7296 results for "canc*"

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• Genome and transcriptome sequence data from a abdominal sarcoma patient

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

  Dataset EGAD00001004919

• Genome and transcriptome sequence data from a metastatic rectal cancer patient

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

  Dataset EGAD00001004918

• Genome and transcriptome sequence data from a metastatic melanoma patient

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

  Dataset EGAD00001004917

• 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

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001004915

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001004914

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

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

  Dataset EGAD00001004913

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

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

  Dataset EGAD00001004912

• Genome and transcriptome sequence data from a metastatic colon cancer patient

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

  Dataset EGAD00001004911

• Genome and transcriptome sequence data from a metastatic serous ovarian cancer patient

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

  Dataset EGAD00001004909

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001004908

• Genome and transcriptome sequence data from a T-cell prolymphocytic leukemia patient

  Genome and transcriptome sequence data from a T-cell prolymphocytic leukemia patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004907

• Genome and transcriptome sequence data from a diffuse large B-cell lymphoma patient

  Genome and transcriptome sequence data from a diffuse large B-cell lymphoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001004906

• Genome and transcriptome sequence data from a metastatic adrenocortical carcinoma patient

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

  Dataset EGAD00001004905

• Genome and transcriptome sequence data from a metastatic neuroendocrine tumor, lung primary patient

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

  Dataset EGAD00001004924

• Genome and transcriptome sequence data from a metastatic choroidal melanoma patient

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

  Dataset EGAD00001004925

• Genome and transcriptome sequence data from a metastatic pancreatic cancer patient

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

  Dataset EGAD00001004926

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001004927

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001004928

• Genome and transcriptome sequence data from a metastatic breast cancer patient

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

  Dataset EGAD00001004929

• Genome and transcriptome sequence data from a metastatic ovarian cancer patient

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

  Dataset EGAD00001004930

• Genome and transcriptome sequence data from a metastatic melanoma patient

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

  Dataset EGAD00001004931

• Genome and transcriptome sequence data from a metastatic pancreatic adenocarcinoma patient

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

  Dataset EGAD00001004932

• Genome and transcriptome sequence data from a metastatic sarcomatoid carcinoma of the lung patient

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

  Dataset EGAD00001004933

• Genome and transcriptome sequence data from a squamous cell carcinoma of the alveolar ridge patient

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

  Dataset EGAD00001004934

• Genome and transcriptome sequence data from a oligometastatic colorectal cancer patient

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

  Dataset EGAD00001004935

• Genome and transcriptome sequence data from a metastatic rectal carcinoma patient

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

  Dataset EGAD00001004936

• 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

• Ovarian cancer sequencing dataset

  Sequencing data from patients with Ovarian cancer. Data utilised in the 'Enhanced detection of circulating tumor DNA by fragment size analysis' manuscript (Mouliere et al, 2018)

  Dataset EGAD00001004939

• 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

• Lung cancer organoids addition

  Organoids are self-organizing 3D structures grown from stem cells that recapitulate essential aspects of organ structure and function. Here we describe a method to establish long-term culture conditions of human airway epithelial organoids that contain all major cell populations and allow personalized human disease modelling. We collected macroscopically inconspicuous lung tissue from non-small-cell lung cancer (NSCLC) patients undergoing medically indicated surgery and isolated epithelial cells to engineer 3D organoids. We exploit the potential to derive sub-clones from AOs to demonstrate the feasibility of CRISPR gene editing. Finally, we show that AOs readily allow modelling of viral infections such as RSV and for the first time demonstrate the possibility to study neutrophil-epithelium interaction in an organoid model. Taken together, we anticipate that human AOs will find broad applications in the study of adult human airway epithelium in health and disease.

  Dataset EGAD00001004943

• Epithelial, fibroblast, myeloid, T cell, primary prostate cancer

  The collection and use of tissue for this study had Melbourne Health institutional review board approval and patients provided written informed consent (Melbourne Health Local Project Number: 2016.087). Following the prostatectomy of 13 patients, ranging from 52 to 78 years of age and from CAPRA-S risk score of 0 (attributed to benign tissue samples, harvested from a site far from a low grade, low volume cancer) to 7 (Supplementary file 2), a four millimeter tissue core was collected from the prostate tumour site, conditional to histopathological verification66,67. If not otherwise specified, all procedures were carried out at 4 °C. Tissue blocks were washed in Phosphate-buffered saline (PBS) solution for 2 minutes and minced for 2 minutes with a scalpel. Homogenised tissue was added to a solution (total volume of 7 ml) composed by of 1 mg/ml collagenase IV (Worthington Biochemical Corp, USA), 0.02 mg/ml DNase 1 (New England Biolabs, USA), 0.2 mg/ml dispase (Merck, USA). The tissue homogenised was serially digested at 37 °C at 180 rpm, through three steps of 5, 10 and 10 minutes of duration, with the final 3 minutes dedicated to sedimentation at 0 rpm. After each digestion step, the supernatant was aspirated and filtered through a 70 μm strainer into a pre-chilled tube, diluting the solution with 15 ml of 2% bovine serum PBS to quench the enzymatic reaction. The resulting cumulative solution was then centrifuged at 1500 rpm for five minutes, with the supernatant collected and the cell pellet resuspended into 1 ml 2% PBS-serum prior to labelling (Fig. S1).

  Dataset EGAD00001004948

• March 2019 data update 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.

  March 2019 data update for cord blood CD34+CD38-, CMP, GMP, MEP, monocyte, erythroid precursor, B cell and primary AML total blasts reference epigenomes generated at Centre for Epigenome Mapping Technologies, Genome Sciences Center, B.C. Cancer Agency as part of the International Human Epigenome Consortium. This dataset contains data for samples: CEMT0158 CEMT0159 CEMT0160 CEMT0161 CEMT0162 CEMT0163 CEMT0164 CEMT0165 CEMT0166 CEMT0167 CEMT0168 CEMT0169 CEMT0170 CEMT0171 CEMT0172 CEMT0189

  Dataset EGAD00001004950

• Single cell RNA sequencing of colorectal cancer with diverse tissue dissociation protocols or fresh vs. frozen cell preparations.

  To evaluate 3 different tissue dissociation protocols or fresh vs. frozen cell preparations, we performed single-cell RNA sequencing on cancer or distant normal tissue dissociates from 2 colorectal cancer patients. Total 18,409 cells from 10 sample preparations were analyzed (5 primary colorectal cancer and 5 matched normal mucosa). The results suggest highly consistent cellular proportions were recovered with different sample preparation methods.

  Dataset EGAD00001005191

• Tobacco exposure and somatic mutations in normal human bronchial epithelium

  That tobacco smoking causes lung cancer is well-established, but we lack quantitative understanding of its effects on genomes of normal bronchial epithelium. We sequenced whole genomes of 632 colonies derived from single bronchial epithelial cells across 16 subjects. Tobacco smoking is the major influence on mutation burden, adding 1000-10,000+ mutations/cell, massively increasing both between-subject and within-subject variance, and generating several distinct signatures of substitutions and indels. A population of cells in subjects with smoking history had mutation burdens equivalent to that expected for never-smokers: these cells lacked tobacco-specific mutational signatures, were four-fold more frequent in ex-smokers than current smokers, and had significantly longer telomeres than their more mutated counterparts. Driver mutations increased in frequency with age, affecting 4-14% of cells in middle-aged never-smokers. In current smokers, ≥25% of cells carried driver mutations and 0-6% cells had 2 or even 3 drivers. Thus, tobacco smoking increases mutation burden, cell-to-cell heterogeneity and driver mutations, but quitting promotes replenishment of bronchial epithelium from mitotically quiescent cells that have avoided tobacco mutagenesis.

  Dataset EGAD00001005193

• IBD-dysplasia

  This data set concerns DNA copy number alterations and mutation data from 30 IBD-associated dysplastic lesions and 13 IBD-associated cancers. DNA was isolated from formalin-fixed, paraffin-embedded material. Whole-genome shallow seq and Truseq amplicon cancer panel (Illumina) were used for detection of DNA copy number alterations and gene mutations, respectively.

  Dataset EGAD00001005196

• Latency and interval therapy affect the evolution in metastatic colorectal cancer

  The cohort of 15 patients included ten patients with available tissue from the primary tumor and ≥1 metastatic site, four patients with pairs of metastases only, and one patient with an anastomotic recurrence five years after initial resection of the primary tumor

  Dataset EGAD00001005226

• Single cell RNA sequencing of colorectal cancer

  To understand intrinsic cancer cell signatures and the surrounding microenvironemt and their interactions, we performed single-cell RNA sequencing on 63,689 cells from 23 patients with 23 primary colorectal cancer and 10 matched normal mucosa samples. Analyzing of primary colorectal cancer and normal mucosa samples show a comprehensive cellular landscape of colon cancer, which is a valuable resource for the development of therapeutic strategies.

  Dataset EGAD00001005198

• Matched WXS of 147 lung cancer patients treated with immunotherapy

  This data includes whole exome sequencing of matched normal-tumor samples of patients who have received immunotherapy. '-1' refers to matched normal sample and '-2' refers to matched tumor sample.

  Dataset EGAD00001005211

• The mutational landscape of normal human endometrial epithelium - Additional Samples

  All normal somatic cells are thought to acquire mutations but understanding of the rates, patterns, causes and consequences of somatic mutation in normal cells is limited. Uterine endometrium adopts multiple physiological states over a lifetime and is lined by a gland-forming epithelium. Whole genome sequencing of normal endometrial glands from women aged 19 to 81 years showed them to be clonal cell populations derived from recent common ancestors, with total mutation burdens that increase with age at ~29 base substitutions/year and which are many-fold lower than endometrial cancers. Normal endometrial glands frequently carry driver mutations in cancer genes. Driver mutation burdens increase with age and correlate negatively with parity. Phylogenetic trees of normal endometrial glands constructed using whole genome sequences indicated that clones with drivers often originate during the first decades and spread to colonise the endometrial epithelial lining. The results show that driver mutation landscapes differ between normal cell types, perhaps shaped by differences in normal tissue physiology, and suggest that the procession of neoplastic changes leading to endometrial cancer is initiated early in life.

  Dataset EGAD00001005214

• RNA sequencing of 31 pancreatic cancer organiods

  RNA sequencing of 31 pancreatic cancer organiods

  Dataset EGAD00001005217

• MutWP4: CRUK Grand Challenge Mutographs of Cancer: Gastric Organoids (2019-08-07)

  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 2019-08-07.

  Dataset EGAD00001005234

• Whole genome sequencing

  The diversity and heterogeneity within high-grade serous ovarian cancer (HGSC) is not well understood. We performed whole genome sequencing on primary and metastatic sites from highly clinically annotated HGSC samples. Samples were obtained pre-treatment based on a laparoscopic triage algorithm from patients who underwent R0 tumor debulking, or received neoadjuvant chemotherapy (NACT) with excellent or poor response.

  Dataset EGAD00001005240

• GBM cancer stem cell lines, RNA-seq and WGS data

  This dataset contains 3 GBM stem cell samples profiled by RNA-seq. Two of those samples have been profiled with WGS as well (with matched blood WGS data available)

  Dataset EGAD00001005077

• Combination Therapies for Personalised Cancer Medicine in drug resistant EGFR mutant lung cancer (2019-06-10)

  This study involves mutagenizing a range of different cell lines with ENU to identify those mutations which engender resistance to targeted treatment. . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005080

• Combination Therapies for Personalised Cancer Medicine in 11-18 (2019-06-10)

  This study involves mutagenizing 11-18 with ENU to identify those mutations which engender resistance to targeted treatment. . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005081

• The identification of genetic vulnerabilities in head and neck cancers for the development of novel treatments. (2019-06-10)

  Exome Sequencing in a set of Asian Head and Neck cancer cell lines, to identify mutations that can be used to genomically classify the cell lines. . This dataset contains all the data available for this study on 2019-06-10.

  Dataset EGAD00001005082

• Cancer and germline exomes, and cancer RNA-seq consisiting of FASTQ paired-end reads from melanoma and lung cancer samples

  Cancer and germline exomes, and cancer RNA-seq consisiting of FASTQ paired-end reads from melanoma and lung cancer samples

  Dataset EGAD00001005097

• Combined MEK and BCL-2/XL inhibition is effective in high-grade serous ovarian cancer patient-derived xenograft models and BIM levels are predictive of responsiveness

  Most patients with late stage high-grade serous ovarian cancer (HGSOC) initially respond to chemotherapy but inevitably relapse and develop resistance, highlighting the need for novel therapies to improve patient outcomes. The MEK/ERK pathway is activated in a large subset of HGSOC, thus making it an attractive therapeutic target. Here, we systematically evaluated the extent of MEK/ERK pathway activation and efficacy of pathway inhibition in a large panel of well-annotated HGSOC patient-derived xenograft (PDX) models. The vast majority of models were nonresponsive to the MEK inhibitor cobimetinib (GDC-0973) despite effective pathway inhibition. Proteomic analyses of adaptive responses to GDC-0973 revealed that GDC-0973 upregulated the pro-apoptotic protein BIM, thus priming the cells for apoptosis regulated by BCL2-family proteins. Indeed, combination of both MEK inhibitor and dual BCL-2/XL inhibitor (ABT-263) significantly reduced cell number, increased cell death and displayed synergy in vitro in most models. In vivo, the GDC-0973 and ABT-263 combination was well tolerated and resulted in greater tumor growth inhibition than single agents. Detailed proteomic and correlation analyses identified two subsets of responsive models – those with high BIM at baseline that was increased with MEK inhibition and those with low basal Bim and high pERK levels. Models with low BIM and low pERK were non-responsive. Our findings demonstrate that combined MEK and BCL-2/XL inhibition has therapeutic activity in HGSOC models and provide a mechanistic rationale for clinical evaluation of this drug combination as well as the assessment of the extent to which BIM and/or pERK levels predict drug combination effectiveness in chemoresistant HGSOC.

  Dataset EGAD00001005111

• Gene expression profile of mesothelial-derived carcinoma-associated fibroblasts

  RNA-sequencing was carried out on ascetic fluid-isolated mesothelial cells from low-grade serous ovarian cancer patients, high-grade serous ovarian cancer patients, chemotherapy-treated high-grade serous ovarian cancer patients and control mesothelial cells obtained from non-oncologic patients to identify differentially expressed genes associated to mesothelial-to-mesenchymal transition process. The dataset contains 18 samples: - Control mesothelial cells: 4 samples - Group 1, high-grade serous ovarian cancer patients: 3 samples - Group 2, chemotherapy-treated high-grade serous ovarian cancer patients: 5 samples - Group 3, low-grade serous ovarian cancer patients: 6 samples

  Dataset EGAD00001005133