Gene Expression Profiling May Refine Cutaneous Melanoma Care

Although American Joint Committee on Cancer (AJCC) staging remains central to cutaneous melanoma management, traditional clinicopathologic factors may not fully capture an individual patient's risk of recurrence or metastasis. Gene expression profiling (GEP) has emerged as a potential complementary tool for refining prognostic assessments and informing individualized management decisions, particularly in patients with early-stage disease.

In an expert discussion with Dermatology Times, Gaurav Singh, MD, a Mohs surgeon at City of Hope Cancer Center in Chicago, examined the clinical utility of GEP testing in cutaneous melanoma, including its potential role in sentinel lymph node biopsy (SLNB) decisions, surveillance strategies, and integration with emerging biomarkers.

Addressing Gaps in Traditional Melanoma Staging

Singh emphasized that although AJCC staging incorporates established prognostic factors such as Breslow thickness, ulceration, and nodal status, it does not account for every characteristic that may influence melanoma outcomes.

Tumor location, mitotic activity, and immunosuppression may contribute to differences in disease behavior, potentially leaving some patients at greater risk than their initial staging suggests.

Singh explained that patients with stage I melanoma may experience recurrence or metastasis despite receiving standard-of-care treatment, whereas some patients with more advanced disease achieve favorable outcomes following surgery and immunotherapy.

He described GEP testing as an additional source of prognostic information that may help identify patients whose tumors demonstrate more aggressive biological characteristics than traditional staging alone would suggest.

Clinical Applications of 31-GEP Testing

Singh highlighted DecisionDx-Melanoma, a 31-gene expression profile assay, as a tool he uses to support risk stratification in patients with stage I and II cutaneous melanoma.

For patients with stage I disease, a high-risk GEP result may prompt additional consideration of SLNB or imaging, particularly when those interventions would not otherwise have been pursued based on conventional clinical characteristics.

In stage II melanoma, Singh described using high-risk results, including Class 2A and 2B classifications, to inform additional imaging and closer surveillance, even among patients with negative sentinel lymph node findings.

He also discussed emerging evidence supporting the prognostic utility of 31-GEP testing, citing analyses of Surveillance, Epidemiology, and End Results (SEER) data and prospective findings from the DECIDE study. According to Singh, these findings suggest that incorporating molecular risk information into clinical decision-making may help identify patients who could benefit from more intensive monitoring.

However, the clinical utility of GEP testing remains an evolving area of investigation, and prognostic findings should not be interpreted as establishing that testing itself improves survival or replaces guideline-based management.

Balancing Prognostic Information With Individualized Care

Despite the potential advantages of molecular testing, Singh cautioned against interpreting GEP results independently of the patient's overall clinical circumstances.

He emphasized that decisions regarding additional imaging, surgery, or surveillance should incorporate functional status, comorbidities, and patient preferences alongside estimated risk.

Singh also noted that GEP testing may offer less clinical utility in certain patients with established stage III disease and confirmed lymph node involvement, depending on the specific management question.

Importantly, he explained that a low-risk GEP result may provide reassurance even when it does not directly alter treatment recommendations. Conversely, high-risk results can help clinicians identify patients who may warrant closer observation without suggesting that disease progression is inevitable.

Integrating GEP With Emerging Biomarkers

Looking ahead, Singh discussed the potential complementary roles of GEP testing and circulating tumor DNA (ctDNA) in melanoma surveillance.

Whereas GEP provides information about a tumor's underlying prognostic characteristics, ctDNA testing may offer a means of monitoring for molecular evidence of disease over time.

In his practice, Singh described considering serial ctDNA testing for patients with high-risk GEP results, using changes in circulating tumor DNA levels to help inform subsequent imaging decisions.

Although ctDNA testing continues to be investigated in melanoma, Singh suggested that integrating molecular prognostic assessments with longitudinal biomarker monitoring could contribute to more individualized surveillance strategies.

Ultimately, Singh emphasized that the value of GEP testing lies in its ability to supplement, rather than replace, established clinical assessments. As evidence continues to develop, the appropriate integration of molecular tools into melanoma care will depend on balancing prognostic information with clinical utility, guideline recommendations, and individual patient needs.

References

1. 1. 31-gene expression profile testing in cutaneous melanoma and survival outcomes in a population-based analysis: A SEER collaboration. (2023). JCO Precision Oncology, 7, e2300044. https://doi.org/10.1200/PO.23.00044
2. A prospective, multicenter analysis of recurrence-free survival after sentinel lymph node biopsy decisions influenced by the 31-GEP. (2025). Cancer Medicine. https://doi.org/10.1002/cam4.70839
3. The prognostic value of the 31-gene expression profile test in cutaneous melanoma: A systematic review and meta-analysis. (2024). https://pubmed.ncbi.nlm.nih.gov/39518150/
4. Bartlett, E. K., et al. (2025). Society of Surgical Oncology consensus statement: Assessing the evidence for and utility of gene expression profiling of primary cutaneous melanoma. Annals of Surgical Oncology. https://doi.org/10.1245/s10434-024-16379-2
5. 31-GEP (DecisionDx): A review of clinical utility and performance in a Mayo Clinic cohort. (2024). https://pubmed.ncbi.nlm.nih.gov/39154363/