Plasma steroid metabolome profiling for the diagnosis of adrenocortical carcinoma

in European Journal of Endocrinology
Correspondence should be addressed to M Kroiss or M Kunz; Email: Kroiss_M@ukw.de or meik.kunz@uni-wuerzburg.de

*(S Schweitzer and M Kunz contributed equally to this work)

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Objective

Current workup for the pre-operative distinction between frequent adrenocortical adenomas (ACAs) and rare but aggressive adrenocortical carcinomas (ACCs) combines imaging and biochemical testing. We here investigated the potential of plasma steroid hormone profiling by liquid chromatography tandem mass spectrometry (LC-MS/MS) for the diagnosis of malignancy in adrenocortical tumors.

Design

Retrospective cohort study of prospectively collected EDTA-plasma samples in a single tertiary reference center.

Methods

Steroid hormone profiling by liquid chromatography tandem mass spectrometry (LC-MS/MS) in random plasma samples and logistic regression modeling.

Results

Fifteen steroid hormones were quantified in 66 ACAs (29 males; M) and 42 ACC (15 M) plasma samples. Significantly higher abundances in ACC vs ACA were observed for 11-deoxycorticosterone, progesterone, 17-hydroxyprogesterone, 11-deoxycortisol, DHEA, DHEAS and estradiol (all P < 0.05). Maximal areas under the curve (AUC) for discrimination between ACA and ACC for single analytes were only 0.76 (estradiol) and 0.77 (progesterone), respectively. Logistic regression modeling enabled the discovery of diagnostic signatures composed of six specific steroids for male and female patients with AUC of 0.95 and 0.94, respectively. Positive predictive values in males and females were 92 and 96%, negative predictive values 90 and 86%, respectively.

Conclusion

This study in a large adrenal tumor patient cohort demonstrates the value of plasma steroid hormone profiling for diagnosis of ACC. Application of LC-MS/MS analysis and of our model may facilitate diagnosis of malignancy in non-expert centers. We propose to continuously evaluate and improve diagnostic accuracy of LC-MS/MS profiling by applying machine-learning algorithms to prospectively obtained steroid hormone profiles.

 

     European Society of Endocrinology

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Figures

  • View in gallery

    Sex-specific steroid hormone patterns in ACA and ACC patients. Log2-transformed absolute quantity of the 15 steroid hormones 11-deoxycorticosterone (DOC), corticosterone (B), aldosterone (A4), progesterone (P4), 17OHP, 21-deoxycortisol (21-DF), 11-deoxycortisol (B), cortisol (F), cortisone (E), DHEA, DHEAS, androstenedione (A4), testosterone, DHT and estradiol (E2) as measured by LC-MS/MS in non-aldosterone-producing adrenocortical adenomas (ACAs) and adrenocortical carcinoma (ACC) patients. (A) In male, significantly increased quantities of 11DOC, A, P4, B and E2 were observed in ACC (right panel, n = 15) vs ACA left panel (n = 29). (B) Female patients showed increased 11DOC, P4, 17-OHP, DHEA, DHEAS and testosterone between ACC (n = 27) and ACA (n = 37). Steroids are color coded in green for mineralocorticoids and precursors, yellow glucocorticoids and precursors, red androgens and gray estradiol. The asterisks indicate P < 0.05.

  • View in gallery

    Optimal diagnostic signatures developed by logistic regression modeling. Separate models for the diagnosis of ACC were developed for male (A, B and C) and female (D, E and F) patients with adrenal tumors. (A) and (D) provide the list of hormones with diagnostic value that have been identified by the model optimization indicated. Akaike Information Criterion (AIC) and area under the curve (AUC) are given. (B) and (E) show receiver operator characteristics (ROC) curved of steroid signatures. (C) and (F) provide the confusion matrix of the selected models: PPV, positive predictive value; NPV, negative predictive value, each for diagnosis of ACC.

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