Circulating tumor cells in bladder cancer: a new horizon of liquid biopsy for precision medicine

Clinical management of bladder cancer (BC) patients offers several challenges such as poor outcome because of elevated recurrence rates and lack of response to chemotherapy [1]. So, there is a need of noninvasive prognostic and predictive tools able to allow risk category assessment and real-time supervision of drug response [2]. Recently, circulating tumor cells (CTCs) have been proposed as prognostic tool able to improve cancer patients' clinical management [3], [4], [5], [6]. CTCs detached from the primary tumor, enter the bloodstream and colonize distant organ, promoting cancer dissemination [7]. Emerging technologies are available to isolate CTC from patient's blood to provide a "liquid biopsy". Such a tool provides a molecular picture of the metastatic disease, useful to assess the cause of drug resistance onset [3, 6, 8], [9], [10], [11], [12], [13], [14]. CTC are very scarce in the blood, so robust methods are still needed for their routine use in laboratory practice [3, 11]. Several technologies have been developed in the last few years [11, 12] and several studies have been performed on the potential use of CTCs in bladder cancer patient clinical management

The role of a new class of long noncoding RNAs transcribed from ultraconserved regions in cancer

Ultraconserved regions (UCRs) represent a relatively new class of non-coding genomic sequences highly conserved between human, rat and mouse genomes. These regions can reside within exons of protein-coding genes, despite the vast majority of them localizes within introns or intergenic regions. Several studies have undoubtedly demonstrated that most of these regions are actively transcribed in normal cells/tissues, where they contribute to regulate many cellular processes. Interestingly, these non-coding RNAs exhibit aberrant expression levels in human cancer cells and their expression profiles have been used as prognostic factors in human malignancies, as well as to unambiguously distinguish among distinct cancer types. In this review, we first describe their identification, then we provide some updated information about their genomic localization and classification. More importantly, we discuss about the available literature describing an overview of the mechanisms through which some transcribed UCRs (T-UCR) contribute to cancer progression or to the metastatic spread. To date, the interplay between T-UCRs and microRNAs is the most convincing evidence linking T-UCRs and tumorigenesis. The limitations of these studies and the future challenges to be addressed in order to understand the biological role of T-UCRs are also discussed herein. We envision that future efforts are needed to convincingly include this class of ncRNAs in the growing area of cancer therapeutics

Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model

After skin cancer, prostate cancer (PC) is the most common cancer among men. The gold standard for PC diagnosis is based on the PSA (prostate-specific antigen) test. Based on this preliminary screening, the physician decides whether to proceed with further tests, typically prostate biopsy, to confirm cancer and evaluate its aggressiveness. Nevertheless, the specificity of the PSA test is suboptimal and, as a result, about 75% of men who undergo a prostate biopsy do not have cancer even if they have elevated PSA levels. Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. The model receives as an input a panel of 4 clinical variables (total PSA, free PSA, p2PSA, and PSA density) plus age. The output of the model is an estimate of the Gleason score of the patient. After training on a dataset of 190 samples and optimization of the variables, the model achieved values of sensitivity as high as 86% and 89% specificity. The efficiency of the method can be improved even further by training the model on larger datasets

Salvage radical prostatectomy after external beam radiation therapy: A systematic review of current approaches

Background: Radical external beam radiotherapy (EBRT) is a standard treatment for prostate cancer patients. Despite this, the rate of intraprostatic relapses after primary EBRT is still not negligible. There is no consensus on the most appropriate management of these patients after EBRT failure. For these patients, local salvage therapy such as radical prostatectomy, cryotherapy, and brachytherapy may be indicated. Objective: The objectives of this review were to analyze the eligibility criteria for careful selection of appropriate patients and to evaluate the oncological results and complications for each method. Methods: A review of the literature was performed to identify studies of local salvage therapy for patients who had failed primary EBRT for localized prostate cancer. Results: Most studies demonstrated that local salvage therapy after EBRT may provide long-term local control in appropriately selected patients, although toxicity is often significant. Conclusions: Our results suggest that for localized prostate cancer recurrence after EBRT, the selection of a local treatment modality should be made on a patient-by-patient basis. An improvement in selection criteria and an integrated definition of biochemical failure for all salvage methods are required to determine which provides the best oncological outcome and least comorbidity

New Cross-Talk Layer between Ultraconserved Non-Coding RNAs, MicroRNAs and Polycomb Protein YY1 in Bladder Cancer

MicroRNAs (miRNAs) are highly conserved elements in mammals, and exert key regulatory functions. Growing evidence shows that miRNAs can interact with another class of non-coding RNAs, so-called transcribed ultraconserved regions (T-UCRs), which take part in transcriptional, post-transcriptional and epigenetic regulation processes. We report here the interaction of miRNAs and T-UCRs as a network modulating the availability of these non-coding RNAs in bladder cancer cells. In our cell system, antagomiR-596 increased the expression of T-UCR 201+. Moreover, T-UCR 8+ silencing increased miR-596 expression, which in turn reduced total T-UCR 283+, showing that the perturbation of one element in this network changes the expression of other interactors. In addition, we identify the polycomb protein Yin Yang 1 (YY1) as mediator of binding between miR-596 and T-UCR 8+. These new findings describe for the first time a network between T-UCRs, miRNAs and YY1 protein, highlighting the existence of an additional layer of gene expression regulation

Peri-Prostatic Adipocyte-Released TGFβ Enhances Prostate Cancer Cell Motility by Upregulation of Connective Tissue Growth Factor

Periprostatic adipose tissue (PPAT) has emerged as a key player in the prostate cancer (PCa) microenvironment. In this study, we evaluated the ability of PPAT to promote PCa cell migration, as well as the molecular mechanisms involved. Methods: We collected conditioned mediums from in vitro differentiated adipocytes isolated from PPAT taken from PCa patients during radical prostatectomy. Migration was studied by scratch assay. Results: Culture with CM of human PPAT (AdipoCM) promotes migration in two different human androgen-independent (AI) PCa cell lines (DU145 and PC3) and upregulated the expression of CTGF. SB431542, a well-known TGFβ receptor inhibitor, counteracts the increased migration observed in presence of AdipoCM and decreased CTGF expression, suggesting that a paracrine secretion of TGFβ by PPAT affects motility of PCa cells. Conclusions: Collectively, our study showed that factors secreted by PPAT enhanced migration through CTGF upregulation in AI PCa cell lines. These findings reveal the potential of novel therapeutic strategies targeting adipocyte-released factors and TGFβ/CTGF axis to fight advanced PCa dissemination

Prognostic accuracy of Prostate Health Index and urinary Prostate Cancer Antigen 3 in predicting pathologic features after radical prostatectomy

Objective: To compare the prognostic accuracy of Prostate Health Index (PHI) and Prostate Cancer Antigen 3 in predicting pathologic features in a cohort of patients who underwent radical prostatectomy (RP) for prostate cancer (PCa). Methods and materials: We evaluated 156 patients with biopsy-proven, clinically localized PCa who underwent RP between January 2013 and December 2013 at 2 tertiary care institutions. Blood and urinary specimens were collected before initial prostate biopsy for [-2] pro-prostate-specific antigen (PSA), its derivates, and PCA3 measurements. Univariate and multivariate logistic regression analyses were carried out to determine the variables that were potentially predictive of tumor volume >0.5. ml, pathologic Gleason sum 657, pathologically confirmed significant PCa, extracapsular extension, and seminal vesicles invasions. Results: On multivariate analyses and after bootstrapping with 1,000 resampled data, the inclusion of PHI significantly increased the accuracy of a baseline multivariate model, which included patient age, total PSA, free PSA, rate of positive cores, clinical stage, prostate volume, body mass index, and biopsy Gleason score (GS), in predicting the study outcomes. Particularly, to predict tumor volume>0.5, the addition of PHI to the baseline model significantly increased predictive accuracy by 7.9% (area under the receiver operating characteristics curve [AUC] = 89.3 vs. 97.2, P>0.05), whereas PCA3 did not lead to a significant increase.Although both PHI and PCA3 significantly improved predictive accuracy to predict extracapsular extension compared with the baseline model, achieving independent predictor status (all P's<0.01), only PHI led to a significant improvement in the prediction of seminal vesicles invasions (AUC = 92.2, P<0.05 with a gain of 3.6%).In the subset of patients with GS 646, PHI significantly improved predictive accuracy by 7.6% compared with the baseline model (AUC = 89.7 vs. 97.3) to predict pathologically confirmed significant PCa and by 5.9% compared with the baseline model (AUC = 83.1 vs. 89.0) to predict pathologic GS 657. For these outcomes, PCA3 did not add incremental predictive value. Conclusions: In a cohort of patients who underwent RP, PHI is significantly better than PCA3 in the ability to predict the presence of both more aggressive and extended PCa