Integrating 31-Gene Expression Profiling With Clinicopathologic Features to Optimize Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction

Abstract

PURPOSE National guidelines recommend sentinel lymph node biopsy (SLNB) be offered to patients with > 10% likelihood of sentinel lymph node (SLN) positivity. On the other hand, guidelines do not recommend SLNB for patients with T1a tumors without high-risk features who have < 5% likelihood of a positive SLN. However, the decision to perform SLNB is less certain for patients with higher-risk T1 melanomas in which a positive node is expected 5%-10% of the time. We hypothesized that integrating clinicopathologic features with the 31-gene expression profile (31-GEP) score using advanced artificial intelligence techniques would provide more precise SLN risk prediction. METHODS An integrated 31-GEP (i31-GEP) neural network algorithm incorporating clinicopathologic features with the continuous 31-GEP score was developed using a previously reported patient cohort (n = 1,398) and validated using an independent cohort (n = 1,674). RESULTS Compared with other covariates in the i31-GEP, the continuous 31-GEP score had the largest likelihood ratio (G2 = 91.3, P < .001) for predicting SLN positivity. The i31-GEP demonstrated high concordance between predicted and observed SLN positivity rates (linear regression slope = 0.999). The i31-GEP increased the percentage of patients with T1-T4 tumors predicted to have < 5% SLN-positive likelihood from 8.5% to 27.7% with a negative predictive value of 98%. Importantly, for patients with T1 tumors originally classified with a likelihood of SLN positivity of 5%-10%, the i31-GEP reclassified 63% of cases as having < 5% or > 10% likelihood of positive SLN, for a more precise, personalized, and clinically actionable SLN-positive likelihood estimate. CONCLUSION These data suggest the i31-GEP could reduce the number of SLNBs performed by identifying patients with likelihood under the 5% threshold for performance of SLNB and improve the yield of positive SLNBs by identifying patients more likely to have a positive SLNB.

Dermatologic Disease-Directed Targeted Therapy (D3T2): The Application of Biomarker-Based Precision Medicine for the Personalized Treatment of Skin Conditions—Precision Dermatology

“…The 10-GEP test was used to identify patients with primary cutaneous melanoma at low risk for nodal metastases (Table 1) [42,61]. The 31-GEP test is primarily useful for risk stratification in patients with early-stage melanoma, where individuals who might benefit from heightened surveillance and closer followup (even though they may have previously been designated as being low risk) are identified (Table 1) [62][63][64][65][66][67][68][69].…”

“…There are four categories: class 1A (0 to 0.41, lowest risk), class 1B (0.42 to 0.49, low risk), class 2A (0.50 to 0.58, high risk), and class 2B (0.59 to 1, highest risk). Hence, lesions that are either class 1B or class 2A are considered to present increased (or intermediate) risk [62][63][64][65][66][67][68][69].…”

“…However, there are no definitive management recommendations regarding sentinel lymph node biopsy when there is discordance between the tumor depth and 31-GEP class result. In addition, the 31-GEP test results are not currently incorporated in the staging criteria of the American Joint Committee on Cancer (AJCC) or the National Comprehensive Cancer Network (NCCN) [62][63][64][65][66][67][68][69].…”

The Integrated 31-Gene Expression Profile Test (i31-GEP) for Cutaneous Melanoma Outperforms the CP-GEP at Identifying Patients who can Forego Sentinel Lymph Node Biopsy when Applying NCCN Guidelines

“…[6][7][8][9][10][11] The 31-GEP continuous risk score has been integrated with clinical and pathological features (Breslow thickness, mitotic rate, ulceration, and age) using a neural network algorithm to determine a precise risk score for SLN positivity (i31-GEP SLNB) and the 31-GEP score was a significant and independent contributor. 12 The i31-GEP algorithm for SLNB was shown to provide benefit for selecting patients for SLNB over treating all with SLNB. 13 This test also provides each patient with their personalized risk of SLN metastasis rather than binning patients as high or low risk, which may miss the nuances associated with each patient's specific tumor, and, ultimately, preclude opportunities for shared decision-making between the patient and clinician.…”

RE: More sentinel lymph node biopsies for thin melanomas after transition to AJCC 8th edition do not increase positivity rate: A Danish population‐based study of 7148 patients

“…Recently, we described the development of a novel neural network algorithm (i31-SLNB) that integrates the continuous 31-GEP test result with MR, age, ulceration, and Breslow thickness for a personalized and precise risk of SLN positivity. 10 The algorithm was developed on a cohort of 1398 patients and independently validated in 1674 patients with Stage I-III melanoma. We analyzed SLNB reduction rates in each T-stage and found that in patients with T1b tumors, the i31-SLNB predicted that 40.9% of patients had a <5% risk of SLN positivity.…”