5709 results for "canc*"

in 20.53 milliseconds.

• Deep-Seq: Resistance to anti-EGFR therapy in colorectal cancer

  In this study we performed ultra deep sequencing of genes associated with anti-EGFR resistance, such as KRAS, BRAF, PIK3CA, and EGFR in 17 plasma-DNA samples from a total of 10 patients treated with anti-EGFR therapy.

  Dataset EGAD00001000688

• Chordoma Sequencing Project Whole Genome

  This is a continuation of the Chordoma Sequencing Project. All cancers arise due to somatically acquired abnormalities in DNA sequence. Systematic sequencing of cancer genomes allows acquisition of complete catalogues of all classes of somatic mutation present in cancer. These mutation catalogues will allow identification of the somatically mutated cancer genes that are operative and characterise patterns of somatic mutation that may reflect previous exogenous and endogenous mutagenic exposures. In this application, we aim to perform whole genome sequencing on 10 chordoma matched genome pairs. RNA Sequencing/Methylation and SNP6 and an additional sequencing of three cancer cell lines will be added to this work.

  Dataset EGAD00001000721

• Transcriptome sequencing of cancer cell lines

  This dataset contains RNA sequencing data for 675 cancer cell lines. RNA libraries were made with the TruSeq RNA Sample Preparation kit (Illumina) according to the manufacturer protocol. The libraries were sequenced on an Illumnia HiSeq 2000

  Dataset EGAD00001000725

• Pseudogene RNAseq

  RNA sequencing to validate findings of somatic pseudogenes acquired during cancer development

  Dataset EGAD00001000732

• Kidney Cancer Heterogeneity and Evolution Revealed by Multi-Region Exome Sequencing

  Paired end Illumina sequencing of whole exomes of multiple tumour regions.

  Dataset EGAD00001000734

• Targeted sequencing of genes recurrently mutated in AML - part2

  Genomic libraries will be generated from total genomic DNA derived from 4000 samples with Acute Myeloid Leukaemia. Libraries will be enriched for a selected panel of genes using a bespoke pulldown protocol. 64 Samples will be individually barcoded and subjected to up to one lanes of Illumina HiSeq. Paired reads will be mapped to build 37 of the human reference genome to facilitate the characterisation of known gene mutations in cancer as well as the validation of potentially novel variants identified by prior exome sequencing.

  Dataset EGAD00001000747

• dataset for BGI bladder cancer project

  dataset for BGI bladder cancer project

  Dataset EGAD00001000758

• dataset for esophageal cancer, 17 pairs for whole-genome sequencing and 71 pairs for whole-exome sequencing

  dataset for esophageal cancer, 17 pairs for whole-genome sequencing and 71 pairs for whole-exome sequencing

  Dataset EGAD00001000760

• Integrated genomic characterization of adrenocortical carcinoma

  Adrenocortical carcinomas (ACC) are aggressive cancers originating in the cortex of the adrenal glands. Despite the overall poor prognosis, ACC outcome is heterogeneous. CTNNB1 and TP53 mutations are frequent in these tumors, but the complete spectrum of genetic changes remains undefined. Exome sequencing and SNP array analysis of 45 ACC revealed recurrent alterations in known drivers (CTNNB1, TP53, CDKN2A, RB1, MEN1) and genes not previously reported to be altered in ACC (ZNRF3, DAXX, TERT and MED12), which were validated in an independent cohort of 77 ACC. The cell-surface transmembrane E3 ubiquitin ligase ZNRF36 was the gene the most frequently altered (21%), and appears as a potential novel tumor suppressor gene related to the ß-catenin pathway.Our integrated genomic analyses led to the identification of two distinct molecular subgroups with opposite outcome. The C1A group of poor outcome ACC was characterized by numerous mutations and DNA methylation alterations, whereas the C1B group with good prognosis displayed a specific deregulation of two miRNA clusters. Thus, aggressive and indolent ACC correspond to two distinct molecular entities, driven by different oncogenic alterations.

  Dataset EGAD00001000764

• Bone Cancer - Rare Types Whole Genome

  We propose to definitively characterise the somatic genetics of a selection of rare bone cancers through generation of comprehensive catalogues of somatic mutations by high coverage genome sequencing.

  Dataset EGAD00001000785