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The SF-1 transcription factor target gene FATE1 encodes a cancer-testis antigen that has an important role in regulating apoptosis and response to chemotherapy in adrenocortical carcinoma (ACC) cells. Autoantibodies directed against FATE1 were previously detected in patients with hepatocellular carcinoma. In this study, we investigated the prevalence of circulating anti-FATE1 antibodies in pediatric and adult patients with adrenocortical tumors using three different methods (immunofluorescence, ELISA and Western blot). Our results show that a pervasive anti-FATE1 immune response is present in those patients. Furthermore, FATE1 expression is a robust prognostic indicator in adult patients with ACC and is associated with increased steroidogenic and decreased immune response gene expression. These data can open perspectives for novel strategies in ACC immunotherapy.
We have previously identified serum miR-483-5p as a preoperative diagnosis and prognosis biomarker for adrenocortical cancer (ACC). Here, we aimed to determine whether circulating miR-483-5p levels measured 3 months post-operatively distinguished patients with good prognosis (no recurrence for at least 3 years; NR3yrs) from patients with poor prognosis (recurrence or death within 3 years after surgery; R < 3yrs). We conducted a single-center retrospective analysis using sera from 48 patients with ACC that were initially non-metastatic and treated by surgery. Sera sampled within 3 months after surgery were available in 26 patients. MiR-483-5p absolute circulating levels were measured using quantitative PCR. Thirteen patients showed a recurrence before 3 years (=R < 3yrs). Thirteen patients showed no recurrence within 3 years, including 11 patients with a follow-up longer than 3 years (=NR3yrs). Serum miR-483-5p levels were higher in R < 3yrs than in NR3yrs: 1,541,990 ± 428,377 copies/mL vs. 388,457 ± 62,169 copies/mL (p = 0.002). Receiver operating characteristic analysis showed that a value of 752,898 copies/mL distinguished R < 3yrs from NR3yrs with 61.5% sensitivity (CI 31.6–86.1) and 100% specificity (CI 71.5–100) with an area under the curve of 0.853. Patients with a value below this threshold had a significantly longer recurrence-free and overall survival. In multivariate analysis, miR-483-5p provided the single best prognostic value for recurrence-free survival (RFS) (hazard ratio (HR) for recurrence 5.98, p < 0.011) but not for overall survival. Our study suggests that serum miR-483-5p is a potent early post-operative biomarker for ACC prognosis that might be a better predictor of RFS than currently used markers.
Mitotane is the only approved drug for advanced adrenocortical carcinoma (ACC) and no biomarkers are available to predict attainment of therapeutic plasma concentrations and clinical response. Aim of the study was to evaluate the suitability of cytochrome P450(CYP)2W1 and CYP2B6 single nucleotide polymorphisms (SNPs) as biomarkers. A multicenter cohort study including 182 ACC patients (F/M = 121/61) treated with mitotane monotherapy after radical resection (group A, n = 103) or in not completely resectable, recurrent or advanced disease (group B, n = 79) was performed. CYP2W1*2, CYP2W1*6, CYP2B6*6 and CYP2B6 rs4803419 were genotyped in germline DNA. Mitotane blood levels were measured regularly. Response to therapy was evaluated as time to progression (TTP) and disease control rate (DCR). Among investigated SNPs, CYP2W1*6 and CYP2B6*6 correlated with mitotane treatment only in group B. Patients with CYP2W1*6 (n = 21) achieved less frequently therapeutic mitotane levels (>14 mg/L) than those with wild type (WT) allele (76.2% vs 51.7%, p = 0.051) and experienced shorter TTP (HR = 2.10, p = 0.019) and lower DCR (chi-square = 6.948, p = 0.008). By contrast, 55% of patients with CYP2B6*6 vs. 28.2% WT (p = 0.016) achieved therapeutic range. Combined, a higher rate of patients with CYP2W1*6WT+CYP2B6*6 (60.6%) achieved mitotane therapeutic range (p = 0.034). In not completely resectable, recurrent or advanced ACC, CYP2W1*6 SNP was associated with a reduced probability to reach mitotane therapeutic range and lower response rates, whereas CYP2B6*6 correlated with higher mitotane levels. The association of these SNPs may predict individual response to mitotane.
Simple Summary
Using a visual-based clustering method on the TCGA RNA sequencing data of a large adrenocortical carcinoma (ACC) cohort, we were able to classify these tumors in two distinct clusters largely overlapping with previously identified ones. As previously shown, the identified clusters also correlated with patient survival. Applying the visual clustering method to a second dataset also including benign adrenocortical samples additionally revealed that one of the ACC clusters is more closely located to the benign samples, providing a possible explanation for the better survival of this ACC cluster. Furthermore, the subsequent use of machine learning identified new possible biomarker genes with prognostic potential for this rare disease, that are significantly differentially expressed in the different survival clusters and should be further evaluated.
Abstract
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.