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Updated: Jun 14, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Integrating the melanoma 31-gene expression profile test with clinical and pathologic features can provide
Chase Kriza1, Brian Martin2, Christine N Bailey2
1ChristianaCare Helen F. Graham Cancer Center & Research Institute, Newark, DE, USA.
Introduction:
Up to 88% of sentinel lymph node biopsies (SLNBs) are negative. The 31-gene expression profile (31-GEP) test can help identify patients with a low risk of SLN metastasis who can safely forego SLNB. The 31-GEP classifies patients as low (Class 1 A), intermediate (Class 1B/2A), or high risk (Class 2B) for recurrence, metastasis, and SLN positivity. The integrated 31-GEP (i31-GEP) combines the 31-GEP risk score with clinicopathologic features using a neural network algorithm to personalize SLN risk prediction.
Methods:
Patients from a single surgical center with 31-GEP results were included (n = 156). An i31-GEP risk prediction < 5% was considered low risk of SLN positivity. Chi-square was used to compare SLN positivity rates between groups.
Results:
Patients considered low risk by the i31-GEP had a 0% (0/30) SLN positivity rate compared to a 31.9% (30/94, p < 0.001) positivity rate in those with > 10% risk. Using the i31-GEP to guide SLNB decisions could have significantly reduced the number of unnecessary SLNBs by 19.2% (30/156, p < 0.001) for all patients and 33.0% (30/91, p < 0.001) for T1-T2 tumors. Patients with T1-T2 tumors and an i31-GEP-predicted SLN positivity risk > 10% had a similar SLN positivity rate (33.3%) as patients with T3-T4 tumors (31.3%).
Conclusion:
The i31-GEP identified patients with < 5% risk of SLN positivity who could safely forego SLNB. Combining the 31-GEP with clinicopathologic features for a precise risk estimate can help guide risk-aligned patient care decisions for SLNB to reduce the number of unnecessary SLNBs and increase the SLNB positivity yield if the procedure is performed.
