Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Gene expression profile alone is inadequate in predicting complete response in multiple myeloma

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE55145 | GEO | 2014/04/21

SECONDARY ACCESSION(S): PRJNA239563

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Gene expression profile alone is inadequate in predicting complete response in multiple myeloma

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

Transcription profiling of bone marrow plasma cells from 320 newly diagnosed multiple myeloma patients

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

Gene expression profiling of multiple myeloma patients included in the HOVON65/GMMG-HD4 trial

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 GeneChip U133 plus 2.0 arrays. Hierarchical clustering identified 10 distinct subgroups. Using this dataset as training data, a prognostic signature was built. The dataset consists of 282 CEL files previously used in the hierarchical clustering study of Broyl et al (Blood, 116(14):2543-53, 2010) outlined above. To this set 8 CEL-files/gene expression profiles were added. Using this set of 290 CEL-files, a prognostic signature of 92 genes (EMC-92-genesignature) was generated by supervised principal components analysis combined with simulated annealing (Kuiper et al.).

2010-06-27 | GSE19784 | GEO

Proteomics-based approach reveals the involvement of SERPINB9 in recurrent and relapsed multiple myeloma

Project description:Multiple myeloma (MM) is a common hematological malignancy with poorly understood recurrence and relapse mechanisms. Notably, bortezomib resistance leading to relapse makes MM treatment significantly challenging. To clarify the drug resistance mechanism, we employed a quantitative proteomics approach to identify differentially expressed protein candidates implicated in bortezomib-resistant recurrent and relapsed MM (RRMM). Bone marrow biopsy specimens from five patients newly diagnosed with MM (NDMM) were compared with those from five patients diagnosed with bortezomib-resistant RRMM using tandem mass tag-mass spectrometry (TMT-MS). Subcellular localization and functional classification of the differentially expressed proteins were determined by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and hierarchical clustering. Top candidates identified were validated with parallel reaction monitoring (PRM) analysis using tissue samples from 11 NDMM and 8 RRMM patients, followed by comparison with the NCBI Gene Expression Omnibus (GEO) dataset of 10 MM patients and 10 healthy controls (Accession No.: GSE80608). Thirty-four differentially expressed proteins in RRMM, including proteinase inhibitor 9 (SERPINB9) were identified by TMT-MS. Subsequent functional enrichment analyses of the identified protein candidates indicated their involvement in regulating cellular metabolism, apoptosis, programmed cell death, lymphocyte-mediated immunity, and defense response pathways in RRMM. The top protein candidate SERPINB9 was confirmed by PRM analysis as well as by comparison with an NCBI GEO dataset. We elucidated the proteome landscape of bortezomib-resistant RRMM and identified SERPINB9 as a promising novel therapeutic target. Our results provide a resource for future studies on the mechanism of RRMM.

2021-03-03 | PXD022182 | Pride

Integration between MCL1 gene co-expression module and the Revised International Staging System enables precise prognostication and prediction of response to proteasome inhibitor-based therapy in individual multiple myeloma

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

Gene expression profiling of newly diagnosed multiple myeloma patients included in the HOVON95 clinical trial

Project description:This gene expression set contains data from patients included in the HOVON95 clinical trial. Using this data the relation between a signature identifying patients with aggressive biology and clinical parameters was studied in newly diagnosed multiple myeloma patients. This dataset was used to identify the relationship between a signature for aggressive disease and clinical parameters.

2022-06-01 | GSE164706 | GEO

Gene expression profiling of elderly newly diagnosed multiple myeloma patients included in the HOVON87/NMSG-18 clinical trial

Project description:This gene expression set contains data from 180 patients included in the HOVON-87/NMSG-18 clinical trial. Using this data the SKY92 classifier was validated in the elderly newly diagnosed multiple myeloma patients (median age = 72). Additional to its proven value in younger patients, the MMprofiler?s SKY92 classifier was found to be a robust marker of high risk patients in the elderly MM population.

2021-01-01 | GSE87900 | GEO

Gene expression profiling of newly diagnosed plasma cell leukemia patients included in the HOVON129 clinical trial

Project description:This gene expression set contains data from patients included in the HOVON129 clinical trial. Using this data the relation between a signature identifying patients with aggressive biology and clinical parameters was studied in newly diagnosed plasma cell leukemia patients. This dataset was used to identify the relationship between a signature for aggressive disease and clinical parameters in plasma cell leukemia.

2022-06-01 | GSE164703 | GEO

Single cell RNA-seq of relapsed and refractory multiple myeloma patients defines new resistance pathways and targeted treatments

Project description:Multiple myeloma (MM) is a neoplastic plasma cell (PC) disorder, characterized by clonal proliferation of malignant PC. Despite extensive research, disease heterogeneity within and between treatment resistant patients is poorly characterized. Here, we conduct a prospective multi-center single-arm clinical trial (NCT04065789), combined with longitudinal single cell RNA-seq to study the molecular dynamics of MM resistance mechanisms. Forty-one newly diagnosed MM patients who either failed to respond or experienced early relapse after a bortezomib-containing induction regimen, were enrolled to evaluate the safety and efficacy of a daratumumab/carfilzomib/lenalidomide/dexamethasone combination. Primary clinical endpoint was safety and tolerability. Secondary endpoints included overall response rate; progression free survival, and overall survival. Treatment was safe and well-tolerated, deep and durable responses were achieved. In prespecified exploratory analyses, comparison of 41 primary-refractory and early-relapsed patients with 11 healthy subjects and 15 newly diagnosed MM patients revealed new MM molecular pathways of resistance, including hypoxia tolerance, protein folding, and mitochondria respiration, that generalized to larger clinical cohorts (CoMMpass). We found peptidylprolyl isomerase A (PPIA), a central gene in the protein-folding response pathway, as a potential new target for resistant MM. CRISPR/Cas9 deletion of PPIA or inhibition of PPIA with cyclosporin A significantly sensitizes MM tumor cells to proteasome inhibitors. Together, our study defines a roadmap for integrating scRNA-seq in clinical trials, identifies a signature of highly resistant MM patients and discovers PPIA as a potent therapeutic target for these tumors.

2021-01-27 | GSE161195 | GEO

Transcriptional characterization of a prospective series of primary plasma cell leukemia revealed genes associated with tumor progression and poorest outcome

Project description:Plasma cell leukemia (PCL) is a rare form of plasma cell dyscrasia that presents either as a progression of previously diagnosed multiple myeloma (MM), namely secondary PCL (sPCL), or as the initial manifestation of disease, namely primary PCL (pPCL). Although presenting signs and symptoms include those seen in MM, pPCL is characterized by several aspects that clearly define more aggressive course. To provide insights into the biology of pPCL, we have investigated the transcriptional profiles of a cohort of 21 newly-diagnosed, homogeneously treated pPCL patients included in a multicenter prospective clinical trial. All but one pPCL had one of the main IGH translocations, whose associated transcriptional signatures resembled those observed in MM. A 503-gene signature was identified that distinguished pPCL from MM, from which emerged 28 genes whose trend in expression levels was found associated with the progressive stages of plasma cell dyscrasia in a large dataset of cases from multiple institutions, including samples from normal donors throughout PCL. The transcriptional pattern of the pPCL series was then evaluated in association with outcome. Three genes were identified having expression levels correlated with response to the first-line treatment with lenalidomide/dexamethasone, whereas a 27-gene signature was identified associated with overall survival independently of molecular alterations, hematological parameters and renal function. Overall, our data contribute to a fine dissection of pPCL and may provide novel insights into the molecular definition of a subgroup of high-risk pPCL.

2013-05-01 | GSE39925 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data