Characterization of Sentinel Lymph Node Immune Signatures and Implications for Risk Stratification for Adjuvant Therapy in Melanoma

• Authors: Norma E Farrow, Duke University; Eda K Holl, Duke University; Jeanne Jung, Emory University; Junheng Gao, Duke University; Sin-Ho Jung, Duke University; Rami N Al-Rohil, Duke University; Maria A Selim, Duke University; Paul J Mosca, Duke University; David W Ollila, University of North Carolina; Scott J Antonia, Duke University; Douglas S Tyler, Duke University; Smita K Nair, Duke University; Georgia M Beasley, Duke University
• Journal Title: ANNALS OF SURGICAL ONCOLOGY
• Volume: Volume 28, Number 7
• Publisher: Springer (part of Springer Nature) | 2021-07-01, Pages 3501-3510
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1245/s10434-020-09277-w
• Subjects: Health Sciences, Medicine and Surgery; Health Sciences, Pathology
• Keywords: sentinel lymph node; Adjuvant Therapy

Abstract

Background: While sentinel lymph node (SLN) biopsy is a standard procedure used to identify patients at risk for melanoma recurrence, it fails to accurately risk stratify certain patients. Since processes in SLNs regulate anti-tumor immune responses, we hypothesize that SLN gene expression may be used for risk stratification. Methods: The Nanostring nCounter® PanCancer Immune Profiling Panel was used to quantify expression of 730 immune-related genes in sixty SLN specimens (31 positive [pSLN], 29 negative [nSLN]) from a retrospective melanoma cohort. A multivariate prediction model for recurrence-free survival (RFS) was created by applying stepwise variable selection to Cox regression models; risk scores calculated using the model were used to stratify patients into low- and high-risk groups. Predictive power of the model was assessed using the Kaplan-Meier and log-rank tests. Results: At a median follow up of 6.3 years, 20 patients (33.3%) developed recurrence (14/31 [45.2%] pSLN and 6/29 [20.7%] nSLN, p=0.0445). A fitted Cox regression model incorporating twelve genes accurately predicted RFS (C-index 0.9919); Improved RFS was associated with increased expression of TIGIT (p = 0.0326), an immune checkpoint, and decreased expression of CXCL16 (p = 0.0273), a cytokine important in promoting dendritic and T cell interactions. Independent of SLN status, our model was able to stratify patients into cohorts at high- and low-risk for recurrence (log-rank p<0.001). Conclusions: SLN gene expression profiles are associated with melanoma recurrence, and may be able to identify patients as high or low risk regardless of SLN status, potentially enhancing patient selection for adjuvant therapy.