RT Journal Article
SR Electronic
T1 Combination Treatment Optimization Using a Pan-Cancer Pathway Model
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.07.05.184960
DO 10.1101/2020.07.05.184960
A1 Robin Schmucker
A1 Gabriele Farina
A1 James Faeder
A1 Fabian Fröhlich
A1 Ali Sinan Saglam
A1 Tuomas Sandholm
YR 2020
UL http://biorxiv.org/content/early/2020/07/06/2020.07.05.184960.abstract
AB The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisti-cated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify potentially novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the mixtures and dosages used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be used for other signaling pathway models also, provided that a suitable predictive model is available.Competing Interest StatementThe authors have declared no competing interest.