The prostate gland is a walnut-sized organ of the male urinary-genital tract that is located below the bladder surrounding the urethra and in front of the rectum. The most important disease affecting prostate is prostate cancer (PCa). PCa is the most common cause of cancer death, and it is the second most common cause of cancer death among men in the Western world. The risk of developing this type of cancer during a lifetime is estimated at 1 in 6 men in the US, and the risk of death due to this disease is 1 in 36. The current diagnosis of PCa is based on a triad consisting of diagnosis, analysis of the levels of PSA (Prostate Specific Antigen) in serum, the digital rectal examination (DRE) and finally the prostate biopsy (PB). When serum PSA levels are above 4 ng/mL and / or when DRE is suspected, the urologist can estimate how likely the patient is affected by PCa and therefore decided the need to practice or not a PB, which is the gold standard for PCa diagnosis. The introduction of PSA testing in the late 80s, has resulted in an improvement of early diagnosis of PCa, at which time options for treatment are effective. However, despite this early detection, mortality PCa has not decreased significantly in the last 50 years. The main limitation of serum PSA as a tumor marker is its lack of specificity (around 30%) at the cut-off value of 4 ng/mL, and also a low negative predictive value (NPV), which results in a high rate of negative biopsies. Elevated PSA levels can also be attributed to other factors such as benign prostatic hyperplasia (BPH), prostatitis, etc. As a consequence of the current screening parameters, around 2/3 of the approximately 1,300,000 biopsies made yearly in the United States and 390,000 in Europe are unnecessary. In contrast, the false positive rate of a biopsy is about zero, although the false negative rate in the first biopsy may oscillates around 20%. As a result of their persistently elevated PSA levels, but negative biopsy results, these men undergo repeated biopsies to rule out PCa. This situation is called the “PSA dilemma”. For these reasons, PCa would benefit from the existence of new markers for screening and also a more specific diagnosis less invasive. Furthermore, an improvement in diagnosis would avoid many unnecessary biopsies and consequently a significant savings in the cost of health today. The search for new markers in PCa is an important field of work in the early detection of this cancer. Urine has been defined as a liquid biopsy of the urogenital tract, and it can provide much more information about these organs (including the prostate) than a tissue biopsy. Urine obtained after DRE can easily serve as a mirror of what is happening within the prostate. Furthermore, urine collection can be accomplished without disruption of clinical standard practice. It can also be repeated several times throughout the course of the prostatic disease. For all of these reasons, urine can serve as a potential source of prostate disease biomarkers. Nevertheless, using urine for biomarker discovery represents an important technical challenge, both in transcriptomic and proteomic approaches. Although there are some studies that focus on those approaches and biomarker discovery and identification, there still exists some controversy regarding the standardization of collection procedures, sample processing, storage and normalization. We hypothesized that the utilization of targeted genomic and proteomic techniques on urine samples from patients suspected of having PCa can provide a pattern of biomarkers able to efficiently distinguish between the presence or absence of a prostate carcinoma and, further, can help to identify clinically significant prostate cancer patients. The main objective of this study is to diagnose asymptomatic PCa by non/minimally-invasive means using RNA or Protein in urine after prostate massage, and to overcome the low specificity of PSA by the use of additional biomarkers to reduce the number of unnecessary biopsies (reduce financial costs for society, reduction in unwanted secondary effects). 1. TRANSCRIPTOMIC APPROACH: In recent years, the explosion of genomic and transcriptomic approaches have resulted in increased biomarker discovery. The recent discovery of Prostate Cancer Gene 3 (PCA3) in urine as a biomarker for the detection of PCa and studies to determine its applicability in routine diagnosis represent a significant success for the scientific community in this field. First, we wanted to characterize a new urine candidate biomarker (PSGR) to be compared with PCA3, and second, we planned to use a panel of biomarkers, in order to improve diagnostic accuracy. Finally, we proposed to better characterize the well-known biomarker PCA3 as a tool for the early detection of pre-neoplastic PCa lesions, such as High grade prostatic intraepithelial neoplasia (HGPIN). 1a) “PSGR and PCA3 as Biomarkers for the Detection of Prostate Cancer in Urine” Rigau M et al., Prostate. 2010 Dec 1;70(16):1760-7. PSGR is a member of the G-protein coupled OR family. PSGR has previously been described to be highly prostate tissue-specific and over-expressed in PCa tissue. Our aim was to test whether PSGR could also be detected by RTqPCR in urine sediment obtained after prostate massage (PM). A total of 215 urine samples were collected from consecutive patients (34% with PCa), who presented for PB due to elevated serum PSA levels (> 4 ng/mL) and/or an abnormal DRE. These samples were analyzed by RTqPCR. By univariate analysis we found that PSGR and PCA3 were significant predictors of PCa. A Reciever Operator Characteristics (ROC) curve was used to assess the outcome predictive values of the individual biomarkers. We obtained the following Area Under the Curve (AUC) values: PSGR (0.68) and PCA3 (0.66). Both markers individually overcame the AUC value for serum PSA (0.60). Finally, we combined those markers to test if a combination of both biomarkers could improve the sensitivity of PCA3 alone. By using a multivariate extension analysis, multivariate ROC (MultiROC), the outcome predictive values of the paired biomarkers were assessed. We obtained an AUC value of 0.73 for the combination of PSGR and PCA3 (PSGRvPCA3). Then, we tested whether a combination of PSGR and PCA3 could improve specificity by fixing the sensitivity at 95%. We obtained specificities of 15% (PSGR) and 17% (PCA3) for each individual marker and 34% for PSGRvPCA3. In summary, a multiplexed model that included PSGR and PCA3 improved the specificity for the detection of PCa, especially in the area of high sensitivity. This could be clinically useful for determining which patients should undergo biopsy. 1b) “A Three-Gene panel on urine increases PSA specificity in the detection of prostate cancer” Rigau et al., Prostate. 2011 Apr 25. doi: 10.1002/pros.21390. Much evidence points to the fact that a single marker may not necessarily reflect the multifactorial and heterogeneous nature of PCa. The principle that underlies the combined biomarker approach is consistent with tests offered for the detection of PCa in tissue specimens and takes into consideration the heterogeneity of cancer development based on a diagnostic profile. The combined model that results from these combinations provides overall increased sensitivity without decreasing the specificity. Following the same approach than in our previous work, we combined three biomarkers to maximize individual specificities. Prostate Specific Membrane Antigen (PSMA), another well-known PCa biomarker, was used to test whether a combination of PSGR, PCA3 and PSMA was able to improve the specificity of the current diagnostic technique. We analyzed post-PM urine samples from 154 consecutive patients (37% with PCa), who presented for PB due to elevated serum PSA levels (>4 ng/mL) and/or an abnormal DRE. We tested whether the putative PCa biomarkers PSMA, PSGR, and PCA3 could be detected by RTqPCR in the post-PM urine sediment. By univariate analysis, we found that the PSMA, PSGR, and PCA3 scores were significant predictors of PCa. We then combined these findings to test if a combination of these biomarkers could improve the specificity of an actual diagnosis. Using a multiplex model (PSGRvPCA3vPSMA), the area under the MultiROC curve (AUCm) was 0.74, 0.77 with PSA and 0.80 with PSA density (PSAD). Fixing the sensitivity at 96%, we obtained a specificity of 34%, 34% with PSA and 40% with PSAD. Afterwards, we specifically tested our model for clinical usefulness in the PSA diagnostic ‘‘gray zone’’ (4–10 ng/mL) on a target subset of 82 men with no prior biopsy (34% with PCa) and a target subset of 77 men with the PSAD information (35% with PCa). Using a multiplex model, the AUCm was 0.82, 0.89 with PSAD. Fixing the sensitivity at 96%, we obtained a specificity of 50% and 62% with PSAD in the gray zone. This model would allow 34% of the patients to avoid unnecessary biopsies in the gray zone (42% when using PSAD). 1c) “Behavior of PCA3 gene in the urine of men with high grade prostatic intraepithelial neoplasia” Morote and Rigau et al., World J Urol. 2010 Dec;28(6):677-80. An ideal biomarker for the early detection of PCa should also differentiate between men with isolated HGPIN and those with PCa. PCA3 is a highly specific PCa gene, and its score in post-PM urine seems to be useful in ruling out PCa, especially after a negative PB. The biopsy finding of an HGPIN is a frequent indication that the PB should be repeated. The aim of this study was to determine the efficacy of post-PM urine PCA3 scores for ruling out PCa in men with previous HGPIN. The PCA3 score was assessed by RTqPCR in 244 post- PM urine samples collected from men subjected to PB (64-isolated HGPIN, 83-PCa, and 97-benign pathology findings (BP)). The median PCA3 score was 1.56 in men with BP, 2.01 in men with isolated HGPIN and 9.06 in men with PCa. A significant difference was observed among the three scores (p < 0.001) and also between HGPIN and PCa (p = 0.008); however, no differences were observed between HGPIN and BP (p = 0.128). The AUC in the ROC analysis was 0.71 in the subset of men with BP and PCa, while it decreased to 0.63 when only men with isolated HGPIN and PCa were included in the analysis. Finally, the median of the PCA3 scores was assessed in men with previously diagnosed unifocal HGPIN (2.63) and in men with previously diagnosed multifocal HGPIN (1.59). No differences were observed between unifocal and multifocal HGPIN (p = 0.56). In conclusion, the efficacy of post-PM urine PCA3 scores in ruling out PCa in men with HGPIN is less than in men with BP. For this reason, when HGPIN is found at PB, these results should be taken into consideration, in order to establish the clinical usefulness of the PCA3 score as a tool for avoiding unnecessary repeated biopsies. 2. PROTEOMIC APPROACH: The high-throughput proteomic analysis of urine samples has recently become a popular approach for the identification of novel biomarkers. Proteins secreted by cancer cells, also referred to as "cancer cell secretomes," are a promising source for biomarker discovery. A great advantage to these cancer-secreted proteins and/or their fragments is that in most cases, they enter body fluids, such as blood or urine, and therefore, can be measured via non-invasive assays. Since the protein products of PCa cells can be detected in urine, their use as a proximal body fluid in the detection of PCa is very attractive. 2a.“The Discovery and Qualification of a Panel of Urine Biomarkers for Prostate Cancer Diagnosis”. In this study we used DIGE proteomic analysis on 30 age-matched, post-PM urine supernatant specimens, in order to identify the differentially expressed proteins in patients with PCa. 24 potential biomarkers were identified, the majority of which were secreted proteins associated with several wellknown, functional cancer pathways. Qualification of 15 of the 24 identified biomarker candidates was then undertaken by relative quantification using an SRM-based assay (target) on 50 post-PM urine supernatant samples (38% with PCa). After statistical analysis, 7 peptides, corresponding to 5 different proteins, were selected. A multiplex ROC curve using those 7 peptides showed an AUC value of 0.93. Fixing the sensitivity at 95%, we achieved a specificity of 78%. 2b. “Qualification and Verification of Urine PCa Candidate Biomarkers with Selected Reaction Monitoring”. The qualification and verification of candidate biomarkers is a critical stage in the great biomarker discovery pipeline. Credentialed biomarkers that have successfully passed through this stage are considered verified biomarkers, which are of high value for translation into large-scale, clinical validation studies. The evaluation of biomarkers in body fluids necessitates the development of robust methods to quantify proteins in body fluids, using large sets of samples. In the present study, we performed the qualification of a set of 42 candidate biomarkers for PCa diagnosis on a set of 107 post- PM urine supernatant samples (36% with PCa) using SRM-based absolute quantification. Before that, urine sample preparation and analytical procedures were optimized for SRM methodology. We standardized preparation of the urine protein samples for SRM analysis by using 9 different protocols. Our final goal was to obtain a panel of biomarkers that alone, or in combination with the existing PCa biomarker, would help us to better define patients with PCa. In addition, due to the large number of samples and their pathological conditions, we would also be able to define candidate prognostic markers. However, this study has yet to be completed. In conclusion, the data presented in this dissertation represent a significant advance in the standard care for PCa diagnosis. Our two approaches (RNA- and Protein-based) have begun to yield promising results, as both have levels of specificity that exceed those of PSA. However, validation studies on larger, multi-centric cohorts of urine samples are needed to end up with a valid PCa biomarker. The obtained results should have a rapid application in the clinics and potentially influence, together with actual screening parameters (serum PSA and DRE), decisions that could improve the health system, as well as clinical, managerial and/or public practices for health outcomes in PCa