6135 results for "canc*"

in 30.59 milliseconds.

• MAF dataset of Colorectal Cancer Synthetic genomes

  This dataset contains genomic variants data in MAF format from four patients synthetically generated

  Dataset EGAD50000000315

• EOSC4Cancer Synthetic Colorectal Cancer Genomic data

  In this study there are stored the synthetic datasets produced by partners involved in EOSC4Cancer. The study contains independent datasets available for the research community. The synthetic genomes have been created trying to mimic real cancer data.

  Study EGAS50000000190

• EOSC4Cancer Longitudinal Synthetic Colorectal Cancer Genomic data developed at BSC

  The synthetic genomes have been created trying to mimic real cancer data of 4 patients (Named 185,186,187 and 188). Mutations are based on real CRC patients from the PCAWG dataset. For each patient, two tumor samples at different time points and one healthy sample have been simulated. The cancer intra-tumor heterogeneity and evolution in the patients is depicted by simulating reads from tumor subclones separately and then mixing them according to their clonal proportions in each sample. For rapid use and transfer only selected chromosomes have been generated for each patient. Chromosomes per patient: -185: chr4, chr5, chr7, chr17 -186: chr1, chr7, chr12, chr17 -187: chr1, chr2, chr5, chr12, chr17 -188: chr2, chr5, chr12, chr13, chr17 Worflows used to create BAM/BAI, VCF and MAF files from FASTQ (Alignment with GRCh38): - https://usegalaxy.eu/published/workflow?id=2c3d05023c02113e - https://usegalaxy.eu/published/workflow?id=1da86d74f8535f4e

  Dataset EGAD50000000276

• VCF dataset of Colorectal Cancer Synthetic genomes

  This dataset contains genomic variants data from VCF files of four patients synthetically generated in the EOSC4Cancer project

  Dataset EGAD50000000314

• BAM/BAI dataset of Colorectal Cancer Synthetic genomes

  This dataset contains binary alignment map (BAM) data with their respective index (BAI) from four patients synthetically generated.

  Dataset EGAD50000000316

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 14)

  The dataset contains whole genome sequencing data of 42 high-grade serous carcinoma (HGSC) patients sequenced with Novoseq 6000. The 100 samples are either fresh frozen tumour samples or blood samples. The files provided are paired fastq files.

  Dataset EGAD50000000507

• IRCC

  DACs of Translational Cancer Medicine laboratory of Candiolo IRCC institute

  Dac EGAC50000000068

• Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities

  Despite molecular and clinical heterogeneity, small cell lung cancer (SCLC) is treated as a single entity with predictably poor results. Using tumor expression data, we applied non-negative matrix factorization, which identified four SCLC subtypes defined largely by differential expression of transcription factors ASCL1, NEUROD1, and POU2F3 or low expression of all three transcription factor signatures accompanied by an Inflamed signature (SCLC-A, N, P and I, respectively). The presence of these subtypes were then validated in tumor gene expression data from the IMpower133 clinical trial.

  Study EGAS00001004888

• The WID-BC-index identifies women with primary poor prognostic breast cancer based on DNA methylation in cervical samples

  A surrogate marker based on the epigenome that captures these components in easily accessible epithelial tissue could potentially identify women at risk. The WID-BC-index identifies women with breast cancer based on a DNA methylation signature derived from cervical smear samples.

  Study EGAS00001005055

• Whole Genome Sequencing of HER2-Positive Metastatic Extramammary Paget’s Disease: A Case Report

  Extramammary Paget’s disease (EMPD) is a rare cancer that occurs within the epithelium of the skin. Whole genome sequencing of the tumor and matched blood was performed. Whole genome sequencing revealed the underlying copy number variation landscape in HER2-positive metastatic EMPD. The presence of alternative signalling pathways and genetic variants suggests potential interactions with HER2 signalling, which possibly contributed to the HER2 overexpression and observed response to HER2-directed therapy combined with other agents in a comprehensive treatment regimen.

  Study EGAS50000000243

• Single cell RNAseq of stenotic, inflamed and non-inflamed transmural lesions from patients with Crohn's disease

  This dataset contains a gene-cell matrix derived from single-cell RNA sequencing (scRNA-seq) data of ileal tissue from Crohn's disease (CD) patients and colorectal cancer (CRC) patients. It includes: Crohn's Disease Patients: A trio of transmural lesions (stenotic, inflamed, and non-inflamed) from each patient. Colorectal Cancer Patients: Unaffected ileal tissue used as external non-inflamed control. Cell Level Metadata: The dataset includes relevant cell-level metadata such as cell type annotations used in the study. Experimental Details: Platform: 10x Genomics Chromium Single Cell 3' GEX Sequencing: Illumina NovaSeq Processing: Data processed with Cell Ranger software. Resulting count matrices were merged for downstream analysis, including integration and dimensionality reduction. Dataset Composition: Crohn's Disease Patients: 10 patients with 3 samples each (non-inflamed, inflamed, stenotic), totaling 30 samples. Colorectal Cancer Patients: 5 patients with 1 sample each of unaffected tissue, totaling 5 samples. Data Provided: Merged Raw Count Matrix: The final merged raw count matrix used for downstream analysis. Cell Metadata File: Contains details of sample, tissue, and patient for each cell in the count matrix. Barcodes File: Indicate each cell barcode which also encodes the sample, tissue, and patient details for each cell. CD.S_Inf: Stenotic Corhn's disease inflamed samples CD.S_Sten: Stenotic CD patient stenosis sample CD.S_Prox: Stenotic CD Patient - proximal non-inflamed sample CC.C_Prox: CRC Patient proximal unaffected sample eg: A barcode 'CC.C_1_Prox_AAGTCGTAGACCCTTA' indicates CRC Patient unaffected proximal sampe from CRC Patient no.1 and the nucleic acid sequence indicate a unique cell from this sample. Total Samples: Crohn's Disease (CD) Patients: 30 samples Colorectal Cancer (CRC) Patients: 5 samples Patient_no Sample Sample_type 1 CC.C_1 CC.C_1_Prox CC.C_Prox 2 CD.S_1 CD.S_1_Prox CD.S_Prox 3 CD.S_1 CD.S_1_Infl CD.S_Infl 4 CD.S_1 CD.S_1_Sten CD.S_Sten 5 CC.C_2 CC.C_2_Prox CC.C_Prox 6 CD.S_2 CD.S_2_Prox CD.S_Prox 7 CD.S_2 CD.S_2_Infl CD.S_Infl 8 CD.S_2 CD.S_2_Sten CD.S_Sten 9 CC.C_3 CC.C_3_Prox CC.C_Prox 10 CC.C_4 CC.C_4_Prox CC.C_Prox 11 CD.S_3 CD.S_3_Prox CD.S_Prox 12 CD.S_3 CD.S_3_Infl CD.S_Infl 13 CD.S_3 CD.S_3_Sten CD.S_Sten 14 CD.S_4 CD.S_4_Prox CD.S_Prox 15 CD.S_4 CD.S_4_Infl CD.S_Infl 16 CD.S_4 CD.S_4_Sten CD.S_Sten 17 CC.C_5 CC.C_5_Prox CC.C_Prox 18 CD.S_5 CD.S_5_Prox CD.S_Prox 19 CD.S_5 CD.S_5_Infl CD.S_Infl 20 CD.S_5 CD.S_5_Sten CD.S_Sten 21 CD.S_6 CD.S_6_Prox CD.S_Prox 22 CD.S_6 CD.S_6_Infl CD.S_Infl 23 CD.S_6 CD.S_6_Sten CD.S_Sten 24 CD.S_7 CD.S_7_Prox CD.S_Prox 25 CD.S_7 CD.S_7_Infl CD.S_Infl 26 CD.S_7 CD.S_7_Sten CD.S_Sten 27 CD.S_8 CD.S_8_Prox CD.S_Prox 28 CD.S_8 CD.S_8_Infl CD.S_Infl 29 CD.S_8 CD.S_8_Sten CD.S_Sten 30 CD.S_9 CD.S_9_Prox CD.S_Prox 31 CD.S_9 CD.S_9_Infl CD.S_Infl 32 CD.S_9 CD.S_9_Sten CD.S_Sten 33 CD.S_10 CD.S_10_Prox CD.S_Prox 34 CD.S_10 CD.S_10_Infl CD.S_Infl 35 CD.S_10 CD.S_10_Sten CD.S_Sten

  Dataset EGAD50000000559

• TOTHER3

  Patritumab deruxtecan in HER2-negative breast cancer: part B results of the window-of-opportunity SOLTI TOT-HER3 trial and biological determinants of early-response

  Dac EGAC50000000255

• Orthogonal proteogenomic analysis identifies the druggable PA2G4-MYC axis in 3q26 AML

  Our aim was to identify regulators of the EVI1/MECOM gene, which is the most lethal oncogenic TF and is highly expressed in AML with chromosome 3q26 abnormalities.

  Study EGAS50000000347

• Mechanisms of duodenal adenoma development in familial polyposis syndromes

  Study EGAS00001006561

• Characterization of chromatin accessibility in metastatic prostate cancer

  The development of resistance to second-generation AR-signaling inhibitors (ARSIs) and progression to metastatic castrate-resistant prostate cancer (mCRPC) represents a huge obstacle to the improvement of patients’ clinical outcomes. Lineage plasticity is a common mechanism that drives the development of mCRPC by changing chromatin conformation at regulatory elements affecting downstream gene activity. ATAC-seq is a popular technique that measures accessible chromatin regions. To better understand of potential epigenomic mechanisms of ARSI resistance in prostate cancer, in this study, we performed ATAC-seq on 70 mCRPC tissue biopsies from the SU2C/PCF West Coast Dream Team (WCDT) cohort.

  Study EGAS00001006698

• DERMATLAS__Leiomyosarcoma_WES

  irtually no other tumour type is associated with so many different forms as skin cancer. Histologically, tumours of the skin may arise from epithelium, including epidermis, hair follicle, sebaceous or sweat gland, melanocytes, dermal-associated mesenchymal structures or tissue resident immune cells, making for a diversity of clinical presentations. Importantly, many skin tumour types have an extremely poor prognosis. Many skin tumour subtypes have never undergone molecular profiling, or if they have, targeted sequencing has been used and the number of cases analyzed has been so limited that deriving firm conclusions about the profile of driver genes, DNA mutational signatures and germline alleles has not been possible For 70 key skin tumour subtypes defined by the World Health Organization, that have not undergone extensive genetic analysis previoulsy, we propose to perform whole exome and transcriptome sequencing, from a range of body sites, to build a genomic atlas of dermatological tumours, including detailed maps of SNVs, copy number alterations, genome-wide methylation and expression profiles. The cases in this cohort are leiomyosarcomas.

  Study EGAS00001007628

• DERMATLAS__Leiomyoma_WES

  Virtually no other tumour type is associated with so many different forms as skin cancer. Histologically, tumours of the skin may arise from epithelium, including epidermis, hair follicle, sebaceous or sweat gland, melanocytes, dermal-associated mesenchymal structures or tissue resident immune cells, making for a diversity of clinical presentations. Importantly, many skin tumour types have an extremely poor prognosis. Many skin tumour subtypes have never undergone molecular profiling, or if they have, targeted sequencing has been used and the number of cases analyzed has been so limited that deriving firm conclusions about the profile of driver genes, DNA mutational signatures and germline alleles has not been possible For 70 key skin tumour subtypes defined by the World Health Organization, that have not undergone extensive genetic analysis previoulsy, we propose to perform whole exome and transcriptome sequencing, from a range of body sites, to build a genomic atlas of dermatological tumours, including detailed maps of SNVs, copy number alterations, genome-wide methylation and expression profiles. The cases in this cohort are leiomyomas.

  Study EGAS00001007629

• DERMATLAS__Leiomyoma_RNAseq

  Virtually no other tumour type is associated with so many different forms as skin cancer. Histologically, tumours of the skin may arise from epithelium, including epidermis, hair follicle, sebaceous or sweat gland, melanocytes, dermal-associated mesenchymal structures or tissue resident immune cells, making for a diversity of clinical presentations. Importantly, many skin tumour types have an extremely poor prognosis. Many skin tumour subtypes have never undergone molecular profiling, or if they have, targeted sequencing has been used and the number of cases analyzed has been so limited that deriving firm conclusions about the profile of driver genes, DNA mutational signatures and germline alleles has not been possible For 70 key skin tumour subtypes defined by the World Health Organization, that have not undergone extensive genetic analysis previoulsy, we propose to perform whole exome and transcriptome sequencing, from a range of body sites, to build a genomic atlas of dermatological tumours, including detailed maps of SNVs, copy number alterations, genome-wide methylation and expression profiles. The cases in this cohort are leiomyomas.

  Study EGAS00001007630

• Buccal Sample Methylation for Breast Cancer Detection

  This study encompasses buccal sample DNA methylation (IlluminaMethylationEPIC array) from breast cancer cases and healthy age-matched controls.

  Study EGAS00001007658

• ATAC-seq of metastatic prostate tumors

  This dataset includes Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) data, in FASTQ format, from 70 metastatic castration-resistant prostate cancer tumor samples from the SU2C/PCF West Coast Dream Team (WCDT) project. The sequencing data is paired-end, 150 bp sequencing data from an Illumina NovaSeq 6000 machine. ATAC-seq libraries were prepared following the protocol described in Buenrostro et al. Nature Methods. 2013 (PMID: 24097267) and Corces et al. Nature Methods. 2017 (PMID: 28846090).

  Dataset EGAD00001009654

• Buccal sample methylation from breast cancer cases

  Buccal sample methylation from breast cancer cases

  Dataset EGAD00010002635

• Integrative genomic analyses of European intrahepatic cholangiocarcinoma: new ROS1 fusion gene and PBX1 as prognostic marker

  Study EGAS00001007525

• Dataset developed for use with EOSC4Cancer of synthetic colorectal cancer tumor/normal pairs.

  This dataset contains 10 tumor and normal pairs synthetic WGS data of colorectal cancer that were simulated in a standard format of Illumina paired-end reads. The NEAT read simulator (version 3.0, https://github.com/zstephens/neat-genreads) was utilized to synthetize these 10 pairs of tumor and normal WGS data. In the procedure of data generation, simulated parameters (i.e., sequencing error statistics, read fragment length distribution and GC% coverage bias) were learned from data models provided by NEAT. The average sequencing depth for tumor and normal samples aimed to reach around 110X and 60X, respectively. For generation of synthetic normal WGS data per each sample, a germline variant profile from a real patient was down-sampled randomly, representing 50% germline variants of a given patient. These were mixed with the other 50% in silico germline variants that were modelled randomly using an average mutation rate (0.001), finally constituting a full germline profile for normal synthetic WGS data. For generation of synthetic tumor WGS data per each sample, a pre-defined somatic short variant profile (SNVs+Indels) learnt from a real CRC patient was added to the germline variant profile used for creating the normal synthetic WGS data of the same patient, consisting of the variants for tumor sample. Neither copy number profile nor structural variation profile was introduced into the tumor synthetic WGS data. Tumor content and ploidy were assumed to be 100% and 2, respectively. For mapping/variant detection, the Sarek pipeline v3.1.2 (https://nf-co.re/sarek/3.1.2) was used, specifically: 1. BWA v0.7.17-r1188 for read mapping 2. GATK v4.3.0.0 for pre-processing BAM file (including markduplicates and recalibration). 2. Mutect2 (GATK v4.3.0.0) for somatic variant calling 3. Strelka2 v2.9.10 for germline and somatic variant calling Metadata information of 10 CRC patients used for the generation of synthetic normal and tumor WGS data: Patient_id Tumor_barcode Normal_barcode Age Sex Tissue Cancer SIM007 SIM007_T SIM007_N 71 F Rectal Primary CRC SIM008 SIM008_T SIM008_N 45 F Colon Neuroendocrine Metastasis CRC SIM010 SIM010_T SIM010_N 62 M Colon Metastasis CRC SIM011 SIM011_T SIM011_N 55 M Colon Neuroendocrine Metastasis CRC SIM012 SIM012_T SIM012_N 57 M Rectal Metastasis CRC SIM013 SIM013_T SIM013_N 69 M Colon Metastasis CRC SIM014 SIM014_T SIM014_N 68 M Colon Neuroendocrine primary CRC SIM015 SIM015_T SIM015_N 58 F Colon Primary CRC SIM016 SIM016_T SIM016_N 49 M Colon/Rectal Primary CRC SIM017 SIM017_T SIM017_N 78 M Colon Neuroendocrine primary CRC

  Dataset EGAD50000000564

• Sequencing data for "Replication timing alterations are associated with mutation acquisition during tumour evolution in breast and lung cancer"

  Repli-seq data for "Replication timing alterations are associated with mutation acquisition during tumour evolution in breast and lung cancer"

  Dataset EGAD00001015363

• Molecular underpinnings of dedifferentiation and aggressiveness in chromophobe renal cell carcinoma

  This study expands our understanding of aggressive chromophobe renal cell carcinoma (ChRCC), provides insight into molecular mechanisms of tumor progression, and informs pathologic classification and diagnostics. Tumor and adjacent normal samples were collected from 11 patients treated in affiliated hospitals of the University of Texas Southwestern Medical Center (UTSW), including Parkland Hospital. Seven of the eleven patients consented to depositing their genomic data to public repository.

  Study EGAS50000000287