An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies

Jennifer O’Neil(Applied Science Laboratories (United States)), Yair Benita(Applied Science Laboratories (United States)), Igor Feldman(Applied Science Laboratories (United States)), Mélissa Chénard(Applied Science Laboratories (United States)), Brian S. Roberts(Applied Science Laboratories (United States)), Yaping Liu(University College of the North), Jing Li(University College of the North), Astrid M. Kral(Applied Science Laboratories (United States)), Serguei Lejnine(Applied Science Laboratories (United States)), Andrey Loboda(Applied Science Laboratories (United States)), William T. Arthur(Applied Science Laboratories (United States)), Răzvan Cristescu(Applied Science Laboratories (United States)), Brian B. Haines(Applied Science Laboratories (United States)), Christopher Winter(Applied Science Laboratories (United States)), Theresa Zhang(Applied Science Laboratories (United States)), Andrew Bloecher(Applied Science Laboratories (United States)), Stuart D. Shumway(Applied Science Laboratories (United States))
Molecular Cancer Therapeutics
March 16, 2016
Cited by 429

Abstract

Combination drug therapy is a widely used paradigm for managing numerous human malignancies. In cancer treatment, additive and/or synergistic drug combinations can convert weakly efficacious monotherapies into regimens that produce robust antitumor activity. This can be explained in part through pathway interdependencies that are critical for cancer cell proliferation and survival. However, identification of the various interdependencies is difficult due to the complex molecular circuitry that underlies tumor development and progression. Here, we present a high-throughput platform that allows for an unbiased identification of synergistic and efficacious drug combinations. In a screen of 22,737 experiments of 583 doublet combinations in 39 diverse cancer cell lines using a 4 by 4 dosing regimen, both well-known and novel synergistic and efficacious combinations were identified. Here, we present an example of one such novel combination, a Wee1 inhibitor (AZD1775) and an mTOR inhibitor (ridaforolimus), and demonstrate that the combination potently and synergistically inhibits cancer cell growth in vitro and in vivo This approach has identified novel combinations that would be difficult to reliably predict based purely on our current understanding of cancer cell biology. Mol Cancer Ther; 15(6); 1155-62. ©2016 AACR.


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