Gene expression profiling of multiple myeloma patients included in the HOVON65/GMMG-HD4 trial
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE19784 | GEO | 2010/06/27
SECONDARY ACCESSION(S): PRJNA122113
REPOSITORIES: GEO
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Project description:In order to identify relevant, molecularly defined subgroups in Multiple Myeloma (MM), gene expression profiling (GEP) was performed on purified CD138+ plasma cells of 320 newly diagnosed myeloma patients included in the Dutch-Belgian/German HOVON-65/ GMMG-HD4 trial using Affymetrix Gene Chip U133 plus 2.0 arrays. Hierarchical clustering identified 10 distinct subgroups. Bone marrow plasma cell samples were obtained from 320 newly diagnosed multiple myeloma patients included in a large multicenter, prospective, randomized phase III trial (HOVON65/GMMG-HD4). Purified myeloma plasma cells samples with a monoclonal plasma cell purity > 80% were used for analysis.
2010-06-27 | E-GEOD-19784 | biostudies-arrayexpress
Project description:We recently identified a gene module of 87 genes co-expressed with MCL1 (MCL1-M), a critical regulator of plasma cell survival. MCL1-M captures both MM cell-intrinsically acting signals and the signals regulating the interaction between MM cells with bone marrow microenvironment. MM can be clustered into MCL1-M high and MCL1-M low subtypes. While the MCL1-M high MMs are enriched in a preplasmablast signature, the MCL1-M low MMs are enriched in B cell-specific genes. In multiple independent datasets, MCL1-M high MMs exhibited poorer prognosis compared to MCL1-M low MMs. Re-analysis of the phase III HOVON-65/GMMG-HD4 showed that only MCL1-M MMs, but not MCL1-M low MMs, benefited from bortezomib-based treatment. To translate the MCL1-M clustering scheme into a platform for individual diagnosis, we refined the classifier genes and developed a support vector machine-based algorithm. Individual MMs with transcriptome assessed at the RNA-seq or U133 plus 2.0 array platform can be robustly assigned as the MCL1-M high or low subtype with high confidence. Analyses of the MM samples in the HOVON-65/GMMG-HD4 trial and APEX trial reinforce that only MCL1-M high MMs benefit from bortezomib-based treatment with a hazard ratio of 0.58 (P = 0.010) and 0.47 (P = 0.009), respectively. Thus, MCL1-M based subtyping assigns MMs into prognostic and predictive molecular subtypes driven by subtype-specific pathogenic pathways.
2024-12-01 | GSE190042 | GEO
Project description:Neural stem cells (NSCs) are considered to be the cell-of-origin of brain tumor stem cells. To identify the genetic pathways responsible for the transformation of normal NSCs to brain-tumor-initiating cells, we used Sleeping Beauty (SB) transposons, to mutagenize NSCs. Mobilized SB transposons induced the immortalization of NSCs. Immortalized NSCs induced tumors upon subcutaneous transplantation in immunocompromized mice. To further classify the immortalized cells and mouse tumors, we performed Gene Set Enrichment Analysis (GSEA) using DNA microarray data. Ten immortalized NSC lines (four WT and six p53 mutant lines) and two normal NSCs were used for RNA extraction and hybridization on Affymetrix microarrays. CEL files were processed using the RMA algorithm to normalize the data. Unsupervised hierarchical clustering was applied to the normalized gene expression data across samples using the R module pvclust (Suzuki and Shimodaira, Bioinformatics 22, 1540-1542, 2006). Hierarchical clustering showed a close similarity in gene expression between all ten immortalized lines, compared with two normal NSCs. We then compared their gene expression profiles to those present in the brain transcriptome database (Cahoy et al., J Neurosci 28, 264-278, 2008). Using five gene sets that are associated with neurons, oligodendrocytes, OPCs, astrocytes and cultured astroglial cells, GSEA enrichment scores were calculated for the ten immortalized NSCs lines against two control normal NSCs. GSEA enrichment analysis was performed using GSEA software v. 2.07 (http://www.broadinstitute.org/gsea). We found a strong enrichment for genes that are differentially expressed in the cultured astroglial cells. Twelve tumors (six WT and six p53 mutant tumors) and three control subcutaneous tissues were used for RNA extraction and hybridization on Affymetrix microarrays. CEL files were processed using the RMA algorithm to normalize the data. Unsupervised hierarchical clustering was applied to the normalized gene expression data across samples using the R module pvclust (Suzuki and Shimodaira, Bioinformatics 22, 1540-1542, 2006). Hierarchical clustering of 12 tumors showed a close similarity between the tumors, compared with three control subcutaneous tissues. We next performed GSEA analysis on the tumors. Using gene sets specific to the four defined GBM subtypes (Verhaak et al., Cancer Cell 17, 98-110, 2010), a GSEA enrichment score was then calculated for the twelve tumor samples against three control subcutaneous tissue samples. GSEA enrichment analysis was performed using GSEA software v. 2.07 (http://www.broadinstitute.org/gsea). This analysis showed that the mouse tumors were significantly enriched for genes specific for the mesenchymal GBM.
2012-03-31 | GSE36897 | GEO
Project description:Molecular profiling of tumors has proven a valuable tool for identification of prognostic and diagnostic subgroups in medulloblastomas, glioblastomas and other cancers. However, the molecular landscape of atypical teratoid / rhabdoid tumors (AT/RTs) remains largely unexplored. To address this issue, we used microarrays to measure the gene expression profiles of 18 AT/RTs, and performed unsupervised hierarchical clustering to determine molecularly similar subgroups. Four major subgroups (clusters) were identified. These did not conform to gender, tumor location, or presence of monosomy 22. Clusters showed distinct gene signatures and differences in enriched biological processes, including elevated expression of choroid plexus genes in Cluster 4. In addition, survival differed significantly by cluster, with shortest survival (mean 4.7 months) in both Clusters 3 and 4 compared to Clusters 1 and 2 (mean 28.1 months). Analysis showed that multiple bone morphogenetic protein (BMP) pathway genes were up-regulated in the short survival clusters, with BMP4 showing the most significant up-regulation (270-fold). Thus, high expression of BMP pathway genes was negatively associated with survival in this dataset. Our study indicates that molecular subgroups exist within AT/RTs, and that molecular profiling of these comparatively rare tumors may be of diagnostic, prognostic and therapeutic value. Key Words: atypical teratoid / rhabdoid tumor; bone morphogenetic protein pathway; BMP4; survival; microarray Molecular profiling of 18 AT/RT patient tumor samples was performed using Affymetrix U133 Plus2 GeneChips. Data were background corrected and normalized using gcRMA (as implemented in Bioconductor). Unsupervised agglomerative hierarchical clustering was performed to identify subsets of AT/RTs with similar gene expression. Limma (moderated t-tests; Bioconductor) was used to identify signature genes for each cluster. Bioinformatics web tool DAVID was used to identify enriched biological processes for each cluster. Survival was analyzed using Kaplan-Meier curves and Cox Hazard Ratio. Bioinformatics tools Gene Set Enrichment (GSEA) and Ingenuity Pathways Analysis were also used to gain further insight into cluster differences.
2011-09-21 | E-GEOD-28026 | biostudies-arrayexpress
Project description:Background: Renal cell carcinoma (RCC) is characterized by a number of diverse molecular aberrations that differ among individuals. Recent approaches to molecularly classify RCC were based on clinical, pathological as well as on single molecular parameters. As a consequence, gene expression patterns reflecting the sum of genetic aberrations in individual tumors may not have been recognized. In an attempt to uncover such molecular features in RCC, we used a novel, unbiased and integrative approach. Methods: We integrated gene expression data from 97 primary RCCs of different pathologic parameters, 15 RCC metastases as well as 34 cancer cell lines for two-way nonsupervised hierarchical clustering using gene groups suggested by the PANTHER Classification System. We depicted the genomic landscape of the resulted tumor groups by means of Single Nuclear Polymorphism (SNP) technology. Finally, the achieved results were immunohistochemically analyzed using a tissue microarray (TMA) composed of 254 RCC. Results: We found robust, genome wide expression signatures, which split RCC into three distinct molecular subgroups. These groups remained stable even if randomly selected gene sets were clustered. Notably, the pattern obtained from RCC cell lines was clearly distinguishable from that of primary tumors. SNP array analysis demonstrated differing frequencies of chromosomal copy number alterations among RCC subgroups. TMA analysis with group-specific markers showed a prognostic significance of the different groups. Conclusion: We propose the existence of characteristic and histologically independent genome-wide expression outputs in RCC with potential biological and clinical relevance. Expression profiling by array, combined data analysis with genomic profiling data. Genomic DNA from renal cell was hybridized to renal cell carcinoma samples and matched normal kidney tissue biopsies, using the Affymetrix GenomewideSNP_6 platform. CEL files were processed using R, Bioconductor and software from the aroma.affymetrix project. Visualized Copy number profiles are accessible through the Progenetix site (www.progenetix.net). CN,raw.csv and segments.csv: Probes are mapped by their position in genome build 36 / HG18. Probes are ordered according to their linear position on the Golden Path.
2012-07-23 | E-GEOD-19949 | biostudies-arrayexpress
Project description:Molecular profiling of tumors has proven a valuable tool for identification of prognostic and diagnostic subgroups in medulloblastomas, glioblastomas and other cancers. However, the molecular landscape of atypical teratoid / rhabdoid tumors (AT/RTs) remains largely unexplored. To address this issue, we used microarrays to measure the gene expression profiles of 18 AT/RTs, and performed unsupervised hierarchical clustering to determine molecularly similar subgroups. Four major subgroups (clusters) were identified. These did not conform to gender, tumor location, or presence of monosomy 22. Clusters showed distinct gene signatures and differences in enriched biological processes, including elevated expression of choroid plexus genes in Cluster 4. In addition, survival differed significantly by cluster, with shortest survival (mean 4.7 months) in both Clusters 3 and 4 compared to Clusters 1 and 2 (mean 28.1 months). Analysis showed that multiple bone morphogenetic protein (BMP) pathway genes were up-regulated in the short survival clusters, with BMP4 showing the most significant up-regulation (270-fold). Thus, high expression of BMP pathway genes was negatively associated with survival in this dataset. Our study indicates that molecular subgroups exist within AT/RTs, and that molecular profiling of these comparatively rare tumors may be of diagnostic, prognostic and therapeutic value. Key Words: atypical teratoid / rhabdoid tumor; bone morphogenetic protein pathway; BMP4; survival; microarray
2011-09-22 | GSE28026 | GEO
Project description:(Purpose) Biological classification of colorectal cancer (CRC) can help to understand its heterogeneous background. The purpose of this study is to classify CRC based on gene expression profiles using formalin-fixed paraffin-embedded (FFPE) samples and to correlate subgroups of CRC with biological features and clinical outcomes. (Results) CRC was clustered into four subgroups by unsupervised hierarchical clustering method. These subgroups show different biological and clinical features. (Conclusion) Gene expression profiles of CRC using FFPE samples distinguish four subgroups that had different biological features and clinical outcomes. These subgroups may explain heterogeneity of CRC and be useful biomarker for clinical. Patients and Methods: One hundred patients with unresectable and advanced or recurrent CRC who underwent the surgical resection from 1998 to 2010 were enrolled in this study. RNA extracted from FFPE samples was subjected to gene expression microarray. After comprehensive gene expression analysis, CRC were classified by an unsupervised hierarchical clustering and a principle component analysis (PCA). Mutation analysis of KRAS, BRAF, PIK3CA and TP53 genes were performed by direct DNA sequencing. Correlation between the biological information, clinicopathological factors and clinical outcomes were analyzed.
2014-01-01 | E-GEOD-33193 | biostudies-arrayexpress
Project description:Multiple Myeloma (MM) is the second most prevalent blood cancer (10%) after non-Hodgkin's lymphoma and represents approximately 1% of all cancers and 2% of all cancer deaths. MM is a complex disease characterized by numerous genetic alterations and recent mRNA profiling studies have attempted to subclassify the disease to build pathogenetic and prognostic models Correct classification of cancer patients into subtypes is a prerequisite for acute diagnosis and effective treatment. Here we use high accuracy, quantitative proteomics to segregate cancer subtypes directly at the level of expressed proteins. Multiple myeloma is a heterogeneous disease in its initial clinical features as well as its outcome. We investigated two subtypes of Multiple Myeloma: multiple myeloma associated with t(4;14) chromosomal translocation as well as t(4;14)-negative MM subtype. The t(4;14) translocation, found in 15% of multiple myeloma cases, indicates a poor prognosis. Super-SILAC mix was combined of cell lysates from 8 diverse cell lines labelled with heavy amino acids (Lys8 and Arg10). The Super-SILAC library is mixed with samples (lysates), and quantitative mass spectrometric analysis is performed using a setup consisting of LC and on a linear ion trap Orbitrap mass spectrometer with high mass accuracy at the MS and MS/MS levels. This way we have analysed 20 patient samples from present MM. Shotgun proteomic analysis yielded a proteome of more than 5300 quantified proteins overall (3000 on an average per individual sample). High accuracy of quantification allowed robust separation of subtypes by hierarchical clustering on the protein level.
2024-07-15 | PXD003138 | Pride
Project description:Triple negative breast cancer (TNBC) accounts for 15-20% of all breast carcinomas and it is clinically characterized by an aggressive phenotype and bad prognosis. TNBC does not benefit from any targeted therapy, so further characterization is needed to define subgroups with potential therapeutic value. In this work, the proteomes of one hundred twenty-five formalin-fixed paraffin-embedded samples from patients diagnosed with triple negative breast cancer were analyzed by mass spectrometry using data-independent acquisition. Hierarchical clustering, probabilistic graphical models and Significance Analysis of Microarrays were used to characterize molecular groups. Additionally, a predictive signature related with relapse was defined. Two molecular groups with differences in several biological processes as glycolysis, translation and immune response, were defined in this cohort, and a prognostic signature based on the abundance of proteins RBM3 and NIPSNAP1 was defined. This predictor split the population into low-risk and high-risk groups. The differential processes identified between the two molecular groups may serve to design new therapeutic strategies in the future and the prognostic signature could be useful to identify a population at high-risk of relapse that could be directed to clinical trials.
2022-02-14 | PXD021491 | Pride
Project description:(Purpose) Biological classification of colorectal cancer (CRC) can help to understand its heterogeneous background. The purpose of this study is to classify CRC based on gene expression profiles using formalin-fixed paraffin-embedded (FFPE) samples and to correlate subgroups of CRC with biological features and clinical outcomes. (Results) CRC was clustered into four subgroups by unsupervised hierarchical clustering method. These subgroups show different biological and clinical features. (Conclusion) Gene expression profiles of CRC using FFPE samples distinguish four subgroups that had different biological features and clinical outcomes. These subgroups may explain heterogeneity of CRC and be useful biomarker for clinical.
2014-01-01 | GSE33193 | GEO