Mireia Olivan

Biological properties of extracellular vesicles and their physiological functions.

In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV-mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd-sourcing, drawing on the unique EV expertise of academia-based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system.

The EMT signaling pathways in endometrial carcinoma.

Endometrial cancer (EC) is the most common gynecologic malignancy of the female genital tract and the fourth most common neoplasia in women. In EC, myometrial invasion is considered one of the most important prognostic factors. For this process to occur, epithelial tumor cells need to undergo an epithelial to mesenchymal transition (EMT), either transiently or stably, and to differing degrees. This process has been extensively described in other types of cancer but has been poorly studied in EC. In this review, several features of EMT and the main molecular pathways responsible for triggering this process are investigated in relation to EC. The most common hallmarks of EMT have been found in EC, either at the level of E-cadherin loss or at the induction of its repressors, as well as other molecular alterations consistent with the mesenchymal phenotype-like L1CAM and BMI-1 up-regulation. Pathways including progesterone receptor, TGFβ, ETV5 and microRNAs are deeply related to the EMT process in EC.

The Present and Future of Prostate Cancer Urine Biomarkers

In order to successfully cure patients with prostate cancer (PCa), it is important to detect the disease at an early stage. The existing clinical biomarkers for PCa are not ideal, since they cannot specifically differentiate between those patients who should be treated immediately and those who should avoid over-treatment. Current screening techniques lack specificity, and a decisive diagnosis of PCa is based on prostate biopsy. Although PCa screening is widely utilized nowadays, two thirds of the biopsies performed are still unnecessary. Thus the discovery of non-invasive PCa biomarkers remains urgent. In recent years, the utilization of urine has emerged as an attractive option for the non-invasive detection of PCa. Moreover, a great improvement in high-throughput “omic” techniques has presented considerable opportunities for the identification of new biomarkers. Herein, we will review the most significant urine biomarkers described in recent years, as well as some future prospects in that field.

MicroRNAs as prognostic markers in ovarian cancer.

Ovarian cancer (OC) is the most lethal gynecological malignancy among women. Over 70% of women with OC are diagnosed in advanced stages and most of these cases are incurable. Although most patients respond well to primary chemotherapy, tumors become resistant to treatment. Mechanisms of chemoresistance in cancer cells may be associated with mutational events and/or alterations of gene expression through epigenetic events. Although focusing on known genes has already yielded new information, previously unknown non-coding RNAs, such as microRNAs (miRNAs), also lead insight into the biology of chemoresistance. In this review we summarize the current evidence examining the role of miRNAs as biomarkers of response and survival to therapy in OC. Beside their clinical implications, we also discuss important differences between studies that may have limited their use as clinical biomarkers and suggest new approaches.

Role of Serum Cholesterol and Statin Use in the Risk of Prostate Cancer Detection and Tumor Aggressiveness

The aim of this study was to analyze the relationship between statin use along with serum cholesterol levels and prostate cancer (PCa) detection and aggressiveness. Statin users of three years or more and serum cholesterol levels (SC) were assessed in 2408 men scheduled for prostate biopsy. SC was classified as normal (NSC: <200 mg/dL) or high (HSC: >200 mg/dL). High-grade PCa (HGPCa) was considered if the Gleason score was greater than 7. Statin users comprised 30.9% of those studied. The PCa detection rate was 31.2% of men on statins and 37% of non-statin users (p < 0.006). The PCa detection rate was 26.3% in men with NSC and 40.6% in those with HSC (p < 0.001). In the subset of NSC men, the PCa rate was 26.5% for statin users and 26.2% for non-users (p = 0.939), while in men with HSC, the PCa rate was 36.4% for statin users and 42.0% for non-statin users (p = 0.063). The HGPCa rate was 41.8% for statin users and 32.5% for non-users (p = 0.012). NSC men had a 53.8% rate of HGPCa, while the rate was only 27.6% in HSC men (p < 0.001). NSC men on statins had an HGPCa rate of 70.2%, while non-statin users had a rate of 41.2% (p < 0.001). The HGPCa rate for HSC men on statins was 18.8%, while the rate was 30.0% (p = 0.011) for non-users. Logistic regression analysis suggested that serum cholesterol levels could serve as an independent predictor of PCa risk, OR 1.87 (95% CI 1.56–2.24) and HGPCa risk, OR 0.31 (95% CI 0.23–0.44), while statin usage could not. Statin treatment may prevent PCa detection through serum cholesterol-mediated mechanisms. A disturbing increase in the HGPCa rate was observed in statin users who normalized their serum cholesterol.

Clinical significance of proliferative inflammatory atrophy finding in prostatic biopsies

Proliferative inflammatory atrophy (PIA) has been involved in prostatic carcinogenesis. However, little is known about the clinical significance of a PIA finding in prostatic biopsies (PBs). The aim of this study is to determine the incidence of prostate inflammatory atrophy (PIA) in prostate biopsies (PBs), its association to high‐grade prostatic intraepithelial neoplasia (HGPIN), prostate cancer (PCa), and tumor aggressiveness.

Targeted proteomics in urinary extracellular vesicles identifies biomarkers for diagnosis and prognosis of prostate cancer

Rapid and reliable diagnosis of prostate cancer (PCa) is highly desirable as current used methods lack specificity. In addition, identification of PCa biomarkers that can classify patients into high- and low-risk groups for disease progression at early stage will improve treatment decision-making. Here, we describe a set of protein-combination panels in urinary extracellular vesicles (EVs), defined by targeted proteomics and immunoblotting techniques that improve early non-invasive detection and stratification of PCa patients.We report a two-protein combination in urinary EVs that classifies benign and PCa patients (ADSV-TGM4), and a combination of five proteins able to significantly distinguish between high- and low-grade PCa patients (CD63-GLPK5-SPHM-PSA-PAPP). Proteins composing the panels were validated by immunohistochemistry assays in tissue microarrays (TMAs) confirming a strong link between the urinary EVs proteome and alterations in PCa tissues. Moreover, ADSV and TGM4 abundance yielded a high diagnostic potential in tissue and promising TGM4 prognostic power. These results suggest that the proteins identified in urinary EVs distinguishing high- and low grade PCa are a reflection of histological changes that may be a consequence of their functional involvement in PCa development. In conclusion, our study resulted in the identification of protein-combination panels present in urinary EVs that exhibit high sensitivity and specificity for PCa detection and patient stratification. Moreover, our study highlights the potential of targeted proteomic approaches–such as selected reaction monitoring (SRM)–as diagnostic assay for liquid biopsies via urinary EVs to improve diagnosis and prognosis of suspected PCa patients.

Simultaneous Treatment with Statins and Aspirin Reduces the Risk of Prostate Cancer Detection and Tumorigenic Properties in Prostate Cancer Cell Lines

Nowadays prostate cancer is the most common solid tumor in men from industrialized countries and the second leading cause of death. At the ages when PCa is usually diagnosed, mortality related to cardiovascular morbidity is high; therefore, men at risk for PCa frequently receive chronic lipid-lowering and antiplatelet treatment. The aim of this study was to analyze how chronic treatment with statins, aspirin, and their combination influenced the risk of PCa detection. The tumorigenic properties of these treatments were evaluated by proliferation, colony formation, invasion, and migration assays using different PCa cell lines, in order to assess how these treatments act at molecular level. The results showed that a combination of statins and aspirin enhances the effect of individual treatments and seems to reduce the risk of PCa detection (OR: 0.616 (95% CI: 0.467–0.812), P < 0.001). However, if treatments are maintained, aspirin (OR: 1.835 (95% CI: 1.068–3.155), P = 0.028) or the combination of both drugs (OR: 3.059 (95% CI: 1.894–4.939), P < 0.001) represents an increased risk of HGPCa. As observed at clinical level, these beneficial effects in vitro are enhanced when both treatments are administered simultaneously, suggesting that chronic, concomitant treatment with statins and aspirin has a protective effect on PCa incidence.

Urinary biomarkers for the detection of prostate cancer in patients with high-grade prostatic intraepithelial neoplasia

High‐grade prostatic intraepithelial neoplasia (HGPIN) is a recognized precursor stage of PCa. Men who present HGPIN in a first prostate biopsy face years of active surveillance including repeat biopsies. This study aimed to identify non‐invasive prognostic biomarkers that differentiate early on between indolent HGPIN cases and those that will transform into actual PCa.

Eficacia del índice de salud prostática para identificar cánceres de próstata agresivos. Una validación institucional

INTRODUCTION New generations of tumor markers used to detect prostate cancer (PCa) should be able to discriminate men with aggressive PCa of those without PCa or nonaggressive tumors. The objective of this study has been to validate Prostate Health Index (PHI) as a marker of aggressive PCa in one academic institution. METHODS PHI was assessed in 357 men scheduled to prostatic biopsy between June of 2013 and July 2014 in one academic institution. Thereafter a subset of 183 men younger than 75 years and total PSA (tPSA) between 3.0 and 10.0 ng/mL, scheduled to it first prostatic biopsy, was retrospectively selected for this study. Twelve cores TRUS guided biopsy, under local anaesthesia, was performed in all cases. Total PSA, free PSA (fPSA), and [-2] proPSA (p2PSA) and prostate volume were determined before the procedure and %fPSA, PSA density (PSAd) and PHI were calculated. Aggressive tumors were considered if any Gleason 4 pattern was found. PHI was compared to %fPSA and PSAd through their ROC curves. Thresholds to detect 90%, 95% of all tumors and 95% and 100% of aggressive tumors were estimated and rates of unnecessary avoided biopsies were calculated and compared. RESULTS The rate of PCa detection was 37.2% (68) and the rate of aggressive tumors was 24.6% (45). The PHI area under the curve was higher than those of %fPSA and PSAd to detect any PCa (0.749 vs 0.606 and 0.668 respectively) or to detect only aggressive tumors (0.786 vs 0.677 and 0.708 respectively), however, significant differences were not found. The avoided biopsy rates to detect 95% of aggressive tumors were 20.2% for PHI, 14.8% for %fPSA, and 23.5% for PSAd. Even more, to detect all aggressive tumors these rates dropped to 4.9% for PHI, 9.3% for %fPSA, and 7.9% for PSAd. CONCLUSIONS PHI seems a good marker to PCa diagnosis. However, PHI was not superior to %fPSA and PSAd to identify at least 95% of aggressive tumors.