Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects

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http://hdl.handle.net/10138/323995

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Julkunen , H , Cichonska , A , Gautam , P , Szedmak , S , Douat , J , Pahikkala , T , Aittokallio , T & Rousu , J 2020 , ' Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects ' , Nature Communications , vol. 11 , 6136 . https://doi.org/10.1038/s41467-020-19950-z

Title: Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects
Author: Julkunen, Heli; Cichonska, Anna; Gautam, Prson; Szedmak, Sandor; Douat, Jane; Pahikkala, Tapio; Aittokallio, Tero; Rousu, Juho
Contributor: University of Helsinki, Institute for Molecular Medicine Finland
University of Helsinki, Computational Systems Medicine
University of Helsinki, Helsinki Institute for Information Technology HIIT
University of Helsinki, Helsinki Institute for Information Technology
University of Helsinki, Helsinki Institute for Information Technology HIIT
Date: 2020-12-01
Language: eng
Number of pages: 11
Belongs to series: Nature Communications
ISSN: 2041-1723
URI: http://hdl.handle.net/10138/323995
Abstract: We present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines. The approach enables comboFM to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. We demonstrate high predictive performance of comboFM in various prediction scenarios using data from cancer cell line pharmacogenomic screens. Subsequent experimental validation of a set of previously untested drug combinations further supports the practical and robust applicability of comboFM. For instance, we confirm a novel synergy between anaplastic lymphoma kinase (ALK) inhibitor crizotinib and proteasome inhibitor bortezomib in lymphoma cells. Overall, our results demonstrate that comboFM provides an effective means for systematic pre-screening of drug combinations to support precision oncology applications. Combinatorial treatments have become a standard of care for various complex diseases including cancers. Here, the authors show that combinatorial responses of two anticancer drugs can be accurately predicted using factorization machines trained on large-scale pharmacogenomic data for guiding precision oncology studies.
Subject: TARGETED THERAPY
CANCER
MELANOMA
SYNERGISM
ALMANAC
PAIRS
113 Computer and information sciences
3122 Cancers
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