cfDNA samples and reveals undiscovered intra-tumour heterogeneity with important scientific and clinical implications. Implementation of ACT-Discover could lead to important insights from existing cohorts, or underpin future prospective studies seeking to characterise the landscape of tumour evolution through liquid biopsy.

  Study EGAS00001007077

• Genome-wide cell-free DNA biological patterns in patients with cancer

  The structure of cell-free DNA (cfDNA) is altered in the blood of cancer patients. From whole genome sequencing we retrieved the cfDNA fragment-end composition using a new software (FrEIA), as well as the cfDNA size and tumor fraction in three independent cohorts (n=925 cancer from >10 types and 321 control samples). At 95% specificity, we detect 72% of cancer samples using at least one cfDNA measure, including 64% of early-stage cancer (n=220). cfDNA detection correlates with a shorter overall (p=0.018) and recurrence-free (p=0.006) survival in patients with resectable esophageal adenocarcinoma. Integrating cfDNA measures with machine learning in an independent test set (n=396 cancer, 90 controls) achieve a detection accuracy of 82% and AUROC of 96%. In conclusion, harnessing the biological features of cfDNA can improve at no-extra cost the diagnostic performance of liquid biopsies.

  Study EGAS00001007400

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

  Data supporting: "Patient-specific detection of cancer genes reveals recurrently perturbed processes in esophageal adenocarcinoma." Mourikis et al. RNAseq (BAM files) 137 samples

  Dataset EGAD00001004776

• Whole genome sequencing data of lung adenocarcinomas

  This dataset includes the whole-genome sequencing data from a study entitled "Tracing Oncogene Rearrangements in the Mutational History of Lung Adenocarcinoma". Whole-genome sequencing libraries were generated by PCR-free methods, and sequencing run was made in HiSeq X Ten machines. PCR duplicates-marked, indel-realigned, and base-recalibrarted BAM files are provided in our dataset.

  Dataset EGAD00001004793

• Genome and transcriptome sequence data from a breast ductal carcinoma patient

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

  Dataset EGAD00001002047

• Recurrent somatic mutations in POLR2A define a distinct subset of meningiomas: Somatic exome variants

  Somatic variants called from whole-exome sequencing of meningioma-blood pairs

  Dataset EGAD00001002650

• Genomic characterisation of MGUS

  The hematological malignancy multiple myeloma (MM), also called Kahler's disease or plasma cell (PC) myeloma, is characterized by a clonal expansion of PCs originating in the bone marrow (BM). The expansion of these cells leads to an overproduction of antibodies and results in typical symptoms such as anemia, renal failure and bone lesions. All cases of MM are preceded by the asymptomatic, non-malignant pre-stage monoclonal gammopathy of undetermined significance (MGUS). Of all MGUS patients, only 1% per year will progress to MM. Despite efforts to elucidate the molecular mechanisms underlying the MGUS-to-MM progression, its pathogenesis still remains largely unknown. Additionally, the genetic profiles of MGUS patients have only been limitedly investigated due to the only incidental finding of MGUS, the difficulties in BM sampling and isolating a sufficient number of aberrant PCs from the BM aspirates of MGUS patients. Consequently, reliable biomarkers to individually predict which MGUS patients will progress to MM and which will not, are lacking. Therefore, it is highly required to study the molecular pathogenesis of MGUS and the role of genetic events in relation to the malignant transformation to MM.

  Dataset EGAD00001006363

• Leiomyosarcoma Cancer Transcriptome Sequencing

  RNA-Seq data (fastq raw read sequences) for French ICGC leiomyosarcoma cancer sequencing project, 78 samples representing 67 donors. Sequencing was performed on Illumina HiSeq. The libraries were then sequenced with a 2 x 75bp paired-end protocol to a minimum mean reads of 50 million.

  Dataset EGAD00001003192

• Single-cell mRNA-sequencing of neuroblastoma

  Profiling of childhood neuroblastoma by single-cell RNA sequencing

  Dataset EGAD00001009766

• Esophageal adenocarcinoma plasma cfDNA samples - PERFECT cohort and nCRT cohort

  The dataset contains 295 plasma cfDNA samples from various stages of resectable esophageal adenocarcinoma from the PERFECT cohort and the nCRT cohort. Shallow WGS was performed on an Illumina Novaseq S4 PE150bp. Samples are provided as raw reads without any prior processing.

  Dataset EGAD00001008316

• Genome and transcriptome sequence data from a metastatic leiomyosarcoma patient

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

  Dataset EGAD00001004916

• Single-cell RNA-sequencing of a multi-region pleural mesothelioma case

  Single-cell RNA-seq (Smart-Seq2) from 3 distant biopsies (costal, mediastinal, diaphragmatic) of a pleural mesothelioma case.

  Dataset EGAD00001015519

• Whole exome sequencing of endometriod ovarian cancer tumours

  Whole Exome sequencing data of tumour samples for 112 patients with endometrioid ovarian carcinoma in FASTQ format. Data was derived as summarized below: Library Preparation: Libraries were prepared from each DNA sample using the Illumina TruSeq Exome Library Prep kit (#FC-150-1002) according to the provided protocol using modifications for working with FFPE sourced material. Libraries were quantified using the Qubit 2.0 Fluorometer and the Qubit DNA HS assay (#Q32854) and the size distribution of fragments was assessed using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Library QC: Exome-captured sequencing library pools were quantified using the Qubit 2.0 Fluorometer and the Qubit DNA HS assay (#Q32854) and the size distribution of fragments was assessed using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Fragment size and quantity measurements were used to calculate molarity for each library pool. Sequencing: Sequencing was performed using the NextSeq 500/550 High-Output v2 (150 cycle) Kit (# FC-404-2002) on the NextSeq 550 platform (Illumina Inc, #SY-415-1002).

  Dataset EGAD00001006389

• T-VEC CBCL scRNA

  We are presenting raw and processed data of our study where we analyze fine-needle aspirate (FNA) samples of primary cutaneous B-cell lymphoma patients undergoing oncolytic virotherapy (https://clinicaltrials.gov/ct2/show/NCT03458117). We are uploading single cell RNA-sequencing and immune repertoire profiling data of 29 FNA samples from four pCBCL patients. The four patients have three different subtypes of primary cutaneous B-cell lymphoma: pCDLBCL-LT, pCFCL and pCMZL. The samples are taken at different time points following T-VEC injection (from baseline up to 91 days after injection). We are uploading the sequences in BAM format and the outputs of the cellranger pipeline (count matrices and filtered VDJ contigs) in CSV format.

  Dataset EGAD00001006829

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Kidney (1)

  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.

  Dataset EGAD00001006427

• Whole Exome Sequencing data of glioma from the Chinese Glioma Genome Atlas (CGGA) project

  Glioma is the most common and aggressive brain cancer in adults. While primary glioma has been widely studied, molecular characterization of recurrent glioma is still rare. The high-quality sequencing data that we generated provides a useful resource for the community. The CGGA project contains over 2,000 samples from Chinese cohorts. It totally includes the whole-exome sequencing (286), DNA methylation (159), mRNA sequencing (1,018), mRNA microarray (301) and microRNA microarray (198) and matched clinical data. CGGA removes the barriers to researchers, providing rapid and convenient access to high-quality functional genomic data resources for biological research and clinical applications.

  Dataset EGAD00001006445

• Impact of genetic variants in clinical outcome of a cohort of patients with oropharyngeal squamous cell carcinoma

  Caracterization of somatic variants in patients with OpSCC

  Dataset EGAD00001006151

• The mutational landscape of human somatic and germline cells

  During the course of a lifetime normal human cells accumulate mutations. Here, using multiple samples from the same individuals we compared the mutational landscape in 29 anatomical structures from soma and the germline. Two ubiquitous mutational signatures, SBS1 and SBS5/40, accounted for the majority of acquired mutations in most cell types but their absolute and relative contributions varied substantially. SBS18, potentially reflecting oxidative damage, and several additional signatures attributed to exogenous and endogenous exposures contributed mutations to subsets of cell types. The mutation rate was lowest in spermatogonia, the stem cell from which sperm are generated and from which most genetic variation in the human population is thought to originate. This was due to low rates of ubiquitous mutation processes and may be partially attributable to a low cell division rate of basal spermatogonia. The results provide important insights into how mutational processes affect the soma and germline.

  Dataset EGAD00001006642

• Phenotypic characterisation of LRRN4CL over-expression

  This study aims to use RNAseq to identify differentially expressed transcripts in human melanoma cells that over-express the cell surface protein, LRRN4CL, relative to empty-vector control cells, to provide mechanistic insight into how LRRN4CL over-expression confers enhanced pulmonary metastatic colonisation abilities.

  Dataset EGAD00001006249

• Strand-specific RNA Sequencing of initial and recurrent gliomas

  Four RNA-sequencing datasets from two patients with initial low-grade glioma and copy number alteration at IDH1 upon recurrence. Data is provided as bam files.

  Dataset EGAD00001003764

• RCRF release 1

  Rare Cancer Research Foundation: leiomyosarcoma, large cell neuroendorcrine carcinoma, clear cell carcinoma

  Dataset EGAD00001010199

• Analysis of resistance to PLX4032 (2019-08-21)

  We have collected material from a patient who had BrafV600E mutant melanoma that was treated with PLX4032. We have germline DNA from the patient and DNA and RNA from distinct lesions before and after treatment with PLX4032. We would like to exome sequence these samples to gain a snap shot of the mechanisms of resistance that are operative. . This dataset contains all the data available for this study on 2019-08-21.

  Dataset EGAD00001005278

• Immune profiling reveals enrichment of distinct immune signatures in oral epithelial dysplasia

  The dataset contains transcriptomic information of 36 oral potentially malignant disorders (OPMD), 14 fibroepithelial polyps (FEP), and 6 early stage oral squamous cell carcinoma (OSCC) from the Asian population. Total RNA was extracted from formalin-fixed paraffin embedded (FFPE) tissue sections. RNA libraries were prepared using the NEB NextUltra RNA kit with Illumina Ribo-Zero rRNA removal as per manufacturer’s instructions. RNA sequencing was performed on the HiSeq2500 platform to generate paired-end 150 nucleotides reads and with a coverage of 50 million reads per sample. Uploaded bam files have been mapped to the GRCh38 human genome using TopHat2. Clinical and demographic data for these patients are available from the associated publications or by request.

  Dataset EGAD00001007970

• 10x Single Cell Gene Expression Library SCRNA10X_CS_CHIP0178_003A

  10x Single Cell Gene Expression library SCRNA10X_CS_CHIP0178_003A for Follicular lymphoma patient sample TFRI_Cont_8

  Dataset EGAD00001010209

• Amplicon sequencing of adenoma to carcinoma paired samples from colon

  Colorectal cancer panel sequencing of 100 adenoma and carcinoma samples . Cancer Hot Spot panel sequencing of 7 samples from one poly.

  Dataset EGAD00001004848