Synergistic effects of complex drug combinations in colorectal cancer cells predicted by logical modelling

Abstract

Drug combinations have been proposed to combat drug resistance in cancer, but due to the large number of possible drug targets, in vitro testing of all possible combinations of drugs is challenging. Computational models of a disease hold great promise as tools for prediction of response to treatment, and here we constructed a logical model integrating signaling pathways frequently dysregulated in cancer, as well as pathways activated upon DNA damage, to study the effect of clinically relevant drug combinations. By fitting the model to a dataset of pairwise combinations of drugs targeting MEK, PI3K, and TAK1, as well as several clinically approved agents (palbociclib, olaparib, oxaliplatin, and 5FU), we were able to perform model simulations that allowed us to predict more complex drug combinations, encompassing sets of three and four drugs, with potentially stronger effects compared to pairwise drug combinations. All predicted third-order synergies, as well as a subset of non-synergies, were successfully confirmed by in vitro experiments in the colorectal cancer cell line HCT-116, highlighting the strength of using computational strategies to rationalize drug testing.

A dynamic Boolean network reveals that the BMI1 and MALAT1 axis is associated with drug resistance by limiting miR-145-5p in non-small cell lung cancer

“…The objective of modeling a complex system, such as the MALAT1/miR-145 axis in drug resistance, is to produce a model that can predict the outcome of each component quantitatively. Therefore, Boolean network modeling (BNM) is the most effective way for integrating available knowledge into a logical framework that is compatible with experimental data [ [8] , [9] , [10] , [11] ]. Signaling molecules (signaling proteins and noncoding RNAs like lncRNAs and miRNAs) are described as nodes, and the interconnections between them are called edges [ [12] , [13] , [14] , [15] , [16] , [17] , [18] ].…”

Quadra-Stable Dynamics of p53 and PTEN in the DNA Damage Response

“…Cell fates are coupled with model attractors (fixed points or cyclic attractors) whose designation and reachability qualities are particularly suited to this approach [ 23 ]. Thus, dynamic Boolean network modeling is the finest option to explore the dynamic behavior of the system [ 24 , 25 , 26 , 27 ]. More details about Boolean modeling can be found in the “Materials and Methods” section.…”

The lncRNA DLX6-AS1/miR-16-5p axis regulates autophagy and apoptosis in non-small cell lung cancer: A Boolean model of cell death

“…Therefore, Boolean network modeling is the most effective way to merge existing data into a logical framework that is congruent with experimental results. Signaling molecules (signaling proteins and noncoding RNAs like lncRNAs and miRNAs) are often called nodes, and the relationships between them are called edges [ 14 , 15 ]. Cell fates correspond to model attractors (endpoints points or cyclic attractors), and their identification and accessibility attributes lend themselves well to this approach [ [16] , [17] , [18] ].…”