Similar Datasets
Project description:Patient-derived tumor xenografts (PDXs) increasingly are being used as preclinical models to study human cancers and to evaluate novel therapeutics, as they reflect clinical cancers more closely than established tumor cell lines. With >100 PDXs established from resected non-small cell lung carcinomas (NSCLC), we reported previously that xenograftability correlates significantly with poorer patient prognosis. In this study, genomic, transcriptomic, and proteomic profiling of 36 PDXs showed greater similarity in somatic alterations between PDX and primary tumors than with cell lines, using publicly available data on the latter. A higher number of somatic alterations among 865 frequently altered genes in the PDX tumors was associated with better overall patient survival (HR=0.15, p=0.00015) compared to patients with corresponding PDXs characterized by a lower number of alterations; this was validated with the TCGA lung cancer patient dataset (HR=0.28, p=0.000022). These passenger-like alterations, identified in PDXs, link cell-cell signaling and adhesion to patient prognosis. Total RNAs from xenograftswere amplified by DASL kit and hybridized to Illumina HT12v4 chip
2016-10-26 | E-GEOD-68929 | biostudies-arrayexpress
Project description:Background: Patient-derived xenograft (PDX) models are a useful tool in cancer biology research. However, the number of lung cancer PDXs is limited Results: In the present study, we successfully established ten PDXs, including three adenocarcinoma (AD), six squamous cell carcinoma (SQ) and one large cell carcinoma (LA), from 30 patients with non-small cell lung cancer (NSCLC) (18 AD, 10 SQ, and 2 LA), mainly in SHO mice (Crlj:SHO-PrkdcscidHrhr). Histology of SQ, advanced clinical stage (III-IV), status of lymph node metastasis (N2-3), and maximum standardized uptake value (SUVmax) ≧10 when evaluated using a delayed 18F-fluoro-2-deoxy-D-glucose positron emission tomography (FDG-PET) scan, was associated with successful PDX establishment. Histological analyses revealed that PDXs showed a histology similar to that of patients’ surgically resected tumors (SRTs), while components of the microenvironments were replaced with murine cells after several passages. Next generation sequencing and microarray analyses demonstrated that after 2 to 6 passages, PDXs preserved the majority of the somatic mutations and mRNA expressions of the corresponding SRTs. Two out of three PDXs with AD histology had EGFR mutations (L858R or exon19 deletion) and were sensitive to EGFR tyrosine kinase inhibitors (EGFR-TKIs), such as gefitinib and osimertinib. Furthermore, in one of the two PDXs with an EGFR mutation, osimertinib resistance was induced that was associated with epithelial-to-mesenchymal transition. Conclusions: This study presented ten serially transplantable PDXs of NSCLC in SHO mice and demonstrated the use of PDXs with an EGFR mutation for analyses of EGFR-TKI resistance.
2019-09-12 | GSE130160 | GEO
Project description:Patient-derived tumor xenografts (PDXs) increasingly are being used as preclinical models to study human cancers and to evaluate novel therapeutics, as they reflect clinical cancers more closely than established tumor cell lines. With >100 PDXs established from resected non-small cell lung carcinomas (NSCLC), we reported previously that xenograftability correlates significantly with poorer patient prognosis. In this study, genomic, transcriptomic, and proteomic profiling of 36 PDXs showed greater similarity in somatic alterations between PDX and primary tumors than with cell lines, using publicly available data on the latter. A higher number of somatic alterations among 865 frequently altered genes in the PDX tumors was associated with better overall patient survival (HR=0.15, p=0.00015) compared to patients with corresponding PDXs characterized by a lower number of alterations; this was validated with the TCGA lung cancer patient dataset (HR=0.28, p=0.000022). These passenger-like alterations, identified in PDXs, link cell-cell signaling and adhesion to patient prognosis.
2016-10-26 | GSE68929 | GEO
Project description:Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbour actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. We generated NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Here, we completed whole exome sequencing on 122 NSCLC PDXs.
| EGAD00001008601 | EGA
Project description:Non-small cell lung cancer (NSCLC) is the leading cause of cancer deaths worldwide. Only a fraction of NSCLC harbour actionable driver mutations and there is an urgent need for patient-derived model systems that will enable the development of new targeted therapies. NSCLC and other cancers display profound proteome remodelling compared to normal tissue that is not predicted by DNA or RNA analyses. We generated NSCLC patient-derived xenografts (PDXs) that recapitulate the histology and molecular features of primary NSCLC. Here, we completed whole exome sequencing on 122 NSCLC PDXs.
| EGAS00001006068 | EGA
Project description:PURPOSE: Although epidermal growth factor receptor (EGFR) mutated adenocarcinomas initially have very high response rates to EGFR tyrosine kinase inhibitors (TKIs), most atients eventually develop resistance. Patient derived xenografts (PDXs) are considered preferred preclinical models to study the biology of patient tumors. EGFR-mutant PDX models may be valuable tools to study the biology of these tumors and to elucidate mechanisms of resistance to EGFR-targeted therapies. METHODS: Surgically resected early stage non-small cell lung carcinoma (NSCLC) tumors were implanted into non-obese diabetic severe combined immune deficient (NODSCID) mice. EGFR TKI treatment was initiated at tumor volumes of 150 mm3. Gene expression analysis was performed using microarray platform. RESULTS: Of 33 lung adenocarcinomas with EGFR activating mutations, only 6 engrafted 18%) and could be propagated beyond passage one. Engraftment was associated with upregulation of genes involved in mitotic checkpoint and cell proliferation. A differentially expressed gene set between engrafting and non-engrafting patients could identify EGFRmutant patients with significantly different prognoses in The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma datasets. The PDXs included models with variable sensitivity to first- and second-generation EGFR TKIs and the monoclonal antibody cetuximab. All EGFR-mutant NSCLC PDXs studied closely recapitulated their corresponding patient tumor phenotype and clinical course, including response pattern to EGFR TKIs. CONCLUSIONS: PDX models closely recapitulate primary tumor biology and clinical outcome. They may serve as important laboratory models to investigate mechanisms of resistance to targeted therapies, and for preclinical testing of novel treatment strategies.
2017-03-28 | GSE63685 | GEO
Project description:Proteogenomics analysis to identify acquired resistance-specific alterations in melanoma PDXs on MAPKi therapy [WES]
| PRJNA1108300 | ENA
Project description:Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. DNA copy number profiles generated with a new tool, ENCODER, were compared to DNA copy number profiles from SNP6, NimbleGen and low-coverage Whole Genome Sequencing.
2014-08-12 | GSE60254 | GEO
Project description:Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. Current methods for detection of copy number aberrations (CNA) from whole-exome sequencing (WES) data are based on the read counts of the captured exons only. However, accurate CNA determination is complicated by the non-uniform read depth and uneven distribution of exons. Therefore, we developed ENCODER (ENhanced COpy number Detection from Exome Reads), which eludes these problems. By exploiting the ‘off-target’ sequence reads, it allows for creation of robust copy number profiles from WES. The accuracy of ENCODER compares to approaches specifically designed for copy number detection, and outperforms current exon-based WES methods, particularly in samples of low quality. DNA copy number profiles generated with a new tool, ENCODER, were compared to DNA copy number profiles from SNP6, NimbleGen and low-coverage Whole Genome Sequencing.
2014-08-12 | GSE60168 | GEO