In addition, free compound competition FAK signaling data were not available inthis case, we thus exploited IC50s of autophosphorylation, which were available instead. The log10 of the IC50s were used to weight the effect of dasatinib on the kinases. Analysis results in 681 significantly hit subnetworks due to the huge kinase profile. Even though more than 5% of the human kinases are targeted by dasatinib and, subsequently, many sub networks are significantly impacted, the top disrupted sub networks are insightful. For instance, the top 10 ranked biological processes are centered on cell cycle arrest, cell growth and apoptosis.
In the highest ranked sub networks, SRC, LYN and EGFR play a pivotal role, which is absolutely consistent with our experimental data where these three proteins were shown with dasatinib gate keeper mutants to strongly contribute to cell viability of HCC297. These results show that the algorithm can provide informative data even in very challenging situations. In comparison to our approach, MLN8054 classical GO enrichment analysis of the 33 bafetinib drug targets result in 33 significant biological processes with high redundancy in the GO tree. Basically, they represent 3 GO terms: cytoskeleton organization, phosphorylation and regulation of stress activated protein kinase signaling pathway. Except phosphorylation which is obvious in a target profile of a kinase inhibitor there is no overlap with theperturbed functional sub networks.
The GO term of cytoskeleton organization contains competed and non competed members of the target profile. The combined attack power of few competed kinases is too low to see perturbation of the large uniform functional sub network which is based on cytoskeleton organization. Enrichment analysis with KEGG and Biocarta pathways yielded no hit. In contrast to GO enrichment analysis, the presented method does not as much rely on accurate annotations. Possible missing annotations of drug targets interacting at the periphery with a functional sub network have only a minor effect on the score. However, we would like to point out that boundaries of pathways and biological processes are very diffuse. Crosstalk between different signaling cascades and metabolic pathways is essential for a living cell.
Integrating protein interactions to peripheral drug targets provides a way out of this dilemma and can catch therefore more relevant processes than GO enrichment. Alternatively, augmenting the drug target profile with their direct interactors, results in a set of 831 proteins. GO enrichment analysis of this set results in 676 biological processes. Again the first hits are related to general phosphorylation which is obvious for a kinase inhibitor profile. At the ninth rank,regulation of programmed cell death, which is related to perturbed apoptosis is presented with 127 proteins of the augmented set. The highest scored perturbed sub network of EGFR signaling is only found at position 368. Even though the disrupted processes are detected with the augmented GO analysis, their ranks are so bad that they would not be considered as relevant. Our approach thus picks the most relevant