Classification of Myeloma Using Global MicroRNA Profiling
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ABSTRACT:
ORGANISM(S): synthetic construct Homo sapiens
PROVIDER: GSE41276 | GEO | 2013/07/31
SECONDARY ACCESSION(S): PRJNA176427
REPOSITORIES: GEO
Similar Datasets
Project description:Myeloma is a heterogeneous disease. In this work we used global microRNA profiling to classify clinical samples from UK MRC Myeloma IX, and developed a microRNA-based M-bM-^@M-^\outcome classifierM-bM-^@M-^] for risk stratification. Global microRNA expression profiling was carried out in 163 newly diagnosed samples from CD138+ cell selection
2013-07-31 | E-GEOD-41276 | biostudies-arrayexpress
Project description:Many innovative techniques and scientific improvements are available to tackle societal concerns, like public health safety and confining the risk of cancerous exposure to chemicals, but have not been thoroughly tested and implicated yet. We investigated if microRNA and mRNA transcription profiles can be implemented in a short-term carcinogen classifier assay. Our study is additionally focusing on the drawbacks of present-day carcinogen screening strategies and also aims to contribute to a more ethical approach towards animal use and welfare within risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment.
2014-04-01 | GSE43977 | GEO
Project description:Many innovative techniques and scientific improvements are available to tackle societal concerns, like public health safety and confining the risk of cancerous exposure to chemicals, but have not been thoroughly tested and implicated yet. We investigated if microRNA and mRNA transcription profiles can be implemented in a short-term carcinogen classifier assay. Our study is additionally focusing on the drawbacks of present-day carcinogen screening strategies and also aims to contribute to a more ethical approach towards animal use and welfare within risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment. [mRNA profling] 96 hepatic samples in total, 8 control untreated samples, replicates per treated group n=4-6
2014-04-01 | E-GEOD-43977 | biostudies-arrayexpress
Project description:Many innovative techniques and scientific improvements are available to tackle societal concerns, like public health safety and confining the risk of cancerous exposure to chemicals, but have not been thoroughly tested and implicated yet. We investigated if microRNA and mRNA transcription profiles can be implemented in a short-term carcinogen classifier assay. Our study is additionally focusing on the drawbacks of present-day carcinogen screening strategies and also aims to contribute to a more ethical approach towards animal use and welfare within risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mRNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment. [microRNA profling] 68 hepatic samples in total, 3 control untreated samples, replicates per treated group n=3-4
2014-04-01 | E-GEOD-43847 | biostudies-arrayexpress
Project description:Many innovative techniques and scientific improvements are available to tackle societal concerns, like public health safety and confining the risk of cancerous exposure to chemicals, but have not been thoroughly tested and implicated yet. We investigated if microRNA and mRNA transcription profiles can be implemented in a short-term carcinogen classifier assay. Our study is additionally focusing on the drawbacks of present-day carcinogen screening strategies and also aims to contribute to a more ethical approach towards animal use and welfare within risk assessment. Since current in vitro and in silico assays are still not able to mimic the in vivo situation accurately we set out to develop an alternative short-term in vivo assay. Five genotoxic, seven non-genotoxic and five non-carcinogen exposure studies were used to investigate if murine hepatic microRNA and mRNA profiles after 7-day exposure are suitable tools to classify carcinogens. Classification analyses showed that a small transcript set, consisting of both microRNA and mRNA, is able to classify the genotoxic, non-genotoxic and non-carcinogens tested with 100% accuracy. The results indicate that microRNAs have the potential to be used as transcriptional classifiers and that a short-term transcriptional classifier assay in mice can be a powerful tool in carcinogenicity risk assessment.
2014-04-01 | GSE43847 | GEO
Project description:There is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we used the hiPSC-based UKK-24h-test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. All compounds were analyzed at the maximal plasma concentration (Cmax) and the 20-fold Cmax for a 24 h incubation period in three independent experiments. Using the 1,000 probe sets with the highest variability and including information on cytotoxicity, a penalized logistic regression based classifier (top-1,000 classifier) reached an AUC, accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96 and 0.88, respectively to correctly classify the compounds as teratogens or non-teratogens. This top-1,000 classifier reached a higher AUC, accuracy and sensitivity than a second classifier (SPS-classifier), which used the number of significant probe sets to classify the compounds. At least, the SPS-classifier had a specificity of 1. Classification at 20-fold Cmax did not lead to better performance metrics than testing at 1-fold Cmax. Finally, inclusion of cytotoxicity information into the classifier clearly improved the performance metrics. In conclusion, although further optimization by additional readouts and inclusion of cell systems that model different developmental processes is required, the UKK-24h-test will support the early discovery-phase detection of human developmental toxicants already in its present form.
2022-06-24 | GSE187001 | 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 | 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 | GSE107843 | 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:PURPOSE. The deregulation of CCND1, CCND2 and CCND3 genes represents a common event in multiple myeloma (MM). The recently proposed TC classification grouped MM patients into five classes on the basis of their cyclins D expression profiles and the presence of the main translocations involving the immunoglobulin heavy-chain locus (IGH) at 14q32. In this study, we provide a molecular characterization of the identified TC groups. MATERIALS AND METHODS. The gene expression profiles of purified plasma cells from 50 MM cases were used to stratify the samples into the five TC classes and identify their transcriptional fingerprints. The cyclin D expression data were validated by means of real-time quantitative PCR analysis; fluorescence in-situ hybridization was used to investigate the cyclin D loci arrangements, and to detect the main IGH translocations and the chromosome 13q deletion. RESULTS. Class-prediction analysis identified 112 probe sets as characterizing the TC1, TC2, TC4 and TC5 groups, whereas the TC3 samples showed heterogeneous phenotypes and no marker genes. The TC2 group, which showed extra copies of the CCND1 locus and no IGH translocations or the chromosome 13q deletion, was characterized by the overexpression of genes involved in protein biosynthesis at translational level. A meta-analysis of published datasets validated the identified gene expression signatures. CONCLUSIONS. Our data contribute to the understanding of the molecular and biological features of distinct MM subtypes. The identification of a distinctive gene expression pattern in TC2 patients may improve risk stratification and indicate novel therapeutic targets. Keywords: other
2005-09-01 | GSE2912 | GEO