Expression proteomics study to determine metallodrug targets and optimal drug combinations.
Ontology highlight
ABSTRACT:
SUBMITTER: Lee RFS
PROVIDER: S-EPMC5431558 | biostudies-literature | 2017 May
REPOSITORIES: biostudies-literature
Publications
Expression proteomics study to determine metallodrug targets and optimal drug combinations.
Lee Ronald F S RFS Chernobrovkin Alexey A Rutishauser Dorothea D Allardyce Claire S CS Hacker David D Johnsson Kai K Zubarev Roman A RA Dyson Paul J PJ
Scientific reports 20170508 1
The emerging technique termed functional identification of target by expression proteomics (FITExP) has been shown to identify the key protein targets of anti-cancer drugs. Here, we use this approach to elucidate the proteins involved in the mechanism of action of two ruthenium(II)-based anti-cancer compounds, RAPTA-T and RAPTA-EA in breast cancer cells, revealing significant differences in the proteins upregulated. RAPTA-T causes upregulation of multiple proteins suggesting a broad mechanism of ...[more]
Similar Datasets
Project description:The FITExP profiling approach was used to identify protein hits from cells incubated with RAPTA-T or RAPTA-EA. Validation experiments with paclitaxel and cisplatin underlined the reliability of the method and hit lists for both test compounds (and?) gave physiologically viable anti-cancer mechanisms. The hit proteins for paclitaxel match with those published and the ones obtained for cisplatin were related to DNA repair, which is in line with the principal mechanism of cisplatin cytotoxicity involving the formation of nuclear DNA lesions. RAPTA-T treatment leads to upregulation of multiple hits suggesting a broad mechanism of action involving both metastasis and tumorigenicity. In contrast, hit proteins identified after incubation with RAPTA-EA are linked to regulation of the oxidative stress response and are therefore thought to be conferred by the EA moiety in the drug. From a therapeutic standpoint, RAPTA-EA could be explored in cancers where EA alone has shown potency, such as chronic lymphocytic leukemias,91 or where EA combined with another agent shows synergy, such as the combination of EA with afatinib, an irreversible epidermal growth factor receptor tyrosine kinase inhibitors for breast cancers.92 On the other hand, due to its broad mechanism of action, RAPTA-T could potentially be more useful if used concomitantly with drugs that target specific cancer pathways and could also play a role in therapies for later stage cancers due to its anti-metastatic properties. Here, application of the FITExP allowed us to gauge the mechanistic pathways involved in the action of two novel ruthenium(II) metallodrugs with very different phenotypic characteristics and from this knowledge infer their role in therapy. Potentially, future application of the FITExP approach in this manner could be useful for identification of cancer chemotherapy drug combinations, where commonly cancer clinical trials fail at phase II where drug combinations are first tried and tested.
2018-02-09 | PXD005766 | Pride
Project description:Targeted therapeutics hold tremendous promise in inhibiting cancer cell proliferation. However, targeting proteins individually can be compensated for by bypass mechanisms and activation of regulatory loops. Designing optimal therapeutic combinations must therefore take into consideration the complex dynamic networks in the cell. In this study, we analyzed the insulin-like growth factor (IGF-1) signaling network in the MDA-MB231 breast cancer cell line. We used reverse-phase protein array to measure the transient changes in the phosphorylation of proteins after IGF-1 stimulation. We developed a computational procedure that integrated mass action modeling with particle swarm optimization to train the model against the experimental data and infer the unknown model parameters. The trained model was used to predict how targeting individual signaling proteins altered the rest of the network and identify drug combinations that minimally increased phosphorylation of other proteins elsewhere in the network. Experimental testing of the modeling predictions showed that optimal drug combinations inhibited cell signaling and proliferation, whereas nonoptimal combination of inhibitors increased phosphorylation of nontargeted proteins and rescued cells from cell death. The integrative approach described here is useful for generating experimental intervention strategies that could optimize drug combinations and discover novel pharmacologic targets for cancer therapy.
| S-EPMC2932856 | biostudies-literature
Project description:Background: National Institute on Aging-Alzheimer's Association (NIA-AA) proposed the AT(N) system based on β-amyloid deposition, pathologic tau, and neurodegeneration, which considered the definition of Alzheimer's disease (AD) as a biological construct. However, the associations between different AT(N) combinations and cognitive progression have been poorly explored systematically. The aim of this study is to compare different AT(N) combinations using recognized biomarkers within the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Methods: A total of 341 participants were classified into cognitively unimpaired (CU; n = 200) and cognitively impaired (CI; n = 141) groups according to the clinical manifestations and neuropsychological tests. Cerebrospinal fluid (CSF) Aβ42 and amyloid-PET ([18F]flutemetamol) were used as biomarkers for A; CSF phosphorylated tau (p-tau) and tau-PET ([18F]flortaucipir) were used as biomarkers for T; CSF total tau (t-tau), hippocampal volume, temporal cortical thickness, [18F]fluorodeoxyglucose (FDG) PET, and plasma neurofilament light (NfL) were used as biomarkers for (N). Binary biomarkers were obtained from the Youden index and publicly available cutoffs. Prevalence of AT(N) categories was compared between different biomarkers within the group using related independent sample non-parametric test. The relationship between AT(N) combinations and 12-year longitudinal cognition was assessed using linear mixed-effects modeling. Results: Among the CU participants, A-T-(N)- was most common. More T+ were detected using p-tau than tau PET (p < 0.05), and more (N)+ were observed using fluid biomarkers (p < 0.001). A+T+(N)+ was more common in the CI group. Tau PET combined with cortical thickness best predicted cognitive changes in the CI group and MRI predicted changes in the CU group. Conclusions: These findings suggest that optimal AT(N) combinations to determine longitudinal cognition differ by cognitive status. Different biomarkers within a specific component for defining AT(N) cannot be used identically. Furthermore, different strategies for discontinuous biomarkers will be an important area for future studies.
| S-EPMC8377373 | biostudies-literature
Project description:Metabolic changes promoting cell survival are involved in metastatic melanoma progression and in the development of drug resistance. In BRAF-inhibitor resistant melanoma cells, we explored the role of FASN, an enzyme involved in lipogenesis overexpressed in metastatic melanoma. Resistant melanoma cells displaying enhanced migratory and pro-invasive abilities increased sensitivity to the BRAF inhibitor PLX4032 upon the molecular targeting of FASN and upon treatment with the FASN inhibitor orlistat. This behavior was associated with a marked apoptosis and caspase 3/7 activation observed for the drug combination. The expression of FASN was found to be inversely associated with drug resistance in BRAF-mutant cell lines, both in a set of six resistant/sensitive matched lines and in the Cancer Cell Line Encyclopedia. A favorable drug interaction in resistant cells was also observed with U18666 A inhibiting DHCR24, which increased upon FASN targeting. The simultaneous combination of the two inhibitors showed a synergistic interaction with PLX4032 in resistant cells. In conclusion, FASN plays a role in BRAF-mutated melanoma progression, thereby creating novel therapeutic opportunities for the treatment of melanoma.
| S-EPMC8126202 | biostudies-literature
Project description:Drug combinations can improve angiostatic cancer treatment efficacy and enable the reduction of side effects and drug resistance. Combining drugs is non-trivial due to the high number of possibilities. We applied a feedback system control (FSC) technique with a population-based stochastic search algorithm to navigate through the large parametric space of nine angiostatic drugs at four concentrations to identify optimal low-dose drug combinations. This implied an iterative approach of in vitro testing of endothelial cell viability and algorithm-based analysis. The optimal synergistic drug combination, containing erlotinib, BEZ-235 and RAPTA-C, was reached in a small number of iterations. Final drug combinations showed enhanced endothelial cell specificity and synergistically inhibited proliferation (p < 0.001), but not migration of endothelial cells, and forced enhanced numbers of endothelial cells to undergo apoptosis (p < 0.01). Successful translation of this drug combination was achieved in two preclinical in vivo tumor models. Tumor growth was inhibited synergistically and significantly (p < 0.05 and p < 0.01, respectively) using reduced drug doses as compared to optimal single-drug concentrations. At the applied conditions, single-drug monotherapies had no or negligible activity in these models. We suggest that FSC can be used for rapid identification of effective, reduced dose, multi-drug combinations for the treatment of cancer and other diseases.
| S-EPMC4473022 | biostudies-literature
Project description:Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets.We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER(+) breast tumor samples, collected before (n?=?58) and after (n?=?60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n?=?18).We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment.To derive the greatest benefit from molecularly targeted drugs it is critical to design combination treatment strategies rationally. Incorporating knowledge of the tumor adaptation process into the design provides an opportunity to match targeted drugs to the evolving tumor phenotype and surmount resistance.
| S-EPMC4062052 | biostudies-literature
Project description:BackgroundIntegrated analyses of plasma proteomic and genetic markers in prospective studies can clarify the causal relevance of proteins and discover novel targets for ischemic heart disease (IHD) and other diseases.ObjectivesThe purpose of this study was to examine associations of proteomics and genetics data with IHD in population studies to discover novel preventive treatments.MethodsWe conducted a nested case-cohort study in the China Kadoorie Biobank (CKB) involving 1,971 incident IHD cases and 2,001 subcohort participants who were genotyped and free of prior cardiovascular disease. We measured 1,463 proteins in the stored baseline samples using the OLINK EXPLORE panel. Cox regression yielded adjusted HRs for IHD associated with individual proteins after accounting for multiple testing. Moreover, cis-protein quantitative loci (pQTLs) identified for proteins in genome-wide association studies of CKB and of UK Biobank were used as instrumental variables in separate 2-sample Mendelian randomization (MR) studies involving global CARDIOGRAM+C4D consortium (210,842 IHD cases and 1,378,170 controls).ResultsOverall 361 proteins were significantly associated at false discovery rate <0.05 with risk of IHD (349 positively, 12 inversely) in CKB, including N-terminal prohormone of brain natriuretic peptide and proprotein convertase subtilisin/kexin type 9. Of these 361 proteins, 212 had cis-pQTLs in CKB, and MR analyses of 198 variants in CARDIOGRAM+C4D identified 13 proteins that showed potentially causal associations with IHD. Independent MR analyses of 307 cis-pQTLs identified in Europeans replicated associations for 4 proteins (FURIN, proteinase-activated receptor-1, Asialoglycoprotein receptor-1, and matrix metalloproteinase-3). Further downstream analyses showed that FURIN, which is highly expressed in endothelial cells, is a potential novel target and matrix metalloproteinase-3 a potential repurposing target for IHD.ConclusionsIntegrated analyses of proteomic and genetic data in Chinese and European adults provided causal support for FURIN and multiple other proteins as potential novel drug targets for treatment of IHD.
| S-EPMC10641761 | biostudies-literature
Project description:Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available in vitro data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non-small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment. SIGNIFICANCE: These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.
| S-EPMC7442591 | biostudies-literature
Project description:Tumors of the head and neck represent a molecularly diverse set of human cancers, but relatively few proteins have actually been shown to drive the disease at the molecular level. To identify new targets for individualized diagnosis or therapeutic intervention, we performed a kinase centric chemical proteomics screen and quantified 146 kinases across 34 head and neck squamous cell carcinoma (HNSCC) cell lines using intensity-based label-free mass spectrometry. Statistical analysis of the profiles revealed significant intercell line differences for 42 kinases (p < 0.05), and loss of function experiments using siRNA in high and low expressing cell lines identified kinases including EGFR, NEK9, LYN, JAK1, WEE1, and EPHA2 involved in cell survival and proliferation. EGFR inhibition by the small molecule inhibitors lapatinib, gefitinib, and erlotinib as well as siRNA led to strong reduction of viability in high but not low expressing lines, confirming EGFR as a drug target in 10-20% of HNSCC cell lines. Similarly, high, but not low EPHA2-expressing cells showed strongly reduced viability concomitant with down-regulation of AKT and ERK signaling following EPHA2 siRNA treatment or EPHA1-Fc ligand exposure, suggesting that EPHA2 is a novel drug target in HNSCC. This notion is underscored by immunohistochemical analyses showing that high EPHA2 expression is detected in a subset of HNSCC tissues and is associated with poor prognosis. Given that the approved pan-SRC family kinase inhibitor dasatinib is also a very potent inhibitor of EPHA2, our findings may lead to new therapeutic options for HNSCC patients. Importantly, the strategy employed in this study is generic and therefore also of more general utility for the identification of novel drug targets and molecular pathway markers in tumors. This may ultimately lead to a more rational approach to individualized cancer diagnosis and therapy.
| S-EPMC3237086 | biostudies-other
Project description:Combinations of anticancer agents may have synergistic anti-tumor effects, but enhanced toxicity often limit their clinical use. The risk that combinations of two or more drugs will cause adverse effects that are more severe than drugs used as monotherpies can be hypothesized from comprehensive analysis of each compound’s activity. We generated microarray gene expression data following a single dose of agents administered individually with that of the agents administered in a combination. The key objective of this initiative is to generate and make publicly available key high-content gene expression data sets for mechanistic hypothesis generation for several anticancer drug combinations. The expectation is that availability of tissue-based genomic information that are derived from target tissues will facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach.
2018-09-11 | GSE119129 | GEO