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

ABSTRACT: Serum steroids are crucial molecules altered in prostate cancer (PCa). Mass spectrometry (MS) is currently the elected technology for the analysis of steroids in diverse biological samples. Steroids have complex biological pathways and stoichiometry and it is important to evaluate their quantitative ratio. MS applications to patient hormone profiling could lead to a diagnostic approach.Here, we employed the Surface Activated Chemical Ionization-Electrospray-NIST (SANIST) developed in our laboratories, to obtain quantitative serum steroid ratio relationship profiles with a machine learning Bayesian model to discriminate patients with PCa. The approach is focused on steroid relationship profiles and disease association.A pilot study on patients affected by PCa, benign prostate hypertrophy (BPH), and control subjects [prostate-specific antigen (PSA) lower than 2.5?ng/mL] was done in order to investigate the classification performance of the SANIST platform. The steroid profiles of 71 serum samples (31 controls, 20 patients with PCa and 20 subjects with benign prostate hyperplasia) were evaluated. The levels of 10 steroids were quantitated on the SANIST platform: Aldosterone, Corticosterone, Cortisol, 11-deoxycortisol, Androstenedione, Testosterone, dehydroepiandrosterone, dehydroepiandrosterone sulfate (DHEAS), 17-OH-Progesterone and Progesterone. We performed both traditional and a machine learning analysis.We show that the machine learning approach based on the steroid relationships developed here was much more accurate than the PSA, DHEAS, and direct absolute value match method in separating the PCa, BPH and control subjects, increasing the sensitivity to 90% and specificity to 84%. This technology, if applied in the future to a larger number of samples will be able to detect the individual enzymatic disequilibrium associated with the steroid ratio and correlate it with the disease. This learning machine approach could be valid in a personalized medicine setting.

SUBMITTER: Albini A

PROVIDER: S-EPMC5895774 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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

Albini Adriana A Bruno Antonino A Bassani Barbara B D'Ambrosio Gioacchino G Pelosi Giuseppe G Consonni Paolo P Castellani Laura L Conti Matteo M Cristoni Simone S Noonan Douglas M DM

Frontiers in endocrinology 20180405

<h4>Background</h4>Serum steroids are crucial molecules altered in prostate cancer (PCa). Mass spectrometry (MS) is currently the elected technology for the analysis of steroids in diverse biological samples. Steroids have complex biological pathways and stoichiometry and it is important to evaluate their quantitative ratio. MS applications to patient hormone profiling could lead to a diagnostic approach.<h4>Methods</h4>Here, we employed the Surface Activated Chemical Ionization-Electrospray-NIS ...[more]

PMID: 29674995

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Project description:Differences of Sex Development (DSD) are a set of conditions where development of chromosomal, gonadal, or anatomical sex is atypical. With overlapping phenotypes and multiple genes involved, poor diagnostic yields are achieved for many of these conditions. The current DSD diagnostic regimen can be augmented by investigating transcriptome/proteome in vivo, but it is hampered by the unavailability of affected gonadal tissue at the relevant developmental stage. We try to mitigate this limitation by reprogramming readily available skin tissue-derived dermal fibroblasts into Sertoli cells (SC), which could then be deployed for different diagnostic strategies. SCs form the target cell type of choice because they act like an organizing center of embryonic gonadal development and many DSD arise when these developmental processes go awry. Methods: We employed a computational predictive algorithm for cell conversions called Mogrify to predict the transcription factors (TFs) required for direct reprogramming of human dermal fibroblasts into SCs. We established trans-differentiation culture conditions where stable transgenic expression of these TFs was achieved in 46, XY adult dermal fibroblasts using lentiviral vectors. The resulting Sertoli like cells (SLCs) were validated for SC phenotype using several approaches. Results: SLCs exhibited Sertoli-like morphological and cellular properties as revealed by morphometry and xCelligence cell behavior assays. They also showed Sertoli-specific expression of molecular markers such as SOX9, PTGDS, BMP4, or DMRT1 as revealed by IF imaging, RNAseq and qPCR. The SLC transcriptome shared about two thirds of its differentially expressed genes with a human adult SC transcriptome and expressed markers typical of embryonic SCs. Notably, SLCs lacked expression of most markers of other gonadal cell types such as Leydig, germ, peritubular myoid or granulosa cells. Conclusions: The trans-differentiation method was applied to a variety of commercially available 46, XY fibroblasts derived from patients with DSD and to a 46, XX cell line. The DSD SLCs displayed altered levels of trans-differentiation in comparison to normal 46, XY-derived SLCs, thus showcasing the robustness of this new trans-differentiation model.

Dataset Information

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

Publications

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

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