Integration of Clinical, Molecular, and Immune Features to Improve Risk Stratification and Outcomes in Melanoma Patients with Sentinel Lymph Node Metastasis
Abstract
Melanoma is the most aggressive form of skin cancer. The risk of melanoma is strongly linked to exposure to ultraviolet radiation. As many military personnel and their families are stationed and/or reside in areas with extensive sun exposure, melanoma has been identified as a Military Relevance Focus Area. Our understanding of melanoma has improved dramatically over the last decade. During this time, in-depth molecular profiling has identified genetic mutations and signaling pathways that are critical to the aggressive behavior of this disease and has contributed to our understanding of the key factors that control the ability of the body’s immune system to attack melanoma. This information has been utilized to develop personalized therapies that target activating mutations (i.e., genetic changes that can act like an “on switch”) in the BRAF gene and also to develop new immune therapies called checkpoint inhibitors that block negative/inhibitory factors in the immune system to activate an anti-tumor response – in other words, “releasing the brakes” on the immune system so it can better function. These advances have already transformed care and significantly improved outcomes of melanoma patients with distant metastatic (stage IV) disease, and they provide a strong rationale to determine if treatment with targeted and/or immune therapies can improve outcomes in patients with earlier stages of disease. While these treatments are very promising, they can have significant side effects and they are very expensive (i.e., greater than $1 million per patient). Further, if they are used in patients who have already been cured of their disease by surgery, such patients have no chance of benefit from this approach, but are still at risk for harm. Thus, the rational and effective use of such therapies in patients with earlier stages of disease critically depends upon the accurate prediction of which patients are at risk for spread of their disease. Melanomas arising in the skin most commonly spread to nearby structures called regional lymph nodes, and such patients are described as having stage III disease. Most patients (~70%) with stage III disease are diagnosed with small (“microscopic”) tumor deposits by a technique termed sentinel lymph node (SLN) biopsy, a minimally invasive procedure in which lymph nodes that receive drainage from the primary melanoma tumor are removed in patients without physical signs of involvement (i.e., no enlarged lymph nodes that can be palpated on exam) but who are otherwise thought to have a risk of regional metastasis. While many of these patients are cured by surgery, a significant subset will develop distant metastases. Currently, several patient and tumor characteristics are used to estimate prognosis in these patients, as defined in the American Joint Committee on Cancer (AJCC) staging system for melanoma. While this system is very useful, additional factors not included in the AJCC staging system may improve our ability to predict clinical outcome in patients with SLN metastases. Importantly, while molecular and immune analyses of patients with large tumors have shown the ability to predict outcome and guide treatment, little is known about such features in these SLN tumor deposits due to technical challenges related to their very small size. An improved understanding of the clinical, pathological, molecular, and immune predictors of clinical outcomes in melanoma patients with microscopic regional lymph node metastases (i.e., SLN metastases) would have significant impact and benefit. Thus, our group has assembled novel and critical resources to address this unmet clinical need. We will use these resources and capabilities to test the central hypothesis that an integrated analysis of clinical, pathological, molecular, and immune features of regional lymph nodes in patients with micrometastatic disease will improve our ability to accurately predict clinical outcomes in these pat
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Oct 29, 2018
- Source ID
- W81XWH1810146
Entities
People
- Michael Tetzlaff
Organizations
- United States Army
- University of Texas at Austin