5711 results for "canc*"

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• Single-cell analysis for metastatic gastric adenocarcinoma

  Intra-tumoral heterogeneity (ITH) is the fundamental property of cancer, however, the origins of ITH remain poorly understood. Here we performed single-cell RNA sequencing of peritoneal carcinomatosis (PC) from patients with advanced gastric adenocarcinoma (GAC), constructed a transcriptome map of 45,048 PC cells, profiled the transcriptome states of diverse tumor cell lineages and types, incisively explored ITH of PC tumor cells, and identified significant correlates with patient survival.

  Study EGAS00001004443

• Field_effect_of_healthy_and_diseased_livers_WGS

  Recent work in the Campbell group has revealed somatic mutations present in normal, non-cancerous human skin. A subset of the mutations conferred selective advantages to the host cells, leading to clonal expansions and raising the risk for future cancer development. Capturing such somatic mutations in normal tissue is important to advance our understanding about carcinogenesis and could provide prospective medical insights. In this project, our goal is to detect somatic mutations in normal (pre-cancerous) liver tissue. Using Laser Microdissection technology, we will dissect individual liver lobules from patient samples and submit these to sequencing. For each patient sample, we aim to sequence multiple lobules to characterise the mutagenic burden. Samples will be taken from patients with different liver disease aetiologies, including alcoholism and obesity, with a view on distinguishing the prevalent mutation types occurring in each disease context. We will perform both whole genome and targeted sequencing, initially using the WTSI cancer panel. Later we aim to use a novel bait set that captures both cancer genes as well as genes relevant to the non-cancerous samples (ie. genes implicated in hereditary disorders, immune sequences).

  Study EGAS00001002413

• ctDNA monitoring using patient-specific sequencing and integration of variant reads - Breast cohort

  Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to individual parts per million. This resulted in median Area Under the Curve (AUC) values of 0.98 in advanced cancers, and 0.80 in early stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that the INVAR may be applied without requiring personalized sequencing panels, so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

  Study EGAS00001004446

• An integrated multi-omic cellular atlas of human breast cancers

  Study EGAS00001005173

• MutWP1__CRUK_Grand_Challenge_Mutographs_of_Cancer__Oesophageal_squamous_cell_carcinoma

  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 EGAS00001002725

• Orphan_Tumour_Study_NB

  The aim of this study is to investigate the transcriptional landscape of human cancer. 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/

  Study EGAS00001003445

• Single-cell profiling of neoantigen-specific T cells in lung cancers treated with neoadjuvant PD-1 blockade

  We study the expression profile of MANA-specific T cells, we used the MANA functional expansion of specific T cells (MANAFEST) assay in neoadjuvant anti-PD-1-treated lung cancers and applied their T cell receptor’s (TCR) complementarity-determining region 3 (CDR3) as a “barcode” to track and analyze their transcriptional programs in the tumor microenvironment using single cell TCR sequencing coupled with single cell RNA sequencing. We revealed distinct phenotype for MANA- and virus-specific clones in TIL and adjacent normal lung, regardless of response. MANA-specific T cells show features that drive the differences between responders and non-responders.

  Study EGAS00001005343

• Residual ctDNA after treatment predicts early relapse in patients with early-stage non-small cell lung cancer

  Study EGAS00001005967

• Whole Exome sequencing of colorectal cancer patients (SG-BULK)

  The study was approved by the institutional review board of Singhealth (2018-2795). Ten 5-µm tissue sections were cut using standard microtomy techniques. Each collected cell population was then extracted using the allprep kit. Extracted material was then quantified using a Qubit fluorometer. DNA-seq libraries were captured to exome regions using xGen Exome Research Panel v1.0 (IDT), and libraries were prepared using the KAPA Hyper prep kit. DNA libraries were sequenced to a target depth of ×200 for tumor sample, ×100 for normal samples on the Illumina HiSeq platform.

  Study EGAS00001006006

• Comparison of sequencing assays for sensitive detection of circulating tumour DNA in stage IA-IV breast cancer

  This study compared different assays for the detection of circulating tumour DNA (ctDNA) in serial plasma from stage IA-IV breast cancer patients, targeting structural variants (SVs), single nucleotide variants (SNVs) and/or somatic copy-number aberrations (SCNAs). SV-multiplex PCR, SNV-/SV-hybrid capture, and different depths of whole-genome sequencing (WGS) were used to evaluate ctDNA levels, demonstrating concordant results. SNV-hybrid capture targeting 1,347-7,491 mutations was the most sensitive assay, detecting 67% (36/54) of samples down to an allele fraction (AF) of 0.00024%. SV-multiplex PCR, targeting 21-47 mutations, detected 63% (34/54) of samples down to 0.00047% AF and has potential as a clinical assay.

  Study EGAS00001006040