This query data were also applied to the original cMap prediction, where the most up and down regulated 200 genes were used as the query signature genes. As expected, the cMap project gave a mix results in both predictions of similar effect drugs and reverse effect drugs. selleck products E2 itself only ranked 828 in the total 1309 compounds. In cMap, the rank was a summary of a drugs prediction results in every sample of all different cell lines. E2 has a lot of samples in the cMap data across all 5 cell line and the enrichment scores of these samples have large varia tions, ranging from 0. 707 to 0. 040 , and this large variation led an insignificant prediction rank. In the reverse effect prediction, Raloxi fene, anti estrogenic modulator, was found to be at rank 9 as expected, but fulvestrant, another anti estro genic modulator, only ranked 861.

A closer look at the detailed cell line results revealed that fulvestrant had a negative enrichment score in the MCF7 cell line but a positive enrichment score in the HL60 cell line and the combined result led to a low rank. Over all, the comparison between prediction results of cMap and BRCA MoNet shows that BRCA MoNet adds consider able prediction power to the existent cMap data and greatly improves the prediction accuracy on both similar and reverse prediction. BRCA MoNet Application Case 2 Prediction of BMS 754807 Treated MCF7 Cell Line One additional dataset treated with drug BMS 754807 was tested against our BRCA MoNet. This dataset came from breast xenograft MCF7 bearing mice treated with BMS 754807.

MBS 784807 is a dual IGF 1R/InsR inhibitor that can synergize hormonal agents and has been shown to be a potential breast cancer drug. Study showed that there is an elevated IGF IR activity specific in triple negative breast cancer and because of that, BMS 784807 could be a possible treatment for triple negative breast cancer. It has been investigated in several Phase I and Phase II Clinical Trials as an anti cancer drug. This dataset was tested against our BRCA MoNet for similar treatment effect predictions. The top ranked MoA was MoA 37. Interestingly, this MoA contains valproic acid, which is ranked number 1 among all the 504 BRCA MoNet drugs. Valproic acid belongs to a general class of drugs called anticonvulsants and was originally used as a non opioid pain reliever. It has also been used to prevent migraine headaches.

Recently, valproic acid has been shown to have great potential as an epigenetic drug for anti cancer activity through inhibiting Cilengitide cancer cell prolif eration in various types of cancer. This prediction result shows that selleck chemicals both drugs with great anti cancer poten tial are actually detected to have similar MoA by BRCA MoNet. This conclusion strongly supports the fact that BRCA MoNet can uncover new drugs anti cancer MoA by assigning it to a known MoA.