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

ABSTRACT: 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.

SUBMITTER: van Beijnum JR

PROVIDER: S-EPMC7564658 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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

van Beijnum Judy R JR Weiss Andrea A Berndsen Robert H RH Wong Tse J TJ Reckman Louise C LC Piersma Sander R SR Zoetemelk Marloes M de Haas Richard R Dormond Olivier O Bex Axel A Henneman Alexander A AA Jimenez Connie R CR Griffioen Arjan W AW Nowak-Sliwinska Patrycja P

Cancers 20200921 9

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 ...[more]

PMID: 32967224

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Project description:Renal cell carcinoma (RCC) is one of the ten most common malignancies. Even though recent paradigm changes led to inclusion of immune checkpoint inhibitor combination therapy in treatment of naive metastatic RCC, insight in the biology and management of RCC would still favour the use of a combination of kinase inhibitors to target multiple pathways, presenting the possibility of enhanced efficacy and reduced development of resistance. We have used the streamlined-feedback system control (s-FSC) technique, a phenotypic approach, to identify optimized drug combinations (ODC) in 5 different human RCC lines. This approach requires no a priori mechanistic information on drug mechanism of action, and uses statistical modelling circumventing the need to experimentally screen large numbers of combinations. Different ODC with up to 90% effectivity were obtained for the 5 cell lines, importantly, by combining drug at doses that individually only inhibit 5-25% cell viability. Cell viability was reduced and apoptosis induced, accompanied by a general inhibition of downstream survival and growth promoting effector kinase RPS6. Global phosphoproteomics analysis of the cell lines revealed that in all cases where the ODC showed >50% efficacy, the most active kinases were targets of the selected drugs in the ODC. Interestingly, we observed considerable overlap in the top 20 most active kinases in all 5 cell lines, suggesting a universal ODC might be effective in all cells. Indeed, a combination of erlotinib and dasatinib, targeting EGFR and EPHA2/SRC signaling respectively, combined with AZD4547 or axitinib (targeting VEGFR and FGFR signaling), displayed excellent activity. The effect of the 786-O cell-line specific ODC was analysed in more detail using phosphoproteomics coupled to kinase activity analysis using the INKA pipeline.approaches. As expected, the activity of the main targets of erlotinib and dasatinib was reduced, but the activity of several untargeted kinases, including AXL, PTK2 and MET, remained high or increased, having potential implications for the development of resistance. Addition of crizotinib, targeting MET and PTK2, to the 3-drug ODC but not exchange with axitinib, further enhanced drug combination efficacy. In conclusion, we show that the phenotypic s-FSC method is highly effective in selecting optimized combinations of drugs, and that phosphoproteomics analysis can help further refine the selection of drugs included in the screen or final drug mixture.

Project description:BACKGROUND:As protein is the basic unit of cell function and biological pathway, shotgun proteomics, the large-scale analysis of proteins, is contributing greatly to our understanding of disease mechanisms. Proteomics study could detect the changes of both protein expression and modification. With the releases of large-scale cancer proteome studies, how to integrate acquired proteomic and phosphoproteomic data in more comprehensive pathway analysis becomes implemented, but remains challenging. Integrative pathway analysis at proteome level provides a systematic insight into the signaling network adaptations in the development of cancer. RESULTS:Here we integrated proteomic and phosphoproteomic data to perform pathway prioritization in breast cancer. We manually collected and curated breast cancer well-known related pathways from the literature as target pathways (TPs) or positive control in method evaluation. Three different strategies including Hypergeometric test based over-representation analysis, Kolmogorov-Smirnov (K-S) test based gene set analysis and topology-based pathway analysis, were applied and evaluated in integrating protein expression and phosphorylation. In comparison, we also assessed the ranking performance of the strategy using information of protein expression or protein phosphorylation individually. Target pathways were ranked more top with the data integration than using the information from proteomic or phosphoproteomic data individually. In the comparisons of pathway analysis strategies, topology-based method outperformed than the others. The subtypes of breast cancer, which consist of Luminal A, Luminal B, Basal and HER2-enriched, vary greatly in prognosis and require distinct treatment. Therefore we applied topology-based pathway analysis with integrating protein expression and phosphorylation profiles on four subtypes of breast cancer. The results showed that TPs were enriched in all subtypes but their ranks were significantly different among the subtypes. For instance, p53 pathway ranked top in the Basal-like breast cancer subtype, but not in HER2-enriched type. The rank of Focal adhesion pathway was more top in HER2- subtypes than in HER2+ subtypes. The results were consistent with some previous researches. CONCLUSIONS:The results demonstrate that the network topology-based method is more powerful by integrating proteomic and phosphoproteomic in pathway analysis of proteomics study. This integrative strategy can also be used to rank the specific pathways for the disease subtypes.

Project description:BackgroundA major goal in the discovery of cellular signaling networks is to identify regulated phosphorylation sites ("phosphosites") and map them to the responsible protein kinases. The V2 vasopressin receptor is a G-protein coupled receptor (GPCR) that is responsible for regulation of renal water excretion through control of aquaporin-2-mediated osmotic water transport in kidney collecting duct cells. Genome editing experiments have demonstrated that virtually all vasopressin-triggered phosphorylation changes are dependent on protein kinase A (PKA), but events downstream from PKA are still obscure.MethodsHere, we used: 1) Tandem mass tag-based quantitative phosphoproteomics to experimentally track phosphorylation changes over time in native collecting ducts isolated from rat kidneys; 2) a clustering algorithm to classify time course data based on abundance changes and the amino acid sequences surrounding the phosphosites; and 3) Bayes' Theorem to integrate the dynamic phosphorylation data with multiple prior "omic" data sets covering expression, subcellular location, known kinase activity, and characteristic surrounding sequences to identify a set of protein kinases that are regulated secondary to PKA activation.ResultsPhosphoproteomic studies revealed 185 phosphosites regulated by vasopressin over 15 min. The resulting groups from the cluster algorithm were integrated with Bayes' Theorem to produce corresponding ranked lists of kinases likely responsible for each group. The top kinases establish three PKA-dependent protein kinase modules whose regulation mediate the physiological effects of vasopressin at a cellular level. The three modules are 1) a pathway involving several Rho/Rac/Cdc42-dependent protein kinases that control actin cytoskeleton dynamics; 2) mitogen-activated protein kinase and cyclin-dependent kinase pathways that control cell proliferation; and 3) calcium/calmodulin-dependent signaling.ConclusionsOur findings identify a novel set of downstream small GTPase effectors and calcium/calmodulin-dependent kinases with potential roles in the regulation of water permeability through actin cytoskeleton rearrangement and aquaporin-2 trafficking. The proposed signaling network provides a stronger hypothesis for the kinases mediating V2 vasopressin receptor responses, encouraging future targeted examination via reductionist approaches. Furthermore, the Bayesian analysis described here provides a template for investigating signaling via other biological systems and GPCRs. Video abstract.

Dataset Information

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

Publications

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

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