Differences in immune profiles of metastatic melanoma patients treated with anti-CTLA-4 and anti-PD-1 combined immunotherapy.

51 Background: Immune checkpoint inhibitors targeting the cytotoxic T-lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1) receptor have significantly improved the outcomes of metastatic melanoma patients resulting in durable response and longer overall survival. However, while some patients exhibit a complete response to immunotherapy, others have demonstrated little to no response. The aim of this study is to examine the immune cell subsets between the good and poor responders to anti-CTLA-4 and anti-PD-1 combined immunotherapy in order to determine potential mechanisms of response and resistance. Methods: Multiplex immunofluorescent staining was performed on pre-treatment formalin-fixed, paraffin-embedded (FFPE) tissue samples from good and poor responders ( n= 17; n= 11 good responders, n= 6 poor responders) for markers including CD8, PD-1, PD-L1, FOXP3 and SOX10. Spectral image analysis was conducted via the Vectra 3.0 imaging system. Quantitative analysis was carried out using the inForm software. Gene signatures associated with the T-cell signalling pathways were identified following RNA sequencing of FFPE samples. Results: Good responders displayed significantly higher levels of baseline intratumoural PD-1 and PD-L1 expression in comparison to the poor responders ( P= 0.01 and P= 0.01 respectively), highlighting their roles as predictive biomarkers of response. Additionally, the CD8 + T-cell to PD-L1 ratio was significantly higher ( P= 0.001) in the tumours of the poor responders, indicating a distinction in the CD8 + T-cell phenotypes between the good and poor responders. Furthermore, analysis of the RNA sequencing data revealed a panel of immune signatures that have been implicated in prognosis and responsiveness to immunotherapy in melanoma. Conclusions: In summary, these findings provide insight into the possible biomarkers of response and resistance to immunotherapy, thus improving our understanding of the mechanisms driving variations in response.