Polyfunctional T-Cell Signatures to Predict Protection from Cytomegalovirus after Lung Transplantation

Abstract

Rationale: Cytomegalovirus (CMV), which is one of the most common infections after lung transplantation, is associated with chronic lung allograft dysfunction and worse post-transplantation survival. Current approaches for at-risk patients include a fixed duration of antiviral prophylaxis despite the associated cost and side effects.

Objectives: We sought to identify a specific immunologic signature that predicted protection from subsequent CMV.

Methods: CMV-seropositive lung transplantation recipients were included in the discovery (n = 43) and validation (n = 28) cohorts. Polyfunctional CMV-specific immunity was assessed by stimulating peripheral blood mononuclear cells with CMV pp65 or IE-1 peptide pools and then by measuring T-cell expression of CD107a, IFN-γ, tumor necrosis factor-α (TNF-α), and IL-2. Recipients were prospectively monitored for subsequent viremia. A Cox proportional hazards regression model that considered cytokine responses individually and in combination was used to create a predictive model for protection from CMV reactivation. This model was then applied to the validation cohort.

Measurements and main results: Using the discovery cohort, we identified a specific combination of polyfunctional T-cell subsets to pp65 that predicted protection from subsequent CMV viremia (concordance index 0.88 [SE, 0.087]). The model included both protective (CD107a(-)/IFN-γ(+)/IL-2(+)/TNF-α(+) CD4(+) T cells, CD107a(-)/IFN-γ(+)/IL-2(+)/TNF-α(+) CD8(+) T cells) and detrimental (CD107a(+)/IFN-γ(+)/IL-2(-)/TNF-α(-) CD8(+) T cells) subsets. The model was robust in the validation cohort (concordance index 0.81 [SE, 0.103]).

Conclusions: We identified and validated a specific T-cell polyfunctional response to CMV antigen stimulation that provides a clinically useful prediction of subsequent cytomegalovirus risk. This novel diagnostic approach could inform the optimal duration of individual prophylaxis.

Keywords: cytomegalovirus; immunologic monitoring; lung transplantation.

Figures

Figure 1.

Gating strategy. A singlet gate identifies single cells that are then subset into viable (or live) cells. The live cells are then subset to lymphocytes. A CD3+ gate then identifies the T cells. T cells are then separated into CD4+and CD8+subsets. Individual cytokine expression was identified within these T-cell subsets. Boolean gating was used to identify different combinations of these markers as per Figure 2. FSC-A = forward scatter pulse area; FSC-H = forward scatter pulse height; FSC-W = forward scatter pulse width; SSC = side scatter; SSC-A = side scatter pulse area; TNF-α = tumor necrosis factor-α.

Figure 2.

Polyfunctional response of one representative subject who subsequently developed cytomegalovirus (CMV) (right) and one subject who did not develop CMV (left) depicted by pie graphs. The different combinations of CD107a expression and intracellular IFN-γ, tumor necrosis factor-α (TNF-α), and IL-2 detection are noted by the different colors of the pie graph. Red, pink, and yellow pie slices are polyfunctional subsets, whereas the blue and purple pie slices are single-expression subsets. The protective subsets used in the final model include CD107a−/IFN-γ+/IL-2+/TNF-α+ CD4+T cells and CD107a−/IFN-γ+/IL-2+/TNF-α+ CD8+ T cells (pink pie slices), as well as the detrimental CD107a+/IFN-γ+/IL-2−/TNF-α− CD8+ T cell subset (dark blue pie slice).

Figure 3.

Effectiveness of log risk score (x-axis) single-cytokine model derived from our discovery cohort to identify patients who develop cytomegalovirus (CMV) (red X) versus those free of CMV (blue +), adjusted for time after completion of prophylaxis (y-axis). Single-cytokine analysis was unable to discriminate a suitable risk score threshold.

Figure 4.

Selection of the best cutoff for log risk score in the final polyfunctional subset model derived from the discovery data. For each candidate cutoff value, log risk scores of the 43 patients were dichotomized by the given threshold and concordance index between the dichotomized log risk scores, and off-prophylaxis times to cytomegalovirus (CMV) infection was computed. The vertical gray line is drawn at –1.2, the best cutoff that gives the highest concordance index. This cutoff value discriminated subsequently infected CMV patients and patients who did not develop CMV. The gray shaded area is drawn over the interval (−1.4, 0.0) that included all cutoff values whose concordance indexes were within 1 SE of the largest concordance index. For more conservative withdrawal of prophylaxis, a cutoff of −1.4 could be used.

Figure 5.

Application of a polyfunctional T-cell subset model to the validation cohort. The vertical gray line is drawn at −1.2 log risk score. The gray shaded area is drawn over the interval (−1.4, 0.0) that included all cutoff values whose concordance indexes were within 1 SE of the largest concordance index. The concordance index between the dichotomized log risk scores and off-prophylaxis times to cytomegalovirus (CMV) infection was 0.81 (SE, 0.103).