Updated project metadata. Global high-throughput phosphoproteomic profiling is increasingly being applied to cancer specimens as a means to identify the oncogenic signaling cascades responsible for promoting disease initiation and disease progression; pathways that are often invisible to genomics analysis. Hence, phosphoproteomic profiling has enormous potential to inform and improve individualized anti-cancer treatment strategies. However, to achieve the adequate phosphoproteomic depth and coverage necessary to identify the activated, and hence, targetable kinases responsible for driving oncogenic signaling pathways; affinity phosphopeptide enrichment techniques are required and often coupled with offline high-pressure liquid chromatographic (HPLC) separation prior to nanoflow liquid chromatography–tandem mass spectrometry (nLC-MS/MS). These complex and time-consuming procedures, limit the utility of phosphoproteomics for the analysis of individual cancer patient specimens in real-time, and restrict phosphoproteomics to specialized laboratories often outside of the clinical setting. To address these limitations, here we have optimized a new protocol, phospho-Heavy-labeled-spiketide FAIMS Stepped-CV DDA (pHASED), that employs online phosphoproteome deconvolution using high-field asymmetric waveform ion mobility spectrometry (FAIMS) and internal phosphopeptide standards to provide accurate label-free quantitation (LFQ) data in real-time. Compared with traditional LFQ using shotgun phosphoproteomics workflows, pHASED provided increased phosphoproteomic depth and coverage (phosphopeptides = 4,617 pHASED, 2,789 LFQ), whilst eliminating the variability associated with offline prefractionation. pHASED was optimized using tyrosine kinase inhibitor (sorafenib) resistant isogenic FLT3-mutant acute myeloid leukemia (AML) cell line models. Bioinformatic analysis identified differential activation of the Serine/threonine protein kinase ataxia-telangiectasia mutated (ATM) pathway, responsible for sensing and repairing DNA damage in sorafenib-resistant AML cell line models, thereby uncovering a potential therapeutic opportunity. Herein, we have optimized a rapid, reproducible, and flexible protocol for the characterization of complex cancer phosphoproteomes in real-time; a step towards the implementation of phosphoproteomics in the clinic to aid in the selection of anti-cancer therapies for patients.