8252 results for "canc*"

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• EORTC RP1335 SPECTA Lung cancer data - Oncomine

  EORTC RP1335 SPECTA Lung cancer data - Oncomine dataset

  Dataset EGAD00001006594

• Validated Single-cell RNA sequencing in early breast cancer

  Single-cell RNA and VDJ sequencing of early breast cancer

  Dataset EGAD00001006608

• 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

• Exome Sequencing of Gastric Cancer Samples

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

  Dataset EGAD00001006957

• 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

• UROMOL 2020 - RNA-seq data validation

  For this project about non-muscle invasive bladder cancer (NMIBC), we analysed total RNA-seq data from 47 patients used for validation. Sequencing of total RNA was performed using KAPA RNA HyperPrep Kit with RiboErase HMR (Roche). RNA input was 100 to 500 ng. The dataset is composed of 94 fastq files.

  Dataset EGAD00001006967

• Targeted sequencing of Human esophageal epithelium microbiopsies

  NOTCH1 mutant clones occupy the majority of normal human esophagus by middle age, but are comparatively rare in esophageal cancers, suggesting NOTCH1 mutations may promote clonal expansion but impede carcinogenesis. Here we test this hypothesis. Visualizing and sequencing NOTCH1 mutant clones in aging normal human esophagus, reveals frequent biallelic mutations that block NOTCH1 signaling.  In mouse esophagus, heterozygous Notch1 mutation confers a competitive advantage over wild type cells, an effect enhanced by loss of the second allele.  Notch1 loss alters transcription but has minimal effects on epithelial structure and cell dynamics. In a carcinogenesis model, Notch1 mutations were less prevalent in tumors than normal epithelium. Deletion of Notch1 reduced tumor growth, an effect recapitulated by anti-NOTCH1 antibody treatment.  We conclude that Notch1 mutations in normal epithelium are beneficial as wild type Notch1 promotes tumor expansion. NOTCH1 blockade has therapeutic potential in esophageal squamous tumors.

  Dataset EGAD00001006969

• Clinical phenotype data

  Clinical data corresponding to the patients with ovarian cancer studied using scRNA-seq and bulk RNA-seq. Variables include molecular subtype, predicted immune phenotype, reviewing pathologist comment, final immune phenotype, histology characterization, and tumor stage.

  Dataset EGAD00001006973

• scRNA-seq using 10X Genomics platform

  Matrices of counts from single-cell RNA-seq data for 15 samples from patients with ovarian cancer, 5 samples for each of the 3 tumor immune phenotypes (Infiltrated, Excluded and Desert). Dissociated cells from each tumor sample have been sorted to isolate live cells from the 3 compartments: tumor, immune and stromal. Each of the compartments has been analyzed separately by scRNAseq, excluding some desert tumors for which cells from the stromal and immune compartments have been pooled. Sequencing was performed using 10X Genomics Chromium Single Cell platform (v2 Chemistry).

  Dataset EGAD00001006974

• Bulk RNAseq fastq files

  RNAseq FASTq files from 15 samples from patients with ovarian cancer, 5 samples for each of the 3 tumor immune phenotypes (Infiltrated, Excluded and Desert).

  Dataset EGAD00001006975

• HER2-negative primary breast cancers WES

  Temporal HER2-negative breast cancers WES

  Dataset EGAD00001006980

• Breast Cancer TNBC Single-Cell RNA-Seq Dataset

  Single-cell RNA-Sequencing of five TNBC primary breast cancers from Wu et al. (2020) EMBO J study. Data was generated using the Chromium controller (10X Genomics) and sequenced on the NextSeq 500 platform.

  Dataset EGAD00001006981

• Single-cell RNA-seq data from metastatic ovarian cancer for quality control study

  This study includes treatment-naïve fresh tissue sample from 4 HGSOC patients.

  Dataset EGAD00001006984

• Single-cell gene expression data from CD8+ T cells from two Austrian COVID19 patients stimulated with wildtype and mutant SARS-Cov-2 peptides

  The data contains single-cell gene sequencing data (10x Genomics) from FACS-purified CD8 T lymphocytes from two Austrian patients. The cells were stimulated with one MHC class I peptides obtained from a common (wild type) variant and an emerging mutant variant of the SARS-Cov-2 virus. Then the samples were multiplexed using hashtag oligos. We provide the raw and aligned sequence data for: i. The single-cell experiments ii. The PCR-amplified samples for enrichment of the hashtag oligo multiplexing barcodes iii. The PCR-amplified samples for enrichment of the T Cell Receptor (TCR) VDJ region for immuno-profiling. The samples and libraries were processed and obtained in collaboration between St. Anna Children's Cancer Research Institute (CCRI), CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, and the Medical University of Vienna. The cell barcodes and processed data has been submitted to the GEO database with GEO accession GSE166651.

  Dataset EGAD00001006995

• WES, sWGS and RNA-seq of Asian breast cancer

  The dataset contains a full genomics characterization of 527 Asian breast tumours. This includes whole-exome sequencing of tumour tissue at 80X, whole-exome sequencing of matched normal (blood) tissue at 40X, shallow-whole genome sequencing at 0.1X for copy number analyses, and RNA-seq of tumour tissue at 40X coverage (>15 million reads). Whole-exome libraries were prepared using the Nextera Rapid Capture Exome Kit; exome capture was performed in pools of 3 and subjected to paired end 75 sequencing on a HiSEQ4000 platform. RNA libraries were prepared using the TruSeq Stranded Total RNA HT kit with Ribo-Zero Gold as per manufacturer’s instructions and also subjected to paired end 75 sequencing on a HiSEQ4000 platform. Uploaded bam files have been mapped to the hs37d5 human genome and processed using the standard GATK pipelines. Paired clinical, demographic, genotyping, and overall survival data for these patients are available from the associated publications or by request.

  Dataset EGAD00001006399

• Transcriptomic profiling of patient-derived xenografts and organoids in prostate cancer

  Dataset contains RNA-seq of 30 samples (normal prostate tissue, human prostate cancer, PDX and organoids).

  Dataset EGAD00001006404

• FIT interval CRCs versus Screen-detected CRCs

  Formalin-fixed, paraffin-embedded samples from 27 FIT interval CRC and 54 screen-detected CRCs collected in a pilot-program of FIT-based CRC screening in the southwest and northwest regions in the Netherlands, were used in this study. DNA was extracted for 1) Shallow Sequencing (copy number analysis) and 2) TSACP Amplicon Cancer Gene Panel (mutations) of 22 FIT Interval CRCs and 45 screen-detected CRCs.

  Dataset EGAD00001006409

• Regulatory T cell transcriptomic reprogramming characterizes adverse events by checkpoint inhibitors in solid tumors

  Tregs were sorted as CD4+CD25+CD127- cells from peripheral blood of patients with advanced metastatic melanoma, stage III(B-D)-IV, who were receiving treatment with anti-PD1 (n =26); and patients with kidney, non-small cell lung, liver and bladder cancer who were receiving treatment with anti-PD1. RNA was extracted and polyA libraries were prepared using the Illumina Truseq sample preparation kit v.2. Single-end sequencing was performed on NextSeq500.

  Dataset EGAD00001006415

• Copy number profiling of tissue and plasma samples from high grade serous epithelial ovarian cancer patients

  This dataset includes 289 samples from 46 high grade serous epithelial ovarian cancer patients. Data are from both tissue samples (either primary tumor, or synchronous metastases) and circulating cell-free DNA (cfDNA) of plasma samples taken during therapy and follow-up.

  Dataset EGAD00001006422

• Pre-clinical evolution of haematological malignancies_WGS

  Leukaemia and related blood cancers occur due to genetic changes that typically accumulate over many years. This study will employ targeted next-generation sequencing to retrace the preclinical evolution of several types of haematological malignancy. Investigating the progression of the earliest pre-malignant ancestral clones promises to offer valuable insights into early leukaemia evolution and therapeutic vulnerabilities of leukaemia stem cells.

  Dataset EGAD00001006423

• Pre-clinical evolution of haematological malignancies_TGS

  Leukaemia and related blood cancers occur due to genetic changes that typically accumulate over many years. This study will employ targeted next-generation sequencing to retrace the preclinical evolution of several types of haematological malignancy. Investigating the progression of the earliest pre-malignant ancestral clones promises to offer valuable insights into early leukaemia evolution and therapeutic vulnerabilities of leukaemia stem cells.

  Dataset EGAD00001006424

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Kidney (1)

  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. 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.

  Dataset EGAD00001006427

• Whole genome sequencing of retinoblastoma reveals the diversity of rearrangements disrupting RB1 and uncovers a treatment related mutational signature

  Background: The development of retinoblastoma is thought to require pathological genetic changes in both alleles of the RB1 gene. However, cases exist where RB1 mutations are undetectable suggesting alternative pathways to malignancy. Methods: We applied comprehensive whole genome sequencing (WGS) and transcriptomics to sporadic retinoblastomas derived from twenty patients attending our clinic, contrasting these results to that obtained through customary clinical testing. We sought RB1 and other driver mutations, investigated mutation burden, mutational signatures and phylogenetic relatedness in one case of bilateral retinoblastoma. Results: At least one RB1 mutation was identified in all retinoblastomas. We confirmed RB1 mutations previously identified by clinical screening, identified three new RB1 mutations and provided clarity to the mechanism behind a further six mutations. Eight tumours carried structural rearrangements involving RB1 ranging from relatively simple to extremely complex rearrangement patterns, including a chromothripsis-like pattern in one tumour. Potential driver mutations included mutations in BCOR (5/20) and amplification of MYCN (2/20) and MDM4 (1/20). We show that RB1 mutations are not mutually exclusive of MYCN amplifications, and further reveal that all tumours demonstrate increased MYCN expression suggesting a universal role in retinoblastoma tumorigenesis.  Bilateral tumours obtained from one patient harboured conserved germline but divergent somatic RB1 mutations, indicating independent evolution. In-keeping with previous WGS of paediatric cancers, the mutation burden in retinoblastomas was extremely low. Mutational signature analysis showed a predominance of signatures associated with cell division and an absence of ultraviolet-related DNA damage. In a tumour exposed to chemotherapy prior to enucleation, a profound platinum-related mutational signature was observed. Conclusions: WGS provides a complete picture of the genomic landscape of retinoblastomas, allowing the discovery of mutations otherwise undetected by conventional clinical screening approaches. The presence of at least one RB1 mutation in all retinoblastomas and the relative paucity of driver mutations in other genes suggests mutations beyond RB1, MYCN and BCOR are rare. Whilst most RB1 mutations are identifiable by clinical screening, the increased resolution and ability to detect otherwise elusive rearrangements of RB1 by WGS, confirming whether they are somatic or germline, has important repercussions on clinical management and advice on recurrence risks.

  Dataset EGAD00001006431

• Single cell karyotype sequencing of 7 samples from colorectal cancer (CRC) patients

  Contains data for all cells sequenced for this study. Data is organized as one bam-file per sample. Individual cells can be identified through the CB tag in the bam-files.

  Dataset EGAD00001006438