Abstract
Systems biology perspectives are crucial for understanding the pathophysiology of complex diseases, and therefore hold great promise for the discovery of novel treatment strategies. Drug combinations have been shown to improve durability and reduce resistance to available first-line therapies in a variety of cancers; however, traditional drug discovery approaches are prohibitively cost and labor-intensive to evaluate large-scale matrices of potential drug combinations. Computational methods are needed to efficiently model complex interactions of drug target pathways and identify mechanisms underlying drug combination synergy. In this study, we employ a computational approach, SynGeNet (Synergy from Gene expression and Network mining), which integrates transcriptomics-based connectivity mapping and network centrality analysis to analyze disease networks and predict drug combinations. As an exemplar of a disease in which combination therapies demonstrate efficacy in genomic-specific contexts, we investigate malignant melanoma. We employed SynGeNet to generate drug combination predictions for each of the four major genomic subtypes of melanoma (BRAF, NRAS, NF1, and triple wild type) using publicly available gene expression and mutation data. We validated synergistic drug combinations predicted by our method across all genomic subtypes using results from a high-throughput drug screening study across. Finally, we prospectively validated the drug combination for BRAF-mutant melanoma that was top ranked by our approach, vemurafenib (BRAF inhibitor) + tretinoin (retinoic acid receptor agonist), using both in vitro and in vivo models of BRAF-mutant melanoma and RNA-sequencing analysis of drug-treated melanoma cells to validate the predicted mechanisms. Our approach is applicable to a wide range of disease domains, and, importantly, can model disease-relevant protein subnetworks in precision medicine contexts.
Highlights
Systems medicine approaches and related computational methods have provided new and powerful means to aid in various aspects of the drug discovery process via modeling complex, multi-dimensional phenotypes that seek to overcome reductionist approaches to discovery science.[1,2] Numerous studies have demonstrated their utility in identifying novel drug targets, predicting on- and off-target mechanisms, and accelerating the translation of drug repurposing efforts.[3,4] Discovering novel uses for existing drugs through drug repurposing or drug repositioning has been an important goal in efforts to advance understanding of systems-level effects of large repertoires of chemical and pharmacological agents, and in doing so, potentially reduce the financial and labor costs associated with the drug discovery process
Mutations in NF1 were observed at 14 different loci, with primarily truncating effects, which is consistent with the knowledge that NF1 serves as a tumor suppressor in melanoma
While we observed a high recall for BRAF, NF1, and triple wild type (TWT) drug combination predictions, we found a lower recall (0.29) for the IPC-298 NRAS-mutant melanoma cell line (Fig. 3a)
Summary
Systems medicine approaches and related computational methods have provided new and powerful means to aid in various aspects of the drug discovery process via modeling complex, multi-dimensional phenotypes that seek to overcome reductionist approaches to discovery science.[1,2] Numerous studies have demonstrated their utility in identifying novel drug targets, predicting on- and off-target mechanisms, and accelerating the translation of drug repurposing efforts.[3,4] Discovering novel uses for existing drugs through drug repurposing or drug repositioning has been an important goal in efforts to advance understanding of systems-level effects of large repertoires of chemical and pharmacological agents, and in doing so, potentially reduce the financial and labor costs associated with the drug discovery process. The cross-talk, redundancy, and feedback loops of signaling pathways regulating these complex diseases can drive resistance to single-target therapies.[5] The design of multi-target agents and rationale drug combinations are aimed at increasing overall efficacy, improve initiation for first-line therapies, reduce and/or prevent drug resistance, and reduce drug toxicities. It is infeasible, with limited resources, to experimentally screen pairwise drug combinations derived from thousands of currently available therapies for synergistic effects across diverse cell lines and human-derived models. The accumulation of ubiquitous drug-induced gene expression profiles in publicly available datasets has permitted widespread connectivity mapping analysis, including the original
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