Integrating 31-Gene Expression Profiling With Clinicopathologic Features to Optimize Cutaneous Melanoma Sentinel Lymph Node Metastasis Prediction. (2021)

• Record Type: Journal Article
• Authors: Whitman, Eric D.; Koshenkov, Vadim P.; Gastman, Brian R.; Lewis, Deri; Hsueh, Eddy C.; Pak, Ho; Trezona, Thomas P.; Davidson, Robert S.; McPhee, Michael; Guenther, J. Michael; Toomey, Paul; Smith, Franz O.; Beitsch, Peter D.; Lewis, James M.; Ward, Andrew; Young, Shawn E.; Shah, Parth K.; Quick, Ann P.; Martin, Brian J.; Zolochevska, Olga; Covington, Kyle R.; Monzon, Federico A.; Goldberg, Matthew S.; Cook, Robert W.; Fleming, Martin D.; Hyams, David M.; Vetto, John T.
• Is Part Of: JCO precision oncology. Volume 5(2021)
• Publication Date: 2021
• Subjects: Precision Medicine; Neoplasms; Pharmacogenetics; Molecular Targeted Therapy; Personalized medicine; Oncology; Pharmacogenomics; Periodical; Periodicals; 616.994
• DOI: 10.1200/PO.21.00162
• Languages: English
• ISSNs: 2473-4284

Abstract

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 (G 2 = 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.