Predictive Biomarkers of Response to Neoadjuvant Immunotherapy in Melanoma

Melanoma is a deadly form of skin cancer, and while new immunotherapy treatments have significantly improved survival, not all patients respond the same way. Doctors currently lack reliable tools to predict who will benefit from neoadjuvant immune checkpoint blockade (ICB), a treatment given before surgery to shrink tumors and improve long-term outcomes. This study aims to develop better biomarkers—biological indicators that can help predict treatment response—so that doctors can personalize therapy for each patient. 

We will study over 70 patients with Stage III melanoma, analyzing their tumors before and after treatment to identify key immune features that may predict who will respond well. Specifically, we will measure tertiary lymphoid structures (TLS)—specialized immune cell clusters within tumors—as well as CXCL13, a protein that helps immune cells communicate, and tumor-infiltrating lymphocytes (TILs), which are immune cells attacking the cancer. We will also assess PD-L1, a protein linked to immune evasion by cancer cells. 

By combining advanced lab techniques with machine learning, we aim to develop a predictive model that can help doctors classify patients into different risk groups and tailor their treatments accordingly. Our goal is to make immunotherapy more precise and effective, ensuring that patients receive the right treatment while minimizing unnecessary side effects. This research could revolutionize melanoma treatment by providing clinicians with better tools to guide therapy decisions, ultimately improving patient survival and quality of life.