BackgroundAdrenocortical carcinoma (ACC) is a rare endocrine malignancy. Tumor-related glucocorticoid excess is present in ~60% of patients and associated with particularly poor prognosis. Results of first clinical trials using immune checkpoint inhibitors were heterogeneous. Here we characterize tumor-infiltrating T lymphocytes (TILs) in ACC in association with glucocorticoids as potential explanation for resistance to immunotherapy.MethodsWe performed immunofluorescence analysis to visualize tumor-infiltrating T cells (CD3+), T helper cells (CD3+CD4+), cytotoxic T cells (CD3+CD8+) and regulatory T cells (Tregs; CD3+CD4+FoxP3+) in 146 ACC tissue specimens (107 primary tumors, 16 local recurrences, 23 metastases). Quantitative data of immune cell infiltration were correlated with clinical data (including glucocorticoid excess).Results86.3% of ACC specimens showed tumor infiltrating T cells (7.7 cells/high power field (HPF)), including T helper (74.0%, 6.7 cells/HPF), cytotoxic T cells (84.3%, 5.7 cells/HPF) and Tregs (49.3%, 0.8 cells/HPF). The number of TILs was associated with better overall survival (HR for death: 0.47, 95% CI 0.25 to 0.87), which was true for CD4+− and CD8+subpopulations as well. In localized, non-metastatic ACC, the favorable impact of TILs on overall and recurrence-free survival was manifested even independently of ENSAT (European Network for the Study of Adrenal Tumors) stage, resection status and Ki67 index. T helper cells were negatively correlated with glucocorticoid excess (Phi=−0.290, p=0.009). Patients with glucocorticoid excess and low TILs had a particularly poor overall survival (27 vs. 121 months in patients with TILs without glucocorticoid excess).ConclusionGlucocorticoid excess is associated with T cell depletion and unfavorable prognosis. To reactivate the immune system in ACC by checkpoint inhibitors, an inhibition of adrenal steroidogenesis might be pivotal and should be tested in prospective studies.

Conditions of impaired adrenal function and tissue destruction, such as in Addison's disease, and treatment resistance of adrenocortical carcinoma (ACC) necessitate improved understanding of the pathophysiology of adrenal cell death. Due to relevant oxidative processes in the adrenal cortex, our study investigated the role of ferroptosis, an irondependent cell death mechanism and found high adrenocortical expression of glutathione peroxidase 4 (GPX4) and long-chain-fatty-acid CoA ligase 4 (ACSL4) genes, key factors in the initiation of ferroptosis. By applying MALDI mass spectrometry imaging to normal and neoplastic adrenocortical tissue, we detected high abundance of arachidonic and adrenic acid, two long chain polyunsaturated fatty acids which undergo peroxidation during ferroptosis. In three available adrenal cortex cell models (H295R, CU-ACC1 and CU-ACC-2) a high susceptibility to GPX4 inhibition with RSL3 was documented with EC 50 values of 5.7 × 10 −8 , 8.1 × 10 −7 and 2.1 × 10 −8 M, respectively, while all nonsteroidogenic cells were significantly less sensitive. Complete block of GPX4 activity by RSL3 led to ferroptosis which was completely reversed in adrenal cortex cells by inhibition of steroidogenesis with ketoconazole but not by blocking the final step of cortisol synthesis with metyrapone. Mitotane, the only approved drug for ACC did not induce ferroptosis, despite strong induction of lipid peroxidation in ACC cells. Together, this report is the first to demonstrate extraordinary sensitivity of adrenal cortex cells to ferroptosis dependent on their active steroid synthetic pathways. Mitotane does not induce this form of cell death in ACC cells.

Background The response of advanced adrenocortical carcinoma (ACC) to current chemotherapies is unsatisfactory and a limited rate of response to immunotherapy was observed in clinical trials. High tumour mutational burden (TMB) and the presence of a specific DNA signature are characteristic features of tumours with mutations in the gene MUTYH encoding the mutY DNA glycosylase. Both have been shown to potentially predict the response to immunotherapy. High TMB in an ACC cell line model has not been reported yet. Design and Methods The JIL-2266 cell line was established from a primary ACC tumour, comprehensively characterised and oxidative damage, caused by a dysfunctional mutY DNA glycosylase, confirmed. Results Here, we characterise the novel patient-derived ACC cell line JIL-2266, which is deficient in MUTYH-dependent DNA repair. JIL-2266 cells have a consistent STR marker profile that confirmed congruousness with primary ACC tumour. Cells proliferate with a doubling time of 41±13 hours. Immunohistochemistry revealed positivity for steroidogenic factor-1. Mass spectrometry did not demonstrate significant steroid hormone synthesis. JIL-2266 have hemizygous mutations in the tumour suppressor gene TP53 (c.859G>T:p.E287X) and MUTYH (c.316C>T:p.R106W). Exome sequencing showed 683 single nucleotide variants and 4 insertions/deletions. We found increased oxidative DNA damage in the cell line and the corresponding primary tumour caused by impaired mutY DNA glycosylase function and accumulation of 8-oxoguanine. Conclusion This model will be valuable as a pre-clinical ACC cell model with high TMB and a tool to study oxidative DNA damage in the adrenal gland.

Adrenocortical carcinoma (ACC) is a rare disease, associated with poor survival. Several “multiple-omics” studies characterizing ACC on a molecular level identified two different clusters correlating with patient survival (C1A and C1B). We here used the publicly available transcriptome data from the TCGA-ACC dataset (n = 79), applying machine learning (ML) methods to classify the ACC based on expression pattern in an unbiased manner. UMAP (uniform manifold approximation and projection)-based clustering resulted in two distinct groups, ACC-UMAP1 and ACC-UMAP2, that largely overlap with clusters C1B and C1A, respectively. However, subsequent use of random-forest-based learning revealed a set of new possible marker genes showing significant differential expression in the described clusters (e.g., SOAT1, EIF2A1). For validation purposes, we used a secondary dataset based on a previous study from our group, consisting of 4 normal adrenal glands and 52 benign and 7 malignant tumor samples. The results largely confirmed those obtained for the TCGA-ACC cohort. In addition, the ENSAT dataset showed a correlation between benign adrenocortical tumors and the good prognosis ACC cluster ACC-UMAP1/C1B. In conclusion, the use of ML approaches re-identified and redefined known prognostic ACC subgroups. On the other hand, the subsequent use of random-forest-based learning identified new possible prognostic marker genes for ACC.