Accuracy of The Cancer Genome Atlas Classification vs American Joint Committee on Cancer Classification for Prediction of Metastasis in Patients With Uveal Melanoma

Abstract

The Cancer Genome Atlas (TCGA) classification is a newly emerging method for prediction of uveal melanoma (UM)-related metastasis and death. Limited information is available regarding the accuracy of the TCGA classification for prediction of metastasis in patients with UM. To investigate the accuracy of the TCGA classification for predicting UM-related metastasis compared with the American Joint Committee on Cancer (AJCC) classification. In this retrospective cohort study, patients with UM treated with plaque radiotherapy at a tertiary referral center from October 1, 2008, to December 31, 2018, were evaluated. All patients with tumors classified according to the American Joint Committee on Cancer Staging Manual, 8th Edition, and who completed pretreatment fine-needle aspiration biopsy sampling for genetic analysis of chromosomes 3 and 8 for TCGA classification were included. Tumors were classified into 4 categories, 17 subcategories, and 4 stages using AJCC classification and further grouped into 4 classes using TCGA classification. Value of TCGA classification vs AJCC classification for predicting UM-related metastasis. Of 642 included patients, 331 (51.6%) were women, and the mean (SD) age was 58.0 (13.8) years. There were 642 tumors from 642 patients classified according to both AJCC and TCGA classifications. The mean (range) follow-up time for the entire cohort was 43.7 (1.4-159.2) months. At 5 years, TCGA classification showed higher value for prediction of metastasis (4 TCGA classes: Wald statistic, 94.8; hazard ratio [HR], 2.8; 95% CI, 2.3-3.5; P < .001; 4 AJCC categories: Wald statistic, 67.5; HR, 2.6; 95% CI, 2.1-3.2; P < .001; 17 AJCC subcategories: Wald statistic, 74.3; HR, 1.3; 95% CI, 1.2-1.3; P < .001; 4 AJCC stages: Wald statistic, 67.0; HR, not applicable; P < .001). After entering TCGA and AJCC classifications into a multivariate model, TCGA classification still showed higher value for prediction of metastasis (TCGA classification: Wald statistic, 61.5; HR, 2.4; 95% CI, 1.9-2.9; P < .001; AJCC classification: Wald statistic, 35.5; HR, 1.9; 95% CI, 1.5-2.4; P < .001). These results suggest that TCGA classification provides accuracy that is superior to AJCC categories, subcategories, and stages for predicting metastasis from UM. When genetic testing is available, TCGA classification can provide a more accurate way to identify patients at high risk of metastasis who might benefit from adjuvant therapy.