Designing combination therapies with modeling chaperoned machine learning
Huynh, Julie M
Aryeh, Kayenat S
Paek, Andrew L
AffiliationUniv Arizona, Mol & Cellular Biol
MetadataShow full item record
PublisherPUBLIC LIBRARY SCIENCE
CitationZhang Y, Huynh JM, Liu GS, Ballweg R, Aryeh KS, et al. (2019) Designing combination therapies with modeling chaperoned machine learning. PLOS Computational Biology 15(9): e1007158. https://doi.org/10.1371/journal.pcbi.1007158
JournalPLOS COMPUTATIONAL BIOLOGY
RightsCopyright © 2019 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractChemotherapy 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.
NoteOpen access journal
VersionFinal published version
SponsorsTLZ (University of Cincinnati); AP (University of Arizona)