On September 23, 2022 Castle Biosciences, Inc. (Nasdaq: CSTL), a company improving health through innovative tests that guide patient care, reported 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 (Press release, Castle Biosciences, SEP 23, 2022, View Source [SID1234621395]). 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.

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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<0.001), DMFS (95% vs. 53%, P<0.001) and MSS (98% vs. 73%, P<0.001) than patients with a high-risk i31-ROR result.
Additionally, i31-ROR correctly reclassified patient risk as being higher or lower than predicted by AJCC8 for MSS, demonstrating the ability of the algorithm to further refine patient risk and help inform more individualized melanoma management plans.
The i31-ROR also had higher sensitivity and higher negative predictive value (NPV) for RFS, DMFS and MSS than SLN status alone (sensitivity of i31-ROR vs. SLN status alone: RFS (66% vs. 46%), DMFS (78% vs. 49%) and MSS (71% vs. 57%); NPV of i31-ROR vs. SLN status alone: RFS (93% vs. 89%), DMFS (97% vs. 93%) and MSS (98% vs. 97%)).
When patients with an i31-SLNB estimated likelihood of SLN positivity of ≥5% (n=298) were analyzed by the i31-ROR algorithm, those with a negative SLN but high-risk i31-ROR result had lower RFS, DMFS and MSS rates than patients with a negative SLN and a low-risk i31-ROR result. Among this same subset of patients, those with a positive SLN but a low-risk i31-ROR result had higher RFS, DMFS and MSS rates than patients with a high-risk i31-ROR result.
Moreover, the study showed that the number of patients undergoing SLNB could potentially be reduced as a subset of patients with an i31-SLNB predicted risk of <5% all received a low-risk i31-ROR result and had RFS, DMFS and MSS rates >98%.
Study conclusions:

Overall, the study demonstrated that DecisionDx-Melanoma, with integrated algorithms that combine the 31-GEP score with a patient’s clinicopathologic factors, provides personalized and accurate survival prognoses, which can help guide risk-aligned patient management.
Further, the study showed that using DecisionDx-Melanoma test results in conjunction with current staging guidelines can help refine patient risk, reduce unnecessary procedures and ultimately improve patient care.
As the National Comprehensive Cancer Network (NCCN) guidelines recommend risk-aligned decisions for individual patients, the use of DecisionDx-Melanoma test results could aid in identifying patients with more or less aggressive cases of melanoma to align treatment decisions more accurately with patient risk and help ensure a more appropriate allocation of healthcare resources.
About DecisionDx-Melanoma

DecisionDx-Melanoma is a gene expression profile risk stratification test. It is designed to inform two clinical questions in the management of cutaneous melanoma: a patient’s individual risk of sentinel lymph node (SLN) positivity and a patient’s personal risk of melanoma recurrence and/or metastasis. By integrating tumor biology with clinical and pathologic factors using a validated proprietary algorithm, DecisionDx-Melanoma is designed to provide a comprehensive and clinically actionable result to guide risk-aligned patient care. DecisionDx-Melanoma has been shown to be associated with improved patient survival and has been studied in more than 9,000 patient samples. DecisionDx-Melanoma’s clinical value is supported by more than 35 peer-reviewed and published studies, providing confidence in disease management plans that incorporate the test’s results. Through June 30, 2022, DecisionDx-Melanoma has been ordered 105,239 times for patients diagnosed with cutaneous melanoma. More information about the test and disease can be found at www.CastleTestInfo.com