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Prediction of drug combination effects with a minimal set of experiments

Abstract

High-throughput drug combination screening provides a systematic strategy to discover unexpected combinatorial synergies in pre-clinical cell models. However, phenotypic combinatorial screening with multi-dose matrix assays is experimentally expensive, especially when the aim is to identify selective combination synergies across a large panel of cell lines or patient samples. Here, we implement DECREASE, an efficient machine learning model that requires only a limited set of pairwise dose–response measurements for accurate prediction of drug combination synergy in a given sample. Using a compendium of 23,595 drug combination matrices tested in various cancer cell lines and malaria and Ebola infection models, we demonstrate how cost-effective experimental designs with DECREASE capture almost the same degree of information for synergy and antagonism detection as the fully measured dose–response matrices. Measuring only the matrix diagonal provides an accurate and practical option for combinatorial screening. The minimal-input web implementation enables applications of DECREASE to both pre-clinical and translational studies.

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Fig. 1: Schematic representation of the computational steps implemented in the DECREASE approach.
Fig. 2: DECREASE accurately predicts combinatorial responses with cost-effective experimental HTS designs.
Fig. 3: DECREASE accurately predicts drug combination landscapes with a fixed-concentration design.
Fig. 4: Selection and use of dose–response matrix rows for the prediction of drug combination effects.
Fig. 5: DECREASE accurately predicts combinatorial responses in antimalaria and antiviral applications.

Data availability

The unpublished in-house anticancer dose–response matrix data for all 210 combinations are available either from the DECREASE web tool (http://decrease.fimm.fi/data_availability) or the GitHub repository (https://github.com/IanevskiAleksandr/DECREASE/tree/master/210_Novel_Anticancer_combinations). The published DLBCL anticancer dose–response matrix data are available at the Tripod portal (http://tripod.nih.gov/matrix-data/m3-btk-6x6-ls)11. The data for 22,737 anticancer drug combination from the study of O’Neil and others3 were downloaded from the original paper (http://mct.aacrjournals.org/content/15/6/1155.figures-only#fragments-additional-data). The published antimalarial dose–response matrix data are available at the Tripod repository (https://tripod.nih.gov/matrix-client/rest/matrix/blocks/506/table)10. The published antiviral dose–response matrix data for Ebola treatment are available from the NCATS Matrix portal (https://matrix.ncats.nih.gov/matrix-client/rest/matrix/blocks/6324/table)35.

Code availability

The source code of the DECREASE prediction algorithm is freely available either at the tool website (http://decrease.fimm.fi/source_code) or GitHub (https://github.com/IanevskiAleksandr/DECREASE) to allow replication of the results and to compare or combine the cNMF algorithm with other prediction models. The source codes of the other algorithms are publicly available (web links and package versions are listed in Supplementary Table 3). The recent Dose model33 implementation was provided by request from its authors, A. Zimmer and U. Alon.

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Acknowledgements

We thank the authors of the original publications (refs. 3,10,11,35) for making their drug combination data matrices publicly available. We thank the authors of the Dose model publication (ref. 33) for providing their MATLAB implementation and guaranteeing its appropriate use in this work, D. Bulanova (FIMM) for her valuable comments and discussions and O. Hansson (FIMM) for technical support with the web server. This work was funded by the Academy of Finland (grants nos. 272577 and 277293 to K.W. and 292611, 279163, 295504, 310507, 313267 and 326238 to T.A.), the European Union’s Horizon 2020 Research and Innovation Programme (ERA PerMed JAKSTAT-TARGET), the Cancer Society of Finland (T.A. and K.W.), Sigrid Jusélius Foundation (K.W. and T.A.) and Novo Nordisk Foundation (NNF17CC0027852 to K.W.). The FIMM High Throughput Biomedine Unit is supported financially by the University of Helsinki and Biocenter Finland (S.P. and J.S.).

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T.A., K.W. and A.I. designed and conceived the study. A.I. developed cNMF and implemented the web platform. A.I. and A.K.G. selected and tested various machine learning methods. P.G. performed the drug combination screening experiments. A.I., A.K.G. and A.K. managed data integration from various sources. A.I. and A.K. prepared the figures and tables for the manuscript. A.I., A.K.G. and T.A. wrote the manuscript. A.K., P.G., S.P., J.S. and K.W. critically reviewed and edited the manuscript. All authors reviewed and approved the final version of the manuscript. A.K.G. and P.G. contributed equally.

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Correspondence to Tero Aittokallio.

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Ianevski, A., Giri, A.K., Gautam, P. et al. Prediction of drug combination effects with a minimal set of experiments. Nat Mach Intell 1, 568–577 (2019). https://doi.org/10.1038/s42256-019-0122-4

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