DecisionDx-Melanoma integrates a patient’s clinicopathologic factors with his/her tumor biology to provide precise, personalized risk estimates, including five-year melanoma-specific survival, recurrence-free survival and distant metastasis-free survival
FRIENDSWOOD, Texas--(BUSINESS WIRE)-- Castle Biosciences, Inc. (Nasdaq: CSTL), a company improving health through innovative tests that guide patient care, today announced the publication of a study in the Journal of the American Academy of Dermatology validating the performance of DecisionDx®-Melanoma’s proprietary algorithm, i31-ROR. i31-ROR is designed to integrate a patient’s tumor biology with clinicopathologic factors to provide the patient’s personalized risk of melanoma recurrence. The study, accessible here, found that DecisionDx-Melanoma’s integrated algorithms (i31-ROR and i31-SLNB) provide more precise risk-stratification and individualized risk estimates, compared to those based on clinicopathologic factors alone, and can ultimately improve treatment decisions.
As expected in the study, the most significant factor in predicting melanoma-specific survival (MSS) was the tumor biology risk as identified by DecisionDx-Melanoma’s 31-gene expression profile (GEP) (multivariate hazard ratio (HR)=20.00). Additionally, DecisionDx-Melanoma, including both algorithms (i31-SLNB and i31-ROR), identified 44% of patients who could potentially forego the sentinel lymph node biopsy (SLNB) surgical procedure while maintaining high survival rates (>98% for recurrence-free survival (RFS), distant metastasis-free survival (DMFS) and MSS) or were re-stratified as being at a higher or lower risk of recurrence or death than initially staged using the American Joint Committee on Cancer 8th edition (AJCC8) staging criteria.
“Current staging practices use key characteristics of a patient’s melanoma tumor to determine how aggressive it is as a means to inform important cancer management decisions, such as intensity of follow-up, surveillance imaging and the need for adjuvant therapy,” said first author Abel Jarell, M.D., dermatologist and dermatopathologist at Northeast Dermatology Associates, PC, Portsmouth, New Hampshire. “DecisionDx-Melanoma takes many of these same characteristics and combines them with the biology of a patient’s tumor to provide patients and clinicians with personalized – instead of population-based – risk estimates that can allow for tailored treatment plans aligned to the patient’s individual risk.”
Integrating Clinicopathologic Factors with Tumor Biology for Precise, Personalized Risk Estimates
DecisionDx-Melanoma is Castle’s molecular risk stratification GEP test that analyzes the expression of 31 genes (31-GEP) within tumor tissue. DecisionDx-Melanoma’s 31-GEP has been shown to be a significant predictor of recurrence and metastatic risk, independent of other clinical factors.1 In addition to the 31-GEP class score (low risk (Class 1A), increased risk (Class 1B/2A) or high risk (Class 2B) of recurrence or metastasis), the test now provides results from two proprietary algorithms, i31-SLNB and i31-ROR, that combine a patient’s 31-GEP score with his/her clinicopathologic factors to provide precise, personalized risk assessments that inform two clinical questions in the management of cutaneous melanoma:
1) A patient's individual risk of sentinel lymph node (SLN) positivity (i31-SLNB algorithm, previously validated);2 and
2) A patient's personal risk of recurrence and/or metastasis (i31-ROR algorithm).
The paper, titled “Optimizing treatment approaches for patients with cutaneous melanoma by integrating clinical and pathologic features with the 31-gene expression profile test,” discusses the development and validation of the i31-ROR algorithm and its use in conjunction with the i31-SLNB algorithm for more comprehensive and refined patient prognoses.
i31-ROR Highlights:
- DecisionDx-Melanoma’s i31-ROR algorithm integrates a patient’s 31-GEP score with his/her clinicopathologic factors, including Breslow thickness, ulceration, mitotic rate, SLN status, age and tumor location. The most significant factor in predicting MSS was the tumor biology risk as identified by the 31-GEP (multivariate HR=20.00).
- With these inputs, i31-ROR provides personalized, not population-based, predictions of five-year MSS, and two additional endpoints not available in AJCC8, RFS and DMFS.
Study highlights:
- In the study, DecisionDx-Melanoma’s i31-ROR algorithm identified patients at the highest and lowest risk for recurrence or metastasis; patients with a low-risk i31-ROR result had significantly higher RFS (91% vs. 45%, P