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

Frontiers in Endocrinology

Douglas M. Noonan¹  Matteo Conti¹  Adriana Albini²  Giuseppe Pelosi³  Laura Castellani⁴  Antonino Bruno⁵  Paolo Consonni⁵  Gioacchino D’Ambrosio⁵  Barbara Bassani⁵  Simone Cristoni⁶ 

[1] Biotechnologies, Bresso, Italy;Department of Medicine and Surgery, University Milano-Bicocca, Milan, Italy;Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy;;I.S.B.—Ion Source &IRCCS MultiMedica, Milan, Italy;Sant’Orsola Hospital, Bologna, Italy;

关键词: steroids;    steroids profile;    prostate cancer;    algorithm;    EU law;   

DOI  :  10.3389/fendo.2018.00110

来源: DOAJ

【 摘 要 】

BackgroundSerum 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.MethodsHere, 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.ResultsA 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.ConclusionWe 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.

【 授权许可】

Unknown