Inflammatory Blood Parameters as Biomarkers for Response to Immune Checkpoint Inhibition in Metastatic Melanoma Patients

Abstract

Objectives: We aimed to investigate whether inflammatory parameters in peripheral blood at baseline and during the first six months of treatment could predict the short- and long-term outcomes of metastatic melanoma patients treated with immune checkpoint inhibitors (ICIs). Methods: This single-center retrospective study considered patients with metastatic melanoma treated with either single or dual checkpoint inhibition. Blood sample tests were scheduled together with 18F-2-fluor-2-desoxy-D-glucose positron emission tomography/computed tomography (FDG-PET/CT) scans at baseline and at three and six months after initiation of ICI treatment. The short-term response to ICIs was assessed using FDG-PET/CT scans. The long-term response to ICIs was assessed using the overall survival OS and progression-free survival PFS as endpoints. Results: A total of 100 patients with metastatic melanoma were included (female, n = 31; male, n = 69). The median age was 68 years (interquartile range (IQR): 53−74 years). A total of 82% of the cohort displayed a disease control (DC), while 18% presented a progressive disease (PD) after six months of ICIs. Patients with DC after six months of ICIs showed a lower median of the neutrophils-to-lymphocytes ratio (NLR) toward patients with PD, with no significant prediction power of NLR neither in the short nor in the long term. The count of neutrophils at the baseline time point (TP 0) (p = 0.037) and erythrocytes three months after treatment start (TP 1) (p = 0.010) were strong predictive parameters of a DC six months after treatment start. Erythrocytes (p < 0.001) and lymphocytes (p = 0.021) were strong biomarkers predictive of a favorable OS. Erythrocytes (p = 0.013) and lymphocytes (p = 0.017) also showed a significant prediction power for a favorable PFS. Conclusions: Inflammatory blood parameters predicted the short- and long-term response to ICIs with a strong predictive power. Our results suggested the validation of inflammatory blood parameters as biomarkers that predict immunotherapies’ efficacity in metastatic melanoma patients. However, confounding factors that interfere with myelopoiesis should also be taken into consideration.

Keywords: CTLA-4; PD-1; blood biomarkers; immunotherapy; melanoma; outcome prediction; positron emission tomography/computed tomography.

Figure 1

Boxplots of all included blood parameters per time point (TP) (at baseline TP 0 and at three and six months after starting immune checkpoint inhibition, respectively designated as TP 1 and TP 2) in patients with disease control at six months (displayed in green) versus patients with progressive disease at the same time (displayed in red).

Figure 2

Receiver operating characteristic (ROC) of our binominal backward regression model (logit model) for short-term response prediction in metastatic melanoma patients treated for six months with immune checkpoint inhibition.

Figure 3

Kaplan–Meier survival curves stratified by the optimal cut-off count for neutrophils (g/L) at baseline in metastatic melanoma patients based on (A) overall survival and (B) progression-free survival.

Figure 4

Kaplan–Meier survival curves stratified by the optimal cut-off count of erythrocytes (per pL) three months after initiation of immune checkpoint inhibition in metastatic melanoma patients based on (A) overall survival and (B) progression-free survival.

Figure 5

Forest plots summarizing the results of a Cox proportional hazard model analysis of overall survival with time-varying covariates: (A) variable; (B) number of patients; (C) hazard ratio (95% CI); (D) p-value. Abbreviations: F = female, M = male, 1 = single ICI, 2 = double ICI, CRP = c-reactive protein, AIC = Akaike information criterion. * and *** indicate significance at the 0.1 and <0.01 levels respectively.

Figure 6

Forest plots summarizing the results of a Cox proportional hazard model analysis of overall survival with optimal cut-off values of time-varying covariates: (A) variable; (B) number of patients; (C) hazard ratio (95% CI); (D) p-value. Abbreviations: F = female, M = male, 1 = single ICI, 2 = double ICI, CRP = c-reactive protein, AIC = Akaike information criterion. *, ** and *** indicate significance at the 0.1, 0.05 and <0.01 levels respectively.

Figure 7

Forest plots summarizing the results of a Cox proportional hazard model analysis of progression-free survival with time-varying covariates: (A) variable; (B) number of patients; (C) hazard ratio (95% CI); (D) p-value. Abbreviations: F = female, M = male, 1 = single ICI, 2 = double ICI, CRP = c-reactive protein, AIC = Akaike information criterion. *, ** and *** indicate significance at the 0.1, 0.05 and <0.01 levels respectively.

Figure 8

Forest plots summarizing the results of a Cox proportional hazard model analysis of progression-free survival with optimal cut-off values of time-varying covariates: (A) variable; (B) number of patients; (C) hazard ratio (95% CI); (D) p-value. Abbreviations: F = female, M = male, 1 = single ICI, 2 = double ICI, CRP = c-reactive protein, AIC = Akaike information criterion. ** and *** indicate significance at the 0.05 and <0.01 levels respectively.