7416 results for "canc*"

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• Low coverage WGS from plasma DNA

  Low coverage whole genome sequencing plasma DNA from 50 male, 54 female non-cancer donors. For the analysis of nucleosomal positioning all data from the non-cancer controls were merged. Furthermore, two patients with metastasized breast cancer were sequenced on a NextSeq with higher depth.

  Dataset EGAD00001002215

• RNA-Seq of tumors

  RNA-Seq on an Ion Torrent Proton of corresponding tumor material of two metastasized breast cancer patients (Breast7, Breast13).

  Dataset EGAD00001002216

• Paired-end sequenced plasma DNA samples

  Merged file of low-coverage WGS from 179 plasma DNA samples from non-cancer controls and cancer patients for assessment of size distribution of plasma nuclear DNA fragments.

  Dataset EGAD00001002217

• Identification of drug resistance genes in cancer cell lines by insertional mutagenesis

  Our aim is to identify genes involved in resistance to anti-cancer therapies. In order to do this we have taken advantage of a lentiviral vector (LV)-based insertional mutagen to mutagenize cancer cell lines. LV-transduced cell lines were then treated with anti-cancer therapies and the emergence of resistant clones scored. DNA from pools of resistant clones was collected, subjected to custom capture by baits designed against the LV sequence, and then sequenced to identify the LV-genomic junction. We hope that the identification of recurrently targeted genes in resistant cell population will allow us to identify genes that mediate drug resistance.

  Dataset EGAD00001002207

• PSCP_mutation analysis in hESCs

  Many studies over the past 10 years, culminating in the recent report of the International Stem Cell Initiative (ISCI, 2011) have shown that hPSC acquire genetic and epigenetic changes during their time in culture. Many of the genetic changes are non-random and recurrent, probably because they provide a selective growth advantage to the undifferentiated cells. Some are shared by embryonal carcinoma cells, the malignant counterparts of ES cells. The origins of these growth advantages are poorly understood, but may come from altered cell cycle dynamics, resistance to apoptosis or altered patterns of differentiation. Less is known about the nature and consequences of epigenetic changes, but it is likely that these similarly affect hPSC behaviour; e.g., enhanced expression of DLK1, an imprinted gene, is associated with altered hPSC growth (Enver et al 2005). Inevitably, these genetic and epigenetic changes will impact on our ability to use hPSC for regenerative medicine, either because malignant transformation of the undifferentiated cells or their differentiated derivatives to be used for transplantation compromises safety, or because they impede the function of those differentiated derivatives, or because they affect the efficiency with which the undifferentiated cells can be expanded and differentiated into desired cell types. Focusing initially upon the existing clinical grade hESC lines, later moving to iPSC, we will Consolidate and extend knowledge of the rate, type and functional impact of the genetic variations that occur during hPSC culture. We will use whole genome and exome sequencing as well as SNP arrays, together with clonal analysis and other cytogenetics techniques. Common changes will be compared with those found in the normal human population, at low frequency in the original cell population or observed during iPSC generation in the HIPSCI project currently based at the WTSI. These studies will provide a better understanding of the range of genetic changes that occur in hPSC beyond the CNVs already identified. In conjunction with cancer genome resources and expertise at WTSI, bioinformatic analyses of these hPSC data will allow us to assess potential impact on hPSC behaviour pertinent to applications in regenerative medicine, notably the likelihood that specific changes arising in undifferentiated PSC cultures may be associated with potential malignant transformation of differentiated progeny. This data is part of a pre-publication release. For information on the proper use of pre-publication data shred by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001002231

• Illumina_RNA_MET

  RNAseq on Illumina HiSeq2000/2500 of colorectal cancer metastasis sample

  Dataset EGAD00001002072

• Illumina_RNA_MET-CELL

  RNAseq on Illumina HiSeq2000/2500 of PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002073

• Illumina_RNA_MET-CELL-GFP

  RNAseq on Illumina HiSeq2000/2500 of WNT reporter of PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002074

• Illumina_RNA_MET-CELL-XEN

  RNAseq on Illumina HiSeq2000/2500 of Patient-derived xenograft derived from PDO culture derived from colorectal cancer metastasis sample

  Dataset EGAD00001002075

• Illumina_RNA_MET-XEN

  RNAseq of Patient-derived xenograft derived from colorectal cancer metastasis sample

  Dataset EGAD00001002076