Study demonstrated improved prediction for sentinel lymph node (SLN) status compared to clinicopathologic features alone
Study also demonstrated that DecisionDx®-Melanoma’s i31-GEP SLNB algorithm provides high correlation between prediction of SLN positivity rates and observed rates
FRIENDSWOOD, Texas--(BUSINESS WIRE)-- Castle Biosciences, Inc. (Nasdaq: CSTL), a company applying innovative diagnostics to inform disease management and improve patient outcomes, today announced the publication of a study validating performance of a novel algorithm designed to integrate the DecisionDx®-Melanoma gene expression profile (GEP) test with clinicopathologic features (i31-GEP SLNB) to determine sentinel lymph node biopsy (SLNB) positivity risk in patients with cutaneous melanoma.
DecisionDx-Melanoma is Castle’s risk-stratification GEP test that is designed to predict 5-year risk of metastasis as well as metastasis to the SLN. The test’s Integrated Test Result (ITR) includes the traditional class designation of lowest risk (Class 1A), increased risk (Class 1B/2A) or highest risk (Class 2B), as well as a more precise risk prediction for both SLNB positivity and risk of recurrence, distant metastasis and melanoma survival in patients with stage I, II or III melanoma through the i31- GEP algorithms (SLNB and Risk of Recurrence). The i31-GEP SLNB and ROR are distinct independently validated algorithms that integrate clinicopathologic features with the DecisionDx-Melanoma score.
“The majority of patients who undergo the SLNB surgical procedure receive a negative result,” said Robert Cook, Ph.D., senior vice president of research and development of Castle Biosciences and study author. “The i31-GEP SLNB clinical validation data showed that integrating clinicopathologic risk factors with the DecisionDx-Melanoma test provided very high correlation between the predicted and the actual, or observed, rates and a high sensitivity in identifying patients at low risk for SLN metastasis who may be able to safely avoid the SLNB procedure. Importantly, the study demonstrated that the DecisionDx-Melanoma test result was the most important variable in predicting SLN positivity.”
The article, titled “Integrating 31-Gene Expression Profiling with Clinicopathologic Features to Optimize Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction,” was published in the peer-reviewed journal JCO® Precision Oncology and can be accessed here. The study highlights the development and validation of the i31-GEP SLNB algorithm.
Study background:
- National guidelines recommend that an SLNB be offered to patients with >10% likelihood of SLN positivity (typically thought to encompass T2-T4 tumors), but do not recommend SLNB for patients who are thought to have