A network-based trans-omics approach for predicting synergistic drug combinations
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PROVIDER: PRJNA1068432 | ENA |
REPOSITORIES: ENA
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Project description:We examined the mode-of-action of synergistic drug combinations by microarray analysis. We focused on the drug combination of capsaicin and mitoxantrone, which were the top predicted drug pair identified by SyndrumNET. Combination therapy can offer greater efficacy on medical treatments. However, the discovery of synergistic drug combinations is challenging. We propose a novel computational method, SyndrumNET, to predict synergistic drug combinations by network propagation with trans-omics analyses. The prediction is based on the topological relationship, network-based proximity, and transcriptional correlation between diseases and drugs. SyndrumNET was applied to analyzing six diseases including asthma, diabetes, hypertension, colorectal cancer, acute myeloid leukemia (AML), and chronic myeloid leukemia (CML), and it outperformed the previous methods in terms of high accuracy. We performed in vitro cell survival assays to validate our prediction for CML. Of the top 17 predicted drug pairs, 14 drug pairs successfully exhibited synergistic anticancer effects. Our mode-of-action analysis also revealed that the drug synergy of the top predicted combination of capsaicin and mitoxantrone was due to the complementary regulation of 12 pathways, including the Rap1 signaling pathway. The proposed method is expected to be useful for various complex diseases.
2024-05-06 | GSE254052 | GEO
Project description:Transcriptional response to standard AML drugs identifies synergistic combinations
| PRJNA944361 | ENA
Project description:Microarrays allow us to monitor the change in transcription of every gene in the genome in response to a change in cellular state. We use cDNA microarrays to measure the response of E. coli to 13 different antibiotics and 3 synergistic combinations. Hierarchichal clustering reveals 4 distinct classes of antibiotics distinguished by their modes of action, and allows us to predict the mechanism for promethazine, a drug whose mode of action has not previously been established. The expression profiles of the synergistic combinations exhibit a complex relationship between the two component antibiotics, with similarity to one of the two drugs, as well as a surprising number of new gene responses exhibited by E. coli in response to neither drug alone. The subset of drugs which act in synergy with each other suggests that only very specific combination of mechanisms give rise to synergistic behavior. Keywords: stress response, antibiotic response, synergy
2008-02-12 | GSE10440 | GEO
Project description:Microarrays allow us to monitor the change in transcription of every gene in the genome in response to a change in cellular state. We use cDNA microarrays to measure the response of E. coli to 13 different antibiotics and 3 synergistic combinations. Hierarchichal clustering reveals 4 distinct classes of antibiotics distinguished by their modes of action, and allows us to predict the mechanism for promethazine, a drug whose mode of action has not previously been established. The expression profiles of the synergistic combinations exhibit a complex relationship between the two component antibiotics, with similarity to one of the two drugs, as well as a surprising number of new gene responses exhibited by E. coli in response to neither drug alone. The subset of drugs which act in synergy with each other suggests that only very specific combination of mechanisms give rise to synergistic behavior. Keywords: stress response, antibiotic response, synergy Each array has two spots to monitor the response of E coli to one of the treatments, and two control spots (ie no treatment). These are background corrected, normalized for total intensity, and then the average volume difference is calculated. Each treatment has two replicates, and the result is average across these two replicates. Our final processed data is a relative volume difference, in which the aforementioned volume difference is divided by an estimate of the error in the data. This relative volume difference gives us greater confidence that the changes we see are real. Any relative volume difference >= 2 or <= -2 (ie where the absolute volume difference is twice as much as the estimated error or more) is considered to be significant. Total of 68 hybridizations: 17 samples X 2 replicates for each sample X (1 sample + 1 control for each replicate)
2008-02-12 | E-GEOD-10440 | biostudies-arrayexpress
Project description:Unlike genomic alterations, gene expression profiles have not been widely used to refine cancer therapies. We analyzed transcriptional changes in acute myeloid leukemia (AML) cell lines in response to standard first-line AML drugs cytarabine and daunorubicin by means of RNA sequencing. Those changes were highly cell- and treatment-specific. By comparing the changes unique to treatment-sensitive and treatment-resistant AML cells, we enriched for treatment-relevant genes. Those genes were associated with drug response-specific pathways, including calcium ion-dependent exocytosis and chromatin remodeling. Pharmacological mimicking of those changes using EGFR and MEK inhibitors enhanced the response to daunorubicin with minimum standalone cytotoxicity. The synergistic response was observed even in the cell lines beyond those used for the discovery, including a primary AML sample. Additionally, publicly available cytotoxicity data confirmed the synergistic effect of EGFR inhibitors in combination with daunorubicin in all 60 investigated cancer cell lines. In conclusion, we demonstrate the utility of treatment-evoked gene expression changes to formulate rational drug combinations. This approach could improve the standard AML therapy, especially in older patients.
2023-06-12 | GSE227269 | GEO
Project description:293T and A549 COVGT#5 reporter cell lines treated with COVGT#5 inducers IFN mix or 3p-hpRNA and synergistic drug combinations to identify co-regulated genes upon COVGT#5 modulation.
2023-04-17 | GSE226667 | GEO
Project description:MotivationDrug combination therapy shows significant advantages over monotherapy in cancer treatment. Since the combinational space is difficult to be traversed experimentally, identifying novel synergistic drug combinations based on computational methods has become a powerful tool for pre-screening. Among them, methods based on deep learning have far outperformed other methods. However, most deep learning-based methods are unstable and will give inconsistent predictions even by simply changing the input order of drugs. In addition, the insufficient experimental data of drug combination screening limits the generalization ability of existing models. These problems prevent the deep learning-based models from being in service.ResultsIn this article, we propose CGMS to address the above problems. CGMS models a drug combination and a cell line as a heterogeneous complete graph, and generates the whole-graph embedding to characterize their interaction by leveraging the heterogeneous graph attention network. Based on the whole-graph embedding, CGMS can make a stable, order-independent prediction. To enhance the generalization ability of CGMS, we apply the multi-task learning technique to train the model on drug synergy prediction task and drug sensitivity prediction task simultaneously. We compare CGMS's generalization ability with six state-of-the-art methods on a public dataset, and CGMS significantly outperforms other methods in the leave-drug combination-out scenario, as well as in the leave-cell line-out and leave-drug-out scenarios. We further present the benefit of eliminating the order dependency and the discrimination power of whole-graph embeddings, interpret the rationality of the attention mechanism, and verify the contribution of multi-task learning.Availability and implementationThe code of CGMS is available via https://github.com/TOJSSE-iData/CGMS.
| S-EPMC10256571 | biostudies-literature
Project description:A Molecular Roadmap for Induced Multi-lineage Trans-differentiation of Fibroblasts by Chemical Combinations
| PRJNA356865 | ENA
Project description:A promising alternative to address the problem of acquired drug resistance is to rely on combination therapies. Identification of the right combinations is often accomplished through trial and error, a labor and resource intensive process whose scale quickly escalates as more drugs can be combined. To address this problem, we present a broad computational approach for predicting synergistic combinations using easily obtainable single drug efficacy, no detailed mechanistic understanding of drug function, and limited drug combination testing. When applied to mutant BRAF melanoma, we found that our approach exhibited significant predictive power. Additionally, we validated previously untested synergy predictions involving anticancer molecules. As additional large combinatorial screens become available, this methodology could prove to be impactful for identification of drug synergy in context of other types of cancers.
| S-EPMC5234777 | biostudies-literature
Project description:Chemotherapeutic treatment regimens often take advantage of synergistic effects of drug combinations. Anticipating that synergistic effects on cell biological level likely manifest on proteome level, the analysis of proteome modulations represents an appropriate strategy to study drug combinations on molecular level. More specifically, the detection of single proteins exhibiting synergistic abundance changes could be helpful to shed light on key molecules, which contribute in mechanisms facilitating the synergistic interaction and therefore represent potential target for specific therapeutic approaches. In the reported study, we investigated the drug combination of cisplatin and the neddylation inhibitor MLN4924 in HCT-116 cells via cell biological analyses and mass spectrometry-based quantitative proteomics. From 1,789 proteins quantified with two unique peptides, activated RNA polymerase II transcriptional coactivator p15 (SUB1) was highlighted as most synergistically regulated protein using a synergistic scoring approach. Western blotting and analyses of cellular processes associated with this protein (DNA damage, oxidative stress and apoptosis) revealed supporting evidence for the synergistic regulation. Whereas the distinct role of SUB1 in the investigated drug combination needs to be elucidated in future studies, the presented results demonstrated the benefit and feasibility of synergistic scoring of proteome alterations to highlight proteins that likely contribute to the underlying molecular mechanisms of synergistic effects.
2018-10-02 | PXD009185 | Pride