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Synergy from Gene Expression and Network Mining (SynGeNet) method predicts genotype-specific synergistic drug combinations in melanoma

ABSTRACT: Synergy from Gene Expression and Network Mining (SynGeNet) method predicts genotype-specific synergistic drug combinations in melanoma

PROVIDER: PRJNA431809 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR6515920_1.fastq.gz	Fastqsanger.gz
	SRR6515920_2.fastq.gz	Fastqsanger.gz
	SRR6515921_1.fastq.gz	Fastqsanger.gz
	SRR6515921_2.fastq.gz	Fastqsanger.gz
	SRR6515912_1.fastq.gz	Fastqsanger.gz

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Synergy from Gene Expression and Network Mining (SynGeNet) method predicts genotype-specific synergistic drug combinations in melanoma

Project description:Using our computational method SynGeNet to evaluate genomic and transcriptomic data characterizing four major genomic subtypes of melanoma, we selected the top ranked drug combination for BRAF-mutation melanoma for subsequent validaiton. Here we present drug-induced gene expression data from the BRAF-mutant A375 melanoma cell line in response to four treatment conditions: vehicle control (DMSO), vemurafenib alone, tretinoin (ATRA) alone and vemurafenib+tretinoin combination.

2018-07-01 | GSE109731 | GEO

Escherichia coli treated with 13 antibiotics, 3 synergistic combinations, and IPTG

2008-02-12 | GSE10440 | GEO

Escherichia coli treated with 13 antibiotics, 3 synergistic combinations, and IPTG

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

A network-based trans-omics approach for predicting synergistic drug combinations

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.

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Synergy from gene expression and network mining (SynGeNet) method predicts synergistic drug combinations for diverse melanoma genomic subtypes.

Project description:Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor)?+?tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.

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Transcriptional response to standard AML drugs identifies synergistic combinations

Project description:Transcriptional response to standard AML drugs identifies synergistic combinations

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A network-based trans-omics approach for predicting synergistic drug combinations

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Transcriptional response to standard AML drugs identifies synergistic combinations

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.

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Transcriptomic profiling of 293T and A549 COVGT#5 reporter cell lines treated with COVGT#5 inducers and synergistic drug combinations.

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.

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