Assessment of MEK-ERK pathway targeting by BRAF, NRAS and KRAS gene mutations in plasma cell dyscrasias
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Luca Agnelli
PROVIDER: E-GEOD-66293 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
Publications
Molecular spectrum of BRAF, NRAS and KRAS gene mutations in plasma cell dyscrasias: implication for MEK-ERK pathway activation.
Lionetti Marta M Barbieri Marzia M Todoerti Katia K Agnelli Luca L Marzorati Simona S Fabris Sonia S Ciceri Gabriella G Galletti Serena S Milesi Giulia G Manzoni Martina M Mazzoni Mara M Greco Angela A Tonon Giovanni G Musto Pellegrino P Baldini Luca L Neri Antonino A
Oncotarget 20150901 27
Multiple myeloma (MM) is a clinically and genetically heterogeneous plasma cell (PC) malignancy. Whole-exome sequencing has identified therapeutically targetable mutations such as those in the mitogen-activated protein kinase (MAPK) pathway, which are the most prevalent MM mutations. We used deep sequencing to screen 167 representative patients with PC dyscrasias [132 with MM, 24 with primary PC leukemia (pPCL) and 11 with secondary PC leukemia (sPCL)] for mutations in BRAF, NRAS and KRAS, which ...[more]
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Project description:Multiple myeloma (MM) is a malignant disorder characterized by the clonal proliferation of plasma cells (PCs) in the bone marrow (BM). The genetic background and clinical course of the disease are largely heterogeneous, and MM pathophysiology ranges from the premalignant condition of monoclonal gammopathy of undetermined significance (MGUS) to smoldering MM, symptomatic MM, and extramedullary MM/plasma cell leukemia (PCL). Recent genome-wide sequencing efforts have provided the rationale for molecularly aimed treatment approaches, identifying mutations that can be specifically targeted, such as those in the mitogen-activated protein kinase (MAPK) pathway, which represent the most prevalent mutations in MM. Among these, mutations affecting BRAF gene, detected in 4-15% of patients, are of potential immediate clinical relevance due to the availability of effective inhibitors of this serine-threonine kinase which are in fact being explored also in myeloma. In this study, we screened by next generation sequencing (NGS) a large and representative series of intramedullary and extramedullary MM patients, including primary and secondary plasma cell leukemia (pPCL and sPCL, respectively), for mutations in BRAF, NRAS and KRAS genes. We evaluated the relationship of identified variants with other clinical and biological features and determined the transcriptional signature associated with MAPK pathway activation in MM. To further elucidate the transcriptional programs modulated by BRAF activation in MM, we used the PLX4032 drug to inhibit BRAF activity in U266 human myeloma cell line (HMCL), carrying K601N mutation and showing constitutive activation of MEK/ERK signaling. After confirming its ability to suppress MAPK pathway and myeloma cell proliferation in culture in the U266 cell line, we investigated the specific modulation of gene expression induced by the drug. U266 cells were treated with PLX4032 (30 µM) or DMSO for 12 hours and subjected to gene expression profiling (GEP) analysis by using Affymetrix GeneChip Human Gene 1.0ST arrays.
2015-07-01 | E-GEOD-66292 | biostudies-arrayexpress
Project description:Multiple myeloma (MM) is a malignant disorder characterized by the clonal proliferation of plasma cells (PCs) in the bone marrow (BM). The genetic background and clinical course of the disease are largely heterogeneous, and MM pathophysiology ranges from the premalignant condition of monoclonal gammopathy of undetermined significance (MGUS) to smoldering MM, symptomatic MM, and extramedullary MM/plasma cell leukemia (PCL). Recent genome-wide sequencing efforts have provided the rationale for molecularly aimed treatment approaches, identifying mutations that can be specifically targeted, such as those in the mitogen-activated protein kinase (MAPK) pathway, which represent the most prevalent mutations in MM. Among these, mutations affecting BRAF gene, detected in 4-15% of patients, are of potential immediate clinical relevance due to the availability of effective inhibitors of this serine-threonine kinase which are in fact being explored also in myeloma. In this study, we screened by next generation sequencing (NGS) a large and representative series of intramedullary and extramedullary MM patients, including primary and secondary plasma cell leukemia (pPCL and sPCL, respectively), for mutations in BRAF, NRAS and KRAS genes. We evaluated the relationship of identified variants with other clinical and biological features and determined the transcriptional signature associated with MAPK pathway activation in MM. To identify transcriptomic profiles possibly related to BRAF, NRAS and KRAS mutations found in our study, we investigated by Affymetrix microarray technology a large fraction (n=142) of the samples analyzed by NGS, including wild-type patients for all the three genes and cases carrying mutations in at least one of them. Assuming that alterations present in a very limited number of malignant PCs might not appreciably affect gene expression, we chose to arbitrarily establish 20% as the lower variant allele frequency cut-off to perform supervised analyses.
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Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al, 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNAâtarget interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al, 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al, 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al, 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al, 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. âpathsâ, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed. GSE70254 and GSE70319 Samples with the same patient number represent the same sample, profiled using two different Platforms.
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Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al., 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNA-target interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al., 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al., 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al., 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al., 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. paths, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed. GSE73452 and GSE73454 Samples with the same patient number represent the same sample, profiled using two different Platforms.
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Project description:The identification of deregulated miRNA in multiple myeloma (MM) has progressively added a further level of complexity to MM biology. In the present study, we take virtue of in silico integrative genomics analysis to generate an unprecedented global view of the transcriptional regulatory networks modulated in MM to define microRNAs impacting in regulatory circuits with potential functional and clinical relevance. miRNA and gene expression profiles in two large representative MM datasets, available from retrospective and prospective clinical trials and encompassing a total of 249 patients at diagnosis, were analyzed by means of two robust computational procedure to identify (i) relevant miRNA/transcription factors/target gene circuits in the disease and (ii) highly modulated miRNA-gene networks in those pathways enriched with miRNA-target gene interactions in specific MM subgroups. The analysis reinforced the pivotal role the miRNA cluster miR-99b/let-7e/miR-125a, specifically deregulated in MM patients with t(4;14) translocation, and disentangled its major relationships with transcriptional relevance. Integrated pathway analyses performed on the expression data of the MM patients stratified according to t(4;14) further allowed to define the pathway composed by the interactions that mainly characterize this MM subset and unravel connected pathways with putative role in the tumor biology. miRNA and transcripts expression data were analyzed using MAGIA2, to identify mixed circuits (triplets) involving miRNA/gene/transcription factor (TF; http://gencomp.bio.unipd.it/magia2/), as previously described [Bisognin A et al., 2012, Nucl Acid Res]. Specifically, Targetscan was used as target prediction algorithm, and Pearson coefficient was used to measure relationships between microRNA and target mRNA expression profiles. Only the most variable 75% genes according to the coefficient of variation were considered. Lower threshold for absolute correlation coefficients within circuits was set to 0.2; 0.4 was used for miRNA/target binary relationships. Micrographite pipeline allows integrating pathway topologies with predicted and validated miRNA-target interactions, to perform integrated analyses of miRNA and gene expression profiles, for the identification of modulated regulatory circuits involved in the disease in terms of both expression variations and differential strength of inferred interactions [Calura E et al., 2014, Nucl Acid Res]. Micrographite has two steps: i) the extension of pathway annotation using miRNA-target interaction and ii) recursive topological pathway analysis on these networks. We considered network topologies derived from KEGG database by Graphite package [Sales G et al., 2012, BMC Bioinformatics] and miRNA-target gene interactions identified by the above-described MAGIA2 analysis. Specifically, a miRNA was added to a pathway-derived network only if one (or more) of its validated or predicted target genes is a pathway component. Then, a modified recursive version of CliPPER topological pathway analysis [Martini P et al., 2013; Nucl Acid Res] was applied to the composite network, as previously described [Calura E et al., 2014, Nucl Acid Res] in order to identify the most important and non-redundant circuit modulated across groups. Briefly, (i) in the first step, the most significant pathways were selected using P<0.1 as cut-off value for significance; (ii) for each dataset, the upper-scored 10th percentile of the portion of these previously selected pathways (i.e. paths, calculated over a 10,000-permutation step) mostly associated with phenotype were selected; and (iii) for each dataset a meta-pathway was assembled using the paths extracted from previous step and finally re-analyzed. GSE73452 and GSE73454 Samples with the same patient number represent the same sample, profiled using two different Platforms.
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2011-05-25 | E-GEOD-29522 | biostudies-arrayexpress
Project description:A SNP microarray and FISH-based procedure to detect allelic imbalances in multiple myeloma: an integrated genomics approach reveals a wide dosage effect on gene and microRNA expression Multiple myeloma (MM) is characterized by marked genomic instability. Beyond structural rearrangements, a relevant role in its biology is represented by allelic imbalances leading to significant variations in ploidy status. To better elucidate the genomic complexity of MM, we analyzed a panel of 45 patients using combined FISH and microarray approaches. Using a self-developed procedure to infer exact local copy numbers for each sample, we identified a significant fraction of patients showing marked aneuploidy. A conventional clustering analysis showed that aneuploidy, chromosome 1 alterations, hyperdiploidy and recursive deletions at 1p and chromosomes 13, 14 and 22 were the main aberrations driving samples grouping. Then, we integrated mapping information with gene and microRNAs expression profiles: a multiclass analysis of the identified clusters showed a marked gene-dosage effect, particularly concerning 1q transcripts, also confirmed by correlating gene expression levels and local copy number alterations. A wide dosage effect affected also microRNAs, indicating that structural abnormalities in MM closely reflect in their expression imbalances. Finally, we identified several loci in which genes and microRNAs expression correlated with loss-of-heterozygosity occurrence. Our results provide insights into the composite network linking genome structure and gene/microRNA transcriptional features in MM. Keywords: Integrated genomics approach based on SNP microarray and FISH procedures to detect allelic imbalances in multiple myeloma. Pathological bone marrow specimens from 41 MM and four plasma cell leukemia (PCL) patients at diagnosis. 250 nanograms of genomic DNA was processed and, in accordance with the manufacturer's protocols, 40 micrograms of fragmented biotin-labelled DNA were hybridized on GeneChip Human Mapping 50K XbaI Arrays (Affymetrix Inc.). The arrays were scanned using the GeneChip Scanner 3000 7G. The images were acquired using Affymetrix GeneChip® Operating Software (GCOS version 1.4). Copy number values for individual SNPs were extracted and converted from CEL files into signal intensities using GTYPE 4.1 and Affymetrix Copy Number Analysis Tool (CNAT 4.0.1) softwares. Genomic Smoothing analysis was performed by using the smoothing window of 0 Mb, and inferred copy number states were derived from a Hidden Markov Model (HMM) based algorithm implemented in CNAT 4.0.1. Circular Binary Segmentation (Ohlsen et al., 2004) was applied using DNAcopy package for R Bioconductor on raw data. FBN procedure was finally applied to infer exact local copy number as described in the mentioned Reference.
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