The most prominent deletions were observed for the transcription factor genes IKFZ1 and PAX5 as well as for CDKN2A, which encodes the tumor suppressor cyclin-dependent kinase inhibitor 2A. Deletion or mutation of IKZF1 or CDKN2A have been described to have a negative prognostic impact. Thus, it appears that the particularly aggressive character of Ph+ ALL is not owed to the constitutive tyrosine kinase activity of BCR-ABL alone, but also to the contributions of other genetic factors. Accordingly, given that many kinase inhibitors are known to be highly pleiotropic drugs, it is not clear how effective the second-generation BCR-ABL inhibitors will be in the long-term and which one will be best suited for therapy of treatment-na?ve Ph+ ALL with wild-type BCR-ABL. Kinase inhibitor target profiles are routinely investigated on a kinomewide level either by large-scale in vitro kinase inhibition or kinase binding competition assays. For a systems-type appreciation of TKI action, however, it is advantageous to employ a cell-specific approach. At the same time, it should include a genome-, transcriptome-, or DAPT proteome-wide dimension. For instance, one method that is widely used determines drug-induced transcriptomic signatures. Here, we chose a systems biology approach that integrated proteomics and computational methods to predict TKI action in a Ph+ ALL-specific context. First, we characterized the global protein binding signatures of nilotinib, dasatinib, bosutinib and bafetinib in Ph+ ALL cells by chemical proteomics, an unbiased, post-genomic drug affinity chromatography SJN 2511 technology enabled by downstream mass spectrometry. In parallel, we compiled proteinprotein interaction data from several public databases and generated Ph+ ALL disease-specific PPI network models, which were based on previously reported copy number alterations. Correlation of the obtained drug-target profiles with the Ph+ ALL PPI network models allowed for the correct prediction of dasatinib as the most efficient drug as determined by subsequent validation experiments. For a proteome-wide understanding of the respective drugprotein interaction networks, we broadened our analysis by including non-kinase targets. Therefore, we performed drug affinity chromatography experiments in the presence of soluble drug, which competes with the respective drug matrix for specific targets and their interaction partners while non-specific proteins remain unaffected. Next, we compared the average spectral counts of regular and competition experiments and determined proteins that were specific for each drug.