The influence of delay in mononuclear cell isolation on acute myeloid leukemia phosphorylation profiles.

Mass-spectrometry (MS) based phosphoproteomics is increasingly used to explore aberrant cellular signaling and kinase driver activity, aiming to improve kinase inhibitor (KI) treatment selection in malignancies. Phosphorylation is a dynamic, highly regulated post-translational modification that may be affected by variation in pre-analytical sample handling, hampering the translational value of phosphoproteomics-based analyses. Here, we investigate the effect of delay in mononuclear cell isolation on acute myeloid leukemia (AML) phosphorylation profiles. We performed MS on immuno-precipitated phosphotyrosine (pY)-containing peptides isolated from AML samples after seven pre-defined delays before sample processing (direct processing, thirty minutes, one hour, two hours, three hours, four hours and 24 h delay). Up to four hours, pY phosphoproteomics profiles show limited variation. However, in samples processed with a delay of 24 h, we observed significant change in these phosphorylation profiles, with differential phosphorylation of 22 pY phosphopeptides (p < 0.01). This includes increased phosphorylation of pY phosphopeptides of JNK and p38 kinases indicative of stress response activation. Based on these results, we conclude that processing of AML samples should be standardized at all times and should occur within four hours after sample collection. SIGNIFICANCE: Our study provides a practical time-frame in which fresh peripheral blood samples from acute myeloid patients should be processed for phosphoproteomics, in order to warrant correct interpretation of in vivo biology. We show that up to four hours of delayed processing after sample collection, pY phosphoproteomic profiles remain stable. Extended delays are associated with perturbation of phosphorylation profiles. After a delay of 24 h, JNK activation loop phosphorylation is markedly increased and may serve as a biomarker for delayed processing. These findings are relevant for biomedical acute myeloid leukemia research, as phosphoproteomic techniques are of particular interest to investigate aberrant kinase signaling in relation to disease emergence and kinase inhibitor response. With these data, we aim to contribute to reproducible research with meaningful outcomes.

Optimized low-dose combinatorial drug treatment boosts selectivity and efficacy of colorectal carcinoma treatment.

The current standard of care for colorectal cancer (CRC) is a combination of chemotherapeutics, often supplemented with targeted biological drugs. An urgent need exists for improved drug efficacy and minimized side effects, especially at late-stage disease. We employed the phenotypically driven therapeutically guided multidrug optimization (TGMO) technology to identify optimized drug combinations (ODCs) in CRC. We identified low-dose synergistic and selective ODCs for a panel of six human CRC cell lines also active in heterotypic 3D co-culture models. Transcriptome sequencing and phosphoproteome analyses showed that the mechanisms of action of these ODCs converged toward MAP kinase signaling and cell cycle inhibition. Two cell-specific ODCs were translated to in vivo mouse models. The ODCs reduced tumor growth by ~80%, outperforming standard chemotherapy (FOLFOX). No toxicity was observed for the ODCs, while significant side effects were induced in the group treated with FOLFOX therapy. Identified ODCs demonstrated significantly enhanced bioavailability of the individual components. Finally, ODCs were also active in primary cells from CRC patient tumor tissues. Taken together, we show that the TGMO technology efficiently identifies selective and potent low-dose drug combinations, optimized regardless of tumor mutation status, outperforming conventional chemotherapy.

Integrating Phenotypic Search and Phosphoproteomic Profiling of Active Kinases for Optimization of Drug Mixtures for RCC Treatment.

Combined application of multiple therapeutic agents presents the possibility of enhanced efficacy and reduced development of resistance. Definition of the most appropriate combination for any given disease phenotype is challenged by the vast number of theoretically possible combinations of drugs and doses, making extensive empirical testing a virtually impossible task. We have used the streamlined-feedback system control (s-FSC) technique, a phenotypic approach, which converges to optimized drug combinations (ODC) within a few experimental steps. Phosphoproteomics analysis coupled to kinase activity analysis using the novel INKA (integrative inferred kinase activity) pipeline was performed to evaluate ODC mechanisms in a panel of renal cell carcinoma (RCC) cell lines. We identified different ODC with up to 95% effectivity for each RCC cell line, with low doses (ED5-25) of individual drugs. Global phosphoproteomics analysis demonstrated inhibition of relevant kinases, and targeting remaining active kinases with additional compounds improved efficacy. In addition, we identified a common RCC ODC, based on kinase activity data, to be effective in all RCC cell lines under study. Combining s-FSC with a phosphoproteomic profiling approach provides valuable insight in targetable kinase activity and allows for the identification of superior drug combinations for the treatment of RCC.