Hits were considered significant when they had an Expectation value of 0. 05

These data are consistent with the correlation of PKC activity with resistance Amuvatinib 分子量 to PI 103 inferred from pathway and substrate phosphorylation analyses in that the averaged intensities of 13 peptides containing the preferred motif for PKC and of eight peptides containing the related xKxSx motif also correlated with the resistance to PI 103, Also consistent with the data shown in Figure 7C, suggesting a correlation between S6K activity and the resistance to JAK i, the combined intensities of 43 phosphopeptides in the con text of the xRxxSx motif also correlated with the resis tance to this compound, Of these, eight and nine phosphopeptides were in the context of the RxRxxSx and KxRxxSx motifs, respectively, which are the preferred recognition motifs of S6K and related upstream kinases such as PKB Akt.

Overall, the data in Figure 8 indicate that the correlation of phosphopeptide intensi ties with resistance AT-406 chemical 構造 and sensitivity to kinase inhibitors is most likely due to differential kinase pathway activation in these cells, rather than just reflecting differences in the expression levels of the phosphoproteins bearing the sites of modification.

Discussion Tumor classification informs therapeutic strategies and provides information on prognosis, A classification based on the likely response to a particular therapy may be of particular importance in the use of agents that target protein and lipid kinases in signaling pathways, These enzymes are drug targets deregulated AG-490 分子量 in essentially all cancers but the lack of robust and generally applicable methods to stratify patients for therapies based on kinase inhibitors limit their clinical applicability, DNA sequencing, expression arrays and proteomic signatures have been used both to clas sify tumors and to indicate the presence of mutation status or expression of genes involved in cancer cell sig naling, However, protein, gene, or mRNA stu dies do not necessarily reflect enzyme activity, which ultimately determines the activation status of signaling pathways involved in cancer cell biology, Protein phosphorylation is the result of the activities of kinases present in the pathways that are being targeted by signaling inhibitors and kinase activity is a major determinant in conferring resistance sensitivity to these compounds.

Therefore, it may be argued that a compre hensive analysis of protein phosphorylation might repre sent an ideal readout to classify tumors based on the likelihood of response to inhibitors that target signaling pathways. In order to investigate whether phosphoproteomics may be used to classify hematological cancers based on their phenotype, and to provide proof of principle of the approach, we analyzed the most abundant phosphopro teomes of nine hematological cancer cell lines. Qualitative analysis of the data based on Gene Ontologies revealed that our analysis was broad and included phosphopeptides from all types of proteins, including those in cell mem branes, cytosol, and subcellular compartments. While membrane proteins were slightly less represented than expected, proteins involved in cell cycle regulation, prolif eration, and translation, and protein kinases were particu larly well represented in our phosphoprotein dataset. As for the quantitative analyses, our data showed that cancer cells of different phenotypes and origin had markedly different patterns of phosphorylation, thus indicating that phosphorylation could be used to classify these cells according to phenotype.