Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1166671 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR30829982.fastq.gz | Fastqsanger.gz | |||
| SRR30829983.fastq.gz | Fastqsanger.gz | |||
| SRR30829984.fastq.gz | Fastqsanger.gz | |||
| SRR30829985.fastq.gz | Fastqsanger.gz | |||
| SRR30829986.fastq.gz | Fastqsanger.gz |
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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.
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Project description:Our ability to predict the effects of drug combinations is limited, in part by limited understanding of how the transcriptional response of two monotherapies results in that of their combination. We performed the first analysis of matched time course RNAseq profiling of cells treated with both single drugs and their combinations. The transcriptional signature of the synergistic combination we studied had unique gene expression not seen in either constituent monotherapy. This can be explained mechanistically by the sequential activation of transcription factors in time in the gene regulatory network. The nature of this transcriptional cascade suggests that drug synergy may ensue when the transcriptional responses elicited by two unrelated individual drugs are correlated. We used these results as the basis of a simple prediction algorithm attaining an AUROC of 0.77 in the prediction of synergistic drug combinations in an independent dataset.
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Project description:We have used an agnostic approach to identify drug combinations by using combination high throughput screening (cHTS) technology and make the surprising discovery that adenosine A2A and beta-2 adrenergic receptor agonists are highly synergistic, selective and novel agents that enhance glucocorticoid activity in B-cell malignancies. We used the microarray study to understand the synergistic mechanism between dexamethasone and A2A receptor agonist or Beta-2 Adrenergic receptor agonist in a multiple myeloma cell line, MM1S. MM1S cells were treated with CGS-21680 or Salmeterol alone, or in combination with dexamethasone for six hours for RNA extraction and hybridization on Affymetrix microarrays.
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Project description:Transcriptional response to standard AML drugs identifies synergistic combinations
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Project description:We have used an agnostic approach to identify drug combinations by using combination high throughput screening (cHTS) technology and make the surprising discovery that adenosine A2A and beta-2 adrenergic receptor agonists are highly synergistic, selective and novel agents that enhance glucocorticoid activity in B-cell malignancies. We used the microarray study to understand the synergistic mechanism between dexamethasone and A2A receptor agonist or Beta-2 Adrenergic receptor agonist in a multiple myeloma cell line, MM1S.
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