RESUMEN
Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease-causing mutations promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLTR2 mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas.
Asunto(s)
Mutación , Receptores Acoplados a Proteínas G , Transducción de Señal , Humanos , Transducción de Señal/genética , Transducción de Señal/fisiología , Receptores Acoplados a Proteínas G/genética , Receptores Acoplados a Proteínas G/metabolismo , Mutación/genética , Neoplasias de la Úvea/genética , Neoplasias de la Úvea/metabolismo , Biología de Sistemas/métodos , Modelos Biológicos , Melanoma/genética , Melanoma/metabolismo , Proteínas de Unión al GTP/genética , Proteínas de Unión al GTP/metabolismoRESUMEN
BACKGROUND: Cancer research is pursued with the goal of positively impacting patients with cancer. Decisions regarding how to allocate research funds reflect a complex balancing of priorities and factors. Even though these are subjective decisions, they should be made with consideration of all available objective facts. An accurate estimate of the affected cancer patient population by mutation is one variable that has only recently become available to inform funding decisions. METHODS: We compared the overall incident burden of mutations within each cancer-associated gene with two measures of cancer research efforts: research grant funding amounts and numbers of academic manuscripts. We ask to what degree the aggregate set of cancer research efforts reflects the relative burdens of the different cancer genetic drivers. We thoroughly investigate the design of our queries to ensure that the presented results are robust and conclusions are well justified. FINDINGS: We find cancer research is generally not correlated with the relative burden of mutation within the different genetic drivers of cancer. CONCLUSIONS: We suggest that cancer research would benefit from incorporating, among other factors, an epidemiologically informed mutation-estimate baseline into a larger framework for funding and research allocation decisions. FUNDING: This work was supported in part by the National Institutes of Health (NIH) P30CA014195 and NIH DP2AT011327.