On April 15, 2021 Castle Biosciences, Inc. (Nasdaq: CSTL), a skin cancer diagnostics company providing personalized genomic information to improve cancer treatment decisions, presented new data on the integration of the DecisionDx-Melanoma test with clinicopathologic features (i31-GEP) at the 10th World Congress of Melanoma and 17th European Association of Dermato-Oncology (EADO) Congress (Press release, Castle Biosciences, APR 15, 2021, View Source [SID1234578095]). DecisionDx-Melanoma is Castle’s prognostic gene expression profile test for cutaneous melanoma with an Integrated Test Result (ITR) designed to provide a more precise risk prediction in patients with stage I, II or III melanoma.

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The ITR is calculated by the independently validated integrated 31-GEP, or i31-GEP, algorithm, designed to provide a more precise and personalized prediction of sentinel lymph node (SLN) positivity in order to guide discussions and recommendations, within current risk-based guidelines, for the SLN biopsy (SLNB) surgical procedure. i31-GEP is an artificial intelligence-based neural network algorithm that integrates the DecisionDx-Melanoma test result with the patient’s traditional clinicopathologic features. The algorithm has been validated in a cohort of 1,674 prospectively tested patients with T1-T4 cutaneous melanoma.

The poster, titled "Integration of the 31-gene expression profile test with clinicopathologic features (i31-GEP) to assess sentinel lymph node positivity risk in patients with cutaneous melanoma," highlights the i31-GEP validation study data and demonstrates that the algorithm provides a more precise, personalized likelihood of sentinel lymph node positivity. The poster can be accessed here.

Study methods and findings:

DecisionDx-Melanoma, using the categories of Class 1A, 1B, 2A and 2B, age and tumor thickness was previously validated in an independent, prospective, multi-center study of 1,421 patients to predict SLNB positivity rates.
An integrated DecisionDx-Melanoma test result (i31-GEP) was developed to integrate DecisionDx-Melanoma’s output, a risk assignment based on gene expression profile analysis, with clinicopathologic risk factors.
The study reviewed the development and validation of the i31-GEP, which deploys a neural network algorithm to integrate the continuous DecisionDx-Melanoma score as well as other histologic and clinical features on a development cohort of 1,398 patients. The i31-GEP algorithm was locked using these 1,398 patients and was then independently validated on an independent, U.S. based cohort of 1,674 patients.
The development phase identified that the DecisionDx-Melanoma score was the most important variable in predicting SLN positivity under both the variable importance assessment function (DecisionDx-Melanoma score = 100, Breslow thickness = 56, Mitotic rate = 25, ulceration = 83 and Age = 0; with 100 being the highest possible value) and log-likelihood value (DecisionDx-Melanoma score = 91.3, Breslow thickness = 53.5, Mitotic rate = 20.7, ulceration = 19.1 and Age = 10.5; with 100 being the highest possible value).
The independent validation phase showed that the i31-GEP provides a highly concordant prediction of SLN positivity rate compared to observed rates(linear regression slope of 0.999, with 1.0 representing complete concordance).
Of patients originally classified with 5-10% SLN positivity risk, i31-GEP reclassified 63% of those patients, whose actual risk of SLN positivity was outside that range in either direction (less than 5% or greater than 10%).
i31-GEP had a high negative predictive value of 98% in patients with T1-T4 tumors.
"Most patients who undergo a sentinel lymph node biopsy procedure receive negative results, indicating that other tools may be needed to better understand and stratify risk for patients with melanoma and define groups who may be able to avoid SLNB entirely," said study author Eric Whitman, M.D., surgical oncologist and medical director at Atlantic Health System Cancer Care in New Jersey. "DecisionDx-Melanoma and i31-GEP can help refine patient classification with respect to the likelihood of sentinel node positivity. These clinical validation data show that the ITR result, which reflects a combination of gene expression analysis and standard clinicopathologic features, can be used to provide a more precise risk prediction and potentially help improve patient selection for SLNB."

About DecisionDx-Melanoma

DecisionDx-Melanoma is a gene expression profile test that uses an individual patient’s tumor biology to predict individual risk of cutaneous melanoma metastasis or recurrence, as well as sentinel lymph node positivity, independent of traditional staging factors, and has been studied in more than 5,700 patient samples. Using tissue from the primary melanoma, the test measures the expression of 31 genes. The test has been validated in four archival risk of recurrence studies of 901 patients and six prospective risk of recurrence studies including more than 1,600 patients. To predict likelihood of sentinel lymph node positivity, the Company utilizes its proprietary algorithm, i31-GEP, to produce an integrated test result. i31-GEP is an artificial intelligence-based neural network algorithm (independently validated in a cohort of 1,674 prospective, consecutively tested patients with T1-T4 cutaneous melanoma) that integrates the DecisionDx-Melanoma test result with the patient’s traditional clinicopathologic features. Impact on patient management plans for one of every two patients tested has been demonstrated in four multicenter and single-center studies including more than 560 patients. The consistent performance and accuracy demonstrated in these studies provides confidence in disease management plans that incorporate DecisionDx-Melanoma test results. Through December 31, 2020, DecisionDx-Melanoma has been ordered more than 68,920 times for use in patients with cutaneous melanoma.