Abstract A109: PRIME: An integrative systems immunology approach to predict anti-PD1 responses and discover new targets for melanoma immunotherapy

Abstract Immune checkpoint therapies (ICT) have provided remarkable improvements in the treatment and clinical response of solid tumors. However, almost half of metastatic melanoma patients develop resistance to ICT because of ‘cold tumor’ formation, characterized by a lack of CD8+ T cell infiltration. Currently, there are no effective tools to predict the development of cold tumors and subsequent lack of response to ICT. Therefore, there is a persisting clinical need for accurate prognostic models and discovery of functional drivers of cold tumors. In this study, we sought to develop a prognostic model based on features that drive poor immunogenicity in metastatic melanomas. Our model development strategy identified immune triggers that predict RECIST outcomes in both cold and hot tumors. We developed a systems immunology approach that identifies immune signatures from whole-transcriptome data across multiple ICT-treated metastatic melanoma cohorts, focusing on biomarkers related to antigen processing and CD8+ T cell priming (APP). These signatures were integrated into a model to identify the most Predictive Ranking-based Immunogenic Markers for Efficacy (PRIME) in anti-PD1 prognostication for melanoma. These immune signatures also guided functional biomarker discovery through unsupervised clustering of gene expression, clinical, and functional data. Biomarkers were further filtered by spatial transcriptomics and single-cell RNA sequencing to highlight those linked with dendritic cells (DCs) and CD8+ T cell priming interactions. These biomarkers were functionally validated using siRNA knockdown in human monocyte-DCs (moDCs) and antigen presentation studies. Our bioinformatic analysis identified 3,359 and 2,902 genes positively and negatively associated with survival in ICT-treated patients. From these, 667 genes from the positive group and 129 genes from the negative group showed a significant correlation with APP. The PRIME prognostic model, integrating components of these signatures, outperformed the existing models (IMPRES and TIDE) in predicting RECIST responses to anti-PD1 ICT in pre-treatment samples of cutaneous metastatic melanoma patients. The spatial transcriptomics of skin metastases and single-cell profiling identified enriched targets without previously known roles in DC's immunogenic functions, two of which were selected for functional validation. We observed that these biomarkers may play a role in sustaining APP through the regulation of peptide processing and costimulatory/coinhibitory machinery in human moDCs. Our findings present PRIME as a novel anti-PD1 prognostication model focused on the immunogenic characteristics of metastatic melanoma. The model’s efficacy likely stems from the functional properties of its subcomponents, highlighting new potential targets for rational combination therapies aimed at enhancing tumor immunogenicity by optimizing CD8+ T cell priming and improving anti-PD1 response outcomes. Citation Format: Saara A Koskela, Otto Pulkkinen, Arfa Mehmood, Eva-Maria Birkman, Tero Aittokallio, Maria Sundvall, Carlos R Figueiredo. PRIME: An integrative systems immunology approach to predict anti-PD1 responses and discover new targets for melanoma immunotherapy [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A109.