13306 results for ""pdc"+"heritage+language"+census+study"

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• Genome and transcriptome sequence data from an extraosseous osteosarcoma patient

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

  Dataset EGAD00001003062

• Genome and transcriptome sequence data from an atypical bronchial carcinoid patient

  Genome and transcriptome sequence data from an atypical bronchial carcinoid patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003063

• Genome and transcriptome sequence data from an ovarian cancer patient

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

  Dataset EGAD00001003064

• Genome and transcriptome sequence data from an appendiceal adenocarcinoma patient

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

  Dataset EGAD00001003066

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

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

  Dataset EGAD00001003067

• 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 EGAD00001003068

• Genome and transcriptome sequence data from a pancreatic neuroendocrine tumor patient

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

  Dataset EGAD00001003069

• 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 EGAD00001003070

• Genome and transcriptome sequence data from a pleural mesothelioma patient

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

  Dataset EGAD00001003071

• 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 EGAD00001003072

• Genome and transcriptome sequence data from a metastatic adenocarcinoma of the pancreas patient

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

  Dataset EGAD00001003073

• 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 EGAD00001003074

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

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

  Dataset EGAD00001003075

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

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

  Dataset EGAD00001003076

• Genome and transcriptome sequence data from a metastatic adenocarcinoma patient

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

  Dataset EGAD00001003077

• 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 EGAD00001003078

• Genome and transcriptome sequence data from a presumed metastatic lung cancer patient

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

  Dataset EGAD00001003079

• 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 EGAD00001003081

• 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 EGAD00001003082

• 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 EGAD00001003083

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

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

  Dataset EGAD00001003084

• 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 EGAD00001003085

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

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

  Dataset EGAD00001003086

• Genome and transcriptome sequence data from a pancreatic neuroendocrine patient

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

  Dataset EGAD00001003089

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

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

  Dataset EGAD00001003087

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

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

  Dataset EGAD00001003088

• 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 EGAD00001003090

• Genome and transcriptome sequence data from a clear cell carcinoma of ovary patient

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

  Dataset EGAD00001003091

• 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 EGAD00001003695

• Genome and transcriptome sequence data from a metastatic meningioma patient

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

  Dataset EGAD00001003697

• Genome and transcriptome sequence data from a thymic carcinoma patient

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

  Dataset EGAD00001003700

• Genome and transcriptome sequence data from a primary unknown patient

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

  Dataset EGAD00001003722

• 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 EGAD00001003730

• 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 EGAD00001003735

• Genome and transcriptome sequence data from a sinus adenocarcinoma patient

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

  Dataset EGAD00001003737

• Genome and transcriptome sequence data from a Ewing sarcoma patient

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

  Dataset EGAD00001003738

• Genome and transcriptome sequence data from a metastatic cholangiocarcinoma patient

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

  Dataset EGAD00001003715

• Genome and transcriptome sequence data from a mullerian mixed tumor with carcinosarcoma of the ovaries patient

  Genome and transcriptome sequence data from a mullerian mixed tumor with carcinosarcoma of the ovaries patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003610

• Spatial heterogeneity of follicular lymphoma

  Follicular lymphoma (FL) is an incurable B cell malignancy characterized by advanced stage disease and a heterogeneous clinical course. Recent genomic studies have focused on profiling “single” FL biopsies over several time-points, however, multi-site sampling in solid cancers has demonstrated profound spatial intra-tumor heterogeneity (ITH) with implications for precision medicine based initiatives. This study examined the extent of spatial heterogeneity in FL by whole exome sequencing 22 synchronously removed spatially separated biopsies from 9 patients. We observed significant differences in the extent of ITH across cases, with two distinct patterns of high and low spatial heterogeneity emerging. Site-specific alterations in genes with biological, prognostic or therapeutic relevance included, TNFRSF14, PIK3CD, TNFAIP3, PTEN, EP300 and XBP1. In depth characterization of these variants using deep-sequencing techniques confirmed their discordant nature, suggesting on-going genetic diversification driving evolution after widespread tumor dissemination. There was evidence of tumors comprising multiple competing subclones, with distinct clusters of mutations demonstrating differential expansions within spatially-separated sites. For cases where spatial tumors were examined at two time-points (FL and transformation to diffuse large B cell lymphoma (DLBCL)), the degree of heterogeneity increased with transformation. Collectively, our results demonstrate that spatial ITH is prevalent in FL. The existence of site-specific aberrations suggests that a single biopsy may not be sufficient in all patients to capture the full genomic complexity present and these spatial variations need to be considered in biomarker-led clinical studies.

  Dataset EGAD00001003553

• Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of the cheek patient

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

  Dataset EGAD00001004603

• Genome and transcriptome sequence data from a metastatic basal cell carcinoma of frontal scalp patient

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

  Dataset EGAD00001004621

• Genome and transcriptome sequence data from a metastatic squamous cell carcinoma of the cervix patient

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

  Dataset EGAD00001004616

• Genome and transcriptome sequence data from a clear cell carcinoma of the left ovary patient

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

  Dataset EGAD00001004608

• Integrative genome profiling in AML

  AML emerges as a consequence of accumulating independent genetic aberrations that direct regulation and/or dysfunction of genes resulting in aberrant activation of signalling pathways, resistance to apoptosis and uncontrolled proliferation. Given the significant heterogeneity of AML genomes, AML patients demonstrate a highly variable response rate and poor median survival in response to current chemotherapy regimens. For the past 4 years we have conducted gene expression profiling on purified bone marrow populations equating to normal haematopoietic stem and progenitor cells from healthy subjects and patients with de novo AML in order to identify AML signatures of aberrantly expressed genes in cancer versus normal. We are now applying a series of bioinformatic methodologies combined with clinical and conventional diagnostic data to establish novel genomics strategies for improved prognostication of AML. Additionally, we use our AML signatures to unravel oncogenic signalling pathway activities in AML patients and test inhibitory drugs for these pathways inn preclinical therapeutic programmes. We consider that superimposing GEP and clinical data for our AML patient cohort with additional data on their mutational status will significantly improve the prognostic power of the study as well as unravel yet unknown mutations associated with aberrant signalling activities of oncogenic pathways.

  Dataset EGAD00001001873

• Small-molecule inhibitors in melanoma - Kenski / Kong - WES (2019-04-11)

  Small-molecule inhibitors targeting the most commonly activated pathway in melanoma, MAPK pathway (either alone or in combination) are already given to melanoma patients for few years, and initially reduce tumour burden dramatically, eventually melanomas become resistant and tumours progress while on treatment. Resistance to this treatment occurs by acquisition of additional mutations or other alterations that affect the mitogen-activated protein kinase (MAPK) pathway by either direct or indirect signalling. Many resistance mechanisms somehow lead to reactivation of extracellular signal-regulated kinase (ERK), thereby restoring signalling of the oncogenic BRAF/MEK/ERK pathway. In addition, PI3K pathway activation contributes to resistance to BRAF inhibition. Less frequent but equally important to the phenomenon of targeted drug resistance is the observation that B15-20% of BRAF mutant melanoma patients fail to respond to BRAF inhibition already early on treatment, owing to intrinsic resistance. These patients have little therapeutic options, unless immunotherapy can be given. To better understand the resistance mechanisms in MAPK inhibitor-treated melanoma patients and melanoma biology, our lab generated a big panel of MAPK inhibitor resistant melanoma cell lines by continuous drug exposure. The understanding of the genetic landscape and gene expression as well as cross resistance to other treatment regimens, and other aspects of melanoma biology such as phenotype switch, will allow us to better exploit new therapeutic strategies for melanoma patients. . This dataset contains all the data available for this study on 2019-04-11.

  Dataset EGAD00001004952

• RNA sequencing of periprostatic fat after a 6 month deep androgen deprivation therapy

  Androgen deprivation therapy treated patients (n=11) were recruited from an open label neoadjuvant phase II study in which patients with high-risk disease received a ‘supercastration’ regimen consisting of degarelix 240/80 mg subcutaneously every four weeks; abiraterone acetate 500 mg orally daily titrating upwards every two weeks by 250 mg to a final dose of 1000 mg daily; bicalutamide 50 mg orally daily; and prednisolone 5 mg orally twice daily for a total of 6 months (Australian New Zealand Clinical Trials Registry 12612000772842). Untreated patients with similar pre-treatment characteristics were obtained from a prospective prostatectomy biorepository22,23. Prior to ligation of the dorsal venous complex and prostate pedicles, the anterior prostate was defatted and the specimen was removed immediately, placed in a sterile container and transferred on ice for long-term storage in the vapour phase of liquid nitrogen. A total of 50–100 µg of adipose tissue was separated from fresh frozen samples stored at −160°C. RNA was isolated using the Qiagen RNeasy Lipid Tissue Mini Kit and eluted in 35 µL nuclease-free water. 0.5–1 µg of total RNA was used as the input for cDNA library synthesis using TruSeq RNA Sample Prep Kit v2 (Illumina), and libraries were constructed according to manufacturer’s instructions. Samples were sequenced on a HiSeq 2500 (Illumina) using 101 base paired-end chemistry, aiming for 50 million mapped paired-end reads per sample.

  Dataset EGAD00001004971

• Exome trios in patients with gastroschisis (2019-04-08)

  Gastroschisis (MIM 230750) is a herniation of the intestines through a defect of the abdominal wall lateral to the umbilicus (usually on the right side), and it is not covered by a membrane [Ledbetter, 2012]. Gastroschisis is a congenital anomaly with increasing incidence, easy prenatal diagnosis and extremely variable postnatal outcomes. On the basis of clinical manifestations, epidemiologic charateristics, and the presence and type of additional malformations, gastroschisis could be considered a heterogeneous condition with no gene/s discovered yet. This congenital anomaly affects approximately 1-3 infancts per 10,000 live births [Calzolari et al.1995;Parker et al.,2010] Current knowledge about causative mutations/variants. To date, no single gene has been linked to gastroschisis. Some publications have tried to link this malformation to variants in genes (such as AEBP1 (adipocyte enhancer binding protein) gene [Feldkamp et al,. 2012] or the VEGF-NOS3 pathway [Lammer et al., 2008]. Previously, a Scribble mutant mouse model (circletail) was reported to exhibit gastroschisis, however recent studies demonstrated that the Scribble knockout fetus exhibits exomphalos phenotype of gastroschisis [Carnagham et al., 2013]. This data is part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/ . This dataset contains all the data available for this study on 2019-04-08.

  Dataset EGAD00001004942

• Somatic mutation and clonal evolution normal breast tissue TGS (2020-01-15)

  Cancer is a genetic disease caused by an accumulation of mutations, however many of these mutations have been identified in pathologically normal tissue. We aim to use laser-capture microscopy (LCM) to sample individual clones from breast tissue to identify whether cancer-associated mutations appear in this normal tissue, assess the mutational burden present, and identify the mutational processes causing these mutations. We will sample from a wide age range of individuals (<20 to >70 years old) to determine whether these processes differ in pre- and post-menopausal women. We will also be comparing the tissue from healthy individuals (samples from breast reduction surgery) to those at elevated risk of breast cancer (mastectomy from BRCA1/2 patients) and those who have breast cancer (adjacent normal, distal normal, and tumour tissue from mastectomy). This will allow us to determine how these processes are different between these groups of individuals, and gain insight into the earliest stages of tumour development. . This dataset contains all the data available for this study on 2020-01-15.

  Dataset EGAD00001005787

• Molecular profiling of tissue autopsies and ctDNA

  The dataset contains high-throughput sequencing data derived from a cancer autopsy series of 10 patients. As part of this study, whole-exome sequencing and RNA-seq were performed for spatially distinct tissue biopsies from the patients. In addition, plasma samples from the patients were sequenced using a custom panelt to profile ctDNA. There are 106 files containing whole-exome sequencing data, 107 files containing RNA-seq data, and 9 files containing plasma sequencing data.

  Dataset EGAD00001007040

• Molecular characterization of NASH-HCC

  We collected 80 NASH-HCCs formalin fixed paraffin-embedded (FFPE) samples from 5 different institutions. NASH was diagnosed in FFPE samples by at least two expert pathologists following a described histological algorithm (Bedossa et al., Hepatology, 2012). All NASH patients included in the study were HBV- and HCV-negative. Patients reporting alcohol consumption ≥ 20 g/day for women and 30 g/day for men, as well as patients with a known liver disease superimposed to NASH were excluded. Tumour and paired non-tumour gDNA of NASH-HCC FFPE samples was submitted to Whole Exome Sequencing (WES). Exome capture and sequencing library preparation were performed using the SureSelect Human All Exon V5, no UTR hybridization capture kit from Agilent (Target Size 50 Mb). Libraries were sequenced on an Illumina HiSeq 4000 instrument with 100-bp paired-end reads.

  Dataset EGAD00001007524