7351 results for "canc*"

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• VCF dataset of Colorectal Cancer Synthetic genomes

  This dataset contains genomic variants data from VCF files of four patients synthetically generated in the EOSC4Cancer project

  Dataset EGAD50000000314

• BAM/BAI dataset of Colorectal Cancer Synthetic genomes

  This dataset contains binary alignment map (BAM) data with their respective index (BAI) from four patients synthetically generated.

  Dataset EGAD50000000316

• TOTHER3

  Patritumab deruxtecan in HER2-negative breast cancer: part B results of the window-of-opportunity SOLTI TOT-HER3 trial and biological determinants of early-response

  Dac EGAC50000000255

• Mechanisms of duodenal adenoma development in familial polyposis syndromes

  Study EGAS00001006561

• NTNU Data Access Committee for “ProstOmics: spatial and bulk multi-omics of prostate cancer” datasets archived in Federated EGA Norway

  The ProstOmics project of NTNU contains spatial and bulk multi-omics datasets of prostate cancer, and access request to these datasets are handled by this Data Access Committee on behalf of NTNU.

  Dac EGAC50000000277

• The University of Hong Kong Gastric Cancer Organoids RHO Signaling Study Data Access Committee

  Dac EGAC00001002705

• Genomic validation of the revised Bethesda criteria in terms of pathogenesis and oncologic significance

  Study EGAS00001003959

• Cancer Genomics, ICR, summary data

  DAC for summary level data from the Cancer Genomics group, Division of Genetics and Epidemiology at the Institute of Cancer Research, Sutton, UK.

  Dac EGAC50000000050

• DAC for "Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis"

  DAC for the data of "Cancer-associated fibroblasts promote drug resistance in ALK-driven lung adenocarcinoma cells by upregulating lipid biosynthesis" with: • Holger Sültmann • h.sueltmann@dkfz.de • German Cancer Research Center (DKFZ) • Ann-Kathrin Daum • a.daum@dkfz.de • German Cancer Research Center (DKFZ)

  Dac EGAC50000000076

• Machine learning to detect the SINEs of cancer

  We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA (cfDNA) through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a novel machine-learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to samples from 5108 individuals, 2037 with cancer and the remainder without cancer. Samples from cancer patients and controls were pre-specified into four cohorts used for: 1) model training, 2) analyte integration and threshold determination, 3) validation, and 4) reproducibility. A-PLUS alone provided a sensitivity of 40.5% across 11 different cancer types in the Validation Cohort, at a specificity of 98.5%. Combining A-PLUS with aneuploidy and 8 common protein biomarkers detected 51% of 1167 cancers at 98.9% specificity. We found that part of the power of A-PLUS could be ascribed to a single feature – the global reduction of AluS sub-family elements in the circulating DNA of cancer patients. We confirmed this reduction through the analysis of another independent dataset obtained with a very different approach (whole genome sequencing). The evaluation of Alu elements therefore has the potential to enhance the performance of several methods designed for the earlier detection of cancer.

  Study EGAS00001007169