Gene expression profiling of multiple myeloma patients included in the HOVON65/GMMG-HD4 trial
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE19784 | GEO | 2010/06/27
SECONDARY ACCESSION(S): PRJNA122113
REPOSITORIES: GEO
Similar Datasets
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:Today’s diagnostic tests for multiple myeloma (MM) reflect the criteria of the updated WHO classification based on biomarkers and clinicopathologic heterogeneity. To that end, we propose a new subtyping of myeloma plasma cells (PC) by B-cell subset associated gene signatures (BAGS), from the normal B-cell hierarchy in the bone marrow (BM). To do this, we combined FACS and GEP data from normal BM samples to generate classifiers by BAGS for the PreBI, PreBII, immature (Im), naïve (N), memory (M) and PC subsets. The resultant tumor assignments in available clinical datasets exhibited similar BAGS subtype frequencies in four cohorts across 1302 individual cases. The prognostic impact of BAGS was analyzed in patients treated with high dose melphalan as first line therapy in three prospective trials: UAMS, HOVON65/GMMG-HD4 and MRC Myeloma IX with Affymetrix U133 plus 2.0 microarray data available from diagnostic myeloma PC samples. The BAGS subtypes were significantly associated with progression free (PFS) and overall survival (OS) (PFS, P=3.05e−06 and OS, P=1.06e−11) in a meta-analysis of 926 pts. The major impact was observed within the PreBII and M subtypes conferred with significant inferior prognosis compared to the Im, N and PC subtypes. Cox proportional hazard meta-analysis documented that the BAGS subtypes added significant and independent prognostic information to the TC classification system and ISS staging. BAGS subtype analysis identified transcriptome differences and a number of novel differentially spliced genes. We have identified hierarchal subtype differences in the myeloma plasma cells, with prognostic impact. This observation support an acquired reversible B-cell trait and phenotypic plasticity as a hallmark, also in MM.
2018-09-27 | GSE99635 | GEO
Project description:Today’s diagnostic tests for multiple myeloma (MM) reflect the criteria of the updated WHO classification based on biomarkers and clinicopathologic heterogeneity. To that end, we propose a new subtyping of myeloma plasma cells (PC) by B-cell subset associated gene signatures (BAGS), from the normal B-cell hierarchy in the bone marrow (BM). To do this, we combined FACS and GEP data from normal BM samples to generate classifiers by BAGS for the PreBI, PreBII, immature (Im), naïve (N), memory (M) and PC subsets. The resultant tumor assignments in available clinical datasets exhibited similar BAGS subtype frequencies in four cohorts across 1302 individual cases. The prognostic impact of BAGS was analyzed in patients treated with high dose melphalan as first line therapy in three prospective trials: UAMS, HOVON65/GMMG-HD4 and MRC Myeloma IX with Affymetrix U133 plus 2.0 microarray data available from diagnostic myeloma PC samples. The BAGS subtypes were significantly associated with progression free (PFS) and overall survival (OS) (PFS, P=3.05e−06 and OS, P=1.06e−11) in a meta-analysis of 926 pts. The major impact was observed within the PreBII and M subtypes conferred with significant inferior prognosis compared to the Im, N and PC subtypes. Cox proportional hazard meta-analysis documented that the BAGS subtypes added significant and independent prognostic information to the TC classification system and ISS staging. BAGS subtype analysis identified transcriptome differences and a number of novel differentially spliced genes. We have identified hierarchal subtype differences in the myeloma plasma cells, with prognostic impact. This observation support an acquired reversible B-cell trait and phenotypic plasticity as a hallmark, also in MM.
2018-09-27 | GSE99634 | GEO
Project description:Today’s diagnostic tests for multiple myeloma (MM) reflect the criteria of the updated WHO classification based on biomarkers and clinicopathologic heterogeneity. To that end, we propose a new subtyping of myeloma plasma cells (PC) by B-cell subset associated gene signatures (BAGS), from the normal B-cell hierarchy in the bone marrow (BM). To do this, we combined FACS and GEP data from normal BM samples to generate classifiers by BAGS for the PreBI, PreBII, immature (Im), naïve (N), memory (M) and PC subsets. The resultant tumor assignments in available clinical datasets exhibited similar BAGS subtype frequencies in four cohorts across 1302 individual cases. The prognostic impact of BAGS was analyzed in patients treated with high dose melphalan as first line therapy in three prospective trials: UAMS, HOVON65/GMMG-HD4 and MRC Myeloma IX with Affymetrix U133 plus 2.0 microarray data available from diagnostic myeloma PC samples. The BAGS subtypes were significantly associated with progression free (PFS) and overall survival (OS) (PFS, P=3.05e−06 and OS, P=1.06e−11) in a meta-analysis of 926 pts. The major impact was observed within the PreBII and M subtypes conferred with significant inferior prognosis compared to the Im, N and PC subtypes. Cox proportional hazard meta-analysis documented that the BAGS subtypes added significant and independent prognostic information to the TC classification system and ISS staging. BAGS subtype analysis identified transcriptome differences and a number of novel differentially spliced genes. We have identified hierarchal subtype differences in the myeloma plasma cells, with prognostic impact. This observation support an acquired reversible B-cell trait and phenotypic plasticity as a hallmark, also in MM.
2018-09-27 | GSE107843 | GEO
Project description:Gene expression profiling of multiple myeloma patients included in the HOVON65/GMMG-HD4 trial
| PRJNA122113 | ENA
Project description:We have analyzed gene expression microarray datasets from four different clinical trials to assess accuracy of gene expression based signature in predicting treatment complete response in patients with multiple myeloma. Two of four datasets were made available via The Intergroupe Francophone du Myélome (IFM) group, and remaining two datasets were downloaded from NCBI GEO portal with accession IDs: GSE19784 (HOVON65/GMMG-HD4 trial) and GSE9782 (APEX/SUMMIT trial). Analysis UUID: datasets_archive--2afcd42a-7e12-11e3-9145-5fcc1e060548--15-Jan-2014-12-23-44-CST. Following dataset provides gene expression microarray dataset for IFM-2005 trial involving 67 newly diagnosed patients with MM. A second and larger dataset involving 136 newly diagnosed patients with MM from IFM-2005 trial can be accessed via NCBI GSE ID: GSE39754.
2014-04-21 | GSE55145 | GEO
Project description:We have analyzed gene expression microarray datasets from four different clinical trials to assess accuracy of gene expression based signature in predicting treatment complete response in patients with multiple myeloma. Two of four datasets were made available via The Intergroupe Francophone du Myélome (IFM) group, and remaining two datasets were downloaded from NCBI GEO portal with accession IDs: GSE19784 (HOVON65/GMMG-HD4 trial) and GSE9782 (APEX/SUMMIT trial). Analysis UUID: datasets_archive--2afcd42a-7e12-11e3-9145-5fcc1e060548--15-Jan-2014-12-23-44-CST. Following dataset provides gene expression microarray dataset for IFM-2005 trial involving 67 newly diagnosed patients with MM. A second and larger dataset involving 136 newly diagnosed patients with MM from IFM-2005 trial can be accessed via NCBI GSE ID: GSE39754. CD-138 purified plasma cells from 67 newly diagnosed patients with MM were profiled using Affymetrix Exon-1.0 ST microarray platform. Pre-processing and normalization of dataset was carried out using dChip (http://www.hsph.harvard.edu/cli/complab/dchip/exon.htm#expressio) and R package - aroma.affymetrx (http://www.aroma-project.org/vignettes/FIRMA-HumanExonArrayAnalsis). Patients subsequently received bortezomib based induction therapy, followed by autologus stem cell transplant. Post-induction treatment response is attached in metadata file.
2014-04-21 | E-GEOD-55145 | biostudies-arrayexpress
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