Format

Send to

Choose Destination
BMC Genomics. 2017 Jan 25;18(Suppl 1):934. doi: 10.1186/s12864-016-3260-7.

DeSigN: connecting gene expression with therapeutics for drug repurposing and development.

Author information

1
Department of Oral & Maxillofacial Clinical Sciences, Faculty of Dentistry, University of Malaya, 50603, Kuala Lumpur, Malaysia.
2
Oral Cancer Research Group, Cancer Research Malaysia, No. 1, Jalan SS12/1A, 47500, Subang Jaya, Selangor, Malaysia.
3
Data Intensive Computing Centre, Research Management & Innovation Complex, University of Malaya, 50603, Kuala Lumpur, Malaysia.
4
Department of Computer System & Technology, Faculty of Computer Science & Information Technology, University of Malaya, 50603, Kuala Lumpur, Malaysia.
5
Centre for Data Science, University of Malaya, 50603, Kuala Lumpur, Malaysia.
6
Division of Medical Oncology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
7
Institute of Mathematical Sciences, University of Malaya, 50603, Kuala Lumpur, Malaysia.
8
Department of Oral & Maxillofacial Clinical Sciences, Faculty of Dentistry, University of Malaya, 50603, Kuala Lumpur, Malaysia. sokching.cheong@cancerresearch.my.
9
Oral Cancer Research Group, Cancer Research Malaysia, No. 1, Jalan SS12/1A, 47500, Subang Jaya, Selangor, Malaysia. sokching.cheong@cancerresearch.my.

Abstract

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.

KEYWORDS:

Cancer; Cell line; DeSigN; Drug repurposing; Gene expression

PMID:
28198666
PMCID:
PMC5310278
DOI:
10.1186/s12864-016-3260-7
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center