Clinicopathologic models predicting non-sentinel lymph node metastasis in cutaneous melanoma patients: Are they useful for patients with a single positive sentinel node?

BACKGROUND AND OBJECTIVES: Of clinically node-negative (cN0) cutaneous melanoma patients with sentinel lymph node (SLN) metastasis, between 10% and 30% harbor additional metastases in non-sentinel lymph nodes (NSLNs). Approximately 80% of SLN-positive patients have a single positive SLN. METHODS: To assess whether state-of-the-art clinicopathologic models predicting NSLN metastasis had adequate performance, we studied a single-institution cohort of 143 patients with cN0 SLN-positive primary melanoma who underwent subsequent completion lymph node dissection. We used sensitivity (SE) and positive predictive value (PPV) to characterize the ability of the models to identify patients at high risk for NSLN disease. RESULTS: Across Stage III patients, all clinicopathologic models tested had comparable performances. The best performing model identified 52% of NSLN-positive patients (SE = 52%, PPV = 37%). However, for the single SLN-positive subgroup (78% of cohort), none of the models identified high-risk patients (SE > 20%, PPV > 20%) irrespective of the chosen probability threshold used to define the binary risk labels. Thus, we designed a new model to identify high-risk patients with a single positive SLN, which achieved a sensitivity of 49% (PPV = 26%). CONCLUSION: For the largest SLN-positive subgroup, those with a single positive SLN, current model performance is inadequate. New approaches are needed to better estimate nodal disease burden of these patients.

Using the Merlin assay for reducing sentinel lymph node biopsy complications in melanoma: a retrospective cohort study.

BACKGROUND: The assessment of the sentinel lymph node is a cornerstone of melanoma staging. However, ~80% of sentinel lymph node biopsies (SLNB) are negative and nontherapeutic, and patients are unnecessarily exposed to surgery-related complications. Here, we gauged the potential of the Merlin assay to reduce SLNB-associated complications. The Merlin assay uses clinicopathologic variables and tumor gene expression profiling to identify low-risk patients who may forgo SLNB. METHODS: We utilized the Merlin test development cohort to determine SLNB complication rates for procedures performed between 2004 and 2018 at Mayo Clinic. Complications evaluated were lymphedema, seroma, infection/cellulitis, hematoma, and wound dehiscence. Patients who underwent a completion lymph node dissection were excluded. RESULTS: A total of 558 patients were included. The overall 90-day complication rate specific to SLNB (1 year for lymphedema) was 17.4%. The most common complications were seroma (9.3%), infection/cellulitis (4.8%), and lymphedema (4.3%). All three were more common in patients with a lower extremity primary tumor location versus other locations. With Merlin test results applied, SLNB-related complications would have decreased by 59%. CONCLUSION: SLNB is a safe procedure but carries a significant complication rate. Merlin testing might reduce the need for SLNB and its associated complications.

Validation of CP-GEP (Merlin Assay) for predicting sentinel lymph node metastasis in primary cutaneous melanoma patients: A U.S. cohort study.

BACKGROUND: Approximately 85% of melanoma patients who undergo a sentinel lymph node biopsy (SLNB) are node-negative. Melanoma incidence is highest in patients ≥65 years, but their SLNB positivity rate is lower than in younger patients. CP-GEP, a model combining clinicopathologic and gene expression variables, identifies primary cutaneous melanoma (CM) patients who may safely forgo SLNB due to their low risk for nodal metastasis. Here, we validate CP-GEP in a U.S. melanoma patient cohort. METHODS: A cohort of 208 adult patients with primary CM from the Mayo Clinic and West Virginia University was used. Patients were stratified according to their risk for nodal metastasis: CP-GEP High Risk and CP-GEP Low Risk. The main performance measures were SLNB reduction rate (RR) and negative predictive value (NPV). RESULTS: SLNB positivity rate for the entire cohort was 21%. Most patients had a T1b (34%) or T2a (31%) melanoma. In the T1-T2 group (153 patients), CP-GEP achieved an SLNB RR of 41.8% (95% CI: 33.9-50.1) at an NPV of 93.8% (95% CI: 84.8-98.3). Subgroup analysis showed similar performance in T1-T2 patients ≥65 years of age (51 patients; SLNB positivity rate, 9.8%): SLNB RR of 43.1% (95% CI: 29.3-57.8) at an NPV of 95.5% (95% CI: 77.2-99.9). CONCLUSION: We confirmed the potential of CP-GEP to reduce negative SLNB in all relevant age groups. Our findings are especially relevant to patients ≥65 years, where surgery is often elective. CP-GEP may guide SLNB decision-making in clinical practice.