7359 results for "canc*"

in 9.04 milliseconds.

• The_life_history_of_colorectal_cancer_metastases_study_WGS_X10

  Whole genome sequencing of a series of metastasis collected from 2 patients at autopsy will be used to construct a life history of the cancer, including phylogenetic relationship between the different metastases an also whether there is evidence of convergence towards common biological processes that underpin the metastatic process but though different mutational events

  Study EGAS00001000864

• Triple_Negative_Breast_Cancer_Whole_Genomes

  We propose to definitively characterise the somatic genetics of Triple negative breast cancer through generation of comprehensive catalogues of somatic mutations in 500 cases by high coverage genome sequencing coupled with integrated transcriptomic and methylation analyses.

  Study EGAS00001000092

• Angiopredict: predicting response for bevacizumab treatment

  In ANGIOPREDICT, academic cancer biologists and industry-based biotechnology researchers worked with clinicians to identify biomarkers to predict whether individual metastatic colorectal cancer patients will respond positively to bevacizumab containing therapy. In this retrospective study, copy number profiles were determined using whole genome shallow sequencing, and progression free survival and treatment data was collected.

  Study EGAS00001002724

• Prognostic_factors_in_prostate_cancer__deep_sequencing_pilot_project_TAPG

  This is a pilot project to determine whether the TAPG FFPE DNA’s are suitable for deep sequencing. If successful an investigation of SNP distribution in a larger cohort will follow.

  Study EGAS00001000879

• TRACERx 100: whole exome data of the first 100 TRACERx tumours

  BACKGROUNDTRACERx (TRAcking Cancer Evolution through therapy (Rx)) is a prospective cohort study designed to investigate intratumor heterogeneity (ITH) in relation to clinical outcome, and to determine the clonal nature of driver events and evolutionary processes in early stage non-small cell lung cancer (NSCLC). METHODSMultiregion high-depth whole-exome sequencing (M-seq) was performed on 100 early stage NSCLC tumors resected prior to systemic therapy. A total of 327 tumor regions were sequenced and analyzed to define evolutionary histories, obtain a census of clonal and subclonal events, and assess the relationship between ITH and recurrence-free survival (RFS). RESULTSWidespread ITH was observed for both somatic copy number alterations (median 48% [0.03-88%]) and mutations (median 30% [0.5-93%]). Driver mutations in EGFR, MET, BRAF and TP53 were almost always clonal. However, heterogeneous driver alterations occurring later in evolution were found in over 75% of tumors and were common in PIK3CA, NF1 and genes involved in chromatin modification and DNA response and repair. Genome doubling and ongoing dynamic chromosomal instability (CIN), illustrated by mirrored subclonal allelic imbalance, were identified as causes of ITH resulting in parallel evolution of driver copy number events, including amplifications of CDK4, FOXA1, and BCL11A. Elevated copy number heterogeneity was associated with shorter RFS (HR=4.9, P=0.00044), which remained significant in a multivariate analysis.CONCLUSIONSITH mediated through CIN, rather than point mutational heterogeneity, was associated with increased risk of relapse, supporting its value as a prognostic predictor, and the need to target this high-risk phenotype.

  Study EGAS00001002247

• Evolutionary analysis of pancreatic cancer and coexistent precursor lesions using whole exome sequencing data

  Most adult carcinomas develop from noninvasive precursor lesions, a progression that is supported by genetic analysis. Further, the evolution of these lesions contextualizes the dynamics of advanced stage disease. We analyzed the somatic variants of co-existing pancreatic cancers and precursor lesions sampled from distinct regions of the same pancreas. After inferring evolutionary relationships, we found that the ancestral cell had initiated and clonally expanded to form one or more lesions, and that subsequent driver gene mutations eventually led to a pancreatic cancer. We also found that this multi-step progression generally spans many years. Our data suggest that independent, high-grade pancreatic lesions observed in a histologic cross section often represent a single neoplasm that can spread and colonize the ductal system, accumulating spatial and genetic divergence over time.

  Study EGAS00001002778

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__pancreas

  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 (Illumina HiSeqX, 40X and 20X depth respectively). 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.

  Study EGAS00001002726

• CancerDetector: Ultrasensitive and Non-Invasive Cancer Detection at the Resolution of Individual Reads using Cell-free DNA Methylation Sequencing Data

  The detection of tumor-derived cell-free DNA in plasma is one of the most promising directions in cancer diagnosis. The major challenge in such approach is how to identify the tiny amount of tumor DNAs out of total cell-free DNAs in blood. Here we propose an ultrasensitive cancer detection method, termed “CancerDetector”, using the DNA methylation profiles of cell-free DNAs. The key of our method is to probabilistically model the joint methylation patterns of multiple adjacent CpG sites on an individual sequencing read, in order to exploit the pervasive nature of DNA methylation for signal amplification. Therefore, CancerDetector can sensitively identify a trace amount of tumor cfDNAs in plasma, at the level of individual reads. We evaluated CancerDetector on the simulated data, and showed a high concordance of the predicted and true tumor burden. Testing CancerDetector on real plasma data demonstrated its high sensitivity and specificity in detecting tumor DNAs. In addition, the predicted tumor burden showed great consistency with tumor size and survival outcome. Note that all of those testing were performed on sequencing data at low to medium coverage (1X to 10X). Therefore, CancerDetector holds the great potential to detect cancer early and cost-effectively.

  Study EGAS00001002728

• ADCC_Exome_Sequencing

  Agilent whole exome hybridisation capture will be performed on genomic DNA derived from Adenoid cystic carcinomas and matched normal DNA from the same patients. Three lanes of Illumina GA sequencing will be performed on the resulting exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes.

  Study EGAS00001000193

• Myeloproliferative_Disorder_Sequencing

  Agilent whole exome hybridisation capture will be performed on genomic DNA derived from 50 myeloproliferative disorder samples and matched normal DNA from the same patients. Three lanes of Illumina GA sequencing will be performed on the resulting 100 exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes. In addition, 500bp, NO_PCR total genomic libraries will be prepared from the same samples and five lanes of Illumina GA sequencing will be analysed to characterise genome wide, somatically acquired structural variation.

  Study EGAS00001000198