ChIP-seq of PDAC tissues grown as patient-derived tumor xenografts
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Illumina HiSeq 2000
ORGANISM(S): Homo sapiens
SUBMITTER: Krutika Satish Gaonkar
PROVIDER: E-MTAB-5632 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Similar Datasets
Project description:Pancreatic adenocarcinoma (PDAC) is one of the most lethal human malignancies and a major health problem. Patient-derived tumor xenografts (PDTXs) have been increasingly used as a prime approach for preclinical studies despite being insufficiently characterized as a model of the human disease and its diversity. Extensive multiomics characterization of these PDTXs have demonstrated their utility as a suitable model for preclinical studies, representing the diversity of the primary cancers. We performed a multi-factorial integrative analysis of genome-wide ChIP-seq on multiple histone modifications, as well as RNA-seq on subcutaneous PDTXs from 24 PDAC samples obtained either surgically or using diagnostic biopsies (endoscopic ultrasound guided fine needle aspirate). In the dataset, ChIP-seq for five distinct histone marks (H3K4me1, H3K27ac, H3K4me3, H3K27me3, and H3K9me3) and RNA-seq was carried out to generate new knowledge on the epigenetic landscapes underlying the heterogeneity of PDAC tissues grown in this manner. <br>Note: Sample phenotypes have been updated on 2020-09-11 to correct the swapped labels between \"classical\" and \"basal\" phenotypes.
2018-05-29 | E-MTAB-5639 | biostudies-arrayexpress
Project description:A major impediment to the effective treatment of patients with PDAC (Pancreatic Ductal Adenocarcinoma) is the molecular heterogeneity of the disease, which is reflected in an equally diverse pattern of clinical responses to therapy. We developed an efficient strategy in which PDAC samples from 17 consecutively patients were obtained by EUS-FNA or surgery, their cells maintained as a primary culture and tumors as breathing tumors by xenografting in immunosuppressed mice. For these patients a clinical follow up was obtained. On the breathing tumors we studied the RNA expression profile by an Affymetrix approach. We observed a significant heterogeneity in their RNA expression profile, however, the transcriptome was able to discriminate patients with long- or short-time survival which correspond to moderately- or poorly-differentiated PDAC tumors respectively. Cells allowed us the possibility to analyze their relative sensitivity to several anticancer drugs in vitro by developing a chimiogram, like an antibiogram for microorganisms, with several anticancer drugs for obtaining an individual profile of drug sensitivity and as expected, the response was patient-dependent. Interestingly, using this approach, we also found that the transcriptome analysis could predict the sensitivity to some anticancer drugs of patients with a PDAC. In conclusion, using this approach, we found that the transcriptome analysis could predict the sensitivity to some anticancer drugs and the clinical outcome of patients with a PDAC. PDAC samples from 17 consecutively patients were obtained by Endoscopic Ultrasound-Guided Fine-Needle Aspiration (EUS-FNA) or surgery. Consents of informed patients were collected, and the ethics review board of the three centers approved the study. All samples were anonymized and obtained in accordance with institutional review boards. EUS-FNA cells were maintained as a primary culture and tumors as breathing tumors by xenografting in immunosuppressed mice. We characterized 17 PDAC-derived xenografts by duplicate. Total RNA was extracted and hybridized on Human Gene 2.0 (Genechip, Affymetrix) as described previously (Hamidi et al. JCI 122: 2092-2103, 2012).
2015-04-09 | E-GEOD-55513 | biostudies-arrayexpress
Project description:Pancreatic adenocarcinoma (PDAC) is one of the most lethal human malignancies and a major health problem. Patient-derived xenografts (PDX) are appearing as a prime approach for preclinical studies despite being insufficiently characterized as a model of the human disease and its diversity. We generated subcutaneous PDX from PDAC samples obtained either surgically or using diagnostic biopsies (endoscopic ultrasound guided fine needle aspirate). The extensive multiomics characterization of the xenografts demonstrated that PDX is a suitable model for preclinical studies, representing the diversity of the primary cancers. We generated subcutaneous PDX from PDAC samples obtained either surgically or using diagnostic biopsies (endoscopic ultrasound guided fine needle aspirate). The variable 'MultiOmicsClassification' indicates the resulting sample's group. 'CIMPclass' is the CpG island methylator phenotype as estimated from the methylation arrays analysis. In this dataset, Illumina Infinium HumanCode-24 BeadChips SNP arrays were used to analyze the DNA xenografts samples from pancreatic ductal adenocarcinoma.
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Project description:A Cartes d'Identit des Tumeurs (CIT) project from the french Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net): Pancreatic adenocarcinoma (PDAC) is one of the most lethal human malignancies and a major health problem. Patient-derived xenografts (PDX) are appearing as a prime approach for preclinical studies despite being insufficiently characterized as a model of the human disease and its diversity. We generated subcutaneous PDX from PDAC samples obtained either surgically or using diagnostic biopsies (endoscopic ultrasound guided fine needle aspirate). The extensive multiomics characterization of the xenografts demonstrated that PDX is a suitable model for preclinical studies, representing the diversity of the primary cancers. the MultiOmicClassification variable indicates groups resulting from the analysis, and the CIMPclass, the CpG Island Methylator Phenotype as estimated by the methylation analysis. This dataset, describes the miRNA-Seq data used in the multiOmics analysis.
2017-12-12 | E-MTAB-5018 | biostudies-arrayexpress
Project description:Pancreatic adenocarcinoma (PDAC) is one of the most lethal human malignancies and a major health problem. Patient-derived xenografts (PDX) are appearing as a prime approach for preclinical studies despite being insufficiently characterized as a model of the human disease and its diversity. We generated subcutaneous PDX from PDAC samples obtained either surgically or using diagnostic biopsies (endoscopic ultrasound guided fine needle aspirate). The extensive multiomics characterization of the xenografts demonstrated that PDX is a suitable model for preclinical studies, representing the diversity of the primary cancers. this dataset, describe the RNA sequencing data used in the multiomics study.
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Project description:Pancreatic adenocarcinoma (PDAC) is one of the most lethal human malignancies and a major health problem. Patient-derived xenografts (PDX) are appearing as a prime approach for preclinical studies despite being insufficiently characterized as a model of the human disease and its diversity. We generated subcutaneous PDX from PDAC samples obtained either surgically or using diagnostic biopsies (endoscopic ultrasound guided fine needle aspirate). The extensive multiomics characterization of the xenografts demonstrated that PDX is a suitable model for preclinical studies, representing the diversity of the primary cancers. the variable MultiOmicsClassification indicates the resulting sample's group. Whole-genome DNA methylation was analyzed using the Illumina Infinium HumanMethylation450 Beadchips. Integragen SA (Evry, France) carried out microarray experiments and hybridized to the BeadChip arrays following the manufacturer’s instructions. Illumina GenomeStudio software was used to extract the beta value DNA methylation score for each locus. We removed data from probes that contained SNPs or overlapped with a repetitive element that was not uniquely aligned to the human genome or regions of insertions and deletions in the human genome. The CpG Island Methylator Phenotype (CIMP) index was determined using methylation Illumina Infinium HumanMethylation450 BeadChips based on previous low throughput work (Toyota, 1999). In brief, all island CpG found to be unmethylated (<20% Beta-value) in all 25 normal pancreatic samples from the ICGC consortium (Nones, 2014) were selected. The CIMP index was calculated independently for each sample as the proportion of methylated (>30% Beta-value) CpGs among the selected normally unmethylated island CpG.
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2010-09-29 | E-GEOD-22007 | biostudies-arrayexpress
Project description:Pancreatic ductal adenocarcinoma (PDAC) remains a major unsolved health problem. Most drugs that pass preclinical tests fail in these patients, emphasizing the need of appropriate preclinical models to test novel anticancer strategies. We developed four orthotopic mouse models employing primary human PDAC cells expressing Firefly and Gaussia luciferases, enabling bioluminescence monitoring of tumor growth and metastasis formation. Additional tumor characterization was performed using MR and high frequency ultrasound imaging. Genomic and immunohistochemical analysis revealed c-Met amplification and overexpression in one of four models. Analysis of c-Met inhibitors in vitro showed that crizotinib had the most potent effect. Moreover, we demonstrated synergistic effects between crizotinib and gemcitabine M-bM-^@M-^S the standard of care therapeutic in PDAC patients - in vitro and in vivo. Importantly, crizotinib reduced the cytidine deaminase activity in PDAC cells causing prolonged activity of gemcitabine due to diminished metabolic inactivation, as measured by LC-MS/MS. This might at least in part explain the observed prolonged survival of concomitantly treated mice with PDAC tumors and metastases. In conclusion, our orthotopic PDAC models enabled PDAC tumor imaging, and showed genetic, histopathological and metastatic features similar to their originator tumors. This allowed the identification of c-Met as a potential therapeutic target in PDAC, and revealed a cytidine deaminase-mediated synergistic mechanism between crizotinib and gemcitabine, a combination of drugs that warrants further investigation for the potential treatment of PDAC patients. 12 test samples in total [4 PDAC models (PDAC-1, PDAC-2, PDAC-3, PDAC-4; in three panel: primary human PDAC, primary tumor culture and mouse sample)] and reference sample (healthy control (mix/pool of healthy volunteers DNA) were analyzed as following (8 hybridizations); PDAC-1 primary human-Cy3 vs PDAC-4 cultured cells -Cy5 PDAC-1 cultured cells-Cy3 vs PDAC-4 mouse-Cy5 PDAC-1 mouse-Cy3 vs reference-Cy5 PDAC-2 primary human-Cy3 vs reference-Cy5 PDAC-2_cultured cells-Cy3 vs PDAC-2 mouse-Cy5 PDAC-3_primary human-Cy3 vs reference-Cy5 PDAC-3_cultured cells-Cy3 vs PDAC-3 mouse-Cy5 PDAC-4 primary human-Cy5 vs reference-Cy3 Normalized log2 ratio of (sample/references) data were calculated for 12 samples [*txt files on Series records]. Description of experimental/analysis design in r-program to generate 12 samples from 8 raw data files is provided in README.txt [Series supplementary file]
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