Serum Steroid Ratio Profiles in Prostate Cancer: A New Diagnostic Tool Toward a Personalized Medicine Approach

Quantification of glucocorticoid and progestogen metabolites in bovine plasma, skimmed milk and saliva by UHPLC-HR-MS with polarity switching

Steroid metabolites are increasingly in focus when searching for novel biomarkers in physiological mechanisms and their disorders. While major steroids such as progesterone and cortisol are well-researched and routinely determined to assess the health, particularly the reproductive status of mammals, the function of potentially biologically active progestogen and glucocorticoid metabolites is widely unexplored. One of the main reasons for this is the lack of comprehensive, sensitive, and specific analytical methods. This is particularly the case when analyzing matrices like milk or saliva obtained by non-invasive sampling with steroid concentrations often below those present in plasma. Therefore, a new UHPLC-HR-MS method based on an Ultimate UHPLC system equipped with an Acquity HSS T3 reversed-phase column and a Q Exactive™ mass spectrometer was developed, enabling the simultaneous chromatographic separation, detection and quantification of eleven isobaric glucocorticoids (11-dehydrocorticosterone (A), corticosterone (B), cortisol (F), cortisone (E), the tetrahydrocortisols (THF): 3α,5α-THF, 3α,5β-THF, 3β,5α-THF, 3β,5β-THF, and the tetrahydrocortisones (THE): 3α,5α-THE, 3α,5β-THE, 3β,5α-THE) and twelve progestogens (progesterone (P4), pregnenolone (P5), the dihydroprogesterones (DHP): 20α-DHP, 20β-DHP, 3α-DHP, 3β-DHP, 5α-DHP, 5β-DHP, and the tetrahydroprogesterones (THP): 3α,5α-THP, 3α,5β-THP, 3β,5α-THP, 3β,5β-THP) in bovine plasma, skimmed milk, and saliva. A simple liquid-liquid extraction (LLE) with MTBE (methyl tert-butyl ether) was used for sample preparation of 500 μL plasma, skimmed milk, and saliva. Heated electrospray ionization (HESI) with polarity switching was applied to analyze steroids in high-resolution full scan mode (HR-FS). The method validation covered the investigation of sensitivity, selectivity, curve fitting, carry-over, accuracy, precision, recovery, matrix effects and applicability. A high sensitivity in the range of pg mL-1 was achieved for all steroids suitable for the analysis of authentic samples.

Steroid Hormones as Modulators of Emotional Regulation in Male Urogenital Cancers

BACKGROUND

Tumors develop within an organism operating in a specific social and physical environment. Cortisol and dehydroepiandrosterone (DHEA), two of the most abundant steroid hormones in humans, are involved in both emotional regulation and the tumor progression. Several studies reported preclinical findings that DHEA can have preventive and therapeutic efficacy in treating major age-associated diseases, including cancer, although the mechanisms of action are not yet defined. The main aim of current study was to investigate the relationship between psychological and physiological emotional regulation and cancer development.

METHOD

This study assessed the quality of life of urogenital cancer male patients using several validated tools, including the Functional Assessment of Cancer Therapy-General and the Profile of Mood States. Saliva samples were collected to monitor peripheral activity of both cortisol and DHEA. It was hypothesized that patients with a better quality of life would have higher levels of the DHEA/cortisol ratios.

RESULTS

We found that the quality of life was positively related to DHEA, but not cortisol levels. Negative mood increases were related to lower levels of DHEA. Logistic regression of the predictors of metastases indicated three main independent factors involved: DHEA, age, and cortisol. In other words, the higher the DHEA levels in comparison to cortisol levels, controlling for age, the lower the probability of metastases.

CONCLUSION

Our results appear to support the hypothesis that emotional dysregulation mediated by DHEA/cortisol activity is a key factor in the probability of metastasis in urogenital cancers.

Rapidity and Precision of Steroid Hormone Measurement

Steroids are present in all animals and plants, from mammals to prokaryotes. In the medical field, steroids are commonly classified as glucocorticoids, mineralocorticoids, and gonadal steroid hormones. Monitoring of hormones is useful in clinical and research fields for the assessment of physiological changes associated with aging, disease risk, and the diagnostic and therapeutic effects of various diseases. Since the discovery and isolation of steroid hormones, measurement methods for steroid hormones in biological samples have advanced substantially. Although immunoassays (IAs) are widely used in daily practice, mass spectrometry (MS)-based methods have been reported to be more specific. Steroid hormone measurement based on MS is desirable in clinical practice; however, there are several drawbacks, including the purchase and maintenance costs of the MS instrument and the need for specialized training of technicians. In this review, we discuss IA- and MS-based methods currently in use and briefly present the history of steroid hormone measurement. In addition, we describe recent advances in IA- and MS-based methods and future applications and considerations.

Two Novel Ceramide-Like Molecules and miR-5100 Levels as Biomarkers Improve Prediction of Prostate Cancer in Gray-Zone PSA

Reliable liquid biopsy-based tools able to accurately discriminate prostate cancer (PCa) from benign prostatic hyperplasia (BPH), when PSA is within the “gray zone” (PSA 4–10), are still urgent. We analyzed plasma samples from a cohort of 102 consecutively recruited patients with PSA levels between 4 and 16 ng/ml, using the SANIST-Cloud Ion Mobility Metabolomic Mass Spectrometry platform, combined with the analysis of a panel of circulating microRNAs (miR). By coupling CIMS ion mobility technology with SANIST, we were able to reveal three new structures among the most differentially expressed metabolites in PCa vs. BPH. In particular, two were classified as polyunsaturated ceramide ester-like and one as polysaturated glycerol ester-like. Penalized logistic regression was applied to build a model to predict PCa, using six circulating miR, seven circulating metabolites, and demographic/clinical variables, as covariates. Four circulating metabolites, miR-5100, and age were selected by the model, and the corresponding prediction score gave an AUC of 0.76 (C.I. = 0.66–0.85). At a specified cut-off, no high-risk tumor was misclassified, and 22 out of 53 BPH were correctly identified, reducing by 40% the false positives of PSA. We developed and applied a novel, minimally invasive, liquid biopsy-based powerful tool to characterize novel metabolites and identified new potential non-invasive biomarkers to better predict PCa, when PSA is uninformative as a tool for precision medicine in genitourinary cancers.

Concentrations of canine prostate specific esterase, CPSE, at baseline are associated with the relative size of the prostate at three-year follow-up

Background

Enlargement of the prostate is associated with prostatic diseases in dogs, and an estimation of prostatic size is a central part in the diagnostic workup. Ultrasonography is often the method of choice, but biomarkers constitute an alternative. Canine prostate specific esterase (CPSE) shares many characteristics with human prostate specific antigen (PSA) and is related to prostate size. In men with clinical symptoms of prostatic disease, PSA concentrations are related to prostate growth. The aims of the present follow-up study were to evaluate if the concentration of CPSE is associated with future growth of the prostate, and if analysis of a panel of 16 steroids gives further information on prostatic growth. Owners of dogs included in a previous study were 3 years later contacted for a follow-up study that included an interview and a clinical examination. The prostate was examined by ultrasonography. Serum concentrations of CPSE were measured, as was a panel of steroids.

Results

Of the 79 dogs included at baseline, owners of 77 dogs (97%) were reached for an interview, and 22 were available for a follow-up examination. Six of the 79 dogs had clinical signs of prostatic disease at baseline, and eight of the remaining 73 dogs (11%) developed clinical signs between baseline and follow-up, information was lacking for two dogs. Development of clinical signs was significantly more common in dogs with a relative prostate size of ≥2.5 at baseline (n = 20) than in dogs with smaller prostates (n = 51). Serum concentrations of CPSE at baseline were not associated with the change in prostatic size between baseline and follow-up. Serum concentrations of CPSE at baseline and at follow-up were positively associated with the relative prostatic size (S_rel) at follow-up. Concentrations of corticosterone (P = 0.024), and the class corticosteroids (P = 0.0035) were positively associated with the difference in S_rel between baseline and follow-up.

Conclusions

The results support the use of CPSE for estimating present and future prostatic size in dogs ≥4 years, and the clinical usefulness of prostatic size for predicting development of clinical signs of prostatic disease in the dog. The association between corticosteroids and prostate growth warrants further investigation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-021-02874-1.

A case of personalized and precision medicine: Pharmacometabolomic applications to rare cancer, microbiological investigation, and therapy

RATIONALE

Advances in metabolomics, together with consolidated genetic approaches, have opened the way for investigating the health of patients using a large number of molecules simultaneously, thus providing firm scientific evidence for personalized medicine and consequent interventions. Metabolomics is an ideal approach for investigating specific biochemical alterations occurring in rare clinical situations, such as those caused by rare associations between comorbidities and immunosuppression.

METHODS

Metabolomic database matching enables clear identification of molecular factors associated with a metabolic disorder and can provide a rationale for elaborating personalized therapeutic protocols. Mass spectrometry (MS) forms the basis of metabolomics and uses mass-to-charge ratios for metabolite identification. Here, we used an MS-based approach to diagnose and develop treatment options in the clinical case of a patient afflicted with a rare disease further complicated by immunosuppression. The patient's data were analyzed using proprietary databases, and a personalized and efficient therapeutic protocol was consequently elaborated.

RESULTS

The patient exhibited significant alterations in homocysteine:methionine and homocysteine:thiodiglycol acid plasma concentration ratios, and these were associated with low immune system function. This led to cysteine concentration deficiency causing extreme oxidative stress. Plasmatic thioglycolic acid concentrations were initially altered and were used for therapeutic follow-up and to evaluate cysteine levels.

CONCLUSIONS

An MS-based pharmacometabolomics approach was used to define a personalized protocol in a clinical case of rare peritoneal carcinosis with confounding immunosuppression. This personalized protocol reduced both oxidative stress and resistance to antibiotics and antiviral drugs.

Recent evolutions of machine learning applications in clinical laboratory medicine

Machine learning (ML) is gaining increased interest in clinical laboratory medicine, mainly triggered by the decreased cost of generating and storing data using laboratory automation and computational power, and the widespread accessibility of open source tools. Nevertheless, only a handful of ML-based products are currently commercially available for routine clinical laboratory practice. In this review, we start with an introduction to ML by providing an overview of the ML landscape, its general workflow, and the most commonly used algorithms for clinical laboratory applications. Furthermore, we aim to illustrate recent evolutions (2018 to mid-2020) of the techniques used in the clinical laboratory setting and discuss the associated challenges and opportunities. In the field of clinical chemistry, the reviewed applications of ML algorithms include quality review of lab results, automated urine sediment analysis, disease or outcome prediction from routine laboratory parameters, and interpretation of complex biochemical data. In the hematology subdiscipline, we discuss the concepts of automated blood film reporting and malaria diagnosis. At last, we handle a broad range of clinical microbiology applications, such as the reduction of diagnostic workload by laboratory automation, the detection and identification of clinically relevant microorganisms, and the detection of antimicrobial resistance.

Using Machine Learning to Aid the Interpretation of Urine Steroid Profiles

BACKGROUND

Urine steroid profiles are used in clinical practice for the diagnosis and monitoring of disorders of steroidogenesis and adrenal pathologies. Machine learning (ML) algorithms are powerful computational tools used extensively for the recognition of patterns in large data sets. Here, we investigated the utility of various ML algorithms for the automated biochemical interpretation of urine steroid profiles to support current clinical practices.

METHODS

Data from 4619 urine steroid profiles processed between June 2012 and October 2016 were retrospectively collected. Of these, 1314 profiles were used to train and test various ML classifiers' abilities to differentiate between “No significant abnormality” and “?Abnormal” profiles. Further classifiers were trained and tested for their ability to predict the specific biochemical interpretation of the profiles.

RESULTS

The best performing binary classifier could predict the interpretation of No significant abnormality and ?Abnormal profiles with a mean area under the ROC curve of 0.955 (95% CI, 0.949–0.961). In addition, the best performing multiclass classifier could predict the individual abnormal profile interpretation with a mean balanced accuracy of 0.873 (0.865–0.880).

CONCLUSIONS

Here we have described the application of ML algorithms to the automated interpretation of urine steroid profiles. This provides a proof-of-concept application of ML algorithms to complex clinical laboratory data that has the potential to improve laboratory efficiency in a setting of limited staff resources.