7790 results for "canc*"

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• Evaluation of capture and amplicon-based targeted sequencing methods on formalin-fixed tumours

  Three capture (Agilent’s SureSelectXT HS, Illumina’s Nextera Rapid Capture Custom, and New England Biolabs’ Next Direct Custom) and one amplicon-based (Qiagen’s Human Breast Cancer Panel) targeted sequencing methods on 6-8 paired blood and FFPE from the Malaysian Breast Cancer Cohort.

  Dataset EGAD00001006879

• Shallow whole genome sequencing on stage 1 epithelial ovarian cancer

  BAM files (aligned against the hg38 genome) from a shallow whole-genome sequencing experiment (median depth 0.5X) on 218 samples from Stage 1 epithelial ovarian cancer biopises. Samples labeled "bis" or "tris" with the same ID are relapses; "left" or "right" samples indicate, in the case of bilateral tumor, from which ovary the sample was taken.

  Dataset EGAD00001006881

• Panel sequencing data of IMPACT2 patients

  Patient samples were sequenced by Foundation Medicine, Inc. (Cambridge, MA), using FoundationOne CDx, a comprehensive NGS-based in vitro diagnostic device designed to capture cancer genes.

  Dataset EGAD00001006887

• 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

• PIK3CA SiMSen-Seq

  Simple, Multiplexed, PCR-based barcoding of DNA for Sensitive mutation detection using Sequencing (SiMSen-Seq) of 11 PIK3CA hotspot mutations in plasma DNA of breast cancer patients.ng

  Dataset EGAD00001006897

• Single cell sequencing of ovarian cancer

  Single cell sequencing of 12 ovarian cancer biopsies from 7 patients.

  Dataset EGAD00001006898

• RNA of peripheral blood for pancreatic cancer and chronic pancreatitis

  Samples of nucleated cells found in peripheral blood from over 300 patients suffering from resectable pancreatic ductal adenocarcinoma, non-resectable pancreatic cancer, chronic pancreatitis, or none of these. Please cite this article when using data: Al-Fatlawi, A.; Malekian, N.; García, S.; Henschel, A.; Kim, I.; Dahl, A.; Jahnke, B.; Bailey, P.; Bolz, S.N.; Poetsch, A.R.; Mahler, S.; Grützmann, R.; Pilarsky, C.; Schroeder, M. Deep Learning Improves Pancreatic Cancer Diagnosis Using RNA-Based Variants. Cancers 2021, 13, 2654. https://doi.org/10.3390/cancers13112654

  Dataset EGAD00001006915

• Exome sequencing of replication repair deficient brain cancers

  This dataset contains exome sequencing from replication repair deficient brain tumor samples.

  Dataset EGAD00001006920

• Transcriptome sequencing of replication repair deficient brain cancers

  Hypermutant tumors which harbor many somatic mutations may obscure the interpretation of targetable genomic events. This dataset contains transcriptome sequencing from 21 replication repair deficient brain tumor samples as well as healthy controls.

  Dataset EGAD00001006921

• Longitudinal single-cell RNA-seq data from metastatic ovarian cancer

  Longitudinal single-cell RNA-seq data of prospectively collected tumor tissue samples before and after chemotherapy from 11 HGSOC patients.

  Dataset EGAD00001006922

• Assessment of cannabidiol and Δ9-tetrahydrocannabiol in mouse models of medulloblastoma

  These data were used in the following publication: Andradas, C.; Byrne, J.; Kuchibhotla, M.; Ancliffe, M.; Jones, A.C.; Carline, B.; Hii, H.; Truong, A.; Storer, L.C.D.; Ritzmann, T.A.; et al. Assessment of Cannabidiol and delta9-Tetrahydrocannabiol in Mouse Models of Medulloblastoma and Ependymoma. Cancers 2021, 13, 330. https://doi.org/10.3390/cancers13020330 There are 4 paired-end RNA-seq samples from paediatric brain cancer cell lines.

  Dataset EGAD00001006923

• MutWP5: CRUK Mutographs of Cancer: Lung: PD37920 (Targeted) (2021-02-02)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Targeted sequencing will be conducted on samples to identify drivers of interest and clonality of the samples, well-performing samples will be sent for subsequent whole-genome sequencing. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006929

• MutWP5: CRUK Mutographs of Cancer: Lung: PD38233 (WG) (2021-02-02)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutation burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this portion of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006930

• MutWP5: CRUK Mutographs of Cancer: Lung: PD43291_Novaseq (Exome) (2021-02-02)

  Sequencing of LCM-derived microbiopsies from explanted lung from COPD patient. Goal to assess the mutational burden, spectrum, and clonal dynamics within the tissue. Deep sampling throughout multiple regions of the lung will determine whether there are differences in smoking-related mutaiton burden in different portions of the lung. Whole-genome sequencing will be conducted on samples identified as promising from the initial targeted data. Results from this poriton of the study will be compared to other individuals with smoking-related diseases (COPD, pulmonary fibrosis, lung cancer), and normal, non-smoking lungs. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006932

• MutWP4: CRUK Grand Challenge Mutographs of Cancer: Pancreatic Organoids (2021-02-02)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006933

• Exome Sequencing of Gastric Cancer Samples

  We perform whole exome sequencing on 50 pairs of gastric cancer and matched normal samples.

  Dataset EGAD00001006957

• Bulk tumor RNAseq of bladder cancer patients

  RNAseq fastq files from 611 bulk pre-treatment tumors from two indications: metastatic urothelial bladder cancer patients (IMvigor210) and metastatic renal cell carcinoma (IMmotion150)

  Dataset EGAD00001006960

• Whole-genome sequencing of gastric cancer with peritoneal metastasis

  Whole-genome sequencing of 135 tumor samples and 98 normal samples of gastric cancer with peritoneal metastasis

  Dataset EGAD00001006963

• MicroRNA profiling by next-generation sequencing for colorectal cancer screening

  The dataset contains raw miRNA sequencing data of plasma samples from 20 newly diagnosed colorectal cancer cases and 20 controls free of colorectal neoplasms matched by age and sex. It includes files in the FASTQ compressed (.gz) format.

  Dataset EGAD00001006966

• Breast Cancer Single-Cell RNA-Seq Dataset

  Single-cell RNA-Sequencing of 26 primary breast cancers from Wu et al. (2021) study. Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq 500 platform.

  Dataset EGAD00001007495

• A life history of colorectal cancer metastases

  Multiple metastatic sites were sampled at autopsy from four patients diagnosed with metastatic colorectal cancer and subjected to whole-genome sequencing using the Illumina HiSeq X Ten platform to identify somatic variants, structural rearrangements and mutational signatures. The number of tumour samples per patient ranged from 6 to 66.

  Dataset EGAD00001007503

• Somatic copy-number alterations in plasma circulating tumor DNA from advanced EGFR-mutated lung adenocarcinoma patients

  This is the raw data obtained from shallow whole-genome sequencing of plasma DNA (plasma-seq) for calling of somatic copy number alterations as well as focal amplifications from patients with lung cancer.

  Dataset EGAD00001007505

• sWGS on cfDNA and matching tumor DNA in pediatric cancer

  This dataset contains shallow whole genome sequencing data from paired cfDNA - tumor DNA samples of various pediatric cancer entities (total 215 samples). Files are provided in fastq format. Samples were sequenced on an Ion Proton sequencer (Thermo Fisher Scientific) or a Hiseq3000 (Illumina). Data analysis is available at https://github.com/rmvpaeme/sWGS_pediatric_cancer.

  Dataset EGAD00001007508

• November 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium.

  November 2020 data update (fastq) for reference epigenomes generated at Centre for Epigenome Mapping Technologies (Canadian Epigenetics, Environment and Health Research Consortium), Genome Sciences Center, B.C. Cancer Agency, Vancouver, Canada as part of the International Human Epigenome Consortium.

  Dataset EGAD00001007529

• 252 human breast cancer samples in WGS and WES

  We analyzed chromothripsis in 252 human breast cancers from two patient cohorts (149 metastatic breast cancers, 63 untreated primary tumors, 29 local relapses, 11 longitudinal pairs) using whole-genome and whole-exome (paired) sequencing. A lot of the WGS samples were sequenced on Illumina HiSeq X-Ten using Illumina TruSeq Nano DNA. For exome sequencing Agilent_SureSelect_V5+UTRs has been used (sequencing on Hiseq2000, Hiseq2500 and Hiseq4000). For exome sequencing Agilent_SureSelect_V5+UTRs has been used (sequencing on Hiseq2000, Hiseq2500 and Hiseq4000).

  Dataset EGAD00001007563