Moreover, BRCA MoNet assesses the therapeu tics influence based on MoA instead of those for individu ally drugs. This network model not only leads to improved prediction results but it also uncovered the underlying selleck products MoA structure of the cMap data that has not been fully discovered before. The case studies we analyzed here returned favorable results and insightful leads. For the E2 treated MCF7 cell line case, the detection power and Inhibitors,Modulators,Libraries insight of the BRCA MoNet E2 related MoA were exploited. The BMS 754807 case showed that BRCA MoNet is capable of assigning new anti cancer drug to the existing anti cancer MoA and yielding insight understanding of drug MoA detection. The UNC breast cancer patients case demonstrated the potential of BRCA MoNet to be used as a tool for perso nalized treatment recommendation based on patients gene Inhibitors,Modulators,Libraries expression.

The BRCA MoNet approach provides added values to the connectivity map project and allowed for new and bet ter capability in identification of possible therapeutic can didates. Future direction will likely lend itself to two paths to expand the MoNet concept to other Inhibitors,Modulators,Libraries cancer and cell lines by incorporating multiple drug treatment dataset, and to mature BRCA MoNets capability of prediction for the real patients. We expect that the rapid development in cancer profiling projects including The Cancer Genome Atlas will greatly benefit our effort in these future directions Method BRCA MoNet workflow The proposed scheme of generating a breast cancer spe cific MoA network or BRCA MoNet from cMap data is summarized in Figure 4.

In the first step, new data pre processing, drug signature selection and clustering algo rithms were developed and applied to identify MoAs. In the second step, the relationship between the MoAs in terms Inhibitors,Modulators,Libraries of their effectiveness was assessed. Based on the MoAs, the BRCA MoNet was constructed to depict the relationship of compound effectiveness. BRCA MoNet and the drug signatures were used for subsequent prediction. Two types of prediction can be carried out with BRCA MoNet including similar prediction and reverse prediction. For the purpose of find the drug effectiveness on a tumor sample, the expression profile of an individual tumor sam ple is used as a query, where reverse prediction is adopted and the query will be inverse correlated against the MoAs to predict treatment effects.

The prediction result includes a list of MoAs ranked in an increasing order of their nega Signature gene set selection and distance assessment The goal of signature gene set selection is to select the genes that are expressed differentially. Since most of the drugs in cMap contains only two samples, the conven tional Inhibitors,Modulators,Libraries differentially analysis algorithms such as t test can not be applied. We proposed the following test statistic to measure if a gene, say i, is consistently differentially expressed in a pair of samples kinase inhibitor Imatinib Mesylate tive correlation to the tumor profile.