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Modeling drug combination effects via latent tensor reconstruction

MOTIVATION: Combination therapies have emerged as a powerful treatment modality to overcome drug resistance and improve treatment efficacy. However, the number of possible drug combinations increases very rapidly with the number of individual drugs in consideration, which makes the comprehensive exp...

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Autores principales: Wang, Tianduanyi, Szedmak, Sandor, Wang, Haishan, Aittokallio, Tero, Pahikkala, Tapio, Cichonska, Anna, Rousu, Juho
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336593/
https://www.ncbi.nlm.nih.gov/pubmed/34252952
http://dx.doi.org/10.1093/bioinformatics/btab308
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author Wang, Tianduanyi
Szedmak, Sandor
Wang, Haishan
Aittokallio, Tero
Pahikkala, Tapio
Cichonska, Anna
Rousu, Juho
author_facet Wang, Tianduanyi
Szedmak, Sandor
Wang, Haishan
Aittokallio, Tero
Pahikkala, Tapio
Cichonska, Anna
Rousu, Juho
author_sort Wang, Tianduanyi
collection PubMed
description MOTIVATION: Combination therapies have emerged as a powerful treatment modality to overcome drug resistance and improve treatment efficacy. However, the number of possible drug combinations increases very rapidly with the number of individual drugs in consideration, which makes the comprehensive experimental screening infeasible in practice. Machine-learning models offer time- and cost-efficient means to aid this process by prioritizing the most effective drug combinations for further pre-clinical and clinical validation. However, the complexity of the underlying interaction patterns across multiple drug doses and in different cellular contexts poses challenges to the predictive modeling of drug combination effects. RESULTS: We introduce comboLTR, highly time-efficient method for learning complex, non-linear target functions for describing the responses of therapeutic agent combinations in various doses and cancer cell-contexts. The method is based on a polynomial regression via powerful latent tensor reconstruction. It uses a combination of recommender system-style features indexing the data tensor of response values in different contexts, and chemical and multi-omics features as inputs. We demonstrate that comboLTR outperforms state-of-the-art methods in terms of predictive performance and running time, and produces highly accurate results even in the challenging and practical inference scenario where full dose–response matrices are predicted for completely new drug combinations with no available combination and monotherapy response measurements in any training cell line. AVAILABILITY AND IMPLEMENTATION: comboLTR code is available at https://github.com/aalto-ics-kepaco/ComboLTR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-83365932021-08-09 Modeling drug combination effects via latent tensor reconstruction Wang, Tianduanyi Szedmak, Sandor Wang, Haishan Aittokallio, Tero Pahikkala, Tapio Cichonska, Anna Rousu, Juho Bioinformatics Biomedical Informatics MOTIVATION: Combination therapies have emerged as a powerful treatment modality to overcome drug resistance and improve treatment efficacy. However, the number of possible drug combinations increases very rapidly with the number of individual drugs in consideration, which makes the comprehensive experimental screening infeasible in practice. Machine-learning models offer time- and cost-efficient means to aid this process by prioritizing the most effective drug combinations for further pre-clinical and clinical validation. However, the complexity of the underlying interaction patterns across multiple drug doses and in different cellular contexts poses challenges to the predictive modeling of drug combination effects. RESULTS: We introduce comboLTR, highly time-efficient method for learning complex, non-linear target functions for describing the responses of therapeutic agent combinations in various doses and cancer cell-contexts. The method is based on a polynomial regression via powerful latent tensor reconstruction. It uses a combination of recommender system-style features indexing the data tensor of response values in different contexts, and chemical and multi-omics features as inputs. We demonstrate that comboLTR outperforms state-of-the-art methods in terms of predictive performance and running time, and produces highly accurate results even in the challenging and practical inference scenario where full dose–response matrices are predicted for completely new drug combinations with no available combination and monotherapy response measurements in any training cell line. AVAILABILITY AND IMPLEMENTATION: comboLTR code is available at https://github.com/aalto-ics-kepaco/ComboLTR. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2021-07-12 /pmc/articles/PMC8336593/ /pubmed/34252952 http://dx.doi.org/10.1093/bioinformatics/btab308 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Biomedical Informatics
Wang, Tianduanyi
Szedmak, Sandor
Wang, Haishan
Aittokallio, Tero
Pahikkala, Tapio
Cichonska, Anna
Rousu, Juho
Modeling drug combination effects via latent tensor reconstruction
title Modeling drug combination effects via latent tensor reconstruction
title_full Modeling drug combination effects via latent tensor reconstruction
title_fullStr Modeling drug combination effects via latent tensor reconstruction
title_full_unstemmed Modeling drug combination effects via latent tensor reconstruction
title_short Modeling drug combination effects via latent tensor reconstruction
title_sort modeling drug combination effects via latent tensor reconstruction
topic Biomedical Informatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336593/
https://www.ncbi.nlm.nih.gov/pubmed/34252952
http://dx.doi.org/10.1093/bioinformatics/btab308
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