Blood immune cells as potential biomarkers predicting relapse-free survival of stage III/IV resected melanoma patients treated with peptide-based vaccination and interferon-alpha

• Published in: Frontiers in oncology 2023, Vol.13, p.1145667-1145667
• Main Authors: Moschella, Federica, Buccione, Carla, Ruspantini, Irene, Castiello, Luciano, Rozo Gonzalez, Andrea, Iacobone, Floriana, Ferraresi, Virginia, Palermo, Belinda, Nisticò, Paola, Belardelli, Filippo, Proietti, Enrico, Macchia, Iole, Urbani, Francesca
• Format: Article
• Language: English
• Subjects: circulating biomarkers; combination therapy; immunotherapy; melanoma; multiparametric flow cytometry; Oncology; peptide vaccination
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2023.1145667

Despite the recent approval of several therapies in the adjuvant setting of melanoma, tumor relapse still occurs in a significant number of completely resected stage III-IV patients. In this context, the use of cancer vaccines is still relevant and may increase the response to immune checkpoint inhibitors. We previously demonstrated safety, immunogenicity and preliminary evidence of clinical efficacy in stage III/IV resected melanoma patients subjected to a combination therapy based on peptide vaccination together with intermittent low-dose interferon-α2b, with or without dacarbazine preconditioning (https://www.clinicaltrialsregister.eu/ctr-search/search, identifier: 2008-008211-26). In this setting, we then focused on pre-treatment patient immune status to highlight possible factors associated with clinical outcome. Multiparametric flow cytometry was used to identify baseline immune profiles in patients' peripheral blood mononuclear cells and correlation with the patient clinical outcome. Receiver operating characteristic curve, Kaplan-Meier survival and principal component analyses were used to evaluate the predictive power of the identified markers. We identified 12 different circulating T and NK cell subsets with significant (p ≤ 0.05) differential baseline levels in patients who later relapsed with respect to patients who remained free of disease. All 12 parameters showed a good prognostic accuracy (AUC>0.7, p ≤ 0.05) and 11 of them significantly predicted the relapse-free survival. Remarkably, 3 classifiers also predicted the overall survival. Focusing on immune cell subsets that can be analyzed through simple surface staining, three subsets were identified, namely regulatory T cells, CD56 CD16 NK cells and central memory γδ T cells. Each subset showed an AUC>0.8 and principal component analysis significantly grouped relapsing and non-relapsing patients (p=0.034). These three subsets were used to calculate a combination score that was able to perfectly distinguish relapsing and non-relapsing patients (AUC=1; p=0). Noticeably, patients with a combined score ≥2 demonstrated a strong advantage in both relapse-free (p=0.002) and overall (p=0.011) survival as compared to patients with a score