One would intuitively expect that the drug with the widest target spectrum, in this case bosutinib, would produce the strongest network effects. However, the network correlation analysis suggested dasatinib to have the most favorable drugprotein interaction profile in Ph+ ALL. This was consistent with the demonstrated important role of, for instance, BCR-ABL, LYN and BTK in Ph+ ALL and the fact that dasatinib displayed the strongest impact on these kinases. Overall, nilotinib, bosutinib and bafetinib were predicted to be inferior to dasatinib. This prediction was well reflected by the IC50��s in cellular proliferation assays and was further improved when based on more detailed genomic information as accessible in the CCLE database. This suggests that incorporation of patient gene signatures, as they will become available in the future, has the potential to produce valuable predictions for individual Ph+ ALL patients. Notably, these observations also correlated well with published, in part preliminary reports from clinical trials with the individual drugs applied as monotherapies or in combination with chemotherapy. Being critical about the correlation analysis also revealed two points worth discussing for future applications. First, although nilotinib is a potent kinase inhibitor, as e.g. observed in Table 3 and Figure S7C in File S1, and its couplable derivate pcnilotinib showed well preserved potency in a c-ABL kinase assay, when linked to beads this compound might have modified binding abilities as indicated by low Axitinib BCR-ABL spectral counts in Table 1 and poor scores in Tables 2 and S1 in File S2. In addition, post-translational modifications on BCR-ABL and its interaction partners in Ph+ ALL cells, as well as the different BCR-ABL isoforms themselves, may influence drug binding properties compared to c-ABL. This highlights the importance of performing experiments in the correct cell type, ideally from patient biopsies, and having detailed information about genetic alterations is likely to be essential as well. As a matter of fact, our correlation analysis performed better with BV-173 than with Z-119 cells. Z-119 cells respond to kinase inhibitors very differently compared to BV-173 cells, as can be for instance appreciated from Figure S7C in File S1, and their genetic alterations were not mapped in detail whereas for BV-173 the CCLE database provided detailed genetic data. To use the correct cell type has the potential to reveal changes at the compound-target interaction level and the genetic alterations can inform on possible downstream signalling changes when mapped onto the appropriate network. In summary, we here MK-0683 present a systems biology-derived network model for assisting implementation of personalized therapy in Ph+ ALL with second-generation BCR-ABL inhibitors.