23 Apr Gene Expression Profile Improves Melanoma Risk Assessment
MedicalResearch: What is the basis and background for performing this study?
Dr. Gerami: Most of the existing literature shows that Sentinel Lymph Node Biopsy (SLNB) will identify 25 to 35 percent of patients who will ultimately die of metastatic melanoma. Hence while SLNB is reported to be the strongest predictor of outcome for melanoma, the vast majority of patients who ultimately die of metastatic melanoma have a negative Sentinel Lymph Node Biopsy result. Hence in this study we aimed to determine whether a GEP assay developed by Castle bioscience could be used independently or in conjunction with SLNB to better detect those patients who are at high risk for developing metastatic disease and dying from melanoma.
MedicalResearch: What are the findings of the study?
Dr. Gerami: Our study, which examined the use of a Gene Expression Profile (GEP) assay developed by Castle Biosciences and Sentinel Lymph Node Biopsy alone and in combination in a multi-center cohort of 217 patients, demonstrated that the use of the GEP identified more than 80 percent of patients who develop melanoma
Combining the two methods showed that patients predicted to be high risk based on the GEP test alone had similar rates of disease progression whether they were SLNB positive or negative. Patients who were SLNB negative and predicted to be low risk using the GEP test had lower rates of disease progression than the SLNB negative group as a whole.
Primary tumor tissue from 217 Stage I, II, III, or IV cutaneous melanoma patients with documented sentinel lymph node biopsy (SLNB) results was analyzed using the DecisionDx-Melanoma GEP test, developed by Castle Biosciences, Inc., under a multicenter, prospectively-planned, archival tissue study protocol. DecisionDx-Melanoma is a noninvasive test developed to identify high risk disease independent of other staging methods, such as AJCC stage and SLNB status. Using tissue from the primary melanoma, the DecisionDx-Melanoma test measures the expression of 31 genes and stratifies patients as either low risk Class 1 or high risk Class 2
The predictive accuracy of the DecisionDx-Melanoma and SLNB prognostic tools were evaluated individually and in combination to assess primary endpoints of disease-free survival (DFS), distant metastasis free survival (DMFS), and overall survival (OS). Independently, the Gene Expression Profile and SLNB tests were shown to stratify patients according to their risk (univariate analysis using Cox proportional hazards p<0.0001 for GEP for all endpoints, SLNB for DFS and DMFS; SLNB for OS p=0.0099).
Multivariate analysis found GEP Class to be independent of SLNB status for all endpoints (p<0.0001) and SLNB status to be independent of GEP Class for DFS (p=0.008) and DMFS (p=0.001) but not OS (p=0.11).
Combining the GEP and SLNB tests showed improvements in stratifying patients who were SLNB negative. The SLNB negative group as a whole had a DFS of 55 percent. Using the GEP test to further stratify those patients resulted in identification of a higher risk (Class 2) sub-group of SLNB negative patients who had a DFS of 35 percent. Likewise, SLNB negative patients who were predicted to be low risk (Class 1) using the GEP had a DFS of 83 percent.
MedicalResearch: What are the conclusions of the study?
Dr. Gerami: Use of the GEP test, both independently and in combination with SLNB will help clinicians identify high-risk SLNB-negative patients with aggressive disease and patients identified as high-risk by conventional parameters who are unlikely to have progression of their disease. Based on data from our study and a prior validation study (published earlier this year in Clinical Cancer Resrearch), this non-invasive GEP prognostic tool could be used to help clinicians more accurately stratify patients as higher versus lower risk. We believe that the use of GEP prognostic testing may identify the majority of patients who are at risk for metastasis.
Gerami P1, Cook RW2, Russell MC3, Wilkinson J4, Amaria RN5, Gonzalez R6, Lyle S7, Jackson GL8, Greisinger AJ9, Johnson CE2, Oelschlager KM2, Stone JF4, Maetzold DJ2, Ferris LK10, Wayne JD11, Cooper C12, Obregon R12, Delman KA3, Lawson D3.
MedicalResearch.com Interview with: Pedram Gerami, M.D. (2015). Gene Expression Profile Improves Melanoma Risk Assessment