11-Gene Expression Signature May Predict Sentinel Lymph Node Metastatic Status in Melanoma Patients

The Director of Cutaneous Clinical and Translational Research, Dr. Ahmad Tarhini, presented research findings aiming to investigate if biomarkers derived from a primary melanoma tumor can predict if regional sentinel lymph node (SLN) metastatic status can be valuable in guiding the decision-making in planning the SLN surgical procedure for candidate patients.

His approach involved performing gene expression profiling primary cutaneous melanoma tumor biopsies of 49 (24 known SLN+, 25 SLN-) patients (T3a/b, T4a/b) who underwent SLN for staging using transcriptome profiling analysis. U133A 2.0 Affymetrix gene chips were used. Significance Analysis of Microarrays (SAM) was used to test the association between gene expression level of the primary tumor and SLN status. Genes with fold change > 1.5 and q value < 0.05 were considered differentially expressed. Pathway analysis was performed using Ingenuity Pathway Analysis. Benjamini and Hochberg method was used to adjust for multiple testing in pathway analysis. All statistical analyses were implemented in R.

The results concluded of the 49 patients with primary cutaneous melanoma, 24 were diagnosed as SLN positive and 25 as SLN negative by routine H&E and immunohistochemistry. Using SLN metastatic status as the outcome, a univariate logistic regression model was fitted with individual probe sets. A total of 251 probe sets were filtered and 11 probe sets were considered as differentially expressed (DE) between SLN-ve and SLN+ve groups, with the selection criterion set at Benjamini- Hochberg method -adjusted p-value below 0.05 and the absolute log fold change above 0.5. Each of the 11 probe sets was matched to a unique known gene, with an 11 gene signature derived and among them, resulting in the expression level of 7 genes significantly reduced in the SLN+ve group compared to the SLN-ve group, while 4 genes were overexpressed in the SLN+ve group. Integrative and interactive heatmaps were produced from hierarchical cluster analysis displaying the 11 differentially expressed genes. Selected genes were uniformly related to tumorigenesis, malignant progression, DNA repair, cell cycle regulation, chemoresistance, immunosuppression and/or involvement in multiple cancer-related pathways. Several selected genes were previously shown to be prognostic in various malignancies.

Dr. Tarhini and his team presented a unique 11-gene expression signature derived from primary melanoma tumor biopsy samples and linked to tumorigenesis and immunosuppression that may be useful for the prognostic stratification of melanoma patients. Additionally, these findings may allow the optimal selection of patients for SLN biopsy.

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