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Angiogenic activity of breast cancer patients' monocytes reverted by combined use of systems modeling and experimental approaches.

Guex N, Crespo I, Bron S, Ifticene-Treboux A, Faes-Van't Hull E, Kharoubi S, Liechti R, Werffeli P, Ibberson M, Majo F, Nicolas M, Laurent J, Garg A, Zaman K, Lehr HA, Stevenson BJ, Rüegg C, Coukos G, Delaloye JF, Xenarios I, Doucey MA - PLoS Comput. Biol. (2015)

Bottom Line: Angiogenesis plays a key role in tumor growth and cancer progression.In silico predicted perturbations were validated experimentally using patient TEM.In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity.

View Article: PubMed Central - PubMed

Affiliation: The Vital-IT, SIB (Swiss Institute of Bioinformatics), University of Lausanne, Lausanne, Switzerland.

ABSTRACT
Angiogenesis plays a key role in tumor growth and cancer progression. TIE-2-expressing monocytes (TEM) have been reported to critically account for tumor vascularization and growth in mouse tumor experimental models, but the molecular basis of their pro-angiogenic activity are largely unknown. Moreover, differences in the pro-angiogenic activity between blood circulating and tumor infiltrated TEM in human patients has not been established to date, hindering the identification of specific targets for therapeutic intervention. In this work, we investigated these differences and the phenotypic reversal of breast tumor pro-angiogenic TEM to a weak pro-angiogenic phenotype by combining Boolean modelling and experimental approaches. Firstly, we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor, suggesting that the tumor microenvironment shapes the highly pro-angiogenic phenotype of TEM. Secondly, we predicted in silico all minimal perturbations transitioning the highly pro-angiogenic phenotype of tumor TEM to the weak pro-angiogenic phenotype of blood TEM and vice versa. In silico predicted perturbations were validated experimentally using patient TEM. In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. Finally, the relapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity. Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM, and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer.

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ivdTEM network topology.Dynamical models of treatments/receptors/cytokines interactions in ivdTEM. Inputs (treatments) and output (receptor and secreted soluble factors) are depicted in yellow and red respectively. Factors that were used as treatment and measured as output are depicted in orange. Combined treatments (network nodes i.e. AND) are represented as small pink circles. Stimulatory and inhibitory effects of single or combined treatments are depicted by black arrow-headed edges and green edges respectively. Circles and diamonds represent soluble factors and receptors respectively. All the links presented are provided in S4 Table. Boolean equations used for representing ivdTEM regulatory networks are provided in S6 Table.
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pcbi.1004050.g003: ivdTEM network topology.Dynamical models of treatments/receptors/cytokines interactions in ivdTEM. Inputs (treatments) and output (receptor and secreted soluble factors) are depicted in yellow and red respectively. Factors that were used as treatment and measured as output are depicted in orange. Combined treatments (network nodes i.e. AND) are represented as small pink circles. Stimulatory and inhibitory effects of single or combined treatments are depicted by black arrow-headed edges and green edges respectively. Circles and diamonds represent soluble factors and receptors respectively. All the links presented are provided in S4 Table. Boolean equations used for representing ivdTEM regulatory networks are provided in S6 Table.

Mentions: The limited amounts of patient TEM and the combinatorial nature of the ligands precluded experimental testing of all the ligand combinations and was the rationale for building an integrative and predictive model of TEM behavior. We used TEM differentiated in vitro to derive a dynamical regulatory network from experimental data obtained with a selected number of ligands (Fig. 2) and used then as a proxy to assess the clinically most relevant ligand combinations. To create the models, data sets of receptor expression (Fig. 2 and S2 Table) and paracrine secretion profiles (Fig. 2 and S3 Table) were combined to infer relevant relationships (or links) between ligands and receptors. Briefly, relevant links were identified based on the amplitude of their expression or secretion changes, their reproducibility, and their coherent variations across the treatments (see Methods). Based on these criteria, amongst 924 possible links (7 receptors × 11 secreted factors × 12 treatments) we retained 74 relevant links (S4 Table). Globally, TNF-α, TGF-β and PlGF appeared as key regulators of TEM network. However, TNF-α in contrast to TGF-β, was strongly regulated by other factors (Fig. 3). Dynamical Boolean modeling was then performed by integrating the retained links into an algorithm for computing Minimal Intervention Set (MIS) of TEM regulatory network. Given a regulatory network, MIS patterns represent a set of simultaneous perturbations (or treatments) to force the network into a desired steady state, where a subset of nodes remain at a fixed expression level of either low or high [41,42]. The term minimal implies that no other sub-set of an MIS pattern can lead to the desired steady state behavior. However, for a given network, there can be more than one MIS patterns to generate the same steady state. The MIS algorithm proposed by Garg et al [43,44] was used for assessing TEM regulatory network by computationally predicting all possible set of up to three simultaneous treatments that can force the TEM network into a weakly (i.e. blood TEM) or highly (i.e. tumor TEM) pro-angiogenic phenotype.


Angiogenic activity of breast cancer patients' monocytes reverted by combined use of systems modeling and experimental approaches.

Guex N, Crespo I, Bron S, Ifticene-Treboux A, Faes-Van't Hull E, Kharoubi S, Liechti R, Werffeli P, Ibberson M, Majo F, Nicolas M, Laurent J, Garg A, Zaman K, Lehr HA, Stevenson BJ, Rüegg C, Coukos G, Delaloye JF, Xenarios I, Doucey MA - PLoS Comput. Biol. (2015)

ivdTEM network topology.Dynamical models of treatments/receptors/cytokines interactions in ivdTEM. Inputs (treatments) and output (receptor and secreted soluble factors) are depicted in yellow and red respectively. Factors that were used as treatment and measured as output are depicted in orange. Combined treatments (network nodes i.e. AND) are represented as small pink circles. Stimulatory and inhibitory effects of single or combined treatments are depicted by black arrow-headed edges and green edges respectively. Circles and diamonds represent soluble factors and receptors respectively. All the links presented are provided in S4 Table. Boolean equations used for representing ivdTEM regulatory networks are provided in S6 Table.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4359163&req=5

pcbi.1004050.g003: ivdTEM network topology.Dynamical models of treatments/receptors/cytokines interactions in ivdTEM. Inputs (treatments) and output (receptor and secreted soluble factors) are depicted in yellow and red respectively. Factors that were used as treatment and measured as output are depicted in orange. Combined treatments (network nodes i.e. AND) are represented as small pink circles. Stimulatory and inhibitory effects of single or combined treatments are depicted by black arrow-headed edges and green edges respectively. Circles and diamonds represent soluble factors and receptors respectively. All the links presented are provided in S4 Table. Boolean equations used for representing ivdTEM regulatory networks are provided in S6 Table.
Mentions: The limited amounts of patient TEM and the combinatorial nature of the ligands precluded experimental testing of all the ligand combinations and was the rationale for building an integrative and predictive model of TEM behavior. We used TEM differentiated in vitro to derive a dynamical regulatory network from experimental data obtained with a selected number of ligands (Fig. 2) and used then as a proxy to assess the clinically most relevant ligand combinations. To create the models, data sets of receptor expression (Fig. 2 and S2 Table) and paracrine secretion profiles (Fig. 2 and S3 Table) were combined to infer relevant relationships (or links) between ligands and receptors. Briefly, relevant links were identified based on the amplitude of their expression or secretion changes, their reproducibility, and their coherent variations across the treatments (see Methods). Based on these criteria, amongst 924 possible links (7 receptors × 11 secreted factors × 12 treatments) we retained 74 relevant links (S4 Table). Globally, TNF-α, TGF-β and PlGF appeared as key regulators of TEM network. However, TNF-α in contrast to TGF-β, was strongly regulated by other factors (Fig. 3). Dynamical Boolean modeling was then performed by integrating the retained links into an algorithm for computing Minimal Intervention Set (MIS) of TEM regulatory network. Given a regulatory network, MIS patterns represent a set of simultaneous perturbations (or treatments) to force the network into a desired steady state, where a subset of nodes remain at a fixed expression level of either low or high [41,42]. The term minimal implies that no other sub-set of an MIS pattern can lead to the desired steady state behavior. However, for a given network, there can be more than one MIS patterns to generate the same steady state. The MIS algorithm proposed by Garg et al [43,44] was used for assessing TEM regulatory network by computationally predicting all possible set of up to three simultaneous treatments that can force the TEM network into a weakly (i.e. blood TEM) or highly (i.e. tumor TEM) pro-angiogenic phenotype.

Bottom Line: Angiogenesis plays a key role in tumor growth and cancer progression.In silico predicted perturbations were validated experimentally using patient TEM.In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity.

View Article: PubMed Central - PubMed

Affiliation: The Vital-IT, SIB (Swiss Institute of Bioinformatics), University of Lausanne, Lausanne, Switzerland.

ABSTRACT
Angiogenesis plays a key role in tumor growth and cancer progression. TIE-2-expressing monocytes (TEM) have been reported to critically account for tumor vascularization and growth in mouse tumor experimental models, but the molecular basis of their pro-angiogenic activity are largely unknown. Moreover, differences in the pro-angiogenic activity between blood circulating and tumor infiltrated TEM in human patients has not been established to date, hindering the identification of specific targets for therapeutic intervention. In this work, we investigated these differences and the phenotypic reversal of breast tumor pro-angiogenic TEM to a weak pro-angiogenic phenotype by combining Boolean modelling and experimental approaches. Firstly, we show that in breast cancer patients the pro-angiogenic activity of TEM increased drastically from blood to tumor, suggesting that the tumor microenvironment shapes the highly pro-angiogenic phenotype of TEM. Secondly, we predicted in silico all minimal perturbations transitioning the highly pro-angiogenic phenotype of tumor TEM to the weak pro-angiogenic phenotype of blood TEM and vice versa. In silico predicted perturbations were validated experimentally using patient TEM. In addition, gene expression profiling of TEM transitioned to a weak pro-angiogenic phenotype confirmed that TEM are plastic cells and can be reverted to immunological potent monocytes. Finally, the relapse-free survival analysis showed a statistically significant difference between patients with tumors with high and low expression values for genes encoding transitioning proteins detected in silico and validated on patient TEM. In conclusion, the inferred TEM regulatory network accurately captured experimental TEM behavior and highlighted crosstalk between specific angiogenic and inflammatory signaling pathways of outstanding importance to control their pro-angiogenic activity. Results showed the successful in vitro reversion of such an activity by perturbation of in silico predicted target genes in tumor derived TEM, and indicated that targeting tumor TEM plasticity may constitute a novel valid therapeutic strategy in breast cancer.

Show MeSH
Related in: MedlinePlus