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Neuroblastoma is an aggressive pediatric cancer that displays tremendous clinical and biological heterogeneity. Approximately 40% of patients present with metastatic disease and have poor outcome; new biomarkers that improve risk classification and prognostication are desperately needed. Applying a machine learning-based approach to analyzing transcriptome-wide signals in multiple populations of neuroblastoma patients spanning clinical stage we identified a combined set of 14 clinical and molecular variables that predicted survival outcome and tumor stage with over 87% accuracy. A systems biology-based analysis of the molecular variables from our predictive set identified 5 major networks with hubs defining 3 major ontological processes: apoptosis, nucleotide biosynthesis, and RNA metabolism. Citation Format: Alex Carlisle, Ivan Caceres, Sonali Mehta, Jay Schindler, Jonathan Sharma. A combined machine learning and bioinformatic analysis approach identifies biological pathways that predict clinical stage and survival outcome in neuroblastoma patients. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3758. doi:10.1158/1538-7445.AM2015-3758