Neuroblastoma, a rare childhood cancer of the sympathetic nervous system, is particularly deadly because it is difficult to detect and thus generally advanced before treatment begins. Scientists know that neuroblastoma develops from embryonic neural crest cells that fail to properly migrate or differentiate, but the details about exactly what causes these cells to go astray have been unclear. Motivated by a desire to better understand the molecular circuitry underlying neuroblastoma and limitations of current methods for predicting disease progression and outcome, researchers from the Kulesa Lab at the Stowers Institute for Medical Research (Kansas City, Missouri) and collaborators at the University of Michigan and Oxford University set out to construct a logic-based model incorporating information about developmental signaling pathways implicated in the disease. The scientists sought to test whether their model could predict disease outcomes more effectively than the current predictive methods, which are based on gene expression information from human patient samples but do not provide much insight about how these molecules interact to participate in disease progression. Using a six-gene input logic model, the team simulated a molecular network of developmental genes and downstream signals that predicted a favorable or unfavorable disease outcome based on the outcome of four cell states related to tumor development - cell differentiation, proliferation, apoptosis, and angiogenesis. The six genes of the model included three receptor tyrosine kinases involved in sympathetic nervous system development and implicated in neuroblastoma - trkA, trkB, and ALK - plus their three ligands.
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