Perturbations of cancer cell metabolism by the antidiabetic drug canagliflozin.
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ABSTRACT:
SUBMITTER: Papadopoli D
PROVIDER: S-EPMC8027095 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We present a novel method for deriving network models from molecular profiles of perturbed cellular systems. The network models aim to predict quantitative outcomes of combinatorial perturbations, such as drug pair treatments or multiple genetic alterations. Mathematically, we represent the system by a set of nodes, representing molecular concentrations or cellular processes, a perturbation vector and an interaction matrix. After perturbation, the system evolves in time according to differential equations with built-in nonlinearity, similar to Hopfield networks, capable of representing epistasis and saturation effects. For a particular set of experiments, we derive the interaction matrix by minimizing a composite error function, aiming at accuracy of prediction and simplicity of network structure. To evaluate the predictive potential of the method, we performed 21 drug pair treatment experiments in a human breast cancer cell line (MCF7) with observation of phospho-proteins and cell cycle markers. The best derived network model rediscovered known interactions and contained interesting predictions. Possible applications include the discovery of regulatory interactions, the design of targeted combination therapies and the engineering of molecular biological networks.
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Project description:OBJECTIVE:To evaluate the cardiovascular safety of canagliflozin, a sodium-glucose cotransporter 2 inhibitor for the treatment of type 2 diabetes mellitus, in direct comparisons with DPP-4 inhibitors (DPP-4i), GLP-1 receptor agonists (GLP-1RA), or sulfonylureas, as used in routine practice. DESIGN:Population based retrospective cohort study. SETTING:Nationwide sample of patients with type 2 diabetes from a large de-identified US commercial healthcare database (Optum Clinformatics Datamart). PARTICIPANTS:Three pairwise 1:1 propensity score matched cohorts of patients with type 2 diabetes 18 years and older who initiated canagliflozin or a comparator non-gliflozin antidiabetic agent (ie, a DPP-4i, a GLP-1RA, or a sulfonylurea) between April 2013 and September 2015. MAIN OUTCOME MEASURES:The primary outcomes were heart failure admission to hospital and a composite cardiovascular endpoint (comprised of being admitted to hospital for acute myocardial infarction, ischemic stroke, or hemorrhagic stroke). Hazard ratios and 95% confidence intervals were estimated in each propensity score matched cohort controlling for more than 100 baseline characteristics. RESULTS:During a 30 month period, the hazard ratio for heart failure admission to hospital associated with canagliflozin was 0.70 (95% confidence interval 0.54 to 0.92) versus a DPP-4i (n=17 667 pairs), 0.61 (0.47 to 0.78) versus a GLP-1RA (20 539), and 0.51 (0.38 to 0.67) versus a sulfonylurea (17 354 ). The hazard ratio for the composite cardiovascular endpoint associated with canagliflozin was 0.89 (0.68 to 1.17) versus a DPP-4i, 1.03 (0.79 to 1.35) versus a GLP-1RA, and 0.86 (0.65 to 1.13) versus a sulfonylurea. Results were similar in sensitivity analyses further adjusting for baseline hemoglobin A1c levels and in subgroups of patients with and without prior cardiovascular disease or heart failure. CONCLUSIONS:In this large cohort study, canagliflozin was associated with a lower risk of heart failure admission to hospital and with a similar risk of myocardial infarction or stroke in direct comparisons with three different classes of non-gliflozin diabetes treatment alternatives as used in routine care.
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Project description:Chemoresistance remains the major barrier to effective ovarian cancer treatment. The molecular features and associated biological functions of this phenotype remain poorly understood. We developed carboplatin-resistant cell line models using OVCAR5 and CaOV3 cell lines with the aim of identifying chemoresistance-specific molecular features. Chemotaxis and CAM invasion assays revealed enhanced migratory and invasive potential in OVCAR5-resistant, compared to parental cell lines. Mass spectrometry analysis was used to analyse the metabolome and proteome of these cell lines, and was able to separate these populations based on their molecular features. It revealed signalling and metabolic perturbations in the chemoresistant cell lines. A comparison with the proteome of patient-derived primary ovarian cancer cells grown in culture showed a shared dysregulation of cytokine and type 1 interferon signalling, potentially revealing a common molecular feature of chemoresistance. A comprehensive analysis of a larger patient cohort, including advanced in vitro and in vivo models, promises to assist with better understanding the molecular mechanisms of chemoresistance and the associated enhancement of migration and invasion.
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Project description:To better understand the disruptions of transcriptional regulations and gene expression in lung cancers, we constructed a multi-omics catalogue of the responses of lung cancer cells to a series of chemical compounds. We generated and analyzed 3,240 RNA-seq and 3,393 ATAC-seq libraries obtained from 23 cell lines treated with 95 well-annotated compounds. To demonstrate the power of the created multi-omics resource, we attempted to identify drugs that could induce the designated changes alone or in combination. The basal multi-omics information was first integrated into co-expression modules. Among these modules, we identified a stress response module that may be a promising drug intervention target, as new combinations of compounds that could be used to regulate this module and the consequent phenotypic appearance of cancer cells have been identified. We believe that the multi-omics profiles generated in this study and the strategy used to stratify them will lead to more rational and efficient development of anticancer drugs.
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Project description:BackgroundCancer cachexia is a metabolic disorder involving perturbed energy balance and altered mitochondrial function. Chemotherapy is a primary treatment option for many types of cancer, but there is substantial evidence that some chemotherapeutic agents can also lead to the development and progression of cachexia. In this study, we apply a comprehensive and systems level metabolomics approach to characterize the metabolic perturbations in murine models of cancer-induced and chemotherapy-induced cachexia. Knowledge of the unique pathways through which cancer and chemotherapy drive cachexia is necessary in order to develop effective treatments.MethodsThe murine Colon26 (C26) adenocarcinoma xenograft model was used to study the metabolic derangements associated with cancer-induced cachexia. In vivo administration of Folfiri (5-fluorouracil, irinotecan, and leucovorin) was used to model chemotherapy-induced cachexia. Comprehensive metabolic profiling was carried out using both nuclear magnetic resonance-based and mass spectrometry-based platforms. Analyses included plasma, muscle, and liver tissue to provide a systems level profiling.ResultsThe study involved four groups of CD2F1 male mice (n = 4-5), including vehicle treated (V), C26 tumour hosts (CC), Folfiri treated (F), and C26 tumour hosts treated with Folfiri (CCF). Significant weight loss including skeletal muscle was observed for each of the experimental groups with the tumour hosts showing the most dramatic change (-3.74 g vs. initial body weight in the CC group). Skeletal muscle loss was evident in all experimental groups compared with V, with the CCF combination resulting in the most severe depletion of quadriceps mass (-38% vs. V; P < 0.001). All experimental groups were characterized by an increased systemic glucose demand as evidenced by decreased levels of circulating glucose (-47% in CC vs. V; P < 0.001) and depletion of liver glucose (-51% in CC vs. V; P < 0.001) and glycogen (-74% in CC vs. V; P < 0.001). The cancer-induced and chemotherapy-induced cachexia models displayed unique alterations in flux through the tricarboxylic acid cycle and β-oxidation pathways. Cancer-induced cachexia was uniquely characterized by a dramatic elevation in low-density lipoprotein particles (+6.9-fold vs. V; P < 0.001) and a significant increase in the inflammatory marker, GlycA (+33% vs. V; P < 0.001).ConclusionsThe results of this study demonstrated for the first time that cancer-induced and chemotherapy-induced cachexia is characterized by a number of distinct metabolic derangements. Effective therapeutic interventions for cancer-induced and chemotherapy-induced cachexia must take into account the specific metabolic defects imposed by the pathological or pharmacological drivers of cachexia.
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Project description:Clinical responses to anti-cancer therapies often only benefit a defined subset of patients. Predicting the best treatment strategy hinges on our ability to effectively translate genomic data into actionable information on drug responses.To achieve this goal, we compiled a comprehensive collection of baseline cancer genome data and drug response information derived from a large panel of cancer cell lines. This data set was applied to identify the signature genes relevant to drug sensitivity and their resistance by integrating CNVs and the gene expression of cell lines with in vitro drug responses. We presented an efficient in-silico pipeline for integrating heterogeneous cell line data sources with the simultaneous modeling of drug response values across all the drugs and cell lines. Potential signature genes correlated with drug response (sensitive or resistant) in different cancer types were identified. Using signature genes, our collaborative filtering-based drug response prediction model outperformed the 44 algorithms submitted to the DREAM competition on breast cancer cells. The functions of the identified drug response related signature genes were carefully analyzed at the pathway level and the synthetic lethality level. Furthermore, we validated these signature genes by applying them to the classification of the different subtypes of the TCGA tumor samples, and further uncovered their in vivo implications using clinical patient data.Our work may have promise in translating genomic data into customized marker genes relevant to the response of specific drugs for a specific cancer type of individual patients.
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Project description:This SuperSeries is composed of the SubSeries listed below.
2015-07-02 | GSE64460 | GEO
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