The autophagy adaptor molecule p62 is included clearance to mitochondria only after their depolarization
Bosutinib, however, has only slightly lower ‘A’ scores for LYN and BTK than dasatinib. The difference between dasatinib and bosutinib might therefore be attributed to the contribution that is made by serine/threonine kinases to the bosutinib network score, which would be in line with the relatively weak effect of bosutinib on tyrosine phosphorylation in BV-173 cells. Additionally, this could be at least in part due to other dasatinib targets, such as TEC or ILK, which are not or only marginally seen with bosutinib. For instance, the entire ILK/LIMS1/PARVIN/RSU1 complex is engaged specifically by dasatinib, produces relatively high ‘A’ scores and is well connected to disease genes through ILK and LIMS1. Interestingly, we have identified BCR-ABL, LYN, BTK, TEC and ILK as dasatinib interactors also in the pool of Ph+ ALL patient PBMCs and subsequently confirmed their expression in each individual patient from the pool by qPCR, which highlights their potential relevance in the context of mediating network-wide drug effects in Ph+ ALL. 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 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 present a systems biology-derived network model for assisting implementation of personalized therapy in Ph+ ALL with second-generation BCR-ABL inhibitors. This model is based on the comprehensive, proteome-wide survey of the drug-target profiles of nilotinib, dasatinib, bosutinib and bafetinib in the context of the complex Ph+ ALL-specific protein-protein interaction network. Correlation analysis elected dasatinib as the most effective network drug for Ph+ ALL. This prediction was validated by cellular proliferation assays. First clinical reports show that dasatinib indeed has favorable efficacy. This type of study was designed to serve the community to evaluate these drugs based on their cellular target profile. In future, as it will not always be feasible to test their effects directly on patient cells, it should be useful to annotate these networks with mutation and expression data to derive a patient-specific simulation. Myofibrillar myopathies are a clinically and genetically heterogeneous group of disorders characterized by ectopic protein aggregates and myofibrillar disorganization.