Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines

Abstract Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were propos...

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Autores principales:	Krzysztof Koras, Ewa Kizling, Dilafruz Juraeva, Eike Staub, Ewa Szczurek
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5ca6b6ee38d84c60b90d71aa1aaab0aa
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spelling	oai:doaj.org-article:5ca6b6ee38d84c60b90d71aa1aaab0aa2021-12-02T18:49:36ZInterpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines10.1038/s41598-021-94564-z2045-2322https://doaj.org/article/5ca6b6ee38d84c60b90d71aa1aaab0aa2021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94564-zhttps://doaj.org/toc/2045-2322Abstract Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel interpretability approach, which in addition to the set of modeled features considers also the genes and processes outside of this set. Our approach outperforms simpler matrix factorization models, achieving R $$=$$ = 0.82 correlation between true and predicted response for the unseen cell lines. The interpretability analysis identifies 67 biological processes that drive the cell line sensitivity to particular compounds. Detailed case studies are shown for PHA-793887, XMD14-99 and Dabrafenib.Krzysztof KorasEwa KizlingDilafruz JuraevaEike StaubEwa SzczurekNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Krzysztof Koras Ewa Kizling Dilafruz Juraeva Eike Staub Ewa Szczurek Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines
description	Abstract Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project cell line and drug features into 10-dimensional hidden representations and a feed-forward neural network to combine them into response prediction. We propose a novel interpretability approach, which in addition to the set of modeled features considers also the genes and processes outside of this set. Our approach outperforms simpler matrix factorization models, achieving R $$=$$ = 0.82 correlation between true and predicted response for the unseen cell lines. The interpretability analysis identifies 67 biological processes that drive the cell line sensitivity to particular compounds. Detailed case studies are shown for PHA-793887, XMD14-99 and Dabrafenib.
format	article
author	Krzysztof Koras Ewa Kizling Dilafruz Juraeva Eike Staub Ewa Szczurek
author_facet	Krzysztof Koras Ewa Kizling Dilafruz Juraeva Eike Staub Ewa Szczurek
author_sort	Krzysztof Koras
title	Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines
title_short	Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines
title_full	Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines
title_fullStr	Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines
title_full_unstemmed	Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines
title_sort	interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5ca6b6ee38d84c60b90d71aa1aaab0aa
work_keys_str_mv	AT krzysztofkoras interpretabledeeprecommendersystemmodelforpredictionofkinaseinhibitorefficacyacrosscancercelllines AT ewakizling interpretabledeeprecommendersystemmodelforpredictionofkinaseinhibitorefficacyacrosscancercelllines AT dilafruzjuraeva interpretabledeeprecommendersystemmodelforpredictionofkinaseinhibitorefficacyacrosscancercelllines AT eikestaub interpretabledeeprecommendersystemmodelforpredictionofkinaseinhibitorefficacyacrosscancercelllines AT ewaszczurek interpretabledeeprecommendersystemmodelforpredictionofkinaseinhibitorefficacyacrosscancercelllines
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Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines

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