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

Chifeng MaHung-I Harry ChenMario FloresYufei Huang & Yidong Chen

Abstract

Background

Connectivity map (cMap) is a recently 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 the 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 for 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 the identification of new therapeutic candidates.

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