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Developing multi-target therapeutics to fine-tune the evolutionary dynamics of the cancer ecosystem.

Xie L, Bourne PE - Front Pharmacol (2015)

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Hunter College, The City University of New York New York, NY, USA ; The Graduate Center, The City University of New York New York, NY, USA.

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Multi-target therapies, either in combination or in sequential order, have been advocated to combat intrinsic and acquired resistance to anti-cancer drugs (Holohan et al., ; Yardley, )... However, the effectiveness of multi-target anti-cancer therapy in the clinic is limited... The selection of cancer cells obeys Darwin's law of evolution... Under the pressure of drug perturbation, the cancer cell can adapt versatile molecular and cellular mechanisms for survival, and often evolves into more aggressive or metastasis phenotypes (Holohan et al., )... At the cellular level, multiple pathways support the survival of cancer cells... On the contrary, the chemotherapy may stimulate the production of immunosuppressive molecules (Shalapour et al., )... As a result, the patient's anti-cancer immune response is inhibited... Based on theoretical, experimental, and clinical results of ecology, microbiology, and cancer research, it has been proposed that tuning the population dynamics of cancer cells can be a powerful strategy in developing an anti-cancer therapy (Korolev et al., )... Correlated with generic variations, drug response phenomics data are available at molecular, cellular, tissue, and organism levels (Zbuk and Eng, )... A number of effective therapies in treating complex diseases may follow this principle (Xie et al., )... For instance, successful anti-psychotic drugs, such as clozapine, mediate their effects through binding entire families of serotonin and dopamine receptors... Polypharmacology aims to design “dirty” drugs that can bind to multiple receptors simultaneously... Its effectiveness in treating systematic diseases has been documented (Xie et al., )... For example, targeted polypharmacological agents have been successfully designed to modulate signaling transduction events (Apsel et al., ).

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Related in: MedlinePlus

Two strategies for anti-cancer therapy. (A) Node killing strategy kills sub-clones of cancer cells through chemo-, targeted-, and immuno-therapy. The adaptive evolution of the cancer often leads to drug resistance. The cancer often turns into a more aggressive form. (B) Edge perturbation strategy aims to disturb the cell-cell interactions of the cancer ecosystem. It may have bigger impact on the cancer as a whole. It is less likely for the cancer to evolve into a drug resistance phenotype, as no sub-clone can gain particular evolutionary advantage.
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Figure 1: Two strategies for anti-cancer therapy. (A) Node killing strategy kills sub-clones of cancer cells through chemo-, targeted-, and immuno-therapy. The adaptive evolution of the cancer often leads to drug resistance. The cancer often turns into a more aggressive form. (B) Edge perturbation strategy aims to disturb the cell-cell interactions of the cancer ecosystem. It may have bigger impact on the cancer as a whole. It is less likely for the cancer to evolve into a drug resistance phenotype, as no sub-clone can gain particular evolutionary advantage.

Mentions: Multi-scale modeling and simulation may play a key role in predicting the evolutionary dynamics of the cancer ecosystem, and identify anti-cancer therapeutic targets for pre-emptive treatment. It is possible to reconstruct context-specific whole cell models by integrating multiple omics data (Karr et al., 2012). Subsequently, their cellular functions can be simulated and predicted at different evolutionary stages under a framework of constraint-based modeling (Bordbar et al., 2014). Using a single cell or sub-clone as the building block, the cancer ecosystem can be modeled as a dynamic cell-cell interaction network, in which the node is a cell, and the edge represents the cell-cell interaction. Each node, or cluster of nodes, has different traits and evolutionary trajectory, yet depend on each other, as shown in Figure 1. A network representation may allow us to understand the emergent properties of the cancer ecosystem. For example, one of intrinsic properties of biological network is “robust-yet-fragile” (Kitano, 2007). The removal of a single node may have little impact on the whole system. However, the weak perturbation of multiple nodes can lead to the system failure, even if these nodes are not deleted. A number of effective therapies in treating complex diseases may follow this principle (Xie et al., 2012). For instance, successful anti-psychotic drugs, such as clozapine, mediate their effects through binding entire families of serotonin and dopamine receptors. The clinical failures of many anti-psychotic drugs can be attributed to them being too selective as designed (Hopkins et al., 2006). In another example, the anti-cancer effect of HIV protease inhibitors is proposed to comes from their weak bindings to multiple kinases (Xie et al., 2011). Moreover, the perturbation of edges may be more effective than nodes to regulate the state transition of a non-linear dynamic system (Tong et al., 2012). An additional advantage of edge perturbation is that the cancer cell has little selection pressure to evolve into a drug resistant phenotype, as the cancer cell will not be killed directly by the drug. In a proof-of-concept study, blocking cell-cell communication inhibited the repopulation of cancer stem cells, thus enhancing the effectiveness of anti-cancer therapy (Kurtova et al., 2015). To capture the whole dynamic spectrum of the cancer ecosystem, mechanistic and quantitative dynamic simulation is needed. Coarse-grained dynamic modeling has successfully identified an optimized sequence of therapies to improve the survival of patients with metastatic castrate resistant prostate cancer (Gallaher et al., 2014). Agent-based modeling that has been successfully applied to study the dynamics of complex systems could be a powerful tool to integrate whole cell models into a dynamic model of the cancer ecosystem (An et al., 2009).


Developing multi-target therapeutics to fine-tune the evolutionary dynamics of the cancer ecosystem.

Xie L, Bourne PE - Front Pharmacol (2015)

Two strategies for anti-cancer therapy. (A) Node killing strategy kills sub-clones of cancer cells through chemo-, targeted-, and immuno-therapy. The adaptive evolution of the cancer often leads to drug resistance. The cancer often turns into a more aggressive form. (B) Edge perturbation strategy aims to disturb the cell-cell interactions of the cancer ecosystem. It may have bigger impact on the cancer as a whole. It is less likely for the cancer to evolve into a drug resistance phenotype, as no sub-clone can gain particular evolutionary advantage.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4585080&req=5

Figure 1: Two strategies for anti-cancer therapy. (A) Node killing strategy kills sub-clones of cancer cells through chemo-, targeted-, and immuno-therapy. The adaptive evolution of the cancer often leads to drug resistance. The cancer often turns into a more aggressive form. (B) Edge perturbation strategy aims to disturb the cell-cell interactions of the cancer ecosystem. It may have bigger impact on the cancer as a whole. It is less likely for the cancer to evolve into a drug resistance phenotype, as no sub-clone can gain particular evolutionary advantage.
Mentions: Multi-scale modeling and simulation may play a key role in predicting the evolutionary dynamics of the cancer ecosystem, and identify anti-cancer therapeutic targets for pre-emptive treatment. It is possible to reconstruct context-specific whole cell models by integrating multiple omics data (Karr et al., 2012). Subsequently, their cellular functions can be simulated and predicted at different evolutionary stages under a framework of constraint-based modeling (Bordbar et al., 2014). Using a single cell or sub-clone as the building block, the cancer ecosystem can be modeled as a dynamic cell-cell interaction network, in which the node is a cell, and the edge represents the cell-cell interaction. Each node, or cluster of nodes, has different traits and evolutionary trajectory, yet depend on each other, as shown in Figure 1. A network representation may allow us to understand the emergent properties of the cancer ecosystem. For example, one of intrinsic properties of biological network is “robust-yet-fragile” (Kitano, 2007). The removal of a single node may have little impact on the whole system. However, the weak perturbation of multiple nodes can lead to the system failure, even if these nodes are not deleted. A number of effective therapies in treating complex diseases may follow this principle (Xie et al., 2012). For instance, successful anti-psychotic drugs, such as clozapine, mediate their effects through binding entire families of serotonin and dopamine receptors. The clinical failures of many anti-psychotic drugs can be attributed to them being too selective as designed (Hopkins et al., 2006). In another example, the anti-cancer effect of HIV protease inhibitors is proposed to comes from their weak bindings to multiple kinases (Xie et al., 2011). Moreover, the perturbation of edges may be more effective than nodes to regulate the state transition of a non-linear dynamic system (Tong et al., 2012). An additional advantage of edge perturbation is that the cancer cell has little selection pressure to evolve into a drug resistant phenotype, as the cancer cell will not be killed directly by the drug. In a proof-of-concept study, blocking cell-cell communication inhibited the repopulation of cancer stem cells, thus enhancing the effectiveness of anti-cancer therapy (Kurtova et al., 2015). To capture the whole dynamic spectrum of the cancer ecosystem, mechanistic and quantitative dynamic simulation is needed. Coarse-grained dynamic modeling has successfully identified an optimized sequence of therapies to improve the survival of patients with metastatic castrate resistant prostate cancer (Gallaher et al., 2014). Agent-based modeling that has been successfully applied to study the dynamics of complex systems could be a powerful tool to integrate whole cell models into a dynamic model of the cancer ecosystem (An et al., 2009).

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Hunter College, The City University of New York New York, NY, USA ; The Graduate Center, The City University of New York New York, NY, USA.

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

Multi-target therapies, either in combination or in sequential order, have been advocated to combat intrinsic and acquired resistance to anti-cancer drugs (Holohan et al., ; Yardley, )... However, the effectiveness of multi-target anti-cancer therapy in the clinic is limited... The selection of cancer cells obeys Darwin's law of evolution... Under the pressure of drug perturbation, the cancer cell can adapt versatile molecular and cellular mechanisms for survival, and often evolves into more aggressive or metastasis phenotypes (Holohan et al., )... At the cellular level, multiple pathways support the survival of cancer cells... On the contrary, the chemotherapy may stimulate the production of immunosuppressive molecules (Shalapour et al., )... As a result, the patient's anti-cancer immune response is inhibited... Based on theoretical, experimental, and clinical results of ecology, microbiology, and cancer research, it has been proposed that tuning the population dynamics of cancer cells can be a powerful strategy in developing an anti-cancer therapy (Korolev et al., )... Correlated with generic variations, drug response phenomics data are available at molecular, cellular, tissue, and organism levels (Zbuk and Eng, )... A number of effective therapies in treating complex diseases may follow this principle (Xie et al., )... For instance, successful anti-psychotic drugs, such as clozapine, mediate their effects through binding entire families of serotonin and dopamine receptors... Polypharmacology aims to design “dirty” drugs that can bind to multiple receptors simultaneously... Its effectiveness in treating systematic diseases has been documented (Xie et al., )... For example, targeted polypharmacological agents have been successfully designed to modulate signaling transduction events (Apsel et al., ).

No MeSH data available.


Related in: MedlinePlus