More careful R115777 research is required for CCBT to develop more substantially. Background In the last decade, a number of drugs targeting specific biologically Inhibitors,Modulators,Libraries relevant kinases have been developed that are becoming common in cancer research as a basis for per sonalized therapy. The idea of treating cancer through inhibition of a specific tyrosine kinase was proven by the discovery Inhibitors,Modulators,Libraries that patients with Chronic Myeloid Leukemia can be successfully treated by inhibiting the tyrosine kinase BCR ABL with the kinase inhibitor Imatinib Mesy late. However, the success rate of any one specific targeted drug for other forms of cancer, such as sarcoma, is limited as the tumors exhibit a wide variety of signaling pathways and are not uniformly dependent on the activity of a specific kinase.

The numerous aberrations in molecular pathways that can produce cancer is one cause to necessitate the use of drug combinations for treatment of individual can cers. Combination therapy design requires a framework for inference of the individual tumor pathways, prediction of tumor sensitivity to targeted drug and algorithms Inhibitors,Modulators,Libraries for selection of the drug combinations under different con straints. The current state of the art in predicting sensitiv ity to drugs is primarily based on assays measuring gene expression, protein abundance and genetic mutations of tumors. these methods often have low accuracy due to the breadth of available expression data coupled with the absence of information on the functional importance of many genetic mutations.

A commonly used method for predicting the success of targeted drugs for a tumor sample is based on the genetic aberrations in the tumor. However, the accuracy of prediction of drug sensitivity based on mutation knowl edge is limited in many forms of tumors as some of the mutations may not be functionally important Inhibitors,Modulators,Libraries or tumors can develop without the known genetic mutations. Statistical tests have been used in to show that genetic mutations can be predictive of the drug sensitivity in non small cell lung cancers but the classification rates of these predictors based on indi vidual mutations for the aberrant samples are still low. For specific diseases, some mutations have been able to predict the patients that will not respond to particular therapies for instance reports a success rate of 87% in predicting non responders to anti EGFR Inhibitors,Modulators,Libraries monoclonal antibodies using the mutational status of KRAS, BRAF, PIK3CA and PTEN.

The prediction of tumor sensitivity to drugs has also been approached selleck kinase inhibitor as a classification prob lem using gene expression profiles. In, gene expression profiles are used to predict the binarized efficacy of a drug over a cell line with the accuracy of the designed classi fiers ranging from 64% to 92%. In, a co expression extrapolation approach is used to predict the binarized drug sensitivity in data points outside the train ing set with an accuracy of around 75%.