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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.
Experimental high-throughput analysis of molecular networks is a central approach to characterize the adaptation of plant metabolism to the environment. However, recent studies have demonstrated that it is hardly possible to predict in situ metabolic phenotypes from experiments under controlled conditions, such as growth chambers or greenhouses. This is particularly due to the high molecular variance of in situ samples induced by environmental fluctuations. An approach of functional metabolome interpretation of field samples would be desirable in order to be able to identify and trace back the impact of environmental changes on plant metabolism. To test the applicability of metabolomics studies for a characterization of plant populations in the field, we have identified and analyzed in situ samples of nearby grown natural populations of Arabidopsis thaliana in Austria. A. thaliana is the primary molecular biological model system in plant biology with one of the best functionally annotated genomes representing a reference system for all other plant genome projects. The genomes of these novel natural populations were sequenced and phylogenetically compared to a comprehensive genome database of A. thaliana ecotypes. Experimental results on primary and secondary metabolite profiling and genotypic variation were functionally integrated by a data mining strategy, which combines statistical output of metabolomics data with genome-derived biochemical pathway reconstruction and metabolic modeling. Correlations of biochemical model predictions and population-specific genetic variation indicated varying strategies of metabolic regulation on a population level which enabled the direct comparison, differentiation, and prediction of metabolic adaptation of the same species to different habitats. These differences were most pronounced at organic and amino acid metabolism as well as at the interface of primary and secondary metabolism and allowed for the direct classification of population-specific metabolic phenotypes within geographically contiguous sampling sites.