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

Abstract

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.

Trial registration: ClinicalTrials.gov NCT02355587 NCT02355574.

FIG 1.

31-GEP improves precision of SLN positivity predictions compared with T stage–based predictions in an independent validation cohort (n = 1,674) with T1-T4 CM. The integration of the 31-GEP score and clinicopathologic features (i31-GEP) is represented by the red line. Gray shading represents 95% CI. The solid black line represents a perfect match of predicted and observed SLN-positive rates. Linear regression shows a y = 0.999x − 0.005 relationship between predicted and observed positivity demonstrating the close alignment of i31-GEP–predicted risk of SLN positivity and observed SLN positivity. 31-GEP, 31-gene expression profile; i31-GEP, integrated 31-gene expression profile; SLN, sentinel lymph node.

FIG 2.

Distribution of SLN positivity risk predicted by i31-GEP by T stage. T1a-LR refers to patients with low risk T1a tumors with no high-risk features documented, and T1a-HR refers to those with a high risk T1a tumor who had risk factors for a positive SLN resulting in a risk between 5% and 10%. The predicted risk was truncated at 20%. T4a risk ranged from 9.5% to 50.0%, and T4b ranged from 9.5% to 58.5%. See Appendix Fig A3 for full distribution of predicted SLN positivity, including distribution for T4 tumors. i31-GEP, integrated 31-gene expression profile; SLN, sentinel lymph node; T1a-HR, high-risk T1a; T1a-LR, low-risk T1a.

FIG 3.

Melanoma survival rates in a subset of 312 patients with long-term follow-up stratified by < 5% and ≥ 5% SLN positivity risk by i31-GEP. The blue line represents the survival of patients with an i31-GEP prediction of SLN positivity < 5%, the red line represents the survival rates of patients with ≥ 5% positivity who had a negative SLN, and the teal line represents the survival rates of patients with ≥ 5% positivity who had a positive SLN. P value on the basis of log-rank test. ^aNumber of events over the full follow-up period. DMFS, distant metastasis–free survival; i31-GEP, integrated 31-gene expression profile; OS, overall survival; RFS, recurrence-free survival; SLN, sentinel lymph node.

FIG A1.

Training and validation cohorts. 31-GEP, 31-gene expression profile; SLN, sentinel lymph node.

FIG A2.

Correlation of individual variables score used in i31-GEP training. Correlation of the (A) continuous 31-GEP score, (B) continuous mitotic rate, (C) continuous Breslow thickness, (D) binary ulceration, and (E) continuous age with SLN positivity. Spearman's correlation (r) and log-likelihood ratios (G² values) demonstrate a significant correlation between all variables used in training. The GEP continuous score had the highest log-likelihood value and, therefore, had the best fit of all the variables. 31-GEP, 31-gene expression profile; i31-GEP, integrated 31-gene expression profile; MR, mitotic rate; SLN, sentinel lymph node.

FIG A3.

Full distribution of SLN positivity risk predicted by i31-GEP by T stage in T1-T4 CM. The black line is 5% and the red line is 10% predicted probability of a positive SLN. i31-GEP, integrated 31-gene expression profile; SLN, sentinel lymph node; T1a-HR, high-risk T1a; T1a-LR, low-risk T1a.