Circulating Interleukin-4 Is Associated with a Systemic T Cell Response against Tumor-Associated Antigens in Treatment-Naïve Patients with Resectable Non-Small-Cell Lung Cancer

Abstract

Spontaneous T cell responses to tumor-associated antigens (TAs) in the peripheral blood of patients with non-small-cell lung cancer (NSCLC) may be relevant for postoperative survival. However, the conditions underlying these T cell responses remain unclear. We quantified the levels of 27 cytokines in the peripheral blood and tumor tissues from treatment-naïve patients with NSCLC (n = 36) and analyzed associations between local and systemic cytokine profiles and both TA-specific T cell responses and clinical parameters. We defined T cell responders as patients with circulating T cells that were reactive to TAs and T cell nonresponders as patients without detectable TA-specific T cells. TA-specific T cell responses were correlated with serum cytokine levels, particularly the levels of interleukin(IL)-4 and granulocyte colony-stimulating factor (G-CSF), but poorly correlated with the cytokine levels in tumor tissues. Nonresponders showed significantly higher serum IL-4 levels than responders (p = 0.03); the predicted probability of being a responder was higher for individuals with low serum IL-4 levels. In multivariable Cox regression analyses, in addition to IL-4 (hazard ratio (HR) 2.8 (95% confidence interval (CI): 0.78-9.9); p = 0.116), the age-adjusted IL-8 level (HR 3.9 (95% CI: 1.05-14.5); p = 0.042) predicted tumor recurrence. However, this study included data for many cytokines without adjustment for multiple testing; thus, the observed differences in IL-4 or IL-8 levels might be incidental findings. Therefore, additional studies are necessary to confirm these results.

Keywords: T cells; cytokine; immunotherapy; lung cancer.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1

Box and whisker plots representing the preoperative serum cytokine levels in all patients (n = 36), responders only (n = 22) and nonresponders only (n = 14). FGF = fibroblast growth factor. G-CSF = granulocyte colony-stimulating factor. IFN = interferon. IL = interleukin. IP10 = interferon-inducible protein 10. MCP-1 = monocyte chemoattractant protein 1. MIP = macrophage-inflammatory protein. PDGF = platelet-derived growth factor. RANTES = regulated upon activation, normal T cell expressed and secreted. TNF = tumor necrosis factor.

Figure 2

Tree-structured dendrogram resulting from the hierarchical clustering analysis based on the correlation matrix of all cytokine and response data. The R-square value is the proportion of variance accounted for by the cluster. For example, the variables IL-4, eotaxin, G-CSF, IL-7 and IL-17A represent a cluster of variables with similar correlations with the response variable (“any_response_PB”; marked with an asterisk). In contrast, the dendrogram shows a marked distance between the variables IL-6 and IL-8 and the response variable, indicating a weak correlation.

Figure 3

Predicted probability of a response obtained from age-adjusted logistic models. As an example, the age-weighted probability of being a responder based on cytokine levels is shown for the serum cytokines IL-4 (a), G-CSF (b), IL-17A (c), IL-9 (d), IL-7 (e), and IL-8 (f). The blue curves illustrate the probability of having serum cytokine levels below the optimal cutoff, and the red lines represent the probability of having serum cytokine levels above the cutoff.

Figure 4

Predicted probability of a TA-specific response based on a multiple logistic model including the variables IL-4, IL-8, and MIP-1b (dichotomized at the optimal cutoff value) and age (continuous variable). The predicted probability of being a responder is visualized based on the three serum cytokines dichotomized at their optimal cutoff values and the variable age. The probabilities of having IL-4 levels above the optimal cutoff value are shown as blue lines, and the probabilities of having IL-4 levels below the cutoff value are shown as red lines. The categorization according to the optimal cutoff value of MIP-1b is shown in the upper and lower rows and the categorization for IL-8 is shown in the left and right columns. The variable age is used as a continuous variable on the x-axis. For example, the probability of being a responder is very high if the serum IL-4 level is below the threshold, the serum IL-8 level is also below the threshold, and the serum MIP-1b level is above the threshold (d). In contrast, the likelihood of being a responder in the older age group is very low if the serum IL-8 level is above the threshold and the serum MIP-1b level is also below the threshold (a). Likelihood of being a responder if the serum IL-8 level is below the threshold and the serum MIP-1b level is also below the threshold (b). Likelihood of being a responder if the serum IL-8 level is above the threshold and the serum MIP-1b level is also above the threshold (c).

Figure 5

Postoperative survival curves predicted using the Cox model. Postoperative RFS after curative-intent surgery for NSCLC was predicted based on the variables serum IL-4 levels and age. Prolonged survival was predicted for younger patients with low serum IL-4 levels than for older patients with high serum IL-4 levels. The favorable effect of younger age on survival was reversed by the combination with higher serum IL-4 levels.

Figure 6

Associations among lymphangiosis carcinomatosa, the TA-specific T cell response, recurrence-free survival (RFS) and crude (a) and age-adjusted (b) serum IL-8 levels.