Castle Biosciences, Inc., a dermatologic diagnostics company, announced a podium presentation on its DecisionDx-SCC test at the American Head & Neck Society (AHNS) 10th International Conference on Head and Neck Cancer, held on July 22-25, 2021.
DecisionDx-SCC is Castle’s prognostic 40-gene expression profile (GEP) test for patients diagnosed with high-risk cutaneous squamous cell carcinoma (SCC), designed to use a patient’s tumor biology to predict individual risk of metastasis for patients with SCC and one or more risk factors.
“Comparison of the 40-Gene Expression Profile Test with Clinicopathologic Risk Factor-Based Assessment to Improve Metastasis Risk Assessment in Cutaneous Squamous Cell Carcinoma of the Head and Neck” presented by Jason G. Newman, M.D., University of Pennsylvania Health System, Philadelphia, on July 23 at 3:30 p.m.-4:20 p.m. Central time.
“Study data demonstrate that DecisionDx-SCC is a valuable complement to traditional and trusted risk assessment systems, including the American Joint Committee on Cancer Eighth Edition (AJCC8) and Brigham and Women's Hospital (BWH) tumor (T) classification,” said Newman. “The data further demonstrate that Castle’s DecisionDx-SCC test can provide clinicians with additional information on a patient’s metastatic risk to help them make more informed choices about their treatment and follow-up care.”
Study methods and findings:
Archival, primary tumor specimens and associated data from a cohort of 278 patients from 33 different clinical sites were included in the study; the patients had high-risk SCC located on the head or neck, and 54 patients (19.4%) developed regional and/or distant metastasis.
All SCC tumor specimens were tested with DecisionDx-SCC and analyzed using Kaplan-Meier for metastasis-free survival (MFS) and Cox regression for risk of regional/distant metastasis.
Patients who received a Class 1 (low biological risk), Class 2A (moderate biological risk) or Class 2B (high biological risk) DecisionDx-SCC result had significantly different three-year MFS rates (92.1%, 76.1% or 44.4%, respectively; p<0.0001, log-rank test) compared to the overall cohort MFS rate of 81.3%.
Univariate Cox regression analysis demonstrated that the GEP test has significant, independent prognostic value. Multivariate Cox regression analysis demonstrated that the DecisionDx-SCC results compared to AJCC8 T staging was the most significant predictor of outcomes with a Hazard Ratio of 9.07 compared to AJCC8 at 2.88. Similar results were shown when the GEP test was compared with BWH T stages and individual clinicopathologic risk factors, such as tumor diameter, deep invasion, poor differentiation and perineural invasion.
The specificity and positive predictive value (PPV) of a high-risk Class 2B DecisionDx-SCC result were improved relative to these metrics for high-stage AJCC8 (T3/T4) and BWH (T2b/T3), while maintaining a similar negative predictive value (NPV).
Overall, the study demonstrated that DecisionDx-SCC offers significant, independent prognostic value for determining a patient’s individual risk of SCC metastasis, and that the test could be used to complement AJCC8 and BWH T staging and a patient’s clinicopathologic risk factor-based assessment.
DecisionDx-SCC is a 40-gene expression profile test that uses an individual patient’s tumor biology to predict individual risk of cutaneous squamous cell carcinoma metastasis for patients with one or more risk factors. The test result, in which patients are stratified into a Class 1 (low), 2A (moderate) or 2B (high) risk category, predicts individual metastatic risk to inform risk-appropriate management.
Peer-reviewed publications have demonstrated that DecisionDx-SCC is an independent predictor of metastatic risk and that integrating DecisionDx-SCC with current prognostic methods can add positive predictive value to clinician decisions regarding staging and management.
Castle Biosciences is a commercial-stage dermatologic diagnostics company focused on providing physicians and their patients with personalized, clinically actionable genomic information to make more accurate treatment decisions.