7794 results for "canc*"

in 10.18 milliseconds.

• Genome and transcriptome sequence data from a metastatic breast carcinoma patient

  Genome and transcriptome sequence data from a metastatic breast carcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003714

• Genome and transcriptome sequence data from a metastatic cholangiocarcinoma patient

  Genome and transcriptome sequence data from a metastatic cholangiocarcinoma patient, generated as part of the BC Cancer Agency's Personalized OncoGenomics (POG) study

  Dataset EGAD00001003715

• Highly differentiated, fusion-negative rhabdomyosarcoma.

  Sequencing was performed using OncoPanel v.2 (OPv2), an Agilent SureSelect custom designed bait set consisting of the coding regions of 504 genes, previously linked to human cancer. Sequencing wa sperformed on an Illumina HiSeq 2500. 14 highly differentiated, fusion-negative rhabdomyosarcoma tumor samples, and 8 non-matched normal skeletal muscle samples weer sequenced. BAM files are available for download.

  Dataset EGAD00001003746

• Raw DNA and RNA data from breast cancer organoids, control samples and biopsies

  Whole genome sequencing data for primary tumors, matching control material from blood and their corresponding organoid. Whole transcriptome data for organoids.

  Dataset EGAD00001003751

• Whole exome Sequencing (WES) of multiple tumor biopsies, patient-derived spheroids and leukocyte DNA from colorectal cancer patients (BAM files)

  WES was performed using the KAPA-Hyper prep kit from Illumina (Roche, Basel, Switzerland) for library construction, followed by exome capture using Niblegen SeqCap EZ Human Exome Library v3.0 (Roche). Reads were mapped using BWA MEM against the humane reference genome HG19.

  Dataset EGAD00001003821

• RRBS sequencing data of ovarian cancer, breast cancer, control tissues, and white blood cell DNA.

  The dataset comprises 8 breast cancer, 11 ovarian cancer, 1 benign tumour, 18 normal tissue, 2 endometrium, and 23 white blood cell samples. Genome wide methylation analysis was performed by Reduced Representation Bisulfite Sequencing (RRBS) on Illumina HiSeq 2500. Data is provided as FASTQ files

  Dataset EGAD00001003822

• Somatic mutation analysis based on Whole exome Sequencing (WES) of multiple tumor biopsies, patient-derived spheroids and leukocyte DNA from colorectal cancer patients (vcf files)

  Somatic mutations were called using whole exome Sequencing (WES) data from colorectal cancer samples (dataset EGAD00001003821) using MuTect2, with matched constitutional WES-data obtained from leukocytes samples as reference.

  Dataset EGAD00001003823

• Identification of cis-regulatory mutations generating de novo edges in personalized cancer gene regulatory networks

  Whole genome sequencing data on 10 human cancer cell lines

  Dataset EGAD00001003824

• Exome sequencing data from tumor progression cohort

  This dataset, named Stockholm tumor progression cohort, contains exome-sequencing samples of matched primary and metastasis samples from 20 metastatic breast cancer patients. All patients have one or more sequenced normal samples as well. The total number of samples is 125. The dataset has been used, apart from other studies, to explore tumor evolution patterns in metastatic breast cancer at Karolinska Institute Stockholm.

  Dataset EGAD00001003837

• Deciphering the genomic, epigenomic and transcriptomic landscapes of pre-invasive lung cancer lesions to determine prognosis

  Background: Lung carcinoma-in-situ (CIS) lesions are the pre-invasive precursor to lung squamous cell carcinoma. However, only half progress to invasive cancer in three years, while a third spontaneously regress. Whether modern molecular profiling techniques can identify those pre-invasive lesions that will subsequently progress and distinguish them from those that will regress is unknown. Methods: Progressive and regressive CIS lesions were laser-captured and their genome, epigenome and transcriptome interrogated. We analysed 83 progressive lesions, 41 regressive and 33 normal epithelial control samples. DNA methylation and gene expression profiles were further validated using publicly available lung cancer data. Results: Somatic mutation burden was higher in progressive lesions than regressive CIS lesions, across base substitutions, rearrangements, and copy number changes. Driver mutations were present in both progressive and regressive CIS lesions, but were more numerous in progressive cases. Progressive and regressive CIS lesions had distinct epigenomic and transcriptional profiles, with a strong chromosomal instability signature. Gene expression, methylation and copy number profiles can all predict accurately which CIS lesions will progress to lung cancer. Conclusion: Pre-invasive CIS lesions that will subsequently progress to invasive lung cancer can be distinguished from those that will regress using molecular profiling. Progression is associated with a strong chromosomal instability signature. These findings inform the development of novel therapeutic targets.

  Dataset EGAD00001003883