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Mathematical modeling of interleukin-27 induction of anti-tumor T cells response.

ABSTRACT: Interleukin-12 is a pro-inflammatory cytokine which promotes Th1 and cytotoxic T lymphocyte activities, such as Interferon-[Formula: see text] secretion. For this reason Interleukin-12 could be a powerful therapeutic agent for cancer treatment. However, Interleukin-12 is also excessively toxic. Interleukin-27 is an immunoregulatory cytokine from the Interleukin-12 family, but it is not as toxic as Interleukin-12. In recent years, Interleukin-27 has been considered as a potential anti-tumor agent. Recent experiments in vitro and in vivo have shown that cancer cells transfected with IL-27 activate CD8+ T cells to promote the secretion of anti-tumor cytokines Interleukin-10, although, at the same time, IL-27 inhibits the secretion of Interferon-[Formula: see text] by CD8+ T cells. In the present paper we develop a mathematical model based on these experimental results. The model involves a dynamic network which includes tumor cells, CD8+ T cells and cytokines Interleukin-27, Interleukin-10 and Interferon-[Formula: see text]. Simulations of the model show how Interleukin-27 promotes CD8+ T cells to secrete Interleukin-10 to inhibit tumor growth. On the other hand Interleukin-27 inhibits the secretion of Interferon-[Formula: see text] by CD8+ T cells which somewhat diminishes the inhibition of tumor growth. Our numerical results are in qualitative agreement with experimental data. We use the model to design protocols of IL-27 injections for the treatment of cancer and find that, for some special types of cancer, with a fixed total amount of drug, within a certain range, continuous injection has better efficacy than intermittent injections in reducing the tumor load while the treatment is ongoing, although the decrease in tumor load is only temporary.

SUBMITTER: Liao KL

PROVIDER: S-EPMC3954918 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Interleukin-27 (IL-27) has shown promise in halting tumor growth and mediating tumor regression in several models, including prostate cancer. We describe our findings on the effects of IL-27 on the gene expression changes of TC2R prostate adenocarcinoma cells. We utilized RNAseq to assess profile differences between empty vector control, vector delivering IL-27 modified at its C-terminus with a non-specific peptide, and IL-27 modified at the C-terminus with a peptide targeting the IL-6-R?. The targeted IL-27 had higher bioactivity and activity in vivo in a recent study by our group, but the mechanisms underlying this effect had not been characterized in detail at the gene expression level on tumor cells. In the present work, we sought to examine potential mechanisms for targeted IL-27 enhanced activity directly on tumor cells. The targeted IL-27 appeared to modulate several changes that would be consistent with an anti-tumor effect, including upregulation in the Interferon (IFN) and Interferon regulatory factor (IRF), oxidative phosphorylation, Janus kinase/Signal transducers and activators of transcription (JAK/STAT), and eukaryotic initiation factor 2 (EIF2) signaling. Of these signaling changes predicted by ingenuity pathway analyses (IPA), the novel form also with the highest significance (-log(Benjamini-Hochberg (B-H)) p-value) was the EIF2 signaling upregulation. We validated this predicted change by assaying for eukaryotic initiation factor 2 alpha (eIF2?), or phosphorylated eIF2? (p-eIF2?), and caspase-3 levels. We detected an increase in the phosphorylated form of eIF2? and in the cleaved caspase-3 fraction, indicating that the EIF2 signaling pathway was upregulated in these prostate tumor cells following targeted IL-27 gene delivery. This approach of targeting cytokines to enhance their activity against cancer cells is a novel approach to help augment IL-27's bioactivity and efficacy against prostate tumors and could be extended to other conditions where it could help interfere with the EIF2? pathway and promote caspase-3 activation.

Project description:BACKGROUND:Intra-and inter-tumoral heterogeneity in growth dynamics and vascularity influence tumor response to radiation therapy. Quantitative imaging techniques capture these dynamics non-invasively, and these data can initialize and constrain predictive models of response on an individual basis. METHODS:We have developed a family of 10 biologically-based mathematical models describing the spatiotemporal dynamics of tumor volume fraction, blood volume fraction, and response to radiation therapy. To evaluate this family of models, rats (n?=?13) with C6 gliomas were imaged with magnetic resonance imaging (MRI) three times before, and four times following a single fraction of 20?Gy or 40?Gy whole brain irradiation. The first five 3D time series data of tumor volume fraction, estimated from diffusion-weighted (DW-) MRI, and blood volume fraction, estimated from dynamic contrast-enhanced (DCE-) MRI, were used to calibrate tumor-specific model parameters. The most parsimonious and well calibrated of the 10 models, selected using the Akaike information criterion, was then utilized to predict future growth and response at the final two imaging time points. Model predictions were compared at the global level (percent error in tumor volume, and Dice coefficient) as well as at the local or voxel level (concordance correlation coefficient). RESULT:The selected model resulted in <?12% error in tumor volume predictions, strong spatial agreement between predicted and observed tumor volumes (Dice coefficient?>?0.74), and high level of agreement at the voxel level between the predicted and observed tumor volume fraction and blood volume fraction (concordance correlation coefficient?>?0.77 and?>?0.65, respectively). CONCLUSIONS:This study demonstrates that serial quantitative MRI data collected before and following radiation therapy can be used to accurately predict tumor and vasculature response with a biologically-based mathematical model that is calibrated on an individual basis. To the best of our knowledge, this is the first effort to characterize the tumor and vasculature response to radiation therapy temporally and spatially using imaging-driven mathematical models.

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Mathematical modeling of interleukin-27 induction of anti-tumor T cells response.

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