DNA methylation profiling has been suggested a reliable technique to distinguish between benign and malignant adrenocortical tumors, a process which with current diagnostic methods remains challenging and lacks diagnostic accuracy of borderline tumors. Accurate distinction between benign and malignant adrenal tumors is of the essence, since ACC is a rare but aggressive endocrine disease with an annual incidence of about 2.0 cases per million people per year. The estimated five-year overall survival rate for ACC patients is <50%. However, available treatment regimens are limited, in which a radical surgical resection is the only curable option. Nevertheless, up to 85% of patients with radical resection show recurrence of the local disease often with concurrent metastases. Adrenolytic therapy with mitotane, administered alone or in combination with cytotoxic agents, is currently the primary (palliative) treatment for patients with advanced ACC and is increasingly used in adjuvant setting to prevent recurrence. Prognostic stratification is important in order to individualize adjuvant therapies. On April 1, 2020, there were 7404 publications on adrenocortical carcinoma (adrenocortical carcinoma) OR adrenocortical carcinoma [MeSH Terms]) OR adrenal cortex cancer[MeSH Terms]) OR adrenal cortical carcinoma [MeSH Terms]) OR adrenal cortex neoplasm [MeSH Terms]) OR adrenocortical cancer [MeSH Terms]), yet the underlying pathophysiology and characteristics of ACC is not fully understood. Knowledge on epigenetic alterations in the process of adrenal tumorigenesis is rapidly increasing and will add to a better understanding of the pathogenesis of ACC. DNA methylation profiling has been heralded as a promising method in the prognostication of ACC. This review summarizes recent findings on epigenetics of ACC and its role in diagnosis, prognosis and therapeutic strategies.

Background Mitotane is the only approved treatment for patients with adrenocortical carcinoma (ACC). A better explanation for the variability in the pharmacokinetics (PK) of mitotane, and the optimization and individualization of mitotane treatment, is desirable for patients. Objectives This study aims to develop a population PK (PopPK) model to characterize and predict the PK profiles of mitotane in patients with ACC, as well as to explore the effect of genetic variation on mitotane clearance. Ultimately, we aimed to facilitate mitotane dose optimization and individualization for patients with ACC. Methods Mitotane concentration and dosing data were collected retrospectively from the medical records of patients with ACC taking mitotane orally and participating in the Dutch Adrenal Network. PopPK modelling analysis was performed using NONMEM (version 7.4.1). Genotypes of drug enzymes and transporters, patient demographic information, and clinical characteristics were investigated as covariates. Subsequently, simulations were performed for optimizing treatment regimens. Results A two-compartment model with first-order absorption and elimination best described the PK data of mitotane collected from 48 patients. Lean body weight (LBW) and genotypes of CYP2C19*2 (rs4244285), SLCO1B3 699A>G (rs7311358) and SLCO1B1 571T>C (rs4149057) were found to significantly affect mitotane clearance (CL/F), which decreased the coefficient of variation (CV%) of the random inter-individual variability of CL/F from 67.0 to 43.0%. Fat amount (i.e. body weight − LBW) was found to significantly affect the central distribution volume. Simulation results indicated that determining the starting dose using the developed model is beneficial in terms of shortening the period to reach the therapeutic target and limit the risk of toxicity. A regimen that can effectively maintain mitotane concentration within 14–20 mg/L was established. Conclusions A two-compartment PopPK model well-characterized mitotane PK profiles in patients with ACC. The CYP2C19 enzyme and SLCO1B1 and SLCO1B3 transporters may play roles in mitotane disposition. The developed model is beneficial in terms of optimizing mitotane treatment schedules and individualizing the initial dose for patients with ACC. Further validation of these findings is still required.

Adrenocortical carcinoma (ACC) has an incidence of about 1.0 per million per year. In general, survival of patients with ACC is limited. Predicting survival outcome at time of diagnosis is a clinical challenge. The aim of this study was to develop and internally validate a clinical prediction model for ACC-specific mortality. Data for this retrospective cohort study were obtained from the nine centers of the Dutch Adrenal Network (DAN). Patients who presented with ACC between 1 January 2004 and 31 October 2013 were included. We used multivariable Cox proportional hazards regression to compute the coefficients for the prediction model. Backward stepwise elimination was performed to derive a more parsimonious model. The performance of the initial prediction model was quantified by measures of model fit, discriminative ability, and calibration. We undertook an internal validation step to counteract the possible overfitting of our model. A total of 160 patients were included in the cohort. The median survival time was 35 months, and interquartile range (IQR) 50.7 months. The multivariable modeling yielded a prediction model that included age, modified European Network for the Study of Adrenal Tumors (mENSAT) stage, and radical resection. The c-statistic was 0.77 (95% Confidence Interval: 0.72, 0.81), indicating good predictive performance. We developed a clinical prediction model for ACC-specific mortality. ACC mortality can be estimated using a relatively simple clinical prediction model with good discriminative ability and calibration.