Circulating immune landscape in melanoma patients undergoing anti-PD1 therapy reveals key immune features according to clinical response to treatment - Sosa Cuevas, Eleonora; Mouret, Stéphane; Vayssière, Guillaume; Kerboua, Siham; Girard, Pauline; Molens, Jean-Paul; Manceau, Marc; Charles, Julie; Saas, Philippe; Aspord, Caroline (Frontiers in Immunology, 2024)
IntroductionImmune checkpoint blockers (ICB) bring unprecedented clinical success, yet many patients endure immune mediated adverse effects and/or fail to respond. Predictive signatures of response to ICB and mechanisms of clinical efficacy or failure remain understudied. DC subsets, in network with conventional αβ T (Tconv), NK, γδ T and iNKT cells, harbor pivotal roles in tumor control, yet their involvement in response to ICB remained underexplored.MethodsWe performed an extensive longitudinal monitoring of circulating immune cells from melanoma patients treated with first-line anti-PD1, before (T0) and during treatment. We assessed the phenotypic and functional features of DC and effector cells’ subsets by multi-parametric flow cytometry and ProcartaPlex® dosages.ResultsWe revealed differences according to response to treatment and modulations of patterns during treatment, highlighting a strong link between the immune landscape and the outcome of anti-PD1 therapy. Responders exhibited higher frequencies of circulating cDC1s, CD8+ T cells, and γδ2+ T cells in central memory (CM) stage. Notably, we observed a distinct remodeling of ICP expression profile, activation status and natural cytotoxicity receptor patterns of immune subsets during treatment. Anti-PD1 modulated DCs’ functionality and triggered deep changes in the functional orientation of Tconv and γδT cells.DiscussionOverall, our work provides new insights into the immunological landscape sustaining favorable clinical responses or resistance to first-line anti-PD1 therapy in melanoma patients. Such exploration participates in uncovering the mechanism of action of anti-PD1, discovering innovative predictive signatures of response, and paves the way to design pertinent combination strategies to improve patient clinical benefits in the future.