Characterization of Sentinel Lymph Node Immune Signatures and Implications for Risk Stratification for Adjuvant Therapy
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ORIGINAL ARTICLE – MELANOMA
Characterization of Sentinel Lymph Node Immune Signatures and Implications for Risk Stratification for Adjuvant Therapy in Melanoma Norma E. Farrow, MD MHS1, Eda K. Holl, PhD1,2, Jeanne Jung3, Junheng Gao, PhD4, Sin-Ho Jung, PhD4, Rami N. Al-Rohil, MBBS5, Maria A. Selim, MD5, Paul J. Mosca, MD PhD1,2, David W. Ollila, MD6, Scott J. Antonia, MD PhD7,8, Douglas S. Tyler, MD9, Smita K. Nair, PhD1,2,5,9, and Georgia M. Beasley, MD MHS1,2 1
Department of Surgery, Duke University Medical Center, Durham, NC; 2Duke Cancer Institute, Durham, NC; 3Emory University, Atlanta, Georgia; 4Department of Biostatistics and Bioinformatics, Duke University, Durham, NC; 5 Department of Pathology, Duke University, Durham, NC; 6Department of Surgery, University of North Carolina Chapel Hill, Chapel Hill, NC; 7Department of Medicine, Duke University, Durham, NC; 8Medical Branch Department of Surgery, University of Texas, Austin; 9Department of Neurosurgery, Duke University, Durham, TX
ABSTRACT Background. Although sentinel lymph node (SLN) biopsy is a standard procedure used to identify patients at risk for melanoma recurrence, it fails to risk-stratify certain patients accurately. Because processes in SLNs regulate anti-tumor immune responses, the authors hypothesized 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 60 SLN specimens (31 positive [pSLNs], 29 negative [nSLNs]) 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 on the basis of the model were used to stratify patients into low- and high-risk groups. The
predictive power of the model was assessed using the Kaplan–Meier and log-rank tests. Results. During a median follow-up period of 6.3 years, 20 patients (33.3%) experienced recurrence (pSLN, 45.2% [14/31] vs nSLN, 20.7% [6/29]; p = 0.0445). A fitted Cox regression model incorporating 12 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, the model in this study was able to stratify patients into cohorts at high and low risk for recurrence (p \ 0.001, log-rank). Conclusions. Expression profiles of the SLN gene 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.
Electronic supplementary material The online version of this article (https://doi.org/10.1245/s10434-020-09277-w) contains supplementary material, which is available to authorized users.
Although surgical resection of the primary tumor and associated tumor-draining lymph nodes
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