12130 results for "cancer rna-seq"

in 13.42 milliseconds.

• Sequence of breast cancer bone metastases PDX from 2 targeted panels

  Sequence of breast cancer bone metastases PDX obtained by 2 targeted panels

  Dataset EGAD00001010856

• Shallow-whole genome sequencing for copy numbers in resectable gastric cancer treated with surgery alone

  Shallow-whole genome sequencing for copy numbers in resectable gastric cancer treated with surgery alone

  Dataset EGAD00001011994

• Geographic variation of mutagenic exposures in kidney cancer genomes – structural variation vcf files (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – structural variation vcf files ( Mutographs )

  Dataset EGAD00001013726

• Geographic and age-related variations in mutational processes in colorectal cancer - filtered vcf files (Mutographs)

  Geographic and age-related variations in mutational processes in colorectal cancer - filtered vcf files (Mutographs)

  Dataset EGAD00001015486

• Temporal Dissection of Tumorigenesis in Primary Cancers

  The earliest genetic abnormalities in cancer represent a unique opportunity for timely clinical diagnosis. Classic deep sequencing of tumors identifies many aberrations acquired later in cancer progression. In this study, data regarding simple mutation and chromosomal aberration were integrated to trace the evolution of cutaneous squamous cell carcinomas and ovarian adenocarcinomas. Only after the second allele of TP53 was lost did the genome enter a window of extreme genomic vulnerability, in both cancer types, eventually acquiring more than 100,000 mutations in skin cancers. Inactivating Notch mutations were also identified as prevalent secondary changes. These results add context to the idea of TP53 mutation as dominant negative and occurring later in tumorigenesis.

  Study phs000418

• Amplicon sequencing of various tumors

  In our previous studies on advanced esophageal, gastric, and colorectal carcinomas, measurement of circulating tumor DNA (ctDNA) in terms of variant allele frequency (VAF) in blood suggested: (1) patients with high VAF have a high chance of relapse; (2) VAF changes in response to treatment; and (3) ctDNA VAF elevation precedes imaging diagnosis of relapse. It is thus necessary to investigate whether these findings can be generalized in a wide cancer spectrum, types of treatment, and relapse phenotypes across cancer types. In the present study, we conduct a non-interventional observational study in a wide cancer spectrum on the basis of technical establishment for ctDNA using our unique digital PCR probe library.

  Study JGAS000366

• MutWP4: CRUK Grand Challenge Mutographs of Cancer: Pancreatic Organoids (2021-02-02)

  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, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure 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. . This dataset contains all the data available for this study on 2021-02-02.

  Dataset EGAD00001006933

• MutWP4: CRUK Grand Challenge Mutographs of Cancer: Gastric Organoids (2019-08-07)

  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, Kings College London will characterise the mutational signatures induced by putative human carcinogens in order to identify the origins of mutational signatures found in human cancers. To achieve this human organoid cell cultures will be exposed to a representative catalogue of known or suspected human carcinogens and mutagens and, using whole genome sequencing, the patterns of mutations induced by them will be determined. Somatic mutational signatures will be subsequently extracted by non-negative matrix factorisation methods and correlated with exposure 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. . This dataset contains all the data available for this study on 2019-08-07.

  Dataset EGAD00001005234

• Phenotypic characterization and prognostic impact of CD103+ tissue-resident memory T cells in diffuse large B cell lymphoma

  Tissue-resident memory T (TRM) cells are a population of memory T cells that stably occupy tissues and play a key role in immunosurveillance, having been linked to favorable survival outcomes in various solid tumors. While TRM cells have been identified in lymph nodes, their phenotype and prognostic significance in B-cell non-Hodgkin lymphoma (B-NHL), including diffuse large B-cell lymphoma (DLBCL), remains poorly characterized. The frequency of CD103 expressing T cells in patient samples with DLCBL was quantified by immunofluorescence (IF) staining of tissue biopsies (n=306) and flow cytometry (n=252) of cell disaggregates, and linked with clinical outcome. The phenotype of TRM cells was characterized by spectral flow cytometry (n=10 DLBCL and 2 reactive lymph node (rLN) samples) and single-cell RNA sequencing (scRNAseq) (n=12 aggressive B cell lymphomas and 4 rLN samples). An additional 51 samples from an external dataset were included to verify the single cell transcriptome findings. The flow cytometry and scRNAseq findings in DLBCL were extrapolated to additional B-NHL entities. Through analysis of these datasets we were able to characterize CD103+ TRM-like cells in DLBCL and other B-NHL entities and identified them to represent a prognostically favorable population with an activated/cytotoxic T cell phenotype.

  Study EGAS50000000943

• Polygenic Risk Score for the Prediction of Breast Cancer Is Related to Lesser Terminal Duct Lobular Unit Involution of the Breast

  The Susan G. Komen Tissue Bank (KTB) at the Indiana University Simon Cancer Center is an annotated biobank, which has recruited healthy women who provided demographic, lifestyle, and cancer-related information via self-administered questionnaire, blood samples and normal breast tissues. H&E slides of the normal breast tissues were scanned for image analysis and a pathologist measured the number of terminal duct lobular units (TDLUs) ("TDLU count"). The acini count per TDLU was quantified using the TDLU analyzer software. In this analysis, we computed the recently developed polygenic risk score (PRS) based on 313 common variants for the prediction of breast cancer and related this PRS to TDLU count and acini count per TDLU. We did this for the overall PRS, the ER-positive PRS and the ER-negative PRS.

  Study phs002062