Genomic heterogeneity of osteosarcoma - implementing order on the level of functional modules
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Eberhard Korsching
PROVIDER: E-MTAB-3034 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:Osteosarcoma (OS) is the most common primary malignant tumor of bone; it occurs most commonly in the metaphyseal regions of long bones in adolescents and young adults. Conventional osteosarcoma is characterized by complex karyotypes with a high degree of aneuploidy and numerous structural aberrations such as copy number alterations (CNAs) and genomic rearrangements. We characterized the landscape of somatic copy number alterations (SCNAs) in a large cohort (n=160) of osteosarcoma (OS) samples using the whole-genome CytoScan High Density arrays. This experiment is an extension of E-MTAB-3998. It contains data for 97 samples that is also included in E-MTAB-3998. The samples derived from E-MTAB-3998 identified using the 'Description' field in the sample attributes.
2017-06-02 | E-MTAB-4815 | biostudies-arrayexpress
Project description:Predictors built from gene expression data accurately predict ER, PR, and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data cannot be used to predict tumor size or lymphatic-vascular invasion. Experiment Overall Design: Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers (ER, PR, HER2), histologic features (grade and lymphatic-vascular invasion), and stage-related information (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy determined by error rates of prediction and by dimensional scaling and visualization of the states under study. Models to predict lymph node metastasis were built by combinations of molecular, histologic and anatomic features.
2008-06-14 | E-GEOD-5460 | biostudies-arrayexpress
Project description:Osteosarcoma (OS) is the most common primary malignant tumor of bone; it occurs most commonly in the metaphyseal regions of long bones in adolescents and young adults. Conventional osteosarcoma is characterized by complex karyotypes with a high degree of aneuploidy and numerous structural aberrations such as copy number alterations (CNAs) and genomic rearrangements. The data comprise of array copy number data (N=113) from pre-therapeutic osteosarcoma. These have been derived from Affymetrix CytoscanHD. The study searches for driver genes and examines cancer evolution. Note (added 8th June 2016): This experiment is extended in an experiment described under accession E-MTAB-4815. It contains data for 97 samples from this experiment are also included in E-MTAB-4815.
2015-11-09 | E-MTAB-3998 | biostudies-arrayexpress
Project description:The interplay between copy number variation (CNV) and differential gene expression may be able to shed light on molecular process underlying breast cancer and lead to the discovery of cancer-related genes. In the current study, genes concurrently identified in array comparative genomic hybridization (CGH) and gene expression microarrays were used to derive gene signatures for Han Chinese breast cancers. We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival. Distributions of signature genes were strongly associated with chromosomal location: chromosome 16 for ER and 17 for HER2. A breast cancer risk predictive model was built based on the first supervised principal component from 16 genes (RCAN3, MCOLN2, DENND2D, RWDD3, ZMYM6, CAPZA1, GPR18, WARS2, TRIM45, SCRN1, CSNK1E, HBXIP, CSDE1, MRPL20, IKZF1, and COL20A1), and distinct survival patterns were observed between the high- and low-risk groups from the combined dataset of 408 microarrays. The risk score was significantly higher in breast cancer patients with recurrence, metastasis, or mortality than in relapse-free individuals (0.241 versus 0, P<0.001). The concurrent gene risk predictive model remained discriminative across distinct clinical ER and HER2 statuses in subgroup analysis. We conclude that parallel analysis of CGH and microarray data, in conjunction with known gene expression patterns, can be used to identify biomarkers with prognostic values in breast cancer. We performed 23 array CGHs and 81 gene expression microarrays in breast cancer samples from Taiwanese women. Genes with coherent patterns of both CNV and differential gene expression were identified from the 21 samples assayed using both platforms. We used these genes to derive signatures associated with clinical ER and HER2 status and disease-free survival.
2014-01-07 | E-GEOD-48390 | biostudies-arrayexpress
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2014-01-07 | E-GEOD-48391 | biostudies-arrayexpress
Project description:Osteosarcoma (OS) and Ewing’s sarcoma (EW) are the two most common pediatric solid tumors, after brain tumors. Multimodal treatments have significantly improved prognosis in localized disease but outcome is still poor in metastatic patients, for whom therapeutic options are often inadequate. Preclinical drug testing to identify promising treatment options that match the molecular make-up of these tumors is hampered by the lack of appropriate and molecularly well-characterized patient-derived models. To address this need, a panel of patient-derived xenografts (PDX) was established by subcutaneous implantation of fresh, surgically resected OS and EW tumors in NSG mice. Tumors were re-transplanted to next mice generations and fragments were collected for histopathological and molecular characterization. A model was considered established after observing stable histological and molecular features for at least three passages. To evaluate the similarity of the model with primary tumor, we performed a global gene expression profiling and tissue microarrays (TMA), to assess tumor specific biomarkers on tissues from OS/EW tumors and their PDXs (1st and 3rd passage). Moreover, we verified the feasibility of these models for preclinical drug testing. We implanted 61 OS and 29 EW samples: 14/38 (37%) primary OS and 9/23 (39%) OS lung metastases successfully engrafted; while among EW, 5/26 (19%) primary samples and 1/3 (33%) metastases were established. Comparison between patient samples and PDXs, highlighted that histology and genetic characteristics of PDXs were stable and maintained over passages. In particular, correlative analysis between OS and EW samples and their PDXs was extremely high (Pearson’s r range r=0.94-0.96), while patient-derived primary cultures displayed reduced correlation with human samples (r=0.90-0.93), indicating that in vitro adaptation superimpose molecular alterations that create genetic diversion from original tumors. No significant differentially expressed gene profile was observed from the comparison between EW samples and PDXs (fold change > 2, adjusted p <0.05 at paired t-test). In OS, the comparison between OS patient-derived tumors and PDX indicated differences in 397 genes, mostly belonging to immune system functional category. This is in line with the idea that human immune cells are gradually replaced by murine counterparts upon engraftment in the mouse. As proof-of concept, two EW PDX and one OS PDX have been treated with conventional and innovated drugs to test their value in terms of drug-sensitivity prediction. Overall, our study indicated that PDX models maintained the histological and genetic markers of the tumor samples and represent reliable models to test sensitivity to novel drug associations.
2019-08-27 | E-MTAB-7568 | biostudies-arrayexpress
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2005-09-22 | E-GEOD-2712 | biostudies-arrayexpress
Project description:The S. cerevisiae hybrid karyotypes were analyzed by array-CGH to identify small regions of duplication or homeologous chromosomal exchange occurring during the strain construction. alpha-type cells. S. cerevisiae vs. Chromosome replacement lines. Biological replicates: 1 control (S. bayanus), 11 Chromosome replacement lines, independently grown and harvested. Two replicate per array.
2010-06-25 | E-GEOD-12776 | biostudies-arrayexpress
Project description:The S. cerevisiae hybrid karyotypes were analyzed by array-CGH to identify small regions of duplication or homeologous chromosomal exchange occurring during the strain construction. a-type cells. S. cerevisiae vs. Chromosome replacement lines. Biological replicates: 1 control (S. bayanus), 11 Chromosome replacement lines, independently grown and harvested. Two replicate per array.
2010-06-25 | E-GEOD-12775 | biostudies-arrayexpress
Project description:Background: Results obtained from our previous study using a label-free quantitation (LF) proteomic approach in which dynamic range compression was used to profile lower abundance proteins, demonstrated few proteins that were differentially abundant within the synovial fluid (SF) when comparing Autologous Chondrocyte Implantation (ACI) responders and non-responders at baseline (1). This study builds upon our previous findings by assessing higher abundance proteins within these SFs; providing a more global proteome analysis from which we can understand more fully the biology underlying ACI success or failure. Methods: Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomics was used to assess SFs from ACI responders (mean Lysholm improvement of 33; n=14) and non-responders (mean Lysholm decrease of 14; n=13) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Differentially abundant proteins were investigated using pathway analyses and the iTRAQ proteomic dataset was combined with our published proteomic dataset of dynamically compressed SFs, from which an interactome network model of systemic protein interactions was generated. Results: iTRAQ proteomics has confirmed our previous finding that there is a marked proteome shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥2.0 fold change between Stages I and II in responders and non-responders, respectively) and has highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, that were not found in the LF study; 16 of which were altered at baseline. Two protein abundance changes (Complement C1S subcomponent and Matrix metalloproteinase 3 (MMP3), have been biochemically validated. Combination of the iTRAQ and LF proteomic datasets has generated in-depth SF proteome information that has been used to generate interactome networks representing ACI success or failure, from which functional pathways that are dysregulated in ACI non-responders have been identified. Conclusions: Several candidate biomarkers for baseline prediction of ACI outcome have been identified. A holistic overview of the SF proteome in responders and non-responders to ACI has been profiled providing a better understanding of the biological pathways underlying clinical outcome, particularly the differential response to cartilage harvest in non-responders.
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