Multiparametric immunophenotypic evaluation to predict immunotherapy response in patients with advanced melanoma

• Grant number: 24/23006-9
• Support Opportunities: Scholarships in Brazil - Master
• Start date: September 01, 2025
• End date: December 31, 2026
• Field of knowledge: Biological Sciences - Immunology - Applied Immunology
• Principal Investigator: Lidia Maria Rebolho Batista Arantes
• Grantee: Letícia Rafaela Ziquiel
• Host Institution: Hospital do Câncer de Barretos. Fundação Pio XII (FP). Barretos , SP, Brazil
• Associated research grant: 19/07111-9 - Immune-checkpoint inhibitors: immunophenotyping and clinical outcome to predict response at Barretos Cancer Hospital, AP.JP

Abstract

INTRODUCTION: Melanoma stands out in the oncological landscape due to its high lethality and invasive capacity. Immunotherapy with immune checkpoint inhibitors (ICI) has demonstrated improvements in survival rates for patients with advanced melanoma. However, there are currently no approved biomarkers for application in clinical practice. In this context, new studies are crucial for the identification of biomarkers, employing techniques such as flow cytometry that enable multiparametric evaluation of potential biological indicators. OBJECTIVE: To characterize the immunophenotypic profile of patients with advanced melanoma treated with ICI at Barretos Cancer Hospital (HCB). MATERIALS AND METHODS: Sixty patients with advanced melanoma treated with ICI will be selected. Peripheral blood samples were collected, processed to obtain peripheral blood mononuclear cells (PBMCs), and stored in liquid nitrogen. In this study, the samples will be evaluated before the first immunotherapy infusion (baseline) and after the third therapy cycle (post-treatment). Cellular phenotypes will be analyzed using the BD FACSymphony A5 flow cytometer. Additionally, plasma samples will undergo Cytometric Bead Array (CBA) analysis to evaluate cytokines. EXPECTED RESULTS: It is hypothesized that patients responding to immunotherapy will exhibit a higher frequency of pro-inflammatory cells and cytokines, while non-responders will show a greater frequency of cells with immunoregulatory markers. Furthermore, it is anticipated that the analyses will identify relevant biomarkers to predict responses to immunotherapy. (AU)