Deciphering the complexity of combinatorial therapies through advanced target engagement deconvolution assays in acute myeloid leukemia

Combinatorial therapy (CT) has demonstrated significant potential in overcoming the limitations of single-drug treatments and mitigating cancer cell resistance in patients. Despite advancements in CT, a substantial knowledge gap persists in deconvoluting drug targets and identifying downstream pathways or mechanisms of action (MoA) due to the inherent complexity of these combinations. This study aims to develop a computational workflow to identify drug targets and elucidate the MoA for two recently proposed CTs for acute myeloid leukemia (AML): LY3009120-Sapanisertib (LS) and Ruxolitinib-Ulixertinib (RU). We employed a comprehensive proteome integral stability and solubility assay (CoPISA), an advanced computational development of PISA, to deconvolute and characterize the protein targets of these drug combinations. Using four AML-related cell lines (MOLM13, SKM, NOMO, and MOLM16), we analyzed protein extracts and intact cells after single and paired drug treatments, compared to control. Our study successfully identified both primary and secondary targets of the aforementioned drug combinations for AML, as well as for the individual drugs. For LS, we identified 564 direct and indirect protein targets, with 55 being primary targets after considering single and paired drug treatments in addition to control. For RU, 793 direct and indirect protein targets were identified, with 65 primary targets implicated in this combinatorial therapy. Pathway analysis revealed significant involvement of apoptotic signaling and cell cycle regulation pathways, suggesting mechanisms through which these drug combinations exert their effects, resulting in reduced toxicity on normal cells and enhanced efficacy on cancer cells. Our workflow, CoPISA, demonstrates substantial potential in the high-throughput analysis of drug-target interactions and the elucidation of drug MoA by facilitating comprehensive comparisons of target sets. This approach is instrumental in understanding the mechanisms behind the reduced toxicity and enhanced efficacy of LS and RU compared to first-line treatments for AML. Furthermore, the proteomics-based insights derived from CoPISA are pivotal in refining combinatorial therapy strategies, thus contributing to the optimization of therapeutic regimens and improving clinical outcomes for AML patients. This advancement in proteomics not only enhances our understanding of drug interactions at the molecular level but also paves the way for personalized medicine approaches in oncology.