Abstract
BackgroundMolecularly 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.MethodsWe have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER+ breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18).ResultsWe find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment.ConclusionsTo derive the greatest benefit from molecularly targeted drugs it is critical to design combination treatment strategies rationally. Incorporating knowledge of the tumor adaptation process into the design provides an opportunity to match targeted drugs to the evolving tumor phenotype and surmount resistance.
Highlights
Targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients
While this strategy is intuitive and may appear straightforward, selecting the best combination of targets to maximize tumor cell death while minimizing collateral damage and toxicity presents a tremendous challenge. It does not take into account the evolving tumor phenotype that emerges through the adaptation process in response to drug perturbation
We evaluated each gene pair for functional relationships based on empirical data with networks from the Integrated Multi-species Prediction (IMP) web server [31]
Summary
Targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. The predominant strategy for overcoming resistance is to combine drugs that act through ancillary mechanisms to block the functional redundancies and compensatory signaling pathways that serve as escape routes for cell survival This strategy is supported by studies showing that complex networks, including the networks of molecular interactions that underlie biological function, are vulnerable to coordinated attacks at multiple targets [7,8], and by functional genomics screens with RNA-mediated interference showing that cells can be increasingly sensitized to a molecularly targeted drug by inhibiting a second complementary target concurrently [9]. To address this challenge we have developed a framework to identify tumor-essential genes as potential drug targets by mining high-throughput transcriptomic data based on coexpression patterns where coexpression serves as a proxy for coregulation or participation in the same biological processes [10,11] We apply this method to tumor samples taken from breast cancer patients undergoing preoperative letrozole treatment. This allows us to identify essential genes in the primary and residual tumors capturing changes in essentiality as the tumors adapt to the drug
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