Identificación de biomarcadores urinarios del cáncer de próstata mediante análisis metabolómico por resonancia magnética

Prostate cancer is the most common non-cutaneous solid neoplasm in men and the third leading cause of cancer-related death, after lung and colorectal cancer. Following surgery, approximately 15-45% of patients experience biochemical recurrence. The lack of aggressiveness biomarkers leads 90% of patients to opt for treatments that are not without risks and have a negative impact on their quality of life. Metabolomics is an emerging research tool with great potential, integrated into systems biology, based on changes in metabolic signaling pathways that reflect the phenotypic and functional changes of malignant cells preceding their neoplastic transformation. Nuclear Magnetic Resonance (NMR) is an important analytical tool for the structural determination of organic compounds, allowing the detection and quantification of compounds in a complex biological matrix. NMR spectroscopy analysis requires minimal sample preparation and, due to its high reproducibility, quantitative nature, and wide linear dynamic range, it is considered a very useful technique. Furthermore, its non-destructive nature allows the observation of metabolite dynamics in the biological matrix and their storage for future analyses or complementary analyses on the same sample. However, the main obstacles to the use of NMR spectroscopy in metabolomics are low sensitivity (detection limit 1-5 μM), material cost, and lack of automation in metabolite identification through relevant databases. • METHODOLOGY: During the period between February 2012 and December 2015, urine samples were obtained from 61 males at the time of transrectal prostate biopsy, who ultimately underwent laparoscopic radical prostatectomy at the University and Polytechnic Hospital La Fe in Valencia, after a diagnosis of prostate cancer. The spectra were acquired and processed in collaboration with Bruker (Bruker Biospin GmbH). The panel of metabolites established in Bruker's procedures was identified and quantified, obtaining their concentration in the urine. The concentration data of the 117 definitive metabolites were used for chemometric analysis. • RESULTS: After a minimum follow-up of 5 years for all patients, the rate of biochemical recurrence was 27.86% (17/61); those who recurred had a median PSA almost twice that of those who did not recur (13.2 versus 7.35 ng/mL). We did not find statistically significant differences in terms of patients' average age or BMI, nor were there significant differences between clinical staging or anatomopathological characteristics of the prostate biopsy, although there were differences in pathological staging and ISUP of the specimen. In the chemometric study, an optimized model with 35 variables was obtained, including PSA, which had a VIP >1. The score plot of the sample showed a certain increase in the discrimination capacity between the two groups, reflected in a higher AUROC (0.88), with a confusion table based on cross-validation (sensitivity: 64.71%, specificity 93.18%, precision 85.25%). • CONCLUSION: Previous studies have identified metabolic profiles with discriminative capacity between benign prostatic pathology (BPH) and malignant pathology (CaP). However, the evidence in the literature suggests that metabolomics could allow for a more accurate prediction of the development of biochemical recurrence after radical prostatectomy than existing clinical criteria. From the results obtained in response to the objectives outlined in this Doctoral Thesis, from a methodological, results, and new research perspective, the following conclusions can be drawn: 1. Metabolomics, as a rapidly growing systematic analytical technique, examines metabolites in complex systems, a consequence of specific cellular processes, and provides sensitive and dynamic phenotypic patterns that more accurately reflect cellular and molecular changes during cancer initiation and progression. 2. The design of our study and the systematization in sample collection before surgery increases the statistical power of the results by reducing the effect of systematic and random errors associated with the analytical stage. 3. The good reproducibility of the quantitative aspects obtained, in combination with multivariate statistical methods, makes 1H-NMR a very reliable tool. 4. Our metabolomic model in urine with all detected metabolites fails to be significantly discriminative between patients with biochemical recurrence and those who will remain disease-free. 5. Our optimized clinical-metabolomic model with PSA and the 35 urine metabolites with a higher contribution to the model show good discriminatory capacity to predict the development of biochemical recurrence. The highlighted components in this regard belong to the metabolism of Galactose, vitamin B6, glycolysis/gluconeogenesis, and glycerophospholipids, among others. 6. Our results should be complemented with larger and stratified samples, which achieve externally validated metabolic fingerprints. The set of identified metabolites will be used to develop a urinary biomarker for the non-invasive diagnosis of CaP and its correlation with tumor aggressiveness. 7. We consider its potential translation into routine clinical practice along with other accepted variables, such as serum PSA level, clinical stage, and tumor grade, determining the cost-effectiveness of radical surgery, since the associated risks (urinary incontinence and erectile dysfunction) would not be justified if we knew in advance that surgery has a low probability of achieving disease remission.