39 between the predicted IC50s and experimental IC50s. Supervised machine learning approaches using genomic signatures achieved a specificity and sensitivity of higher than 70% for prediction of drug response in. Tumor sensitivity Tubacin FDA prediction has also been considered as a drug induced Inhibitors,Modulators,Libraries topology alteration using phospho proteomic signals and prior biological knowledge of a generic pathway and a molecular tumor profile based prediction. Most interestingly, in the recent cancer cell line ency clopedia study, the authors characterize a large set of cell lines with numerous associated data measurement sets gene and protein expression pro files, mutation profiles, methylation data along with the response of around 500 of Inhibitors,Modulators,Libraries these cells lines across 24 anti cancer drugs.

One of the goals of the study was to enable predictive modeling of cancer Inhibitors,Modulators,Libraries drug sensitivity. For gener ating predictive models, the authors considered regression based analysis across input features of gene and protein expression profiles, mutation profiles and methylation data. The performance of the predictive models using 10 fold cross validation ranged between 0. 1 to 0. 8. In particular, the correlation coefficient for prediction of sensitivity using genomic signatures for the drug Erlotinib across 450 cell lines was 0. 35. Erlotinib is a commonly used tryosine kinase inhibitor selected primarily as an EGFR inhibitor. However, studies have shown Inhibitors,Modulators,Libraries that these tar geted drugs often have numerous side targets that can play significant roles in the effectiveness of the inhibitor drugs.

The target inhibition profiles of drugs and sensitivity of trainings set of drugs can provide significant information for enhanced prediction of anti cancer drug sensitivity as we have recently shown. By incorporating the drug target interaction data and sensitivities Inhibitors,Modulators,Libraries of training drugs with genomic signatures, we were able to achieve a cor relation coefficient of 0. 79 for prediction of Erlotinib sensi tivity using 10 fold cross validation. The result illustrates the fundamental concept of the importance of drug target interaction and functional data under which we develop the sensitivity prediction method presented in this paper. By developing a framework around the functional and tar get information extracted from the primary tumor drug screen performed by our collaborators, we seek to develop find FAQ a cohesive approach to sensitivity prediction and com bination therapy design. This necessitates the generation of the tumor pathway structure for individual patients to decide on the target inhibitors for therapy based on the personalized patient pathways. We envision that the overall schematic of the design of personalized pathways and personalized therapy will be similar to the workflow shown in Figure 1.