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Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization - PubMed

  • ️Fri Jan 01 2021

Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization

Simone Spolaor et al. Front Genet. 2021.

Abstract

Combination therapies proved to be a valuable strategy in the fight against cancer, thanks to their increased efficacy in inducing tumor cell death and in reducing tumor growth, metastatic potential, and the risk of developing drug resistance. The identification of effective combinations of drug targets generally relies on costly and time consuming processes based on in vitro experiments. Here, we present a novel computational approach that, by integrating dynamic fuzzy modeling with multi-objective optimization, allows to efficiently identify novel combination cancer therapies, with a relevant saving in working time and costs. We tested this approach on a model of oncogenic K-ras cancer cells characterized by a marked Warburg effect. The computational approach was validated by its capability in finding out therapies already known in the literature for this type of cancer cell. More importantly, our results show that this method can suggest potential therapies consisting in a small number of molecular targets. In the model of oncogenic K-ras cancer cells, for instance, we identified combination of up to three targets, which affect different cellular pathways that are crucial for cancer proliferation and survival.

Keywords: cancer; combination chemotherapy; fuzzy modeling; global optimization; multi-objective optimization; therapeutic targets.

Copyright © 2021 Spolaor, Scheve, Firat, Cazzaniga, Besozzi and Nobile.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1

The interaction map of the programmed cell death model, showing the mutual regulation of the components. In the figure, single molecules are represented by red circles; blue rounded rectangles represent proteins; orange rectangles denote processes; green hexagons represent the outputs of the model. Positive and negative regulations are denoted as arrows and blunt-ended arrows, respectively. Figure adapted from Nobile et al. (2020).

Figure 2
Figure 2

Pareto dominant solutions obtained by NSGA-II using 2 objectives (f↑apo and f↓com) in PKA low (left) and high (right) conditions.

Figure 3
Figure 3

Simulation of the DFM in the PKA low condition, using the perturbations “Ca2+ is high, Bcl2 is low” (left) and “Ca2+ is high, Bcl2 is low, CI is low” (right).

Figure 4
Figure 4

Dynamics of the unperturbed DFM, in the case of low (left) and high (right) PKA expression.

Figure 5
Figure 5

Simulation of the DFM in the PKA high condition, using the perturbations “ERK is low, UPR is high” (left) and “ERK is low, UPR is high, CI is Low” (right).

Figure 6
Figure 6

Pareto dominant solutions obtained by NSGA-II using 3 objectives (f↑apo, f↓nec, and f↓com) in the PKA low condition (left) and PKA high condition (right). Solutions' color is represented by RGB triplets, according to their fitness values (i.e., red as change in apoptosis, green as change in necrosis and blue as complexity of the solution). Projections of the solutions on 2D planes are represented in gray.

Figure 7
Figure 7

Simulation of the DFM in the PKA low condition, using the perturbations “CI IS high, ERK IS low, UPR IS high” (left) and “CI IS low, ERK IS low, UPR IS high” (right).

Figure 8
Figure 8

Simulation of the DFM in the PKA low condition, using the perturbations “DeltaPsi IS low, ERK IS low, UPR IS high” (left) and “Ca2+ IS high, ERK IS low, UPR IS high” (right).

Figure 9
Figure 9

Simulation of the DFM in the PKA high condition, using the perturbations “Ca2+ IS high, ERK IS low” (left) and “DeltaPsi IS low, UPR IS high” (right).

Figure 10
Figure 10

Simulation of the DFM in the PKA high condition, using the perturbations “Ca2+ IS high, ERK IS low, N-glycosylation IS low” (left) and “DeltaPsi IS low, ERK IS low, UPR IS high” (right).

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