Project description:High-grade osteosarcoma is a tumor with a complex genomic profile, occurring primarily in adolescents with a second peak at middle age. The extensive genomic alterations obscure the identification of genes driving tumorigenesis during osteosarcoma development. In order to identify such driver genes, we integrated DNA copy number profiles (Affymetrix SNP 6.0) of 32 diagnostic biopsies with 84 expression profiles (Illumina Human-6 v2.0) of high-grade osteosarcoma as compared with its putative progenitor cells, i.e. mesenchymal stem cells (n=12) or osteoblasts (n=3). In addition, we performed paired analyses between copy number and expression profiles of a subset of 29 patients for which both DNA and mRNA profiles were available. Integrative analyses were performed in Nexus Copy Number software and statistical language R. Paired analyses were performed on all probes detecting significantly differentially expressed genes in corresponding LIMMA analyses. For both non-paired and paired analyses, copy number aberration frequency was set to >35%. Non-paired and paired integrative analyses resulted in 45 and 101 genes, respectively, which were present in both analyses using different control sets. Paired analyses detected >90% of all genes found with the corresponding non-paired analyses. Remarkably, approximately twice as many genes as found in the corresponding non-paired analyses were detected. Affected genes were intersected with differentially expressed genes in osteosarcoma cell lines, resulting in 31 new osteosarcoma driver genes. Cell division related genes, such as MCM4 and LATS2, were overrepresented and genomic-instability was predictive for metastasis-free survival, suggesting that deregulation of the cell cycle is a driver of osteosarcomagenesis. This SuperSeries is composed of the following subset Series: GSE28974: Genome-wide gene expression profiling of mesenchymal stem cells GSE33153: Copy number analysis of high-grade osteosarcoma GSE33382: Genome-wide gene expression analysis of high-grade osteosarcoma For data processing, we refer to the individual series. We performed both non-paired and paired integrative analyses on SNP and gene expression data. Non-paired integrative analysis was performed by importing lists of differentially expressed genes into the Copy Number module of Nexus software version 5 (BioDiscovery, CA). Based on the length of the gene list, Nexus software performs a Fisher's exact test in order to determine whether the number of differentially expressed genes in a specific region with a significant copy number alteration is larger than expected by chance. Genes present in such regions of copy number alteration with FDR-adjusted P-values (Q-bounds in Nexus software) < 0.05 were returned from this integrative analysis. Nexus software only reports genes which are both gained and overexpressed, or both deleted and downregulated. For the paired integrative analysis, copy number data of all autosomal overlapping genes between the copy number and gene expression arrays were exported from Nexus software, and converted into a binary file containing all genes with a gain (1) and no gain (0), and a similar binary file for losses. As in the non-paired integrative analysis, we did not apply any restrictions on the size of copy number alterations. Gene expression data of each probe for each sample were normalized against average gene expression of the corresponding probes over all control samples (either expression data from 12 MSCs, or from 3 osteoblasts). This was performed by subtracting the average expression of the control samples from the expression levels of the sample of interest, since these are log-transformed expression values. For both analyses, only genes that were significantly differentially expressed between the 84 osteosarcoma samples and the specific control set were analyzed, in order to make sure that no genes returned from the integrative analysis were not significantly differentially expressed. Subse quently, genes that overlapped between the copy number binary files and that matched the fold change of expression (upregulation for genes with gains, and downregulation for genes with losses) were returned.

Project description:Conventional high-grade osteosarcoma is a primary malignant bone tumor, which is most prevalent in adolescence. Survival rates of osteosarcoma patients have not improved significantly in the last 25 years. Aiming to increase this survival rate, a variety of model systems are used to study osteosarcomagenesis and to test new therapeutic agents. Such model systems are typically generated from an osteosarcoma primary tumor, but undergo many changes due to culturing or interactions with a different host species, which may result into differences in gene expression between primary tumor cells, and tumor cells from the model system. We aimed to investigate whether gene expression profiles of osteosarcoma cell lines and xenografts are still comparable to those of the primary tumor. Osteosarcoma can be subdivided into several histological subtypes, of which osteoblastic, chondroblastic, and fibroblastic osteosarcoma are the most frequent ones. Using nearest shrunken centroids classification, we have generated an expression profile that can predict these histological subtypes in both osteosarcoma biopsies (n=66), as well as in two osteosarcoma model systems, i.e. osteosarcoma cell lines (n=13) and xenografts (n=18). Based on the preservation of mRNA expression profiles that are characteristic for the histological subtype we propose that these model systems are representative to the primary tumor from which they are derived. Genome-wide expression profiling was performed on pre-treatment diagnostic biopsies of 76 resectable high-grade osteosarcoma patients from the EuroBoNet consortium (www.eurobonet.eu). Samples with a main histological subtype (n=66) were selected for subsequent subtype analyses. Out of the EuroBoNeT panel of 19 cell lines, 13 cell lines were recorded to belong to a main histological subtype. This set of 13 cell lines contained 4 cell lines derived from fibroblastic, and 9 cell lines derived from osteoblastic osteosarcomas. The 13 osteosarcoma cell lines IOR/MOS, IOR/OS10, IOR/OS14, IOR/OS15, IOR/OS18, IOR/OS9, IOR/SARG, KPD, MG-63, MHM, OHS, OSA, and ZK-58 were maintained in RPMI 1640 (Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal calf serum and 1% Penicillin/Streptomycin (Invitrogen) as previously described (PMID: 19787792). The osteosarcoma xenograft model is described in Kresse et al. (PMID: 21713766) In short, human tumors were implanted directly from patient samples and successively passaged subcutaneously in nude mice. Eighteen different xenografts were used, of which 3 were derived from chondroblastic, and 15 from osteoblastic osteosarcomas. Osteosarcoma and xenograft tissue was handled as previously described in Buddingh, Kuijjer et al. (PMID: 21372215) Osteosarcoma cell lines were prepared as in Ottaviano et al. (PMID: 19787792) RNA isolation, synthesis of cDNA, cRNA amplification, and hybridization of cRNA onto the Illumina Human-6 v2.0 Expression BeadChips were performed as described in Buddingh, Kuijjer, et al. (PMID: 21372215) Microarray data processing and quality control were performed using the statistical language R as described in Buddingh, Kuijjer, et al. (PMID: 21372215) We performed a factorial LIMMA analysis comparing 50 osteoblastic, 9 chondroblastic, and 7 fibroblastic osteosarcomas pre-chemotherapy biopsies. Probes with Benjamini and Hochberg False discovery rate-adjusted P-values < 0.05 were considered to be significantly differentially expressed. The gene expression profile was generated on the dataset of biopsies using Bioconductor package PAMR. Internal cross-validation was performed 50 times. A threshold was selected where the error rate of the prediction profile was minimal. The minimum error rate was representative of 50 independent simulations. In order to minimize optimization bias, we validated the profile on an independent dataset of osteosarcoma biopsies (n=5), containing 1 chondroblastic osteosarcoma and 4 osteoblastic osteosarcomas. We subsequently applied the validated prediction profile to two independent datasets, the first consisting of gene expression data of osteosarcoma cell lines, the second of xenografts. Expression of the probes that composed the prediction profile was verified using a factorial LIMMA analysis, comparing chondroblastic, fibroblastic, and osteoblastic osteosarcoma biopsy samples.

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Genome-wide gene expression profiling of mesenchymal stem cells

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