RNAseq data of 81 liver cancer cell lines
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE97098 | GEO | 2019/06/17
REPOSITORIES: GEO
Similar Datasets
Project description:Liver cancer is a dreadful disease with limited therapeutic options. How to translate genomic findings into biomarkers and precision treatment is challenging. A large cell line-based platform was established, termed as Liver Cancer Model Repository. Heterogeneities in patients were faithfully recapitulated by 81 liver cancer cell lines at genomic and transcriptomic levels. This platform provides a rich resource and models to study cancer drivers and drug responses in liver cancer.
| EGAS00001002237 | EGA
Project description:Whole genome sequencing was performed for 81 liver cancer cell lines. Additional whole exome sequencing was performed for a subset of 11 liver cancer cell lines. SK_HEP_1 was also provided, though considered not hepatic origin. These sequencing data provided the detailed genomic characterization of liver cancer models.
| EGAD00001003165 | EGA
Project description:For many years, immortalized cell lines have been used as model systems for cancer research. Cell line panels were established for basic research and drug development, but did not cover the full spectrum of leukemia and lymphoma. Therefore, we now developed a novel panel (LL-100), 100 cell lines covering 22 entities of human leukemia and lymphoma including T-cell, B-cell and myeloid malignancies. Importantly, all cell lines are unequivocally authenticated and assigned to the correct tissue. Cell line samples were proven to be free of mycoplasma and virus contamination. Whole exome sequencing (WES) and RNA sequencing (RNA-seq) of the hundred authenticated leukemia-lymphoma cell lines were conducted with a uniform methodology to complement existing data on these publicly available cell lines. This part captures WES. This data set will be useful for understanding the function of oncogenes and tumor suppressor genes and to develop targeted therapies.
2019-05-28 | E-MTAB-7722 | biostudies-arrayexpress
Project description:We evaluated linked-read whole genome sequencing (WGS) for detection of structural chromosomal rearrangements in primary samples of varying DNA quality from 12 patients diagnosed with ALL. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements, aneuploidy assessment and gene deletions. Additional RNA-sequencing and copy number aberrations (CNA) data from Illumina Infinium arrays were also generated and assessed against the linked-read WGS data. RNA-sequencing data was used to support structural chromosomal rearrangements detected in the linked-read WGS data by detecting expressed fusion genes as a consequence of the rearrangements. Illumina Infinium arrays (450k array and/or SNP array) were used to assess CNA status to further support the findings in the linked-read WGS data. The processed CNA data from the primary ALL patient samples has been deposited to GEO. RNA-sequencing, linked-read WGS data, and raw SNP array data from the primary ALL patient samples will not be deposited because the patient/parent consent does not cover depositing data that may be used for large-scale determination of germline variants in a repository. The ALL samples were collected 10-20 years ago from pediatric patients aged 2-15 years, some whom have deceased. The linked-read WGS data and the RNA-sequencing data sets generated in the study are available upon reasonable request from the corresponding author Jessica.Nordlund@medsci.uu.se.
2020-03-02 | GSE117693 | GEO
Project description:We evaluated linked-read whole genome sequencing (WGS) for detection of structural chromosomal rearrangements in primary samples of varying DNA quality from 12 patients diagnosed with ALL. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements, aneuploidy assessment and gene deletions. Additional RNA-sequencing and copy number aberrations (CNA) data from Illumina Infinium arrays were also generated and assessed against the linked-read WGS data. RNA-sequencing data was used to support structural chromosomal rearrangements detected in the linked-read WGS data by detecting expressed fusion genes as a consequence of the rearrangements. Illumina Infinium arrays (450k array and/or SNP array) were used to assess CNA status to further support the findings in the linked-read WGS data. The processed CNA data from the primary ALL patient samples has been deposited to GEO. RNA-sequencing, linked-read WGS data, and raw SNP array data from the primary ALL patient samples will not be deposited because the patient/parent consent does not cover depositing data that may be used for large-scale determination of germline variants in a repository. The ALL samples were collected 10-20 years ago from pediatric patients aged 2-15 years, some whom have deceased. The linked-read WGS data and the RNA-sequencing data sets generated in the study are available upon reasonable request from the corresponding author Jessica.Nordlund@medsci.uu.se.
2020-03-02 | GSE116057 | GEO
Project description:We evaluated linked-read whole genome sequencing (WGS) for detection of structural chromosomal rearrangements in primary samples of varying DNA quality from 12 patients diagnosed with ALL. Linked-read WGS enabled precise, allele-specific, digital karyotyping at a base-pair resolution for a wide range of structural variants including complex rearrangements, aneuploidy assessment and gene deletions. Additional RNA-sequencing and copy number aberrations (CNA) data from Illumina Infinium arrays were also generated and assessed against the linked-read WGS data. RNA-sequencing data was used to support structural chromosomal rearrangements detected in the linked-read WGS data by detecting expressed fusion genes as a consequence of the rearrangements. Illumina Infinium arrays (450k array and/or SNP array) were used to assess CNA status to further support the findings in the linked-read WGS data. The processed CNA data from the primary ALL patient samples has been deposited to GEO. RNA-sequencing, linked-read WGS data, and raw SNP array data from the primary ALL patient samples will not be deposited because the patient/parent consent does not cover depositing data that may be used for large-scale determination of germline variants in a repository. The ALL samples were collected 10-20 years ago from pediatric patients aged 2-15 years, some whom have deceased. The linked-read WGS data and the RNA-sequencing data sets generated in the study are available upon reasonable request from the corresponding author Jessica.Nordlund@medsci.uu.se.
2020-03-02 | GSE117692 | GEO
Project description:Here we analyzed mouse derived samples to investigate origin, subtypes, functions and cell-cell interactions of cancer-associated fibroblasts in desmoplastic liver metastasis. Hepatic stellate cell-derived cancer-associated fibroblasts were isolated from three different models of liver metastasis induced by intra-splenic injection of cell lines Panc02, KPCY and CMT93 and analyzed by bulk RNA sequencing. In addition, CAF from Panc02- and CMT93-induced liver metastasis were analyzed by single cell sequencing.
2021-11-01 | GSE160541 | GEO
Project description:Whole genome sequencing (WGS) of tongue cancer samples and cell line was performed to identify the fusion gene translocation breakpoint. WGS raw data was aligned to human reference genome (GRCh38.p12) using BWA-MEM (v0.7.17). The BAM files generated were further analysed using SvABA (v1.1.3) tool to identify translocation breakpoints. The translocation breakpoints were annotated using custom scripts, using the reference GENCODE GTF (v30). The fusion breakpoints identified in the SvABA analysis were additionally confirmed using MANTA tool (v1.6.0).
2024-02-17 | E-MTAB-12534 | biostudies-arrayexpress
Project description:Single Gland Whole-exome sequencing: building on our prior description of multi-region WES of colorectal tumors and targeted single gland sequencing (E-MTAB-2247), we performed WES of multiple single glands from different sides (right: A and left: B) of two tumors in this study (tumor O and U) on the illumina platform using the Agilent SureSelect 2.0 or illumina Nextera Rapid Capture Exome kit (SureSelect or NRCE, as indicated in the naming of fastq files). Colorectal Cancer Xenograft Whole-exome sequencing: The HCT116 and LoVo Mismatch-Repair-deficient colorectal adenocarcinoma cell lines were obtained from the ATCC and cultured under standard conditions. For both cell lines, a single âfoundingâ cell was cloned and expanded in vitro to ~6M cells. Two aliquots of ~1M cells were subcutaneously injected into opposite flanks (right and left) of a nude mouse and tumors allowed to reach a size of ~1B cells (1cm3) before the animal was sacrificed. Tumor tissue was collected separately from the right and left lesions and DNA was extracted for WES using the illumina TruSeq Exome kit or Nextera Rapid Capture Exome expanded Kits (Truseq or NRCEe), as was DNA from the first passage population (a polyclonal tissue culture for HCT116 and a polyclonal xenograft sample for LoVo), which were employed as a control to study mutation accumulation in culture and post xenotransplantation.
2017-05-01 | E-MTAB-5547 | biostudies-arrayexpress
Project description:The study of human liver cancer has been hampered by the lack of reliable models that faithfully recapitulate the physiopathology of the patient’s original tumour ex vivo. Here, we describe the first culture system to allow the establishment and long-term expansion of human primary liver cancer (PLC) organoids (called tumoroids) from the three most common PLC subtypes: Hepatocellular carcinoma (HCC), Cholangiocarcinoma (CC) and mixed HCC/CC tumours (CHC). We evaluated whether tumoroid lines maintain the expression profile and the genomic landscape of the original tumor they derived from by performing genome-wide (RNAseq/WES) analyses before and after culture. These analyses reveal that the cultures closely recapitulate the gene expression and the mutational landscape of the original tumor, allowing discrimination between different tumor subtypes (HCC, CHC, CC).
2017-11-13 | GSE84073 | GEO