scholarly journals Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma

2019 ◽  
Vol 105 (3) ◽  
pp. e307-e318 ◽  
Author(s):  
Vasileios Chortis ◽  
Irina Bancos ◽  
Thomas Nijman ◽  
Lorna C Gilligan ◽  
Angela E Taylor ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Adrenocortical Carcinoma ◽  
Postoperative Recurrence ◽  
Urine Samples ◽  
Gas Chromatography Mass Spectrometry ◽  
R0 Resection ◽  
Steroid Profiling ◽  
Steroid Profiles ◽  
Urine Steroid

Abstract Context Urine steroid metabolomics, combining mass spectrometry-based steroid profiling and machine learning, has been described as a novel diagnostic tool for detection of adrenocortical carcinoma (ACC). Objective, Design, Setting This proof-of-concept study evaluated the performance of urine steroid metabolomics as a tool for postoperative recurrence detection after microscopically complete (R0) resection of ACC. Patients and Methods 135 patients from 14 clinical centers provided postoperative urine samples, which were analyzed by gas chromatography–mass spectrometry. We assessed the utility of these urine steroid profiles in detecting ACC recurrence, either when interpreted by expert clinicians or when analyzed by random forest, a machine learning-based classifier. Radiological recurrence detection served as the reference standard. Results Imaging detected recurrent disease in 42 of 135 patients; 32 had provided pre- and post-recurrence urine samples. 39 patients remained disease-free for ≥3 years. The urine “steroid fingerprint” at recurrence resembled that observed before R0 resection in the majority of cases. Review of longitudinally collected urine steroid profiles by 3 blinded experts detected recurrence by the time of radiological diagnosis in 50% to 72% of cases, improving to 69% to 92%, if a preoperative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22% to 39% of patients. Specificities varied considerably, ranging from 61% to 97%. The computational classifier detected ACC recurrence with superior accuracy (sensitivity = specificity = 81%). Conclusion Urine steroid metabolomics is a promising tool for postoperative recurrence detection in ACC; availability of a preoperative urine considerably improves the ability to detect ACC recurrence.

scholarly journals Study of urine steroid profiles by gas chromatography-mass spectrometry in patients with adrenocortical cancer in the course of treatment

2020 ◽  
Vol 49 ◽  
Author(s):  
L. I. Velikanova ◽  
N. V. Vorokhobina ◽  
Z. R. Shafigullina ◽  
V. Yu. Bokhian ◽  
I. S. Stilidi ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Metastatic Disease ◽  
Postoperative Period ◽  
Gas Chromatography Mass Spectrometry ◽  
Disease Relapse ◽  
Urine Excretion ◽  
Chromatography Mass Spectrometry ◽  
Steroid Profiles ◽  
Urine Steroid

Background: Adrenocortical carcinoma (ACC) is a rare and aggressive disease. There are only few studies evaluating the diagnostic value of gas chromatography-mass spectrometry (GC-MS) for detection of ACC recurrence after surgery. It is necessary to conduct an in-depth study to search for the most informative markers of the disease relapse.Aim: To study urine steroid metabolism by GC-MS during treatment to identify early signs of metastatic disease and relapse.Materials and methods: Thirty nine (39) ACC patients were examined before and after surgery, in the early postoperative period (< 1 year) and late postoperative period (at 2 to 5 years). Ten (10) patients were disease-free at less than 1 year after surgery. Twenty nine (29) patients had metastases in lungs and other organs: 14, within 1 year after surgery, and 15, at 2 to 5 years. The control group included 25 patients with nonfunctioning adrenocortical adenomas (NAA) without malignant characteristics at histological examination. Urine steroid profiles were assessed with a gas chromatograph-mass spectrometer Shimadzu GCMS-QP2020.Results: As assessed by GC-MS, 16 major ACC biomarkers were found before surgery, including etiocholanolone, dehydroepiandrosterone (DHEA) and its metabolites, pregnanediol, pregnanetriol, 5-ene-pregnenes, and tetrahydro-11-deoxycortisol (THS). Their urine excretion was increased compared to that in the patients with NAA (р < 0.002). A non-classic 5-ene-pregnene, 3β,16,20-pregnenetriol (3β,16,20-dP3), was identified, with its urine excretion of > 500 mcg/day that was typical for ACC patients. After surgery, decreased urinary excretion of THS (р < 0.0001) and 3β,16,20-dP3 (р < 0.0001), increased 3α,16,20-dP3/3β,16,20-dP3 ratio (р = 0.003), compared to those before surgery, were indicative of the absence of any metastases. No difference of urine THS excretion and 3α,16,20-dP3/3β,16,20-dP3 ratio from the corresponding values before surgery (p > 0.05) is a sign of metastatic diseases in the ACC patients at less than 1 year after the surgery, of the disease relapse at 2 to 5 years, and of the disease relapse after chemotherapy. In addition, in the ACC patients with metastatic disease within 1 year after surgery, increased progestogen urine excretion was found. Urine excretion of DHEA and its metabolites in the patients with the disease relapse after chemotherapy was not different from those in the ACC patients before surgery (p > 0.05).Conclusion: Determination of urine excretion of THS, DHEA and its metabolites, etiocholanolone, 5-ene-pregnenes, 3β,16,20-dP3, and 3α,16,20-dP3/3β,16,20-dP3 ratio by GC-MS is of utmost importance in the monitoring of treatment for ACC and early diagnosis of the disease progression.

Interpretable machine learning model to detect chemically adulterated urine samples analyzed by high resolution mass spectrometry

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Gabriel L. Streun ◽  
Andrea E. Steuer ◽  
Lars C. Ebert ◽  
Akos Dobay ◽  
Thomas Kraemer
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Liquid Chromatography ◽  
High Resolution ◽  
Retention Time ◽  
Drug Testing ◽  
High Resolution Mass Spectrometry ◽  
Urine Samples ◽  
Machine Learning Model ◽  
Resolution Mass

Abstract Objectives Urine sample manipulation including substitution, dilution, and chemical adulteration is a continuing challenge for workplace drug testing, abstinence control, and doping control laboratories. The simultaneous detection of sample manipulation and prohibited drugs within one single analytical measurement would be highly advantageous. Machine learning algorithms are able to learn from existing datasets and predict outcomes of new data, which are unknown to the model. Methods Authentic human urine samples were treated with pyridinium chlorochromate, potassium nitrite, hydrogen peroxide, iodine, sodium hypochlorite, and water as control. In total, 702 samples, measured with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry, were used. After retention time alignment within Progenesis QI, an artificial neural network was trained with 500 samples, each featuring 33,448 values. The feature importance was analyzed with the local interpretable model-agnostic explanations approach. Results Following 10-fold cross-validation, the mean sensitivity, specificity, positive predictive value, and negative predictive value was 88.9, 92.0, 91.9, and 89.2%, respectively. A diverse test set (n=202) containing treated and untreated urine samples could be correctly classified with an accuracy of 95.4%. In addition, 14 important features and four potential biomarkers were extracted. Conclusions With interpretable retention time aligned liquid chromatography high-resolution mass spectrometry data, a reliable machine learning model could be established that rapidly uncovers chemical urine manipulation. The incorporation of our model into routine clinical or forensic analysis allows simultaneous LC-MS analysis and sample integrity testing in one run, thus revolutionizing this field of drug testing.

Isolated 17, 20 Lyase Deficiency Secondary to a Novel CYB5A Variant: Comparison of Steroid Metabolomic Findings with Published Cases Provides Diagnostic Guidelines and Greater Insight into Its Biological Role

2020 ◽  
Vol 93 (7-8) ◽  
pp. 483-496
Author(s):  
Meera Shaunak ◽  
Norman F. Taylor ◽  
David Hunt ◽  
Justin H. Davies
Keyword(s):  
Mass Spectrometry ◽  
Ambiguous Genitalia ◽  
Relative Increase ◽  
Testicular Microlithiasis ◽  
Lyase Deficiency ◽  
Chain Cleavage ◽  
Steroid Profiles ◽  
Steroid Analysis ◽  
17 Hydroxyprogesterone ◽  
Urine Steroid

Objective: The objective of this study was to report CYB5A deficiency, to discuss the contribution of steroid metabolomics to diagnosis and interpretation, and to highlight the presence of testicular microlithiasis. Methods: Two siblings with ambiguous genitalia at birth were later found to carry novel CYB5A variants, with resulting isolated 17, 20 lyase deficiency. We compared urine steroid data obtained between birth and adulthood with that from other cases. Results: Neonatal urine steroid profiles show a relative increase of 16-hydroxylated pregnenolone metabolites. Thereafter, there are no distinguishing features until puberty, when sex steroid deficiency drives gonadotrophin production, resulting in marked increases of 17-hydroxyprogesterone metabolites derived from the gonads. This excess may be revealed pre-pubertally by gonadotrophin stimulation testing. Novel findings are first, a considerable capacity for DHEA synthesis in the neonatal period compared to childhood and adulthood, suggesting that DHEAS production is much less dependent on CYB5A at birth; second, no consistent change in “backdoor pathway” intermediates; third, side chain cleavage of cortisol is largely unaffected, supporting the existence of a different lyase not dependent on CYB5A; fourth, increased 17-hydroxyprogesterone metabolites and very low androgen metabolites are diagnostic post-pubertally. Conclusion: This is the fourth disease-causing variant in CYB5A in isolated 17, 20 lyase deficiency and the first associated with testicular microlithiasis. Establishing a biochemical diagnosis pre-pubertally should now be possible using urine steroid profiling, supported by synacthen and gonadotrophin stimulation testing. We recommend liquid chromatography-mass spectrometry/mass spectrometry rather than immunoassay for serum steroid analysis, early methaemoglobin measurement and surveillance should testicular microlithiasis be detected.

scholarly journals Comparison of Strategies for the Determination of Sterol Sulfates via GC-MS Leading to a Novel Deconjugation-Derivatization Protocol

Molecules ◽  
2019 ◽  
Vol 24 (13) ◽  
pp. 2353 ◽  
Author(s):  
Julia Junker ◽  
Isabelle Chong ◽  
Frits Kamp ◽  
Harald Steiner ◽  
Martin Giera ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography Mass Spectrometry ◽  
Liquid Chromatography Mass Spectrometry ◽  
Chemical Cleavage ◽  
Advantages And Disadvantages ◽  
Chromatography Mass Spectrometry ◽  
Steroid Profiling ◽  
Health And Disease ◽  
Indispensable Tool

Sulfoconjugates of sterols play important roles as neurosteroids, neurotransmitters, and ion channel ligands in health and disease. In most cases, sterol conjugate analysis is performed with liquid chromatography-mass spectrometry. This is a valuable tool for routine analytics with the advantage of direct sterol sulfates analysis without previous cleavage and/or derivatization. The complementary technique gas chromatography-mass spectrometry (GC-MS) is a preeminent discovery tool in the field of sterolomics, but the analysis of sterol sulfates is hampered by mandatory deconjugation and derivatization. Despite the difficulties in sample workup, GC-MS is an indispensable tool for untargeted analysis and steroid profiling. There are no general sample preparation protocols for sterol sulfate analysis using GC-MS. In this study we present a reinvestigation and evaluation of different deconjugation and derivatization procedures with a set of representative sterol sulfates. The advantages and disadvantages of trimethylsilyl (TMS), methyloxime-trimethylsilyl (MO-TMS), and trifluoroacetyl (TFA) derivatives were examined. Different published procedures of sterol sulfate deconjugation, including enzymatic and chemical cleavage, were reinvestigated and examined for diverse sterol sulfates. Finally, we present a new protocol for the chemical cleavage of sterol sulfates, allowing for simultaneous deconjugation and derivatization, simplifying GC-MS based sterol sulfate analysis.

THC and CBD concentrations in blood, oral fluid and urine following a single and repeated administration of “light cannabis”

2020 ◽  
Vol 58 (5) ◽  
pp. 682-689 ◽  
Author(s):  
Roberta Pacifici ◽  
Simona Pichini ◽  
Manuela Pellegrini ◽  
Maria Concetta Rotolo ◽  
Raffaele Giorgetti ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Oral Fluid ◽  
Repeated Administration ◽  
Urine Samples ◽  
Gas Chromatography Mass Spectrometry ◽  
Sample Collection ◽  
Valuable Alternative ◽  
The Mean ◽  
Single And Repeated Administration

AbstractBackground“Light cannabis” is a product legally sold in Europe with Δ9-tetrahydrocannabinol (THC) concentration lower than 0.2% and variable cannabidiol (CBD) content. We studied THC and CBD excretion profiles in blood, oral fluid (OF) and urine after smoking one or four light cannabis cigarettes.MethodsBlood, OF and urine samples were obtained from six healthy light cannabis consumers after smoking one 1 g cigarette containing 0.16% THC and 5.8% CBD and from six others after smoking four 1 g cigarettes within 4 h. Sample collection began 0.5 and 4.5 h after smoking one or four cigarettes, respectively. Cannabinoid concentrations were quantified by gas chromatography-mass spectrometry (GC-MS).ResultsAt the first collection, the highest THC and CBD concentrations occurred in blood (THC 7.0–10.8 ng/mL; CBD 30.2–56.1 ng/mL) and OF (THC 5.1–15.5 ng/mL; CBD 14.2–28.1 ng/mL); similar results occurred 0.5 h after the last of four cigarettes in blood (THC 14.1–18.2 ng/mL, and CBD 25.6–45.4 ng/mL) and OF (THC 11.2–24.3 ng/mL; CBD 14.4–37.0 ng/mL). The mean OF to blood ratio ranged from 0.6 to 1.2 after one and 0.6 to 1.9 after four light cannabis cigarettes. THC/CBD ratios in blood and OF were never greater than 2. Urinary 11-nor-9-carboxy-THC concentrations peaked 8 h after one and four cigarettes.ConclusionsOF was a valuable alternative to blood in monitoring consumption of light cannabis. Blood and OF THC/CBD concentration ratios, never exceeded 2, possibly providing a useful biomarker to identify light cannabis vs illegal higher THC cannabis use, where THC/CBD ratios are generally greater than 10.

scholarly journals Metabolomic Approach for Discrimination of Cultivation Age and Ripening Stage in Ginseng Berry Using Gas Chromatography-Mass Spectrometry

Molecules ◽  
2019 ◽  
Vol 24 (21) ◽  
pp. 3837 ◽  
Author(s):  
Seong-Eun Park ◽  
Seung-Ho Seo ◽  
Eun-Ju Kim ◽  
Dae-Hun Park ◽  
Kyung-Mok Park ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Random Forest ◽  
Gas Chromatography Mass Spectrometry ◽  
Learning Approach ◽  
Ripening Stage ◽  
Ripening Stages ◽  
Machine Learning Approach ◽  
Metabolomic Approach

The purpose of this study was to analyze metabolic differences of ginseng berries according to cultivation age and ripening stage using gas chromatography-mass spectrometry (GC-MS)-based metabolomics method. Ginseng berries were harvested every week during five different ripening stages of three-year-old and four-year-old ginseng. Using identified metabolites, a random forest machine learning approach was applied to obtain predictive models for the classification of cultivation age or ripening stage. Principal component analysis (PCA) score plot showed a clear separation by ripening stage, indicating that continuous metabolic changes occurred until the fifth ripening stage. Three-year-old ginseng berries had higher levels of valine, glutamic acid, and tryptophan, but lower levels of lactic acid and galactose than four-year-old ginseng berries at fully ripened stage. Metabolic pathways affected by different cultivation age were involved in amino acid metabolism pathways. A random forest machine learning approach extracted some important metabolites for predicting cultivation age or ripening stage with low error rate. This study demonstrates that different cultivation ages or ripening stages of ginseng berry can be successfully discriminated using a GC-MS-based metabolomic approach together with random forest analysis.

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