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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

Computational identification of hubs associated with the response to checkpoint blockade.(a) Weighted gene correlation network analysis: the x axis depicts the differential expression of the genes, the y-axis the Kwithin value, a measure of connectivity; the genes are colour-coded per module similar to Fig 2c (blue codes for genes within the immune module; cyan for genes within the cancer module) (b) Prior knowledge-based graphical reconstruction of the wiring diagram of the immune module and (c) the cancer module. (d) Tumor growth curve of AB1-HA tumour-bearing mice treated with anti-CTLA4 in combination with competitive NOS2 inhibitor L-NNA, showing abrogation of anti-CTLA4 efficacy.
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f3: Computational identification of hubs associated with the response to checkpoint blockade.(a) Weighted gene correlation network analysis: the x axis depicts the differential expression of the genes, the y-axis the Kwithin value, a measure of connectivity; the genes are colour-coded per module similar to Fig 2c (blue codes for genes within the immune module; cyan for genes within the cancer module) (b) Prior knowledge-based graphical reconstruction of the wiring diagram of the immune module and (c) the cancer module. (d) Tumor growth curve of AB1-HA tumour-bearing mice treated with anti-CTLA4 in combination with competitive NOS2 inhibitor L-NNA, showing abrogation of anti-CTLA4 efficacy.

Mentions: Having identified that there are striking changes in the expression levels of the immune and cancer modules in responders versus non-responders, we reasoned that targeting these modules with drugs could potentially increase the response rate to anti-CTLA-4. We targeted the modules by selectively perturbing the biological activity of hub genes18. We used two different approaches to identify hub genes within the response-associated modules. First, we used an unbiased approach, based on overall strength of the correlation patterns between genes within the same module using weighted gene correlation network analysis (Fig. 3a)18. In the second approach, we employed experimentally supported molecular interaction data from prior studies to reconstruct the wiring diagram of the modules, using the Ingenuity Systems Knowledgebase19. This analysis identified major hub genes in the immune and cancer modules (Fig. 3b,c). Nitric oxide synthase 2 (NOS2) was one of the highest ranked hubs in both independent analyses. Although our analysis identified NOS2 as a hub in the cancer module, it was highly upregulated in responders, while the majority of genes in that module were downregulated, suggesting a reciprocal relationship with other genes in the module (Fig. 3a,b). NOS2 is a transcriptionally regulated isoform of NOS, which catalyses the production of NO from L-arginine, particularly in response to cytokines, generating sustained, high output quantities of NO20. To determine if NOS2 plays a major role in the response to CTLA-4 blockade, we treated mice with established AB1-HA tumours with anti-CTLA-4 in combination with competitive NOS2 inhibitor L-NG-nitroarginine. The data show that treatment efficacy decreased significantly when this hub was inhibited (Fig. 3d). Having established a role for NOS2 in treatment response, we then wanted to determine if this pathway could be harnessed to increase the response rate. Given that selective drugs that enhance NOS2 activity are not available, we used isosorbide dinitrate (ISDN) as a NO generator. ISDN treatment alone did not have any effect on tumour outgrowth, but in combination with anti-CTLA-4 it very significantly improved the cure rate from 10% to 80%, displaying a clear synergistic effect (Fig. 4a).


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)

Computational identification of hubs associated with the response to checkpoint blockade.(a) Weighted gene correlation network analysis: the x axis depicts the differential expression of the genes, the y-axis the Kwithin value, a measure of connectivity; the genes are colour-coded per module similar to Fig 2c (blue codes for genes within the immune module; cyan for genes within the cancer module) (b) Prior knowledge-based graphical reconstruction of the wiring diagram of the immune module and (c) the cancer module. (d) Tumor growth curve of AB1-HA tumour-bearing mice treated with anti-CTLA4 in combination with competitive NOS2 inhibitor L-NNA, showing abrogation of anti-CTLA4 efficacy.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4508665&req=5

f3: Computational identification of hubs associated with the response to checkpoint blockade.(a) Weighted gene correlation network analysis: the x axis depicts the differential expression of the genes, the y-axis the Kwithin value, a measure of connectivity; the genes are colour-coded per module similar to Fig 2c (blue codes for genes within the immune module; cyan for genes within the cancer module) (b) Prior knowledge-based graphical reconstruction of the wiring diagram of the immune module and (c) the cancer module. (d) Tumor growth curve of AB1-HA tumour-bearing mice treated with anti-CTLA4 in combination with competitive NOS2 inhibitor L-NNA, showing abrogation of anti-CTLA4 efficacy.
Mentions: Having identified that there are striking changes in the expression levels of the immune and cancer modules in responders versus non-responders, we reasoned that targeting these modules with drugs could potentially increase the response rate to anti-CTLA-4. We targeted the modules by selectively perturbing the biological activity of hub genes18. We used two different approaches to identify hub genes within the response-associated modules. First, we used an unbiased approach, based on overall strength of the correlation patterns between genes within the same module using weighted gene correlation network analysis (Fig. 3a)18. In the second approach, we employed experimentally supported molecular interaction data from prior studies to reconstruct the wiring diagram of the modules, using the Ingenuity Systems Knowledgebase19. This analysis identified major hub genes in the immune and cancer modules (Fig. 3b,c). Nitric oxide synthase 2 (NOS2) was one of the highest ranked hubs in both independent analyses. Although our analysis identified NOS2 as a hub in the cancer module, it was highly upregulated in responders, while the majority of genes in that module were downregulated, suggesting a reciprocal relationship with other genes in the module (Fig. 3a,b). NOS2 is a transcriptionally regulated isoform of NOS, which catalyses the production of NO from L-arginine, particularly in response to cytokines, generating sustained, high output quantities of NO20. To determine if NOS2 plays a major role in the response to CTLA-4 blockade, we treated mice with established AB1-HA tumours with anti-CTLA-4 in combination with competitive NOS2 inhibitor L-NG-nitroarginine. The data show that treatment efficacy decreased significantly when this hub was inhibited (Fig. 3d). Having established a role for NOS2 in treatment response, we then wanted to determine if this pathway could be harnessed to increase the response rate. Given that selective drugs that enhance NOS2 activity are not available, we used isosorbide dinitrate (ISDN) as a NO generator. ISDN treatment alone did not have any effect on tumour outgrowth, but in combination with anti-CTLA-4 it very significantly improved the cure rate from 10% to 80%, displaying a clear synergistic effect (Fig. 4a).

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