Real-World Evidence Shows Clinicians Appropriately Use the Prognostic 40-Gene Expression Profile (40-GEP) Test for High-Risk Cutaneous Squamous Cell Carcinoma (cSCC) Patients.

Treatment decisions for patients with cutaneous squamous cell carcinoma (cSCC) are traditionally based upon clinicopathologic risk factors and staging systems. Due to the accuracy limitations of these resources in predicting poor outcomes, there is a clinically significant need for more accurate methods of risk assessment. The 40-gene expression profile (40-GEP) test was developed to augment metastatic risk prediction of high-risk cSCC patients and has been validated in two independent, multi-center studies involving over 1,000 patients. This study substantiates that the 40-GEP is appropriately utilized by clinicians and that the personalized risk-stratification results are impactful in guiding risk-aligned patient management.

The 31-gene expression profile stratifies recurrence and metastasis risk in patients with cutaneous melanoma.

Aim: Sentinel node biopsy is a prognostic indicator of melanoma recurrence. We hypothesized that adding the primary melanoma molecular signature from the 31-gene expression profile (31-GEP) test could refine the risk of recurrence prognosis for patients with stage I-III melanoma. Materials & methods: Four hundred thirty-eight patients with stage I-III melanoma consecutively tested with the 31-GEP were retrospectively analyzed. The 31-GEP stratified patients as low-risk (Class 1A), intermediate-risk (Class 1B/2A) or high risk (Class 2B) of recurrence or metastasis. Results: The 31-GEP significantly stratified patient risk for recurrence-free survival (p < 0.001), distant metastasis-free survival (p < 0.001) and melanoma-specific survival (p < 0.001) and was a significant, independent predictor of metastatic recurrence (hazard ratio: 5.38; p = 0.014). Conclusion: The 31-GEP improves prognostic accuracy in stage I-III melanoma.

Lay abstract Cutaneous melanoma is a type of skin tumor affecting 100,000 new patients each year. Even with the best tools available today, knowing which patients will die from their cancer can be challenging. Using individual tumors from over 400 patients, we analyzed the expression of 31 genes from each tumor. Doing this helped us split the patients into groups who are more or less likely to die from their tumor. By combining this technique with current medical practices and guidelines, we hope to help identify which patients may or may not benefit from more intense therapies.