Literature DB >> 26983881

An Unbiased Oncology Compound Screen to Identify Novel Combination Strategies.

Jennifer O'Neil1, Yair Benita2, Igor Feldman2, Melissa Chenard2, Brian Roberts2, Yaping Liu3, Jing Li3, Astrid Kral2, Serguei Lejnine2, Andrey Loboda2, William Arthur2, Razvan Cristescu2, Brian B Haines2, Christopher Winter2, Theresa Zhang2, Andrew Bloecher2, Stuart D Shumway2.   

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. ©2016 American Association for Cancer Research.

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Year:  2016        PMID: 26983881     DOI: 10.1158/1535-7163.MCT-15-0843

Source DB:  PubMed          Journal:  Mol Cancer Ther        ISSN: 1535-7163            Impact factor:   6.261


  62 in total

1.  Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning Model.

Authors:  Heming Zhang; Jiarui Feng; Amanda Zeng; Philip Payne; Fuhai Li
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

Review 2.  Machine learning approaches to drug response prediction: challenges and recent progress.

Authors:  George Adam; Ladislav Rampášek; Zhaleh Safikhani; Petr Smirnov; Benjamin Haibe-Kains; Anna Goldenberg
Journal:  NPJ Precis Oncol       Date:  2020-06-15

Review 3.  Predictive approaches for drug combination discovery in cancer.

Authors:  Seyed Ali Madani Tonekaboni; Laleh Soltan Ghoraie; Venkata Satya Kumar Manem; Benjamin Haibe-Kains
Journal:  Brief Bioinform       Date:  2018-03-01       Impact factor: 11.622

4.  Prediction of drug combination effects with a minimal set of experiments.

Authors:  Aleksandr Ianevski; Anil K Giri; Prson Gautam; Alexander Kononov; Swapnil Potdar; Jani Saarela; Krister Wennerberg; Tero Aittokallio
Journal:  Nat Mach Intell       Date:  2019-12-09

5.  Anticancer drug synergy prediction in understudied tissues using transfer learning.

Authors:  Yejin Kim; Shuyu Zheng; Jing Tang; Wenjin Jim Zheng; Zhao Li; Xiaoqian Jiang
Journal:  J Am Med Inform Assoc       Date:  2021-01-15       Impact factor: 4.497

Review 6.  Targeting the PI3K pathway and DNA damage response as a therapeutic strategy in ovarian cancer.

Authors:  Tzu-Ting Huang; Erika J Lampert; Cynthia Coots; Jung-Min Lee
Journal:  Cancer Treat Rev       Date:  2020-04-10       Impact factor: 12.111

Review 7.  Charting the Fragmented Landscape of Drug Synergy.

Authors:  Christian T Meyer; David J Wooten; Carlos F Lopez; Vito Quaranta
Journal:  Trends Pharmacol Sci       Date:  2020-02-26       Impact factor: 14.819

8.  Machine Learning for Cancer Drug Combination.

Authors:  Ziyan Wang; Hongyang Li; Yuanfang Guan
Journal:  Clin Pharmacol Ther       Date:  2020-02-11       Impact factor: 6.875

9.  DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy.

Authors:  Hui Liu; Wenhao Zhang; Bo Zou; Jinxian Wang; Yuanyuan Deng; Lei Deng
Journal:  Nucleic Acids Res       Date:  2020-01-08       Impact factor: 16.971

10.  Deep learning of pharmacogenomics resources: moving towards precision oncology.

Authors:  Yu-Chiao Chiu; Hung-I Harry Chen; Aparna Gorthi; Milad Mostavi; Siyuan Zheng; Yufei Huang; Yidong Chen
Journal:  Brief Bioinform       Date:  2020-12-01       Impact factor: 11.622

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