FoxP3 Expression in Tumor-Infiltrating Lymphocytes as Potential Predictor of Response to Immune Checkpoint Inhibitors in Patients with Advanced Melanoma and Non-Small Cell Lung Cancer

Simple Summary In the last decade, immunotherapy has revolutionized the treatment of malignant melanoma and non-small cell lung cancer. Even though both tumor types have the highest tumor mutational burden, a non-negligible portion of patients do not benefit from checkpoint inhibitor treatment. The antitumor immune response is a complex multistep process of interactions between the microenvironment and the tumor. Therefore, it is crucial to seek new biomarkers to help predict the response to immunotherapy treatment. The aim of our study was to describe tumor tissue, its microenvironment, and immune profile, and correlate it with the response to treatment. Immune checkpoint inhibitors (ICI) are the main therapy currently used in advanced malignant melanoma (MM) and non-small cell lung cancer (NSCLC). Despite the wide variety of uses, the possibility of predicting ICI efficacy in these tumor types is scarce. The aim of our study was to find new predictive biomarkers for ICI treatment. We analyzed, by immunohistochemistry, various cell subsets, including CD3+, CD8+, CD68+, CD20+, and FoxP3+ cells, and molecules such as LAG-3, IDO1, and TGF beta. Comprehensive genomic profiles were analyzed. We evaluated 46 patients with advanced MM (31) and NSCLC (15) treated with ICI monotherapy. When analyzing the malignant melanoma group, shorter median progression-free survival (PFS) was found in tumors positive for nuclear FoxP3 in tumor-infiltrating lymphocytes (TILs) (p = 0.048, HR 3.04) and for CD68 expression (p = 0.034, HR 3.2). Longer PFS was achieved in patients with tumors with PD-L1 TPS >= 1 (p = 0.005, HR 0.26). In the NSCLC group, only FoxP3 positivity was associated with shorter PFS and OS. We found that FoxP3 negativity was linked with a better response to ICI in both histological groups.

Automated summary

Immunotherapy has revolutionized the treatment of malignant melanoma and non-small cell lung cancer in the last decade. However, a significant proportion of patients do not benefit from checkpoint inhibitor treatment, necessitating the search for new biomarkers to predict treatment response. This study aimed to analyze tumor tissue and immune profiles to identify predictive biomarkers for immune checkpoint inhibitor treatment.