Metastatic adrenocortical carcinoma displays higher mutation rate and tumor heterogeneity than primary tumors

Abstract

Adrenocortical cancer (ACC) is a rare cancer with poor prognosis and high mortality due to metastatic disease. All reported genetic alterations have been in primary ACC, and it is unknown if there is molecular heterogeneity in ACC. Here, we report the genetic changes associated with metastatic ACC compared to primary ACCs and tumor heterogeneity. We performed whole-exome sequencing of 33 metastatic tumors. The overall mutation rate (per megabase) in metastatic tumors was 2.8-fold higher than primary ACC tumor samples. We found tumor heterogeneity among different metastatic sites in ACC and discovered recurrent mutations in several novel genes. We observed 37-57% overlap in genes that are mutated among different metastatic sites within the same patient. We also identified new therapeutic targets in recurrent and metastatic ACC not previously described in primary ACCs.

Fig. 1

Metastatic ACC has a higher mutation burden compared to primary ACC. Total number of mutations per megabase in each tumor sample of both metastatic and primary ACC (TCGA). Each bar on x-axis represents a patient or a tumor sample (a). Total mutations per megabase in metastatic lung ACC and its counterpart primary ACC tumor from the same patient (b). Metastatic ACC mutation rate compared to other cancer types including primary ACC (TCGA) (c). Mutation rate in the primary ACC (TCGA) based on tumor stage (d). Relative proportions of the six possible base-pair mutations in 33 metastatic ACC (e) and primary ACC (f). The top genes that are frequently mutated in metastatic (g) and primary ACC (h)

Fig. 2

Genotyping of metastatic ACC shows tumor heterogeneity among different metastatic tumor sites. The principal component analysis (PCA) of all the missense, nonsense, and splice site mutations in 33 metastatic adrenocortical tumors (a). Heatmap and clustering of the overlapping variants within the genes in metastatic lung (b) and other tumor sites (c) from five different patients (Case 1 with hypermutation phenotype was excluded). The region containing maximum similarity was reanalyzed to better represent the shared genes that are mutated. Heatmap of the total number of overlapping and non-overlapping variants within the genes that are mutated in multiple patients with metastasis in liver (d), and peritoneum (e). Red bar in the heatmap indicates the gene is mutated, whereas the blue represents that the corresponding gene is wild-type in each sample

Fig. 3

Different metastatic ACC sites within the same patient are predominantly homogenous. Heatmap and clustering of the total number of overlapping and non-overlapping variants within the genes that are mutated in multiple tumor sites from each patient (a–d). The list of genes with shared mutations between lung and other tissue in the hypermutated patient (d) were shown in a box. Red bar in the heatmap indicates the gene is mutated, whereas the blue represents that the corresponding gene is wild-type in each sample

Fig. 4

Copy number variation in metastatic tumors. High-level amplifications and deletions in metastatic ACC tumors. Regions of TERT, CDKN2A, and CHEK2/ZNF3 that are common in primary tumors and observed in metastatic tumors are highlighted. Red and blue represent chromosomal gain and loss, respectively

Fig. 5

Key molecular pathways altered in metastatic ACC. Network of canonical pathways that are significantly altered in metastatic ACC based on ingenuity pathway analysis (IPA) (a). Pathways that are predominantly altered in metastatic ACC including ERBB4 signaling (b), retinoic acid receptor signaling (c), G-protein-coupled receptor signaling (d), and platelet-derived growth factor receptor signaling (e). The number within each box represents the percentage of gene mutations in these tumors

Fig. 6

Potential drug candidates for metastatic ACC. Recurrent gene mutations in metastatic ACC and the druggable targets based on location (a) and type (b). List of selected drugs and number of patients with mutations that can be targeted with the agent (c)