{"id":48740,"date":"2022-09-23T13:02:07","date_gmt":"2022-09-23T11:02:07","guid":{"rendered":"https:\/\/pharma-trend.com\/en\/castle-biosciences-announces-publication-of-study-demonstrating-the-integration-of-decisiondx-melanoma-and-clinicopathologic-factors-provides-optimized-personalized-survival-prognoses-for-patie\/"},"modified":"2022-09-23T13:02:07","modified_gmt":"2022-09-23T11:02:07","slug":"castle-biosciences-announces-publication-of-study-demonstrating-the-integration-of-decisiondx-melanoma-and-clinicopathologic-factors-provides-optimized-personalized-survival-prognoses-for-patie","status":"publish","type":"post","link":"https:\/\/pharma-trend.com\/en\/castle-biosciences-announces-publication-of-study-demonstrating-the-integration-of-decisiondx-melanoma-and-clinicopathologic-factors-provides-optimized-personalized-survival-prognoses-for-patie\/","title":{"rendered":"Castle Biosciences Announces Publication of Study Demonstrating the Integration of DecisionDx\u00ae-Melanoma and Clinicopathologic Factors Provides Optimized, Personalized Survival Prognoses for Patients with Cutaneous Melanoma"},"content":{"rendered":"
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\nDecisionDx-Melanoma integrates a patient\u2019s 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<\/i>\n<\/p>\n

FRIENDSWOOD, Texas–(BUSINESS WIRE)–$CSTL<\/a> #CSTL<\/a>–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<\/i> validating the performance of DecisionDx\u00ae<\/sup>-Melanoma\u2019s proprietary algorithm, i31-ROR. i31-ROR is designed to integrate a patient\u2019s tumor biology with clinicopathologic factors to provide the patient\u2019s personalized risk of melanoma recurrence. The study, accessible here<\/a>, found that DecisionDx-Melanoma\u2019s 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.\n<\/p>\n

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\nAs expected in the study, the most significant factor in predicting melanoma-specific survival (MSS) was the tumor biology risk as identified by DecisionDx-Melanoma\u2019s 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<\/sup> edition (AJCC8) staging criteria.\n<\/p>\n

\n\u201cCurrent staging practices use key characteristics of a patient\u2019s 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,\u201d said first author Abel Jarell, M.D., dermatologist and dermatopathologist at Northeast Dermatology Associates, PC, Portsmouth, New Hampshire. \u201cDecisionDx-Melanoma takes many of these same characteristics and combines them with the biology of a patient\u2019s tumor to provide patients and clinicians with personalized \u2013 instead of population-based \u2013 risk estimates that can allow for tailored treatment plans aligned to the patient\u2019s individual risk.\u201d\n<\/p>\n

\nIntegrating Clinicopathologic Factors with Tumor Biology for Precise, Personalized Risk Estimates<\/b>\n<\/p>\n

\nDecisionDx-Melanoma is Castle\u2019s molecular risk stratification GEP test that analyzes the expression of 31 genes (31-GEP) within tumor tissue. DecisionDx-Melanoma\u2019s 31-GEP has been shown to be a significant predictor of recurrence and metastatic risk, independent of other clinical factors.1<\/sup> 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\u2019s 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:\n<\/p>\n

\n1) A patient’s individual risk of sentinel lymph node (SLN) positivity <\/b>(i31-SLNB algorithm, previously validated);2<\/sup> and\n<\/p>\n

\n2) A patient’s personal risk of recurrence and\/or metastasis<\/b> (i31-ROR algorithm).\n<\/p>\n

\nThe paper, titled \u201cOptimizing treatment approaches for patients with cutaneous melanoma by integrating clinical and pathologic features with the 31-gene expression profile test,\u201d 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.\n<\/p>\n

\ni31-ROR Highlights:<\/i>\n<\/p>\n