Analyzing causal relationships in proteomic profiles using CausalPath

Generating synthetic signaling networks for in silico modeling studies

Predictive models of signaling pathways have proven to be difficult to develop. Traditional approaches to developing mechanistic models rely on collecting experimental data and fitting a single model to that data. This approach works for simple systems but has proven unreliable for complex systems such as biological signaling networks. Thus, there is a need to develop new approaches to create predictive mechanistic models of complex systems. To meet this need, we developed a method for generating artificial signaling networks that were reasonably realistic and thus could be treated as ground truth models. These synthetic models could then be used to generate synthetic data for developing and testing algorithms designed to recover the underlying network topology and associated parameters. We defined the reaction degree and reaction distance to measure the topology of reaction networks, especially to consider enzymes. To determine whether our generated signaling networks displayed meaningful behavior, we compared them with signaling networks from the BioModels Database. This comparison indicated that our generated signaling networks had high topological similarities with BioModels signaling networks with respect to the reaction degree and distance distributions. In addition, our synthetic signaling networks had similar behavioral dynamics with respect to both steady states and oscillations, suggesting that our method generated synthetic signaling networks comparable with BioModels and thus could be useful for building network evaluation tools.

Protein Kinase Signaling Networks Driven by Oncogenic Gq/11 in Uveal Melanoma Identified by Phosphoproteomic and Bioinformatic Analyses

Metastatic uveal melanoma (UM) patients typically survive only 2 to 3 years because effective therapy does not yet exist. Here, to facilitate the discovery of therapeutic targets in UM, we have identified protein kinase signaling mechanisms elicited by the drivers in 90% of UM tumors: mutant constitutively active G protein α-subunits encoded by GNAQ (Gq) or GNA11 (G11). We used the highly specific Gq/11 inhibitor FR900359 (FR) to elucidate signaling networks that drive proliferation, metabolic reprogramming, and dedifferentiation of UM cells. We determined the effects of FR on the proteome and phosphoproteome of UM cells as indicated by bioinformatic analyses with CausalPath and site-specific gene set enrichment analysis. We found that inhibition of oncogenic Gq/11 caused deactivation of PKC, Erk, and the cyclin-dependent kinases CDK1 and CDK2 that drive proliferation. Inhibition of oncogenic Gq/11 in UM cells with low metastatic risk relieved inhibitory phosphorylation of polycomb-repressive complex subunits that regulate melanocytic redifferentiation. Site-specific gene set enrichment analysis, unsupervised analysis, and functional studies indicated that mTORC1 and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 2 drive metabolic reprogramming in UM cells. Together, these results identified protein kinase signaling networks driven by oncogenic Gq/11 that regulate critical aspects of UM cell biology and provide targets for therapeutic investigation.

Discovery of oncogenic ROS1 missense mutations with sensitivity to tyrosine kinase inhibitors

ROS1 is the largest receptor tyrosine kinase in the human genome. Rearrangements of the ROS1 gene result in oncogenic ROS1 kinase fusion proteins that are currently the only validated biomarkers for targeted therapy with ROS1 TKIs in patients. While numerous somatic missense mutations in ROS1 exist in the cancer genome, their impact on catalytic activity and pathogenic potential is unknown. We interrogated the AACR Genie database and identified 34 missense mutations in the ROS1 tyrosine kinase domain for further analysis. Our experiments revealed that these mutations have varying effects on ROS1 kinase function, ranging from complete loss to significantly increased catalytic activity. Notably, Asn and Gly substitutions at Asp2113 in the ROS1 kinase domain were found to be TKI-sensitive oncogenic variants in cell-based model systems. In vivo experiments showed that ROS1 D2113N induced tumor formation that was sensitive to crizotinib and lorlatinib, FDA-approved ROS1-TKIs. Collectively, these findings highlight the tumorigenic potential of specific point mutations within the ROS1 kinase domain and their potential as therapeutic targets with FDA-approved ROS1-TKIs.

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology in both normal and cancer cells. Advances in mass spectrometry enable high-throughput, accurate, and sensitive measurement of PTM levels to better understand their role, prevalence, and crosstalk. Here, we analyze the largest collection of proteogenomics data from 1,110 patients with PTM profiles across 11 cancer types (10 from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns of changes in protein acetylation and phosphorylation involved in hallmark cancer processes. These patterns revealed subsets of tumors, from different cancer types, including those with dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated with immune response driven by acetylation, affected kinase specificity by crosstalk between acetylation and phosphorylation, and modified histone regulation. Overall, this resource highlights the rich biology governed by PTMs and exposes potential new therapeutic avenues.