CaArray_jacob-00182: Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE68465 | GEO | 2015/05/02
SECONDARY ACCESSION(S): PRJNA282809
REPOSITORIES: GEO
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Project description:Here we report a large, training*testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas. jacob-00182 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A Organism: Homo sapiens (ncbitax) Material Types: cell, synthetic_RNA, organism_part, whole_organism, total_RNA Disease States: Lung Adenocarcinoma
2015-05-02 | E-GEOD-68465 | biostudies-arrayexpress
Project description:To rapidly identify new prognostic imaging biomarkers, we propose a bioinformatics approach that integrates gene expression and image data and leverages public gene expression data. We demonstrate our approach in non-small cell lung carcinoma patients for whom CT, PET/CT and gene expression data are available but without clinical follow-up. We extracted 180 image features and 56 high quality gene expression clusters, represented by metagenes. 115 image features were expressed in terms of metagenes, using sparse linear regression and cross-validation, with an accuracy of 65-86%. After mapping the signatures to a public gene expression dataset, 26 image features were significantly associated with recurrence-free survival and 22 with overall survival. A multivariate analysis identified multiple image features that were prognostic, independent of clinical covariates. Identifying prognostic imaging biomarkers by linking images and gene expression with outcomes in public gene expression datasets promises to accelerate the role of imaging in personalized medicine. We studied 26 cases of NSCLC with both PET/CT and microarray data under IRB approval from Stanford University and the Veterans Administration Palo Alto Health Care System. The collection of tissue samples consisted of a distribution of poorly- to well-differentiated adenocarcinomas and squamous cell cancers. The surgeon had removed necrotic debris during excision and sampled cavitary lesions to include as much solid component as practical. Then, from the excised tumor, he cut a 3 to 5 mm thick slice along its longest axis, and froze it within 30 minutes of excision. We retrieved the frozen tissue and extracted the RNA that was then processed by the Stanford Functional Genomics Facility using Illumina Whole Genome Bead Chips (Human HT-12 v3.0)
2012-05-31 | E-GEOD-28827 | biostudies-arrayexpress
Project description:Clinical FFPE tissue proteomic analyses were performed for early lung adenocarcinomas including adenocarcinoma in-situ (AIS), minimally invasive adenocarcinoma (MIA) and lepidic predominant invasive adenocarcinoma (LPA).
2020-01-30 | PXD017334 | JPOST Repository
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:Genetically engineered mouse models of lung cancer have demonstrated an important role in understanding the function of novel lung cancer oncogenes and tumor suppressor genes identified in genomic studies of human lung cancer. Further, these models are important platforms for pre-clinical therapeutic studies. Here, we generated a mouse model of lung adenocarcinoma driven by mutation of the Discoidin Domain Receptor 2 (DDR2) gene combined with loss of TP53. DDR2L63V;TP53L/L mice developed poorly differentiated lung adenocarcinomas in all transgenic animals analyzed with a latency of 40-50 weeks and a median survival of 67.5 weeks. Mice expressing wild-type DDR2 with combined TP53 loss did not form lung cancers. DDR2L63V; TP53L/L tumors displayed robust expression of DDR2 and immunohistochemical markers of lung adenocarcinoma comparable to previously generated models of lung adenocarcinoma though also displayed concomitant expression of the squamous cell markers p63 and SOX2. Tumor-derived cell lines were not solely DDR2 dependent and displayed up-regulation of and partial dependence on MYCN. Combined treatment with the BET inhibitor JQ1 and the mutltitargeted DDR2 inhibitor dasatinib inhibited tumor growth in vitro and in vivo. Together, these results suggest that DDR2 mutation can drive lung cancer initiation in vivo and provide a novel mouse model for lung cancer therapeutics studies. We used microarrays to detail the gene expression profile of mouse primary lung tumor cell lines driven by DDR2L63V;p53L/L and KrasG12D;p53L/L.
2015-01-14 | GSE64907 | GEO
Project description:To rapidly identify new prognostic imaging biomarkers, we propose a bioinformatics approach that integrates gene expression and image data and leverages public gene expression data. We demonstrate our approach in non-small cell lung carcinoma patients for whom CT, PET/CT and gene expression data are available but without clinical follow-up. We extracted 180 image features and 56 high quality gene expression clusters, represented by metagenes. 115 image features were expressed in terms of metagenes, using sparse linear regression and cross-validation, with an accuracy of 65-86%. After mapping the signatures to a public gene expression dataset, 26 image features were significantly associated with recurrence-free survival and 22 with overall survival. A multivariate analysis identified multiple image features that were prognostic, independent of clinical covariates. Identifying prognostic imaging biomarkers by linking images and gene expression with outcomes in public gene expression datasets promises to accelerate the role of imaging in personalized medicine.
2012-06-01 | GSE28827 | GEO
Project description:Genetically engineered mouse models of lung cancer have demonstrated an important role in understanding the function of novel lung cancer oncogenes and tumor suppressor genes identified in genomic studies of human lung cancer. Further, these models are important platforms for pre-clinical therapeutic studies. Here, we generated a mouse model of lung adenocarcinoma driven by mutation of the Discoidin Domain Receptor 2 (DDR2) gene combined with loss of TP53. DDR2L63V;TP53L/L mice developed poorly differentiated lung adenocarcinomas in all transgenic animals analyzed with a latency of 40-50 weeks and a median survival of 67.5 weeks. Mice expressing wild-type DDR2 with combined TP53 loss did not form lung cancers. DDR2L63V; TP53L/L tumors displayed robust expression of DDR2 and immunohistochemical markers of lung adenocarcinoma comparable to previously generated models of lung adenocarcinoma though also displayed concomitant expression of the squamous cell markers p63 and SOX2. Tumor-derived cell lines were not solely DDR2 dependent and displayed up-regulation of and partial dependence on MYCN. Combined treatment with the BET inhibitor JQ1 and the mutltitargeted DDR2 inhibitor dasatinib inhibited tumor growth in vitro and in vivo. Together, these results suggest that DDR2 mutation can drive lung cancer initiation in vivo and provide a novel mouse model for lung cancer therapeutics studies. We used microarrays to detail the gene expression profile of mouse primary lung tumor cell lines driven by DDR2L63V;p53L/L and KrasG12D;p53L/L. When DDR2L63V; p53 and KrasG12D;p53L/L mice developed lung tumors confirmed by MRI, the mice were sacrificed and lung tumor nodules were harvested, finely minced, and cultured in 100 mm dishes with RPMI 1640/10% FBS/1% pen-strep/2mM L-Glutamine. After 3 passages, frozen stocks of these short-term cultures were prepared, and further characterized by genotyping. DDR2 expression was induced by treating cells with 2 ug/ml DOX every 2 to 3 days and confirmed by western blot analysis. The cells were cultured in RPMI 1640/10% FBS/1% pen-strep/2mM L-Glutamine. All cells were cultured at 37oC in a humidified incubator with 5% CO2.
2015-01-14 | E-GEOD-64907 | biostudies-arrayexpress
Project description:The aim of our study was to discover a miR marker panel prognostic of 5-year survival in OSCC patients that may be utilized in parallel with the current clinical covariates. We assessed differential expression of miRNAs genome-wide via deep sequencing in 20 tumor tissue samples. We also attempted to identify deregulated miR expression signatures that may serve as the prognostic marker of cancer survival. Selected miR marker-based panel then may serve as a guide for selection of appropriate follow-up chemo/radiation treatment, significantly improving the clinical management of OSCC and the overall survival rate.
2014-10-21 | GSE52633 | GEO
Project description:Methods: We used an ex vivo culture model and measured gene expression changes in human lung fibroblasts after stimulation with BMPs and their antagonists using HEEBO microarrays. The in vitro data were correlated with in vivo observations in published expression datasets of human lung adenocarcinomas. Results: We have systematically analyzed the response to BMP2, BMP4, BMP7 and their antagonists, gremlin and noggin, to define common and specific gene expression patterns. A BMP2 induced gene expression signature was defined, which is specific for stromal fibroblasts. Gene expression profiles from lung adenocarcinoma biopsies were analyzed to determine the prognostic significance of the Fibroblast specific BMP2 induced gene list. This gene list successfully segregated patients with different prognostic outcome in 3 datasets. In a small dataset (Garber et al.) there was a strong trend for a worse prognosis of patients with adenocarcinomas of all stages over-expressing the Fibroblast specific BMP2 induced gene list. In two larger datasets with stage I adenocarcinomas we observed a significantly worse disease-free (p=0.002, Lee et al. and p=0.002, Bhattacharjee et al.) and overall survival (p=0.0002). Conclusions: The effects of BMPs and their antagonists are heterogeneous in different cell types. The gene expression pattern induced by BMP2 in primary lung fibroblasts may predict outcomes of patients with lung adenocarcinomas. A stimulus or stress experiment design type is where that tests response of an organism(s) to stress/stimulus. e.g. osmotic stress, behavioral treatment stimulus_or_stress_design
2016-02-19 | E-GEOD-66108 | biostudies-arrayexpress
Project description:The World Health Organization has subclassified adenocarcinoma based upon predominant cell morphology and growth pattern such as bronchioloalveolar carcinoma (BAC), adenocarcinoma with mixed subtypes (AC-mixed), and homogenously invasive tumors with a variety of histological patterns Pure invasive adenocarcinomas are often devoid of bronchioloalveolar morphology. The clinical importance of lung adenocarcinoma invasion is supported by several recent studies indicating that the risk of death in non-mucinous BAC is significantly lower than that of pure invasive tumors and in tumors with greater than 0.6 cm of fibrosis or linear invasion (J Thorac Oncol 6:244-285) To identify human tumor cell signatures associated with lung adenocarcinoma subtype and invasion, we performed microarray gene expression profiling of microdissected tumor cells noninvasive AC and AC-Mixed invasive tumors. 17 cases of noninvasive AC tumors and 23 cases of AC-mixed subtype invasive lung adenocarcinomas resected from 2002 to 2006 were examined (Columbia Lung Adenocarcinoma dataset)
2011-09-14 | E-GEOD-27716 | biostudies-arrayexpress