Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2013;7 Suppl 5(Suppl 5):S5.
doi: 10.1186/1752-0509-7-S5-S5. Epub 2013 Dec 9.

BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database

Chifeng Ma, Hung-I Chen, Mario Flores, Yufei Huang, Yidong Chen

BRCA-Monet: a breast cancer specific drug treatment mode-of-action network for treatment effective prediction using large scale microarray database

Chifeng Ma et al. BMC Syst Biol. 2013.

Abstract

Background: Connectivity map (cMap) is a recent developed dataset and algorithm for uncovering and understanding the treatment effect of small molecules on different cancer cell lines. It is widely used but there are still remaining challenges for accurate predictions.

Method: Here, we propose BRCA-MoNet, a network of drug mode of action (MoA) specific to breast cancer, which is constructed based on the cMap dataset. A drug signature selection algorithm fitting the characteristic of cMap data, a quality control scheme as well as a novel query algorithm based on BRCA-MoNet are developed for more effective prediction of drug effects.

Result: BRCA-MoNet was applied to three independent data sets obtained from the GEO database: Estrodial treated MCF7 cell line, BMS-754807 treated MCF7 cell line, and a breast cancer patient microarray dataset. In the first case, BRCA-MoNet could identify drug MoAs likely to share same and reverse treatment effect. In the second case, the result demonstrated the potential of BRCA-MoNet to reposition drugs and predict treatment effects for drugs not in cMap data. In the third case, a possible procedure of personalized drug selection is showcased.

Conclusions: The results clearly demonstrated that the proposed BRCA-MoNet approach can provide increased prediction power to cMap and thus will be useful for identification of new therapeutic candidates.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Result of cell line investigation, signature selection, and quality control. A) The clustergram of expression samples from (HDAC) families of enzymes. Hierarchical clustering of 175 expression samples treated by drug TRS A. Rows and columns represent genes and samples, respectively. Columns were labeled with cell line (top) or concentrations (down). Clusters can be clearly observed and further examination of samples in the same cluster reveal that they are all from the same cell line. However, no such correspondence presented for the drug concentrations. This suggested that drug effectiveness is cell line dependent. B) Example of Quality Control. The heatmap and pair-wise two-sample scatter-plot of 4 cMap samples from the same drug were shown. They revealed that only two samples showed similarly and the other two did not. In this example, sample s2 and s4 were removed as noise. C) Example of Signature Gene Set Selection. Two-sample scatter-plots of the selected gene signatures for three drugs were plotted. The red cross dots represented the selected genes and the black dots represented the rest of gene. Tables contain the symbols and expression up- or down- regulation for the selected genes of the three drugs.
Figure 2
Figure 2
BRCA-MoNet. Each node represents a drug. A group of nodes linked by edges of the same color represent a MoA. The black edge linked two MoAs that show correlated effects.
Figure 3
Figure 3
Prediction result of breast cancer patient. A) Top MoAs for reverse effect prediction for UNC lumA patients. Color of the heat map indicates the predicted rank of the MoA in an increasing order from red to yellow. B) Drugs of BRC-MoA24.
Figure 4
Figure 4
The workflow of proposed BRCA-MoNet. The arrow shows the work flow of the project. The whole project can be divided into three parts: 1. Data extraction and preprocessing; 2. Quality Control and signature selection; 3. BRCA-MoNet construction and prediction for new query.
Figure 5
Figure 5
Pseudo code of the proposed gene set selection scheme.
Figure 6
Figure 6
Pseudo code of the proposed quality control scheme.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

See all "Cited by" articles

References

    1. Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med. 2009;7(8):790–800. doi: 10.1056/NEJMra0801289. - DOI - PubMed
    1. Riedel RF. et al.A genomic approach to identify molecular pathways associated with chemotherapy resistance. Mol Cancer Ther. 2008;7(10):3141–9. doi: 10.1158/1535-7163.MCT-08-0642. - DOI - PubMed
    1. Schlueter PJ, Peterson RT. Systematizing serendipity for cardiovascular drug discovery. Circulation. 2009;7(3):255–63. doi: 10.1161/CIRCULATIONAHA.108.824177. - DOI - PMC - PubMed
    1. Ebi H. et al.Relationship of deregulated signaling converging onto mTOR with prognosis and classification of lung adenocarcinoma shown by two independent in silico analyses. Cancer Res. 2009;7(9):4027–35. doi: 10.1158/0008-5472.CAN-08-3403. - DOI - PubMed
    1. Hait WN, Hambley TW. Targeted cancer therapeutics. Cancer Res. 2009;7(4):1263–7. doi: 10.1158/0008-5472.CAN-08-3836. discussion 1267. - DOI - PubMed

Publication types