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Confrontation of fibroblasts with cancer cells in vitro: gene network analysis of transcriptome changes and differential capacity to inhibit tumor growth.

Alexeyenko A, Alkasalias T, Pavlova T, Szekely L, Kashuba V, Rundqvist H, Wiklund P, Egevad L, Csermely P, Korcsmaros T, Guven H, Klein G - J. Exp. Clin. Cancer Res. (2015)

Bottom Line: Confrontation-specific differences were stronger and were characterized by changes in a number of pathways, including Rho, the YAP/TAZ cascade, NF-kB, and TGF-beta signaling, as well as the transcription factor RELA.We also demonstrated functional congruence between the in vitro and ex vivo models.The microarray data are made available via the Gene Expression Omnibus as GSE57199.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden. andrej.alekseenko@scilifelab.se.

ABSTRACT

Background: There is growing evidence that emerging malignancies in solid tissues might be kept under control by physical intercellular contacts with normal fibroblasts.

Methods: Here we characterize transcriptional landscapes of fibroblasts that confronted cancer cells. We studied four pairs of in vitro and ex vivo fibroblast lines which, within each pair, differed in their capacity to inhibit cancer cells. The natural process was modeled in vitro by confronting the fibroblasts with PC-3 cancer cells. Fibroblast transcriptomes were recorded by Affymetrix microarrays and then investigated using network analysis.

Results: The network enrichment analysis allowed us to separate confrontation- and inhibition-specific components of the fibroblast transcriptional response. Confrontation-specific differences were stronger and were characterized by changes in a number of pathways, including Rho, the YAP/TAZ cascade, NF-kB, and TGF-beta signaling, as well as the transcription factor RELA. Inhibition-specific differences were more subtle and characterized by involvement of Rho signaling at the pathway level and by potential individual regulators such as IL6, MAPK8, MAP2K4, PRKCA, JUN, STAT3, and STAT5A.

Conclusions: We investigated the interaction between cancer cells and fibroblasts in order to shed light on the potential mechanisms and explain the differential inhibitory capacity of the latter, which enabled both a holistic view on the process and details at the gene/protein level. The combination of our methods pointed to proteins, such as members of the Rho pathway, pro-inflammatory signature and the YAP1/TAZ cascade, that warrant further investigation via tools of experimental perturbation. We also demonstrated functional congruence between the in vitro and ex vivo models. The microarray data are made available via the Gene Expression Omnibus as GSE57199.

No MeSH data available.


Related in: MedlinePlus

Overview of the transcriptional change during the confrontation with tumor cells in the eight fibroblast samples. The principal component (PC) analysis was performed of log2-transformed fold change expression values from Affymetrix for each gene in each of the 16 samples The text labels refer to cell sample IDs (see Methods) and are centered at the respective coordinates without offset. PCs from 1 to 4 are plotted as X and Y axes pair-wise. These four components took into account 49.9, 19.2, 11.2, and 7.4 % of the fold change variance, respectively. The analysis was meant to identify if any samples stand out compared to others in terms of overall expression
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Fig1: Overview of the transcriptional change during the confrontation with tumor cells in the eight fibroblast samples. The principal component (PC) analysis was performed of log2-transformed fold change expression values from Affymetrix for each gene in each of the 16 samples The text labels refer to cell sample IDs (see Methods) and are centered at the respective coordinates without offset. PCs from 1 to 4 are plotted as X and Y axes pair-wise. These four components took into account 49.9, 19.2, 11.2, and 7.4 % of the fold change variance, respectively. The analysis was meant to identify if any samples stand out compared to others in terms of overall expression

Mentions: For an overview of the transcriptional changes during the confrontation of fibroblasts with PC-3 cancer cells, we performed the principal component analysis (PCA) of the fold change values on all genes in each of the eight samples. Fig. 1 shows that the samples distributed relatively evenly in the space of the first four principal components. Together, the four principal components took into account more than 87 % of total fold change variance. It is important to note that this analysis gave the most general view of the transcriptional landscape, where the most influential genes were those most different across all the 16 samples. In the following analysis, we will focus on more specific features, related to the confrontation with tumor cells and the capacity to inhibit the latter. Due to the common origin, the two in vitro samples Wh1 and Cr9 occupied close positions in each of the four PCA dimensions. However with the exception of component 3, they do not appear as an extreme group compared to the six ex vivo samples. Furthermore, Fig. 1 addresses the fact that some of the post-confrontation samples (namely those with Wh1, Cr9, and PdSFB) might contain relatively high amounts of tumor cells, and thus introduced a potential bias compared to the non-contaminated cells. However when compared to the rest of the samples, they did not stand out in any of the plotted components. From this we concluded that the cancer cell contamination was unlikely to have a larger impact on the analysis compared to other factors that determined variability between the samples.Fig. 1


Confrontation of fibroblasts with cancer cells in vitro: gene network analysis of transcriptome changes and differential capacity to inhibit tumor growth.

Alexeyenko A, Alkasalias T, Pavlova T, Szekely L, Kashuba V, Rundqvist H, Wiklund P, Egevad L, Csermely P, Korcsmaros T, Guven H, Klein G - J. Exp. Clin. Cancer Res. (2015)

Overview of the transcriptional change during the confrontation with tumor cells in the eight fibroblast samples. The principal component (PC) analysis was performed of log2-transformed fold change expression values from Affymetrix for each gene in each of the 16 samples The text labels refer to cell sample IDs (see Methods) and are centered at the respective coordinates without offset. PCs from 1 to 4 are plotted as X and Y axes pair-wise. These four components took into account 49.9, 19.2, 11.2, and 7.4 % of the fold change variance, respectively. The analysis was meant to identify if any samples stand out compared to others in terms of overall expression
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4472614&req=5

Fig1: Overview of the transcriptional change during the confrontation with tumor cells in the eight fibroblast samples. The principal component (PC) analysis was performed of log2-transformed fold change expression values from Affymetrix for each gene in each of the 16 samples The text labels refer to cell sample IDs (see Methods) and are centered at the respective coordinates without offset. PCs from 1 to 4 are plotted as X and Y axes pair-wise. These four components took into account 49.9, 19.2, 11.2, and 7.4 % of the fold change variance, respectively. The analysis was meant to identify if any samples stand out compared to others in terms of overall expression
Mentions: For an overview of the transcriptional changes during the confrontation of fibroblasts with PC-3 cancer cells, we performed the principal component analysis (PCA) of the fold change values on all genes in each of the eight samples. Fig. 1 shows that the samples distributed relatively evenly in the space of the first four principal components. Together, the four principal components took into account more than 87 % of total fold change variance. It is important to note that this analysis gave the most general view of the transcriptional landscape, where the most influential genes were those most different across all the 16 samples. In the following analysis, we will focus on more specific features, related to the confrontation with tumor cells and the capacity to inhibit the latter. Due to the common origin, the two in vitro samples Wh1 and Cr9 occupied close positions in each of the four PCA dimensions. However with the exception of component 3, they do not appear as an extreme group compared to the six ex vivo samples. Furthermore, Fig. 1 addresses the fact that some of the post-confrontation samples (namely those with Wh1, Cr9, and PdSFB) might contain relatively high amounts of tumor cells, and thus introduced a potential bias compared to the non-contaminated cells. However when compared to the rest of the samples, they did not stand out in any of the plotted components. From this we concluded that the cancer cell contamination was unlikely to have a larger impact on the analysis compared to other factors that determined variability between the samples.Fig. 1

Bottom Line: Confrontation-specific differences were stronger and were characterized by changes in a number of pathways, including Rho, the YAP/TAZ cascade, NF-kB, and TGF-beta signaling, as well as the transcription factor RELA.We also demonstrated functional congruence between the in vitro and ex vivo models.The microarray data are made available via the Gene Expression Omnibus as GSE57199.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, Tumor and Cell Biology (MTC), Karolinska Institutet, Stockholm, Sweden. andrej.alekseenko@scilifelab.se.

ABSTRACT

Background: There is growing evidence that emerging malignancies in solid tissues might be kept under control by physical intercellular contacts with normal fibroblasts.

Methods: Here we characterize transcriptional landscapes of fibroblasts that confronted cancer cells. We studied four pairs of in vitro and ex vivo fibroblast lines which, within each pair, differed in their capacity to inhibit cancer cells. The natural process was modeled in vitro by confronting the fibroblasts with PC-3 cancer cells. Fibroblast transcriptomes were recorded by Affymetrix microarrays and then investigated using network analysis.

Results: The network enrichment analysis allowed us to separate confrontation- and inhibition-specific components of the fibroblast transcriptional response. Confrontation-specific differences were stronger and were characterized by changes in a number of pathways, including Rho, the YAP/TAZ cascade, NF-kB, and TGF-beta signaling, as well as the transcription factor RELA. Inhibition-specific differences were more subtle and characterized by involvement of Rho signaling at the pathway level and by potential individual regulators such as IL6, MAPK8, MAP2K4, PRKCA, JUN, STAT3, and STAT5A.

Conclusions: We investigated the interaction between cancer cells and fibroblasts in order to shed light on the potential mechanisms and explain the differential inhibitory capacity of the latter, which enabled both a holistic view on the process and details at the gene/protein level. The combination of our methods pointed to proteins, such as members of the Rho pathway, pro-inflammatory signature and the YAP1/TAZ cascade, that warrant further investigation via tools of experimental perturbation. We also demonstrated functional congruence between the in vitro and ex vivo models. The microarray data are made available via the Gene Expression Omnibus as GSE57199.

No MeSH data available.


Related in: MedlinePlus