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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.
Impact of the Chemokine Receptors CXCR4 and CXCR7 on Clinical Outcome in Adrenocortical Carcinoma
(2020)
Chemokine receptors have a negative impact on tumor progression in several human cancers and have therefore been of interest for molecular imaging and targeted therapy. However, their clinical and prognostic significance in adrenocortical carcinoma (ACC) is unknown. The aim of this study was to evaluate the chemokine receptor profile in ACC and to analyse its association with clinicopathological characteristics and clinical outcome. A chemokine receptor profile was initially evaluated by quantitative PCR in 4 normal adrenals, 18 ACC samples and human ACC cell line NCI-H295. High expression of CXCR4 and CXCR7 in both healthy and malignant adrenal tissue and ACC cells was confirmed. In the next step, we analyzed the expression and cellular localization of CXCR4 and CXCR7 in ACC by immunohistochemistry in 187 and 84 samples, respectively. These results were correlated with clinicopathological parameters and survival outcome. We detected strong membrane expression of CXCR4 and CXCR7 in 50% of ACC samples. Strong cytoplasmic CXCR4 staining was more frequent among samples derived from metastases compared to primaries (p=0.01) and local recurrences (p=0.04). CXCR4 membrane staining positively correlated with proliferation index Ki67 (r=0.17, p=0.028). CXCR7 membrane staining negatively correlated with Ki67 (r=−0.254, p=0.03) but positively with tumor size (r=0.3, p=0.02). No differences in progression-free or overall survival were observed between patients with strong and weak staining intensities for CXCR4 or CXCR7. Taken together, high expression of CXCR4 and CXCR7 in both local tumors and metastases suggests that some ACC patients might benefit from CXCR4/CXCR7-targeted therapy.