Designing combination therapies with modeling chaperoned machine learning.

Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a com...

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Autores principales:	Yin Zhang, Julie M Huynh, Guan-Sheng Liu, Richard Ballweg, Kayenat S Aryeh, Andrew L Paek, Tongli Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2019
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f501b1749c324b698edad8e4e09907c9
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spelling	oai:doaj.org-article:f501b1749c324b698edad8e4e09907c92021-12-02T19:57:52ZDesigning combination therapies with modeling chaperoned machine learning.1553-734X1553-735810.1371/journal.pcbi.1007158https://doaj.org/article/f501b1749c324b698edad8e4e09907c92019-09-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1007158https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.Yin ZhangJulie M HuynhGuan-Sheng LiuRichard BallwegKayenat S AryehAndrew L PaekTongli ZhangPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 15, Iss 9, p e1007158 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Yin Zhang Julie M Huynh Guan-Sheng Liu Richard Ballweg Kayenat S Aryeh Andrew L Paek Tongli Zhang Designing combination therapies with modeling chaperoned machine learning.
description	Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational design of combination therapies, we developed a comprehensive computational model that incorporates the available biological knowledge and relevant experimental data on the life-and-death response of individual cancer cells to cisplatin or cisplatin combined with the TNF-related apoptosis-inducing ligand (TRAIL). The model's predictions, that a combination treatment of cisplatin and TRAIL would enhance cancer cell death and exhibit a "two-wave killing" temporal pattern, was validated by measuring the dynamics of p53 accumulation, cell fate, and cell death in single cells. The validated model was then subjected to a systematic analysis with an ensemble of diverse machine learning methods. Though each method is characterized by a different algorithm, they collectively identified several molecular players that can sensitize tumor cells to cisplatin-induced apoptosis (sensitizers). The identified sensitizers are consistent with previous experimental observations. Overall, we have illustrated that machine learning analysis of an experimentally validated mechanistic model can convert our available knowledge into the identity of biologically meaningful sensitizers. This knowledge can then be leveraged to design treatment strategies that could improve the efficacy of chemotherapy.
format	article
author	Yin Zhang Julie M Huynh Guan-Sheng Liu Richard Ballweg Kayenat S Aryeh Andrew L Paek Tongli Zhang
author_facet	Yin Zhang Julie M Huynh Guan-Sheng Liu Richard Ballweg Kayenat S Aryeh Andrew L Paek Tongli Zhang
author_sort	Yin Zhang
title	Designing combination therapies with modeling chaperoned machine learning.
title_short	Designing combination therapies with modeling chaperoned machine learning.
title_full	Designing combination therapies with modeling chaperoned machine learning.
title_fullStr	Designing combination therapies with modeling chaperoned machine learning.
title_full_unstemmed	Designing combination therapies with modeling chaperoned machine learning.
title_sort	designing combination therapies with modeling chaperoned machine learning.
publisher	Public Library of Science (PLoS)
publishDate	2019
url	https://doaj.org/article/f501b1749c324b698edad8e4e09907c9
work_keys_str_mv	AT yinzhang designingcombinationtherapieswithmodelingchaperonedmachinelearning AT juliemhuynh designingcombinationtherapieswithmodelingchaperonedmachinelearning AT guanshengliu designingcombinationtherapieswithmodelingchaperonedmachinelearning AT richardballweg designingcombinationtherapieswithmodelingchaperonedmachinelearning AT kayenatsaryeh designingcombinationtherapieswithmodelingchaperonedmachinelearning AT andrewlpaek designingcombinationtherapieswithmodelingchaperonedmachinelearning AT tonglizhang designingcombinationtherapieswithmodelingchaperonedmachinelearning
_version_	1718375815531462656

Designing combination therapies with modeling chaperoned machine learning.

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