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Network analysis of immunotherapy-induced regressing tumours identifies novel synergistic drug combinations.

Lesterhuis WJ, Rinaldi C, Jones A, Rozali EN, Dick IM, Khong A, Boon L, Robinson BW, Nowak AK, Bosco A, Lake RA - Sci Rep (2015)

Bottom Line: Here, we provide proof of concept for the validity of this approach in a murine mesothelioma model, which displays a dichotomous response to anti-CTLA4 immune checkpoint blockade.Targeting the modules via selective modulation of hub genes or alternatively by using repurposed pharmaceuticals selected on the basis of their expression perturbation signatures dramatically enhanced the efficacy of CTLA4 blockade in this model.Our approach provides a powerful platform to repurpose drugs, and define contextually relevant novel therapeutic targets.

View Article: PubMed Central - PubMed

Affiliation: 1] National Centre for Asbestos Related Diseases [2] School of Medicine and Pharmacology, University of Western Australia, The Harry Perkins Institute of Medical Research, 5th Floor, QQ Block, 6 Verdun Street, Nedlands WA 6009, Australia.

ABSTRACT
Cancer immunotherapy has shown impressive results, but most patients do not respond. We hypothesized that the effector response in the tumour could be visualized as a complex network of interacting gene products and that by mapping this network we could predict effective pharmacological interventions. Here, we provide proof of concept for the validity of this approach in a murine mesothelioma model, which displays a dichotomous response to anti-CTLA4 immune checkpoint blockade. Network analysis of gene expression profiling data from responding versus non-responding tumours was employed to identify modules associated with response. Targeting the modules via selective modulation of hub genes or alternatively by using repurposed pharmaceuticals selected on the basis of their expression perturbation signatures dramatically enhanced the efficacy of CTLA4 blockade in this model. Our approach provides a powerful platform to repurpose drugs, and define contextually relevant novel therapeutic targets.

No MeSH data available.


Related in: MedlinePlus

Network analysis of gene expression data from regressing versus non-regressing tumours identifies associated modules.(a) Unsupervised hierarchical cluster analysis of microarray data from unilaterally removed AB1-HA tumours from responders (RS), non-responders (NR) and untreated mice (UT), with tumor growth curves. (b) A co-expression network was constructed by applying the WGCNA algorithm. Eight modules were identified (tagged by colour coding). (c) Modules were related to treatment response by identifying differentially expressed genes between responders and non-responders, and plotting the differential t-statistics as box-and-whisker plots on a module-by-module basis. The dashed horizontal lines correspond to FDR < 0.001. (d) Canonical pathways enriched in module 2 (immune module). Data analysis was performed with Ingenuity Systems software. (e) AB1-HA bearing mice were treated with anti-CTLA4 200 μg on day 6 after tumor inoculation with or without a CD8 depleting antibody 150 μg i.v., one day before anti-CTLA4, followed by 100 μg i.p. every 3 days, 6 dosages in total (PBS n = 3; anti-CTLA4 n = 10; anti-CTLA4 + anti-CD8 n = 5) (**p < 0.01). (f) Canonical pathways enriched in module 4 (cancer module).
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f2: Network analysis of gene expression data from regressing versus non-regressing tumours identifies associated modules.(a) Unsupervised hierarchical cluster analysis of microarray data from unilaterally removed AB1-HA tumours from responders (RS), non-responders (NR) and untreated mice (UT), with tumor growth curves. (b) A co-expression network was constructed by applying the WGCNA algorithm. Eight modules were identified (tagged by colour coding). (c) Modules were related to treatment response by identifying differentially expressed genes between responders and non-responders, and plotting the differential t-statistics as box-and-whisker plots on a module-by-module basis. The dashed horizontal lines correspond to FDR < 0.001. (d) Canonical pathways enriched in module 2 (immune module). Data analysis was performed with Ingenuity Systems software. (e) AB1-HA bearing mice were treated with anti-CTLA4 200 μg on day 6 after tumor inoculation with or without a CD8 depleting antibody 150 μg i.v., one day before anti-CTLA4, followed by 100 μg i.p. every 3 days, 6 dosages in total (PBS n = 3; anti-CTLA4 n = 10; anti-CTLA4 + anti-CD8 n = 5) (**p < 0.01). (f) Canonical pathways enriched in module 4 (cancer module).

Mentions: We treated mice with anti-CTLA4 or PBS, surgically removed one of the tumours 7 days after treatment administration, at which time regressor and progressor tumours are macroscopically identical. Gene expression profiling by microarray was performed on these tumours and also on PBS treated controls. The data were analysed by unsupervised hierarchical clustering, which revealed that the three experimental groups had distinct gene expression profiles; responders were clustered separate from non-responders and untreated samples (Fig. 2a).


Network analysis of immunotherapy-induced regressing tumours identifies novel synergistic drug combinations.

Lesterhuis WJ, Rinaldi C, Jones A, Rozali EN, Dick IM, Khong A, Boon L, Robinson BW, Nowak AK, Bosco A, Lake RA - Sci Rep (2015)

Network analysis of gene expression data from regressing versus non-regressing tumours identifies associated modules.(a) Unsupervised hierarchical cluster analysis of microarray data from unilaterally removed AB1-HA tumours from responders (RS), non-responders (NR) and untreated mice (UT), with tumor growth curves. (b) A co-expression network was constructed by applying the WGCNA algorithm. Eight modules were identified (tagged by colour coding). (c) Modules were related to treatment response by identifying differentially expressed genes between responders and non-responders, and plotting the differential t-statistics as box-and-whisker plots on a module-by-module basis. The dashed horizontal lines correspond to FDR < 0.001. (d) Canonical pathways enriched in module 2 (immune module). Data analysis was performed with Ingenuity Systems software. (e) AB1-HA bearing mice were treated with anti-CTLA4 200 μg on day 6 after tumor inoculation with or without a CD8 depleting antibody 150 μg i.v., one day before anti-CTLA4, followed by 100 μg i.p. every 3 days, 6 dosages in total (PBS n = 3; anti-CTLA4 n = 10; anti-CTLA4 + anti-CD8 n = 5) (**p < 0.01). (f) Canonical pathways enriched in module 4 (cancer module).
© Copyright Policy - open-access
Related In: Results  -  Collection

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Show All Figures
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f2: Network analysis of gene expression data from regressing versus non-regressing tumours identifies associated modules.(a) Unsupervised hierarchical cluster analysis of microarray data from unilaterally removed AB1-HA tumours from responders (RS), non-responders (NR) and untreated mice (UT), with tumor growth curves. (b) A co-expression network was constructed by applying the WGCNA algorithm. Eight modules were identified (tagged by colour coding). (c) Modules were related to treatment response by identifying differentially expressed genes between responders and non-responders, and plotting the differential t-statistics as box-and-whisker plots on a module-by-module basis. The dashed horizontal lines correspond to FDR < 0.001. (d) Canonical pathways enriched in module 2 (immune module). Data analysis was performed with Ingenuity Systems software. (e) AB1-HA bearing mice were treated with anti-CTLA4 200 μg on day 6 after tumor inoculation with or without a CD8 depleting antibody 150 μg i.v., one day before anti-CTLA4, followed by 100 μg i.p. every 3 days, 6 dosages in total (PBS n = 3; anti-CTLA4 n = 10; anti-CTLA4 + anti-CD8 n = 5) (**p < 0.01). (f) Canonical pathways enriched in module 4 (cancer module).
Mentions: We treated mice with anti-CTLA4 or PBS, surgically removed one of the tumours 7 days after treatment administration, at which time regressor and progressor tumours are macroscopically identical. Gene expression profiling by microarray was performed on these tumours and also on PBS treated controls. The data were analysed by unsupervised hierarchical clustering, which revealed that the three experimental groups had distinct gene expression profiles; responders were clustered separate from non-responders and untreated samples (Fig. 2a).

Bottom Line: Here, we provide proof of concept for the validity of this approach in a murine mesothelioma model, which displays a dichotomous response to anti-CTLA4 immune checkpoint blockade.Targeting the modules via selective modulation of hub genes or alternatively by using repurposed pharmaceuticals selected on the basis of their expression perturbation signatures dramatically enhanced the efficacy of CTLA4 blockade in this model.Our approach provides a powerful platform to repurpose drugs, and define contextually relevant novel therapeutic targets.

View Article: PubMed Central - PubMed

Affiliation: 1] National Centre for Asbestos Related Diseases [2] School of Medicine and Pharmacology, University of Western Australia, The Harry Perkins Institute of Medical Research, 5th Floor, QQ Block, 6 Verdun Street, Nedlands WA 6009, Australia.

ABSTRACT
Cancer immunotherapy has shown impressive results, but most patients do not respond. We hypothesized that the effector response in the tumour could be visualized as a complex network of interacting gene products and that by mapping this network we could predict effective pharmacological interventions. Here, we provide proof of concept for the validity of this approach in a murine mesothelioma model, which displays a dichotomous response to anti-CTLA4 immune checkpoint blockade. Network analysis of gene expression profiling data from responding versus non-responding tumours was employed to identify modules associated with response. Targeting the modules via selective modulation of hub genes or alternatively by using repurposed pharmaceuticals selected on the basis of their expression perturbation signatures dramatically enhanced the efficacy of CTLA4 blockade in this model. Our approach provides a powerful platform to repurpose drugs, and define contextually relevant novel therapeutic targets.

No MeSH data available.


Related in: MedlinePlus