Dataset Information

Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors.

ABSTRACT:

Context

Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2-11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values.

Objective

Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy.

Design

Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19-84 yr) and 45 ACC patients (20-80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26-201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids.

Results

Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88% (area under the curve = 0.96) when using only the nine most differentiating markers.

Conclusions

Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.

SUBMITTER: Arlt W

PROVIDER: S-EPMC3232629 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Publications

Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors.

Arlt Wiebke W Biehl Michael M Taylor Angela E AE Hahner Stefanie S Libé Rossella R Hughes Beverly A BA Schneider Petra P Smith David J DJ Stiekema Han H Krone Nils N Porfiri Emilio E Opocher Giuseppe G Bertherat Jerôme J Mantero Franco F Allolio Bruno B Terzolo Massimo M Nightingale Peter P Shackleton Cedric H L CH Bertagna Xavier X Fassnacht Martin M Stewart Paul M PM

The Journal of clinical endocrinology and metabolism 20110914 12

<h4>Context</h4>Adrenal tumors have a prevalence of around 2% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2-11% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values.<h4>Objective</h4>Here we developed a novel steroid metabolomic approach, mass spectr ...[more]

PMID: 21917861

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Project description:BACKGROUND:Cross-sectional imaging regularly results in incidental discovery of adrenal tumours, requiring exclusion of adrenocortical carcinoma (ACC). However, differentiation is hampered by poor specificity of imaging characteristics. We aimed to validate a urine steroid metabolomics approach, using steroid profiling as the diagnostic basis for ACC. METHODS:We did a prospective multicentre study in adult participants (age ?18 years) with newly diagnosed adrenal masses. We assessed the accuracy of diagnostic imaging strategies based on maximum tumour diameter (?4 cm vs <4 cm), imaging characteristics (positive vs negative), and urine steroid metabolomics (low, medium, or high risk of ACC), separately and in combination, using a reference standard of histopathology and follow-up investigations. With respect to imaging characteristics, we also assessed the diagnostic utility of increasing the unenhanced CT tumour attenuation threshold from the recommended 10 Hounsfield units (HU) to 20 HU. FINDINGS:Of 2169 participants recruited between Jan 17, 2011, and July 15, 2016, we included 2017 from 14 specialist centres in 11 countries in the final analysis. 98 (4·9%) had histopathologically or clinically and biochemically confirmed ACC. Tumours with diameters of 4 cm or larger were identified in 488 participants (24·2%), including 96 of the 98 with ACC (positive predictive value [PPV] 19·7%, 95% CI 16·2-23·5). For imaging characteristics, increasing the unenhanced CT tumour attenuation threshold to 20 HU from the recommended 10 HU increased specificity for ACC (80·0% [95% CI 77·9-82·0] vs 64·0% [61·4-66.4]) while maintaining sensitivity (99·0% [94·4-100·0] vs 100·0% [96·3-100·0]; PPV 19·7%, 16·3-23·5). A urine steroid metabolomics result indicating high risk of ACC had a PPV of 34·6% (95% CI 28·6-41·0). When the three tests were combined, in the order of tumour diameter, positive imaging characteristics, and urine steroid metabolomics, 106 (5·3%) participants had the result maximum tumour diameter of 4 cm or larger, positive imaging characteristics (with the 20 HU cutoff), and urine steroid metabolomics indicating high risk of ACC, for which the PPV was 76·4% (95% CI 67·2-84·1). 70 (3·5%) were classified as being at moderate risk of ACC and 1841 (91·3%) at low risk (negative predictive value 99·7%, 99·4-100·0). INTERPRETATION:An unenhanced CT tumour attenuation cutoff of 20 HU should replace that of 10 HU for exclusion of ACC. A triple test strategy of tumour diameter, imaging characteristics, and urine steroid metabolomics improves detection of ACC, which could shorten time to surgery for patients with ACC and help to avoid unnecessary surgery in patients with benign tumours. FUNDING:European Commission, UK Medical Research Council, Wellcome Trust, and UK National Institute for Health Research, US National Institutes of Health, the Claire Khan Trust Fund at University Hospitals Birmingham Charities, and the Mayo Clinic Foundation for Medical Education and Research.

Project description:BackgroundAutonomous cortisol secretion (ACS), resulting from cortisol-producing adenomas (CPA), causes endogenous steroid-induced osteoporosis (SIOP). However, the risk of endogenous SIOP cannot be explained by cortisol excess alone, and how other steroid metabolites affect bone status is unclear.MethodsACS was diagnosed as serum cortisol ≥1.8 μg/dL after the 1-mg dexamethasone suppression test (DST-cortisol). Using liquid chromatography tandem mass spectrometry, 21 plasma steroid metabolites were measured in 73 patients with ACS and 85 patients with non-functioning adrenal tumors (NFAT). Expression of steroidogenic enzymes and relevant steroid metabolites were analyzed in some of CPA tissues.FindingsDiscriminant and principal component analyses distinguished steroid profiles between the ACS and NFAT groups in premenopausal women. Premenopausal women with ACS exhibited higher levels of a mineralocorticoid metabolite, 11-deoxycorticosterone (11-DOC), and lower levels of androgen metabolites, dehydroepiandrosterone-sulfate, and androsterone-glucuronide. In premenopausal women with ACS, DST-cortisol negatively correlated with trabecular bone score (TBS). Additionally, 11-DOC negatively correlated with lumbar spine-bone mineral density, whereas androsterone-glucuronide positively correlated with TBS. The CPA tissues showed increased 11-DOC levels with increased expression of CYP21A2, essential for 11-DOC synthesis. Adrenal non-tumor tissues were atrophied with reduced expression of CYB5A, required for androgen synthesis.InterpretationThis study demonstrates that unbalanced production of adrenal steroid metabolites, derived from both adrenal tumor and non-tumor tissues, contributes to the pathogenesis of endogenous SIOP in premenopausal women with ACS.FundingJSPS KAKENHI, Secom Science and Technology Foundation, Takeda Science Foundation, Japan Foundation for Applied Enzymology, AMED-CREST, JSTA-STEP, JST-Moonshot, and Ono Medical Research Foundation.

Dataset Information

Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors.

Context

Objective

Design

Results

Conclusions

Publications

Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors.

Similar Datasets

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