Functional phosphoproteomics and network models to elucidate mechanisms of deregulated signaling

Deregulated cellular signaling is a hallmark of cancers such as melanoma, the majority of which have a phosphomimetic mutation of the BRAF kinase with significant phenotypic implications. Quantitative phosphoproteomics is a powerful method to study such deregulated signaling processes, but low phosphoproteome coverage and the need for sophisticated computational methods often constrain the obtained biological insights. We applied a phosphoproteomics method which combines high-sensitivity detection and precise multiplexed quantification to profile the response of three BRAF-mutated cell lines to the BRAF inhibitor Dabrafenib based on more than 50k phosphopeptides. Further, multiplexing of six different Dabrafenib treatment time points resulted in a quantitative time-resolved profile for more than 80k phosphopeptides in the melanoma cell line A2058. We used computational methods such as clustering, kinase activity prediction with DecoupleR and the KinaseLibrary as well the causal reasoning method PHONEMEs to extract mechanistic insights from these datasets using network models. To deepen our understanding of the functionality of the observed phosphorylation events, we performed solubility proteome profiling experiments at the phosphoproteome level across three cell lines. This approach enabled us to correlate specific phosphorylation events with protein condensation processes and to elucidate the effects of BRAF mutations on transcription. These findings highlight the potential of deep phosphoproteome profiling on abundance as well as functionality level combined with computational methods to study drug effects on signaling networks in cancer.