Predicted ‘wiring landscape’ of Ras network in 29 human tissues

Ras is an important hub protein at the head of numerous signaling pathways and plays a starring role in various types of cancers, notably in pancreas, colon and lung adenomas. The usual suspects are three oncogenic isoforms - i.e. HRAS, KRAS and NRAS - that are highly mutated and drive tumorigenesis. Our study is based on the paradigm of network medicine that sees disease as a perturbation of a network of interconnected proteins finely orchestrating cell's physiology and phenotype through the onset of downstream signal transduction. As such, we built a mechanistic model of the interactions of the three Ras oncoproteins with their direct interactors (known as 'effectors'), with protein abundances and binding affinities being the system's parameters, in order to study elementary pathological and physiological conditions of Ras network. Using high-quality proteomic data from 29 (healthy) human tissues, we quantified the amount of individual Ras-effector complexes, and characterized the (stationary, reference) Ras “wiring landscape” specific to each tissue. We simulated mutant- and stimulus-induced network re-configurations, miming respectively cancerous and physiological state, and compared them to the reference network. Moreover, we investigated the contribution of the input parameters (binding affinities and effector concentrations) in determining the complex formations underlying the specific wiring landscape, by 3D data interpolation onto (tissue-specific) surfaces. This revealed that high affinity - more than high concentration, - is critical for complex formation. As a consequence, we analyzed local and global binding affinity fluctuations and assessed their impact on the system's robustness. Further research will aim at the calibration of the binding affinity parameters, based on the Ras-effector complexes and the activation of the associated downstream pathway.