7790 results for "canc*"

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• Heterogeneous Genomic Evolution and Immune Microenvironments in Metastatic Lung Cancer

  WES data of paired primary and metastatic tumors

  Dataset EGAD00001006213

• Whole exome sequencing of long-term, never relapse exceptional responders of trastuzumab-treated HER2+ metastatic breast cancer

  The dataset contains Whole Exome Sequencing data (BAM files) of 22 samples from HER2+ metastatic breast patients. For 9 of the 13 tumours samples there are paired controls available from normal tissue. There are 8 tumours samples that are from treatment-responder patients and 5 tumours samples from non responder patients.

  Dataset EGAD00001006217

• Cancer Alliance RNA-Seq PolyA

  PolyA selection transcriptome profiling by high-throughput for individualized cancer interpretation

  Dataset EGAD00001006234

• February 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.

  February 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 EGAD00001006220

• SPECTA Lung cancer RNA FASTQ files

  SPECTA Lung cancer RNA FASTQ files (Illumina TST170 targeted analysis)

  Dataset EGAD00001006222

• SPECTA Lung cancer DNA FASTQ files

  SPECTA Lung cancer DNA FASTQ files (Illumina TST170 targeted analysis)

  Dataset EGAD00001006223

• Cancer Alliance RNA-Seq total RNA

  Total RNA transcriptome profiling by high-throughput for individualized cancer interpretation

  Dataset EGAD00001006235

• Neoplastic pancreatic cysts and associated cancers

  Data from a study of 148 samples from IPMNs, MCNs, and small associated invasive carcinomas from 18 patients using whole exome or targeted sequencing. Sequencing data from 77 samples out of the 148 samples in the complete study are available in this dataset, based on the permissions given by the participating patients and the informed consents.

  Dataset EGAD00001006229

• Distribution of ctDNA levels in plasma of early-stage non-small cell lung cancer patients measured using personalised ctDNA analysis

  Blood-based assays have shown increasing ability to detect circulating tumour DNA (ctDNA) in patients with early-stage cancer. However, detection of ctDNA in patients with non-small cell lung cancer (NSCLC) has continued to prove challenging. We performed retrospective analysis to quantify ctDNA levels in a cohort of 100 patients with early-stage NSCLC prior to treatment with curative intent. Where tumour tissue was available for whole exome sequencing, mutations identified were used to define patient-specific sequencing assays. For those 90 patients, plasma cell-free DNA was sequenced to high depth across capture panels targeting a median of 328 mutations specific to each patient. Data was analysed using Integration of Variant Reads (INVAR), detecting ctDNA in 66.7% of patients, including 52.7% (29 of 55) patients with stage I disease and >88% detection for patients with stage II and III disease (16/18 and 15/17). ctDNA was detected in plasma at fractional concentrations as low as 9.1x10-6, and in patients with tumour volumes as low as 0.23 cm3. A 36-gene sequencing panel (InVisionFirst-LungTM) was used to analyse plasma DNA in 27 samples including the 10 cases without tumour exome data, and detected ctDNA in 59% of samples tested (16 of 27). Across the entire cohort, detection rates were higher in squamous cell carcinoma patients compared to adenocarcinoma patients (81% vs. 59%). Detection of ctDNA prior to treatment was associated with significantly shorter time free from relapse, across all patients and in patient subgroups, with Hazard Ratios ranging from 2.25 to >11. Our analysis indicates that for patients with stage I NSCLC, the median ctDNA fraction in plasma is approx. 12 parts per million (0.0012%). This indicates the limits of detection that would be required for ctDNA-based liquid biopsies to detect ctDNA in the majority of patients with early-stage NSCLC.

  Dataset EGAD00001006230

• Cancer Alliance WGS

  Whole genome sequencing for individualized cancer interpretation

  Dataset EGAD00001006233

• Cancer Alliance Exome

  Exome sequencing for individualized cancer interpretation

  Dataset EGAD00001006236

• Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

  Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

  Dataset EGAD00001006237

• RNA-seq data from 121 tumor samples with muscle invasive bladder cancer.

  In this study, a total of 300 patients with MIBC receiving chemotherapy were included; 62 received NAC before cystectomy and 245 received first-line chemotherapy upon detection of locally-advanced (T4b) or metastatic disease. Treatment response, defined as pathological downstaging (< pTa,CIS,N0) after NAC or complete or partial response after first-line treatment (RECIST criteria). RNA-seq was performed using the QuantSeq kit FWD HT kit (Lexogen) using 500 ng input RNA from 121 tumor samples. Data provided here consist of 780 fastq files for RNA-seq.

  Dataset EGAD00001006238

• WES data from 165 tumor/germline samples with muscle invasive bladder cancer.

  In this study, a total of 300 patients with MIBC receiving chemotherapy were included; 62 received NAC before cystectomy and 245 received first-line chemotherapy upon detection of locally-advanced (T4b) or metastatic disease. Treatment response, defined as pathological downstaging (< pTa,CIS,N0) after NAC or complete or partial response after first-line treatment (RECIST criteria). WES was performed using DNA from 165 tumors (76x median coverage) and associated germline DNA (46x median coverage). Data provided here consist of 5,828 fastq files for WES.

  Dataset EGAD00001006239

• RNA-seq data of breast cancer HCI011 model from Ros et al (2020)

  RNA-seq data of the HCI011 and HCI011R models, GDC032 treated and control (total of 21 samples), from the paper: FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (Ros et al, 2020)

  Dataset EGAD00001006245

• RNA-SEQ for the Caldas Lab breast cancer PDTX collection

  RNA-SEQ for the Caldas Lab breast cancer PDTX collection. This includes both single and paired end runs

  Dataset EGAD00001006307

• whole exome sequencing (WES) data of the HIPO head and neck cancer (HNC, n=83)

  Paired whole exome sequencing data of the HIPO head and neck cancer (HNC) (n=83), using Agilent SureSelect V4+UTRs and V6+UTRs with the sequencing platforms HiSeq2000 and HiSeq2500. The reads were aligned to hg19. This is part of project H019.

  Dataset EGAD00001006336

• Whole Exome Sequencing of High grade T1 non-muscle invasive bladder cancer

  The dataset consists of a multisample VCF (version 4.1) and the corresponding annotated MAF file, containing the somatic point mutations found from exome sequencing across the high-grade T1 bladder cancer cohort (HGT1, n=61 samples). The VCF file is in accordance with the HTS format specifications (https://samtools.github.io/hts-specs/). The dataset comprises also a CSV file with clinical data.

  Dataset EGAD00001006346

• Sequencing data for oesophageal and related samples - Katz-Summercorn, Jammula et al (RNA)

  Data supporting: “Multi-omic cross-sectional cohort study of pre-malignant Barrett’s esophagus reveals structural variation and retrotransposon activity occur early in cancer evolution.” Katz-Summercorn, Jammula et al. RNAseq (BAM files)

  Dataset EGAD00001006353

• RNA-seq of Bone Metastasis from breast and prostate cancers

  RNA-seq of Bone Metastasis from breast and prostate cancer (4 breast and 5 prostate samples). Dataset contains BAM files from RNA-seq performed using Illumina HiSeq 2500.

  Dataset EGAD00001006356

• Gene expression profiles of single disseminated breast cancer cells

  A set of 56 EpCAM-positive cells derived from bone marrow aspirates of breast cancer patients or patients without a cancererous disease (30 cells from 21 M0-stage and 11 cells from five M1-stage breast cancer patients, 15 cells from seven non-cancer patients serving as controls). EpCAM-positive cells from breast cancer patients were considered disseminated tumor cells as they harbored copy number alterations and showed high expression of the epithelial marker EpCAM and the mammary luminal progenitor marker KIT in comparison to EpCAM-positive bone marrow cells from non-cancer patients. Paired-end RNA-Sequencing of the samples was performed on Illumina NovaSeq6000, raw data are provided in the Fastq format.

  Dataset EGAD00001006359

• Data for the study "Plasma ctDNA is a tumor tissue surrogate and enables clinical-genomic stratification of metastatic bladder cancer"

  Sequencing data from patients with bladder cancer. BAM files from targeted DNA sequencing of bladder cancer driver genes in 344 circulating tumor DNA and tumor tissue samples. BAM files from whole exome sequencing of 49 circulating tumor DNA and tumor tissue samples. Paired FASTQ files from RNA sequencing of 86 tumor tissue samples.

  Dataset EGAD00001006362

• 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