Performance of DNA-based biomarkers for classification of adrenocortical carcinoma: a prognostic study

Lippert, J., (et al) 2023. European Journal of Endocrinology, 189(2), pp 262-270


Adrenocortical carcinoma (ACC) is a rare aggressive malignancy with heterogeneous clinical outcomes. Recent studies proposed a combination of clinical/histopathological parameters (S-GRAS score) or molecular biomarkers (BMs) to improve prognostication. We performed a comparative analysis of DNA-based BMs by evaluating their added prognostic value to the S-GRAS score.

Design and methods

A total of 194 formalin-fixed, paraffin-embedded (FFPE) ACC samples were analysed, including a retrospective training cohort (n = 107) and a prospective validation cohort (n = 87). Targeted DNA sequencing and pyrosequencing were used to detect somatic single-nucleotide variations in ACC-specific genes and methylation in the promoter region of paired box 5 (PAX5). The European Network for the Study of Adrenocortical Tumors (ENSAT) tumour stage, age, symptoms at presentation, resection status, and Ki-67 were combined to calculate S-GRAS. Endpoints were overall (OS), progression-free (PFS), and disease-free survival (DFS). Prognostic role was evaluated by multivariable survival analysis and their performance compared by Harrell's concordance index (C index).


In training cohort, an independent prognostic role was confirmed at multivariate analysis for two DNA-based BMs: alterations in Wnt/β-catenin and Rb/p53 pathways and hypermethylated PAX5 (both P< .05 for PFS and DFS, hazard ratio [HR] 1.47-2.33). These were combined to S-GRAS to obtain a combined (COMBI) score. At comparative analysis, the best discriminative prognostic model was COMBI score in both cohorts for all endpoints, followed by S-GRAS score (C index for OS 0.724 and 0.765, PFS 0.717 and 0.670, and DFS 0.699 and 0.644, respectively).


Targeted DNA-based BM evaluated on routinely available FFPE samples improves prognostication of ACC beyond routinely available clinical and histopathological parameters. This approach may help to better individualise patient's management.