Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multiple myeloma phosphotyrosine proteomic profile associated with FGFR3 expression, ligand activation, and drug inhibition.

ABSTRACT: Signaling by growth factor receptor tyrosine kinases is manifest through networks of proteins that are substrates and/or bind to the activated receptors. FGF receptor-3 (FGFR3) is a drug target in a subset of human multiple myelomas (MM) and is mutationally activated in some cervical and colon and many bladder cancers and in certain skeletal dysplasias. To define the FGFR3 network in multiple myeloma, mass spectrometry was used to identify and quantify phosphotyrosine (pY) sites modulated by FGFR3 activation and inhibition in myeloma-derived KMS11 cells. Label-free quantification of peptide ion currents indicated the activation of FGFR3 by phosphorylation of tandem tyrosines in the kinase domain activation loop when cellular pY phosphatases were inhibited by pervanadate. Among the 175 proteins that accumulated pY in response to pervanadate was a subset of 52 including FGFR3 that contained a total of 61 pY sites that were sensitive to inhibition by the FGFR3 inhibitor PD173074. The FGFR3 isoform containing the tandem pY motif in its activation loop was targeted by PD173074. Forty of the drug-sensitive pY sites, including two located within the 35-residue cytoplasmic domain of the transmembrane growth factor binding proteoglycan (and multiple myeloma biomarker) Syndecan-1/CD138, were also stimulated in cells treated with the ligand FGF1, providing additional validation of their link to FGFR3. The identification of these overlapping sets of co-modulated tyrosine phosphorylations presents an outline of an FGFR3 network in the MM model and demonstrates the potential for pharmacodynamic monitoring by label-free quantitative phospho-proteomics.

SUBMITTER: St-Germain JR

PROVIDER: S-EPMC2775037 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Drug inhibition profile prediction for NFκB pathway in multiple myeloma.

Project description:Nuclear factor κB (NFκB) activation plays a crucial role in anti-apoptotic responses in response to the apoptotic signaling during tumor necrosis factor (TNFα) stimulation in Multiple Myeloma (MM). Although several drugs have been found effective for the treatment of MM by mainly inhibiting NFκB pathway, there are not any quantitative or qualitative results of comparison assessment on inhibition effect between different drugs either used alone or in combinations. Computational modeling is becoming increasingly indispensable for applied biological research mainly because it can provide strong quantitative predicting power. In this study, a novel computational pathway modeling approach is employed to comparably assess the inhibition effects of specific drugs used alone or in combinations on the NFκB pathway in MM and to predict the potential synergistic drug combinations.

| S-EPMC3051063 | biostudies-literature

Aurora kinase and FGFR3 inhibition results in significant apoptosis in molecular subgroups of multiple myeloma.

Project description:Aberrant expression of proteins involved in cell division is a constant feature in multiple myeloma (MM), especially in high-risk disease. Increasingly, therapy of myeloma is moving towards individualization based on underlying genetic abnormalities. Aurora kinases are important mediators of cell cycle and are up regulated in MM. Functional loss of Aurora kinases results in genetic instability and dysregulated division leading to cellular aneuploidy and growth arrest. We investigated the role of Aurora kinase inhibition in MM, using a small molecule inhibitor A1014907. Low nanomolar A1014907 concentrations induced aneuploidy in MM cell lines independent of underlying cytogenetic abnormalities by inhibiting Aurora Kinases. However, A1014907 induced more pronounced and dose dependent apoptosis in cell lines with t(4;14) translocation. Translocation t(4;14) is observed in about 15% of patients with MM leading to constitutive activation of FGFR3 in two-thirds of these patients. Further investigation of the mechanism of action of A1014907 revealed potent FGFR3 pathway inhibition only in the sensitive cell lines. Thus, our results show that aurora kinase inhibition causes cell cycle arrest and aneuploidy with minimal apoptosis whereas inhibiting both aurora kinase and FGFR3 activity induced potent apoptosis in MM cells. These results support clinical evaluation of A1014907 in MM patients with t(4;14) translocation and/or FGFR3 expression.

| S-EPMC6195373 | biostudies-literature

Ligand-associated ERBB2/3 activation confers acquired resistance to FGFR inhibition in FGFR3-dependent cancer cells.

Project description:Somatic alterations of fibroblast growth factor receptors (FGFRs) have been described in a wide range of malignancies. A number of anti-FGFR therapies are currently under investigation in clinical trials for subjects with FGFR gene amplifications, mutations and translocations. Here, we develop cell line models of acquired resistance to FGFR inhibition by exposure of cell lines harboring FGFR3 gene amplification and translocation to the selective FGFR inhibitor BGJ398 and multitargeted FGFR inhibitor ponatinib. We show that the acquisition of resistance is rapid, reversible and characterized by an epithelial to mesenchymal transition and a switch from dependency on FGFR3 to ERBB family members. Acquired resistance was associated with demonstrable changes in gene expression including increased production of ERBB2/3 ligands, which were sufficient to drive resistance in the setting of FGFR3 dependency but not dependency on other FGFR family members. These data support the concept that activation of ERBB family members is sufficient to bypass dependency on FGFR3 and suggest that concurrent inhibition of these two pathways may be desirable when targeting FGFR3-dependent cancers.

| S-EPMC4261066 | biostudies-literature

Inhibition of thioredoxin 1 leads to apoptosis in drug-resistant multiple myeloma.

Project description:Multiple myeloma (MM) is a hematological malignancy characterized by the aberrant accumulation of clonal plasma cells in the bone marrow. Despite recent advancement in anti-myeloma treatment, MM remains an incurable disease. This study showed higher intrinsic oxidative stress and higher Trx1 and TrxR1 protein levels in MM cells compared to normal cells. Drug-induced Trx1 (PX-12) and TrxR1 (Auranofin) inhibition disrupted redox homeostasis resulting in ROS-induced apoptosis in MM cells and a reduction in clonogenic activity. Knockdown of either Trx1 or TrxR1 reduced MM cell viability. Trx1 inhibition by PX-12 sensitized MM cells to undergo apoptosis in response to the NF-κβ inhibitors, BAY 11-7082 and curcumin. PX-12 treatment decreased the expression of the NF-κβ subunit p65 in MM cells. Bortezomib-resistant MM cells contained higher Trx1 protein levels compared to the parental cells and PX-12 treatment resulted in apoptosis. Thus, increased Trx1 enhances MM cell growth and survival and exerts resistance to NF-κβ inhibitors. Therefore inhibiting the thioredoxin system may be an effective therapeutic strategy to treat newly diagnosed as well as relapsed/refractory MM.

| S-EPMC4558160 | biostudies-literature

Drug resistance-related changes in gene expression profile in multiple myeloma

Project description:1) We identified the genes whose expression was up- and down-regulated by the adhesion to bone marrow stromal cells in human multiple myeloma cell line RPMI8226. 2) We identified the genes whose expression was up- and down-regulated by the PI3K inhibitor PF-04691502 in human multiple myeloma cell line RPMI8226. We isolated mRNA from the multiple myeloma cell line RPMI8226 under drug-resistant conditions, and subjected them to gene expression profiling using an Agilent GeneChip Array.

2015-03-04 | E-GEOD-66466 | biostudies-arrayexpress

Drug resistance-related changes in gene expression profile in multiple myeloma

2015-03-04 | GSE66466 | GEO

NEDD8 Inhibition Overcomes CKS1B-Induced Drug Resistance by Upregulation of p21 in Multiple Myeloma.

Project description:CKS1B is significantly upregulated in multiple myeloma and associated with poor prognosis. The identification of novel therapies is essential for effective treatment of patients resistant to chemotherapy. The NEDD8 inhibitor MLN4924 selectively targets SCF(Skp2) activation and offers a more specific approach to protein degradation inhibition than total proteasomal inhibition. The goal of this study was to evaluate whether MLN4924 is effective in high CKS1B conditions and identify mechanisms regulating drug potency.Bortezomib and MLN4924 sensitivity was assessed through proliferation, viability, clonogenic potential, and senescence induction in cells overexpressing CKS1B. The mechanism for MLN4924 sensitivity was elucidated by immunoblot analysis of SCF(skp) substrates and confirmed by shRNA knockdown. The clinical relevance of the NEDD8 pathway was examined in gene expression profiles (GEP) derived from healthy people, patients with monoclonal gammopathy of undetermined significance (MGUS), and multiple myeloma.Cells overexpressing CKS1B were resistant to bortezomib but sensitive to MLN4924. Treatment of CKS1B-overexpressing cells with MLN4924 decreased proliferation, clonogenicity, and induced senescence. MLN4924, but not bortezomib, induced stabilization of p21 and knockdown of p21 resulted in loss of MLN4924 sensitivity. Patients with MGUS and multiple myeloma exhibited increased expression of NEDD8 pathway genes relative to normal plasma cells. Multiple myeloma patients with high NEDD8 expression were linked to bortezomib resistance in clinical trials, and had inferior outcomes.Our data demonstrate that cells with elevated CKS1B expression are resistant to bortezomib but sensitive to MLN4924 and offer a mechanism through the stabilization of p21. These findings provide rationale for targeting the NEDD8 pathway in multiple myeloma patients exhibiting elevated expression of CKS1B. Clin Cancer Res; 21(24); 5532-42. ©2015 AACR.

| S-EPMC4804624 | biostudies-literature

Single-cell profiling of multiple myeloma reveals molecular response to FGFR3 inhibitor despite clinical progression.

Project description:Genomic characterization of cancer has enabled identification of numerous molecular targets, which has led to significant advances in personalized medicine. However, with few exceptions, precision medicine approaches in the plasma cell malignancy multiple myeloma (MM) have had limited success, likely owing to the subclonal nature of molecular targets in this disease. Targeted therapies against FGFR3 have been under development for the past decade in the hopes of targeting aberrant FGFR3 activity in MM. FGFR3 activation results from the recurrent transforming event of t(4;14) found in ∼15% of MM patients, as well as secondary FGFR3 mutations in this subgroup. To evaluate the effectiveness of targeting FGFR3 in MM, we undertook a phase 2 clinical trial evaluating the small-molecule FGFR1-4 inhibitor, erdafitinib, in relapsed/refractory myeloma patients with or without FGFR3 mutations (NCT02952573). Herein, we report on a single t(4;14) patient enrolled on this study who was identified to have a subclonal FGFR3 stop-loss deletion. Although this individual eventually progressed on study and succumbed to their disease, the intended molecular response was revealed through an extensive molecular characterization of the patient's tumor at baseline and on treatment using single-cell genomics. We identified elimination of the FGFR3-mutant subclone after treatment and expansion of a preexisting clone with loss of Chromosome 17p. Altogether, our study highlights the utility of single-cell genomics in targeted trials as they can reveal molecular mechanisms that underlie sensitivity and resistance. This in turn can guide more personalized and targeted therapeutic approaches, including those that involve FGFR3-targeting therapies.

| S-EPMC10240837 | biostudies-literature

Predicting multi-level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression.

Project description:BACKGROUND:Multiple myeloma (MM), like other cancers, is caused by the accumulation of genetic abnormalities. Heterogeneity exists in the patients' response to treatments, for example, bortezomib. This urges efforts to identify biomarkers from numerous molecular features and build predictive models for identifying patients that can benefit from a certain treatment scheme. However, previous studies treated the multi-level ordinal drug response as a binary response where only responsive and non-responsive groups are considered. METHODS:It is desirable to directly analyze the multi-level drug response, rather than combining the response to two groups. In this study, we present a novel method to identify significantly associated biomarkers and then develop ordinal genomic classifier using the hierarchical ordinal logistic model. The proposed hierarchical ordinal logistic model employs the heavy-tailed Cauchy prior on the coefficients and is fitted by an efficient quasi-Newton algorithm. RESULTS:We apply our hierarchical ordinal regression approach to analyze two publicly available datasets for MM with five-level drug response and numerous gene expression measures. Our results show that our method is able to identify genes associated with the multi-level drug response and to generate powerful predictive models for predicting the multi-level response. CONCLUSIONS:The proposed method allows us to jointly fit numerous correlated predictors and thus build efficient models for predicting the multi-level drug response. The predictive model for the multi-level drug response can be more informative than the previous approaches. Thus, the proposed approach provides a powerful tool for predicting multi-level drug response and has important impact on cancer studies.

| S-EPMC5946496 | biostudies-literature

A Phosphotyrosine Proteomic Screen Identifies Multiple Tyrosine Kinase Signaling Pathways Aberrantly Activated in Malignant Mesothelioma.

Project description:Malignant mesothelioma (MM) is a highly aggressive cancer that is refractory to all current chemotherapeutic regimens. Therefore, uncovering new rational therapeutic targets is imperative in the field. Tyrosine kinase signaling pathways are aberrantly activated in many human cancers and are currently being targeted for chemotherapeutic intervention. Thus, we sought to identify tyrosine kinases hyperactivated in MM. An unbiased phosphotyrosine proteomic screen was employed to identify tyrosine kinases activated in human MM cell lines. From this screen, we have identified novel signaling molecules, such as JAK1, STAT1, cortactin (CTTN), FER, p130Cas (BCAR1), SRC and FYN as tyrosine phosphorylated in human MM cell lines. Additionally, STAT1 and SRC family kinases (SFK) were confirmed to be active in primary MM specimens. We also confirmed that known signal transduction pathways previously implicated in MM, such as EGFR and MET signaling axes, are co-activated in the majority of human MM specimens and cell lines tested. EGFR, MET, and SFK appear to be co-activated in a significant proportion of MM cell lines, and dual inhibition of these kinases was demonstrated to be more efficacious for inhibiting MM cell viability and downstream effector signaling than inhibition of a single tyrosine kinase. Consequently, these data suggest that TKI mono-therapy may not represent an efficacious strategy for the treatment of MM, due to multiple tyrosine kinases potentially signaling redundantly to cellular pathways involved in tumor cell survival and proliferation.

| S-EPMC2909631 | biostudies-literature

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