7075 results for "canc*"

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• August 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.

  August 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 EGAD00001006383

• Normal sample for patient SA300

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA300

  Dataset EGAD00001009369

• Normal sample for patient SA495

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA495

  Dataset EGAD00001009372

• Normal sample for patient SA291

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA291

  Dataset EGAD00001009368

• Normal sample for patient SA676

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA676

  Dataset EGAD00001009376

• 24 Chordoma samples (WES,WGS)

  Case series of the rare tumor entity chordoma. 9 cases sequenced with Whole Exome Sequencing (WES) and 2 cases sequenced with Whole Genome Sequencing (WGS) were recruited from the personalized oncology program NCT-MASTER/DKTK-MASTER at the German Cancer Research Center. One of the WES patients was re-sequenced at a later time point when he relapsed, this resequencing was done by WGS. Therefore there are 11 patients, one of which with two samples, all of which were sequenced with matched normal controls, amounting to a total number of 24 NGS samples.

  Dataset EGAD00001004825

• Normal sample for patient SA677

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA677

  Dataset EGAD00001009377

• Preferential infiltration of unique Vγ9Jγ2‐Vδ2 T cells into glioblastoma multiforme

  Glioblastoma multiforme (GBM) is clinically highly aggressive as a result of evolutionary dynamics induced by cross-talk between cancer cells and a heterogeneous group of immune cells in tumor microenvironment. The brain harbors limited numbers of immune cells with few lymphocytes and macrophages; thus, innate‐like lymphocytes, such as γδ T cells, have important roles in antitumor immunity. Here, we characterized GBM‐infiltrating γδ T cells, which may have roles in regulating the GBM tumor microenvironment and cancer cell gene expression. V(D)J repertoires of tumor‐infiltrating and blood‐circulating γδ T cells from four patients were analyzed by next-generation sequencing-based T-cell receptor (TCR) sequencing in addition to mutation and immune profiles in four GBM cases. In all tumor tissues, abundant innate and effector/memory lymphocytes were detected, accompanied by large numbers of tumor‐associated macrophages and closely located tumor‐infiltrating γδ T cells, which appear to have anti-tumor activity. The immune-related gene expression analysis using the TCGA database showed that the signature gene expression extent of γδ T cells were more associated with those of cytotoxic T and Th1 cells and M1 macrophages than those of Th2 cells and M2 macrophages. Although the most abundant γδ T cells were Vγ9Vδ2 T cells in both tumor tissues and blood, the repertoire of intratumoral Vγ9Vδ2 T cells was distinct from that of peripheral blood Vγ9Vδ2 T cells and was dominated by Vγ9Jγ2 sequences, not by canonical Vγ9JγP sequences that are mostly commonly found in blood γδ T cells. Collectively, unique GBM‐specific TCR clonotypes were identified by comparing TCR repertoires of peripheral blood and intra‐tumoral γδ T cells. These findings will be helpful for the elucidation of tumor-specific antigens and development of anticancer immunotherapies using tumor-infiltrating γδ T cells.

  Dataset EGAD00001004862

• Evolutionary trajectories and clonal migration underlying tumor progression and lymph node metastasis in resectable lung cancer

  In this prospective study, targeted deep sequencing was performed on a total of 160 primary tumors (474 regions) and 112 lymph nodes from 125 patients with stage I-III lung cancer (LuCaTH). Progressive evolution at clonal divergence scale was observed while specific driver events were positively selected for clonal sweeps during tumor development. Between-region genetic divergence (BRGD) of tumors were assessed and positively correlated with tumor differentiation. A machine learning algorithm was employed to evaluate clinicopathological and molecular parameters of primary tumors underlying lymph node metastasis. By analyzing clonal lineages and metastatic trajectories across multiple nodal stations, we unraveled a common sequential LNM seeding pattern but with divergent modes of clonal spread.

  Dataset EGAD00001007587

• Tumour sample for patient SA285

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA285

  Dataset EGAD00001009543

• Normal sample for patient SA271

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA271

  Dataset EGAD00001009559

• Shallow whole genome sequencing from plasma DNA of BC, CRC and NSCLC 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 and deletions from patients with breast, colorectal and non-small cell lung cancer.

  Dataset EGAD00001006288

• Dataset-linking-WGS-via-README-for-EGAS00001004884

  This dataset contains 139 Tumor and Control WGS files for samples for Gerhauser et al.,Cancer Cell, 2018, 34:996-1011. WGS and sequencing protocol was earlier described in Weischenfeldt et al, Cancer Cell, 2013.

  Dataset EGAD00001007669

• RNA-seq data from the tumor samples of a head and neck cancer patient

  This dataset contains paired-end RNA sequencing data (2x50 bp) from the three synchronous primary tumors and the recurrence of a head and neck cancer patient.

  Dataset EGAD00001006654

• Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors.

  Whole-genome sequencing of high-grade serous ovarian cancer (HGSC) tumours and matched normals from long-term survivors performed as part of the Multidisciplinary Ovarian Cancer Outcomes Group (MOCOG) study. The dataset includes fastq files from 58 HGSC tumours (53 primary tumours and 5 recurrent tumours) and 53 matched normals from 53 long-term survivor patients. Sequence libraries were generated from tumour and matched normal genomic DNA using the KAPA HyperPrep PCR-free library preparation kit (Roche) according to manufacturer’s instructions. Sequencing was carried out by the Kinghorn Centre for Clinical Genomics Sequencing Laboratory (Sydney, Australia) on the HiSeq X Ten System (Illumina) to a minimum base coverage of 30-fold for normal DNA and 60-fold for tumour DNA samples.

  Dataset EGAD00001009398

• Strand-seq of hematopoietic stem and progenitor cells along human aging

  Somatic mosaicism (SM), referring to the presence of somatic mutations in sub-populations of cells within healthy individuals, is associated with an increased risk of a variety of diseases, including cancer. Blood is at particularly high risk of SM, given its rapid turnover and functionally- heterogeneous cell-type composition. While the roles of point mutations and large-scale rearrangements in blood SM have been scrutinised in recent years, the functional impact of mosaic structural variants (mSVs) remains poorly understood. Using haplotype-resolved single-cell multi-omics based on Strand-seq technology, we explored the mSV landscape of human hematopoietic stem and progenitor cells (HSPCs).

  Dataset EGAD00001009402

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: pancreas (2019-04-03)

  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 (Illumina HiSeqX, 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. . This dataset contains all the data available for this study on 2019-04-03.

  Dataset EGAD00001004893

• Normal sample for patient SA423

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA423

  Dataset EGAD00001009370

• Tumor-derived cell lines as pharmacogenomic models to predict therapeutic vulnerabilities in hepatocellular carcinoma

  Hepatocellular carcinoma (HCC) is a heterogeneous aggressive malignancy with low efficacy of current therapies at advanced stages. We integrated molecular and pharmacological profiling of a large panel of liver cancer cell lines (LCCL) to assess their clinical relevance as HCC preclinical models and identify new effective therapies and biomarkers of response. Here, we performed multi-omic analysis including whole-exome, RNA and microRNA sequencing in a series 34 LCCL. Molecular profiles of LCCL and primary HCC were compared and we searched for molecular features associated with drug response. Our panel of LCCL faithfully recapitulated the most aggressive molecular “proliferation class” of HCC.

  Dataset EGAD00001004938

• Sequencing files for "A Unifying Paradigm for Transcriptional Heterogeneity and Squamous Features in Pancreatic Ductal Adenocarcinoma."

  Files from DNA and RNA sequencing from primary tumors and metastases from pancreatic cancer patients along with matched normal tissues.

  Dataset EGAD00001005519

• Field effect of healthy and diseased livers (2019-04-08)

  Recent work in the Campbell group has revealed somatic mutations present in normal, non-cancerous human skin. A subset of the mutations conferred selective advantages to the host cells, leading to clonal expansions and raising the risk for future cancer development. Capturing such somatic mutations in normal tissue is important to advance our understanding about carcinogenesis and could provide prospective medical insights. In this project, our goal is to detect somatic mutations in normal (pre-cancerous) liver tissue. Using Laser Microdissection technology, we will dissect individual liver lobules from patient samples and submit these to sequencing. For each patient sample, we aim to sequence multiple lobules to characterise the mutagenic burden. Samples will be taken from patients with different liver disease aetiologies, including alcoholism and obesity, with a view on distinguishing the prevalent mutation types occurring in each disease context. We will perform targeted sequencing, initially using the WTSI cancer panel. Later we aim to use a novel bait set that captures both cancer genes as well as genes relevant to the non-cancerous samples (ie. genes implicated in hereditary disorders, immune sequences). . This dataset contains all the data available for this study on 2019-04-08.

  Dataset EGAD00001004941

• Exploring the extracellular transcriptome in seminal plasma for non-invasive prostate cancer diagnosis

  mRNA capture sequencing data of 57 seminal plasma samples.

  Dataset EGAD00001007688

• Genome-wide 5-mC profiling in plasma of metastatic ALK-rearranged non-small cell lung cancer (NSCLC) patients receiving tyrosine kinase inhibitor therapy.

  Longitudinal plasma samples (n = 79) of 21 ALK-positive NSCLC patients and 13 healthy donors were collected alongside 15 ALK-positive tumor tissue and 10 healthy lung tissue specimens. All plasma and tissue samples were analyzed by cell-free DNA methylation immunoprecipitation sequencing to generate genome-wide 5-mC profiles. Paired cfMeDIP on NextSeq 550 using KAPA Hyper Prep Kit was done.

  Dataset EGAD00001009412

• Sequencing data for oesophageal and related samples - OACs 496 release (WGS)

  Data from 496 OCCAMS (Oesophageal Cancer Clinical And Molecular Stratification) cases. WGS BAM files 496x oesophageal adenocarcinoma samples 496x normal samples

  Dataset EGAD00001007785

• Cancer Alliance WGS

  Whole genome sequencing for individualized cancer interpretation

  Dataset EGAD00001006233

• The 5-hydroxymethylcytosine Landscape of Prostate Cancer

  This dataset consists of genome-wide 5hmC methylomes at various stages of prostate cancer, including not only 93 metastases from castration-resistant prostate cancer (mCRPC) patients, but also 5hmC patterns in cell-free DNA (cfDNA). There are 2000 runs in total as fastq files.

  Dataset EGAD00001008462

• Tumour sample for patient SA589

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA589

  Dataset EGAD00001009195

• Illumina short-reads and 10X Genomics linked-reads sequencing data for MCF7 and a primary breast tumor sample

  This dataset contains whole genome sequencing data from Illumina short-reads sequencing (2X150bp) and 10X Genomics linked-reads sequencing. Both the sequencing technologies were used to sequence MCF7 cell line and a primary breast triple-negative cancer sample. The fastq of paired-end reads for both the samples sequenced with both the technologies is available.

  Dataset EGAD00001005724

• Tumour sample for patient SA221

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA221

  Dataset EGAD00001009351

• Acquired RAD51C promoter methylation loss causes PARP inhibitor resistance in high grade serous ovarian carcinoma

  Analysis of RAD51C promoter methylation using targeted bisulfite sequencing (amplicon sequencing) in ovarian cancer pre-clinical models and patient samples.

  Dataset EGAD00001007799

• Normal sample for patient SA675

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA675

  Dataset EGAD00001009375

• Lowpass whole genome sequencing of single circulating tumor cells (CTCs) in ALK-rearranged non-small-cell lung cancer resistant to ALK inhibitors

  Lowpass whole genome sequencing of 43 single CTCs and one tumor biopsy

  Dataset EGAD00001007700

• cfMeDIP data for 30 CPC-GENE samples

  We analyzed the cell free DNA methylomes using 30 plasma samples from patients with localized prostate cancer in the CPC-GENE project. Methylation was profiled using the methylated DNA immunoprecipitation coupled to next generation sequencing (MeDIP) technology.

  Dataset EGAD00001007972

• Single Cell Genome Sequence for DLP+ library A118368B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA605 passage 3 on DLP+ library A118368B

  Dataset EGAD00001009432

• Comprehensive RNA repository of tissue and plasma from patients with esophageal cancer or precursor lesions

  A comprehensive RNA repository (both coding and non-coding) from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy esophageal and disease tissue sample were collected. This dataset includes both mRNA and small RNA sequencing data (fastq.gz files) of all tissue and plasma samples. In total,102 RNA-seq libraries from 51 samples (17 plasma and 34 tissue samples) were sequenced (plasma mRNA libraries were sequenced twice).

  Dataset EGAD00001006857

• Whole-exome sequencing of breast cancer metastasis and corresponding blood samples

  Whole-exome sequencing (WES) of 216 breast cancer metastasis-normal pairs from patients who underwent a biopsy in the context of the SAFIR01, SAFIR02, SHIVA or MOSCATO prospective trials (France).

  Dataset EGAD00001002747

• Deep targeted DNA sequencing dataset for the study "Molecular characteristics in Burkitt lymphoma over age groups"

  We performed deep targeted DNA sequencing with a panel of 134 selected cancer-related genes previously identified to be recurrently mutated in BL/B-AL. A Nextera rapid capture custom kit was designed using the Illumina DesignStudio. For every gene, all regions in which previous mutations had been described were covered. For 40 of these genes, the entire coding sequence was covered. Targeted deep sequencing was performed on a MiSeq-Sequencer using MiSeq Reagent Kit v2 (300 cycle) with 24 samples per run. There are 396 samples in this targetedDNAseq-dataset with 298 tumors (288 patients and 10 cell lines) and 98 normals. There are 132 female, 262 male, and 2 unknown gender samples in this dataset.

  Dataset EGAD00001007708

• Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer

  The dataset for Multimodal Genomic Features Predict Outcome of Immune Checkpoint Blockade in Non-small Cell Lung Cancer includes 106 bam files from whole exome next-generation sequencing on the Illumina HiSeq2500. The samples analyzed include matched tumor/normal samples from non-small cell lung cancer patients treated with immunotherapy.

  Dataset EGAD00001005796

• Genome and transcriptome sequence data from a uveal melanoma patient

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

  Dataset EGAD00001010956

• Single Cell Genome Sequence for DLP+ library A96130A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA609 passage 8 on DLP+ library A96130A

  Dataset EGAD00001009454

• Tumour sample for patient SA230

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA230

  Dataset EGAD00001009524

• Single Cell Genome Sequence for DLP+ library A95670B

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 6 on DLP+ library A95670B

  Dataset EGAD00001009449

• Tumour sample for patient SA586

  Whole genome sequencing of tumour sample for triple negative breast cancer patient SA586

  Dataset EGAD00001009193

• Normal sample for patient SA218

  Whole genome sequencing of normal sample for triple negative breast cancer patient SA218

  Dataset EGAD00001009220

• Whole genome sequencing informs treatment decision of end-stage metastatic cutaneous angiosarcoma in a patient with Xeroderma Pigmentosum

  Whole genome sequencing (WGS) detects all mutations in a cancer. “Mutational signatures” are patterns of mutations that report the DNA damage and subsequent DNA repair processes that have occurred in cancers. We present a patient with Xeroderma Pigmentosum that developed metastatic angiosarcoma, unresponsive to all lines of sarcoma therapy. Primary tumour WGS revealed a hypermutated tumour, including clonal ultraviolet light-induced mutational patterns (Signature 7) and subclonal signatures of activating mutations of DNA Polymerase-epsilon (POLE)(Signature 10). These signatures are associated with response to immune-checkpoint blockade. Immunohistochemistry confirmed high PD-L1 expression in metastatic deposits. The patient was commenced on anti-PD-L1 therapy and has responded.

  Dataset EGAD00001004786

• Single Cell Genome Sequence for DLP+ library A95722A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 6 on DLP+ library A95722A

  Dataset EGAD00001009451

• Single Cell Genome Sequence for DLP+ library A96154A

  Single Cell Genome Sequence for triple negative breast cancer patient SA1135, patient SA1162SA on DLP+ library A96154A

  Dataset EGAD00001009456

• Human primary and metastatic colorectal cancer (CRC) samples

  Bank of human both primary and metastastic colorectal cancer sample RNAseq

  Dataset EGAD00001009712

• Single Cell Genome Sequence for DLP+ library A96161A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA604 passage 8 on DLP+ library A96161A

  Dataset EGAD00001009457

• Single Cell Genome Sequence for DLP+ library A96171A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2, patient-derived xenograft SA611 passage 3 on DLP+ library A96171A

  Dataset EGAD00001009458