The role of immune profile in predicting outcomes in cancer patients treated with immunotherapy

Abstract

Background: Despite the efficacy of immunotherapy, only a small percentage of patients achieves a long-term benefit in terms of overall survival. The aim of this study was to define an immune profile predicting the response to immune checkpoint inhibitors (ICIs).

Methods: Patients with advanced solid tumors, who underwent ICI treatment were enrolled in this prospective study. Blood samples were collected at the baseline. Thirteen soluble immune checkpoints, 3 soluble adhesion molecules, 5 chemokines and 11 cytokines were analyzed. The results were associated with oncological outcomes.

Results: Regardless of tumor type, patients with values of sTIM3, IFNα, IFNγ, IL1β, IL1α, IL12p70, MIP1β, IL13, sCD28, sGITR, sPDL1, IL10 and TNFα below the median had longer overall survival (p<0.05). By using cluster analysis and grouping the patients according to the trend of the molecules, two clusters were found. Cluster A had a significantly higher mean progression free survival (Cluster A=11.9 months vs Cluster B=3.5 months, p<0.01), a higher percentage of disease stability (Cluster A=34.5% vs. Cluster B=0%, p<0.05) and a lower percentage of disease progression (Cluster A=55.2% vs. Cluster B = 94.4%, p=0.04).

Conclusion: The combined evaluation of soluble molecules, rather than a single circulating factor, may be more suitable to represent the fitness of the immune system status in each patient and could allow to identify two different prognostic and predictive outcome profiles.

Keywords: chemokines; cytokines; immunotherapy; soluble immune check-points; tumor biomarker.

Figure 1

Multiple Soluble ICs and cytokines/chemokines are correlated with OS. Each value of soluble factor, regardless of cancer type, was dichotomized as under the median or above the median. Kaplan-Meier evaluation showed that low values of soluble CD28, GITR, PDL1, TIM3, INFα, INFγ, IL1β, IL10, IL1α, IIL12p70, IL13, MIP1β and TNFα were associated with better OS (p<0.05).

Figure 2

Multiple Soluble ICs and cytokines/chemokines are correlated with PFS. Kaplan-Meier evaluation showed that, dichotomizing values of soluble factors under or above the median, low levels of soluble CD28, GITR, PDL1, IL10 and IL13, were associated with longer PFS (p<0.05).

Figure 3

Unsupervised hierarchical cluster analysis. (A) The heat-map of cluster analysis. Soluble molecule tested are listed in the top of the figure. The unsupervised hierarchical cluster analysis identified 2 distinct clusters of patients based on the soluble immune profile associated with a different oncological outcome: Cluster A (green box) and Cluster B (red box). The color intensity of every single square in the heat-map is directly associated with the measured concentration in pg/ml. Each square in heat-map represents the higher value (red), equal value (black) or lower level (green) of signal of any given tested soluble molecules for each tested patient, (B) Oncological outcomes were reported for each cluster. Cluster A was associated with longer PFS and higher SD rate than Cluster B (11.9 months vs. 3.5 months, and 34.5% vs.0, respectively).

Figure 4

Progression free survival. As highlighted in Kaplan Meier curves, patients in Cluster A showed a significantly longer PFS than patients in Cluster B, 11.9 months vs. 3.5 months, p<0.01.

Figure 5

By means of cluster analysis, the circulating immune profile detected at baseline made it possible to identify two distinct groups of patients (Cluster A and B) characterized by the activation of different immune pathways resulting in two distinct clinical outcomes.