Dartmouth College Dartmouth Digital Commons Dartmouth Scholarship Faculty Work 4-30-2014 Predicting Targeted Drug Combinations Based on Pareto Optimal Patterns of Coexpression Network Connectivity Nadia M. Penrod Dartmouth College Casey S. Greene Dartmouth College Jason H. Moore Dartmouth College Follow this and additional works at: https://digitalcommons.dartmouth.edu/facoa Part of the Medical Genetics Commons, Neoplasms Commons, and the Therapeutics Commons Dartmouth Digital Commons Citation Penrod, Nadia M.; Greene, Casey S.; and Moore, Jason H., "Predicting Targeted Drug Combinations Based on Pareto Optimal Patterns of Coexpression Network Connectivity" (2014). Dartmouth Scholarship. 895. https://digitalcommons.dartmouth.edu/facoa/895 This Article is brought to you for free and open access by the Faculty Work at Dartmouth Digital Commons. It has been accepted for inclusion in Dartmouth Scholarship by an authorized administrator of Dartmouth Digital Commons. For more information, please contact
[email protected]. Penrod et al. Genome Medicine 2014, 6:33 http://genomemedicine.com/content/6/4/33 RESEARCH Open Access Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity Nadia M Penrod1, Casey S Greene2,3 and Jason H Moore2,3* Abstract Background: Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets.