MelanomaLNs · Immune signatures in sentinel lymph nodes to predict distant metastases in malignant melanoma

Malignant melanoma is a deadly disease with a 3% lifetime incidence. Currently, no clinically approved companion diagnostics exist for immunotherapy in melanoma, and sentinel lymph node (sLN) evaluation remains the cornerstone of clinical decision-making. A sensitive, robust, and clinically applicable method for identifying patients at risk of developing metastatic disease is urgently needed.Studies in murine models have shown that lymph nodes (LNs) play a dual role in tumor immunity, both supporting immune activation essential for immunotherapy and paradoxically promoting metastatic spread by inducing systemic tolerance. We hypothesized that sLNs exhibit both pro- and anti-tumorigenic roles, varying across patients and disease stages. In a study of 69 melanoma patients, we applied spatial transcriptomics and proteomics to analyze sLNs, revealing immunological organization patterns associated with these opposing roles. A machine learning model trained on this data predicted distant metastases with 93% and 79% accuracy in involved and uninvolved LNs, respectively, demonstrating strong potential to improve clinical diagnostics.Building on these findings, we propose developing a multiplexed imaging panel to profile immune organization in both primary tumors and lymph nodes to guide patient diagnosis and stratification. This panel will be designed for compatibility with clinical workflows and tested on an independent validation cohort of 500 melanoma patients. To pave the way for commercialization, we will explore the market and value chain, identify and engage with relevant industry players and set up a commercialization plan with the aim of positioning our innovation as the new standard for diagnosing and managing melanoma patients. Our diagnostic assay will enhance treatment decisions by identifying high-risk patients at diagnosis, ensuring they receive timely intervention while sparing low-risk patients from unnecessary, potentially harmful and costly treatments.