Proteomic approaches for disentangling oncogenic kinase signalling and for advancing personalized therapies
Pedro Rodríguez Cutillas
Instituto de Parasitología y Biomedicina López-Neyra (CSIC), Granada
Dysregulated kinase signalling drives cancer progression and therapeutic resistance, yet genomic biomarkers alone often fail to capture functional pathway activity. This presentation explores quantitative phosphoproteomics as a systems-level approach to directly measure kinase signalling networks and enable personalised oncology. Because each phosphorylation site represents a direct readout of kinase activity, phosphoproteomics integrates the effects of mutations, expression changes, post-translational modifications, and upstream regulatory inputs.
Using acute myeloid leukaemia (AML) as a model, we show that phosphoproteomic inference of pathway activity predicts responses to targeted therapies—including PI3K/mTOR, FLT3, MEK, PKC, and CK2 inhibitors—more accurately than mutation status alone. Kinase substrate enrichment analysis and network-circuitry modelling reveal that resistance frequently arises through parallel or rewired signalling pathways. Integration with machine learning enables patient stratification, identification of predictive phospho-signatures, and validation in independent clinical cohorts.
We further demonstrate that signalling circuitry is plastic and can be therapeutically reprogrammed: inducing myeloid differentiation reshapes kinase networks and sensitises resistant AML cells to targeted inhibition. Together, these findings position phosphoproteomics as a key functional platform for next-generation precision cancer medicine