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greenelab / deep-review

A collaboratively written review paper on deep learning, genomics, and precision medicine
https://greenelab.github.io/deep-review/
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DeSigN: connecting gene expression with therapeutics for drug repurposing and development #973

Open renesugar opened 5 years ago

renesugar commented 5 years ago

Background The drug discovery and development pipeline is a long and arduous process that inevitably hampers rapid drug development. Therefore, strategies to improve the efficiency of drug development are urgently needed to enable effective drugs to enter the clinic. Precision medicine has demonstrated that genetic features of cancer cells can be used for predicting drug response, and emerging evidence suggest that gene-drug connections could be predicted more accurately by exploring the cumulative effects of many genes simultaneously.

Results We developed DeSigN, a web-based tool for predicting drug efficacy against cancer cell lines using gene expression patterns. The algorithm correlates phenotype-specific gene signatures derived from differentially expressed genes with pre-defined gene expression profiles associated with drug response data (IC50) from 140 drugs. DeSigN successfully predicted the right drug sensitivity outcome in four published GEO studies. Additionally, it predicted bosutinib, a Src/Abl kinase inhibitor, as a sensitive inhibitor for oral squamous cell carcinoma (OSCC) cell lines. In vitro validation of bosutinib in OSCC cell lines demonstrated that indeed, these cell lines were sensitive to bosutinib with IC50 of 0.8–1.2 μM. As further confirmation, we demonstrated experimentally that bosutinib has anti-proliferative activity in OSCC cell lines, demonstrating that DeSigN was able to robustly predict drug that could be beneficial for tumour control.

Conclusions DeSigN is a robust method that is useful for the identification of candidate drugs using an input gene signature obtained from gene expression analysis. This user-friendly platform could be used to identify drugs with unanticipated efficacy against cancer cell lines of interest, and therefore could be used for the repurposing of drugs, thus improving the efficiency of drug development.

https://doi.org/10.1186/s12864-016-3260-7

Summary:

As gene expression changes during cancer treatment, DeSigN could be used to update treatment as cancer cells react to changes caused by the current treatment.

"DeSigN (Differentially Expressed Gene Signatures - Inhibitors), a CMap-inspired bioinformatics pipeline that enables gene expression patterns from experimental data to be linked to gene expression patterns associated with drug response in a cancer cell line database."

DeSigN uses 707 human cancer cell lines in GDSC that have well-characterized gene expression and drug response data.

References:

1) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5310278

2) https://www.ncbi.nlm.nih.gov/pubmed/17008526

3) https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5439177/

evancofer commented 5 years ago

@renesugar Can you include the entire abstract and a doi-based link to the paper? I think this issue is a good example of how to format it.