Global high-throughput profiling of oncogenic signalling pathways by phosphoproteomics are increasingly being applied to cancer specimens. Quantitative unbiased phosphoproteomic profiling of cancer cells identifies oncogenic signalling processes that drive disease initiation and progression that are hidden to genomics approaches, thereby aiding in the development of individualised cancer treatment strategies. However, extensive sample preparation time and complex chromatographic separation techniques needed to achieve adequate phosphoproteomic depth, limits the utility of these techniques to aid in the design of treatment strategies for highly heterogeneous and aggressive forms of cancers in real-time. Over the last few years, our laboratory has performed phosphoproteomic profiling using isogenic cellular models and patient samples employing tandem mass tags (TMT-10plex) coupled with TiO2 enrichment and HILIC prefractionation prior to nRP-LC-MS/MS. Aiming to maintain deep phosphoproteomic coverage while decreasing sample preparation time, the variably associated with offline prefractionation, and issues limiting the assessment of additional cancer phosphoproteomes in real-time, we have optimised a new protocol to identify drug targets to guide treatment in the clinic. Phospho Heavy-labelled-spiketide FAIMS Stepped-CV DDA or pHASED employs online phosphoproteome deconvolution and label-free quantitation using internal standards to normalise expression/phosphorylation of cancer specimens run at any time. Here we compared pHASED and our traditional TMT-DDA phosphoproteomics protocols/techniques using acute myeloid leukaemia and paediatric high-grade glioma cell lines. pHASED decreased sample preparation time, provided more in-depth phosphoproteomic coverage, and identified more unique, high-fidelity phosphopeptides in real-time (7,925 pHASED, 4,508 TMT-DDA, respectively). The utility of pHASED was tested using cytotoxicity assays to assess the sensitivity of cancers to therapies predicted by pHASED compared to controls. pHASED is now being used to identify drug targets to aid in treatment selection for patients with cancers lacking effective treatments or for patients where genomic approaches have failed to identify therapeutic targets both at diagnosis and following disease progression.