PurposeClinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis.MethodsA nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases - men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set.ResultsExpression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67–0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression.ConclusionA genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.

Purpose Prostate cancer patients with locally advanced disease after radical prostatectomy (RP) are candidates for secondary therapy. However, this higher risk population is heterogeneous and many will not metastasize even when conservatively managed. Given the limited specificity of pathologic features to predict metastasis, newer risk-prediction models are needed. This represents a validation study of a genomic classifier (GC) that predicts post-RP metastasis in a high-risk population. Materials and Methods A case-cohort design was used to sample 1,010 post-RP patients at high risk of recurrence treated between 2000-2006. Patients had preoperative PSA >20 ng/mL, Gleason ≥8, pT3b or GPSM score ≥10. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded. 20% random sampling created a subcohort that included all cases with metastasis. 22-marker GC scores were generated for 219 patients with available genomic data. Receiver operating characteristic and decision curves, competing risk, and weighted regression models assessed GC performance. Results GC had area under the curve of 0.79 for predicting 5-year metastasis post-RP. Decision curves showed that net benefit of GC exceeded clinical-only models. GC was the predominant predictor of metastasis in multivariable analysis. Cumulative incidence of metastasis at 5 years post-RP was 2.4%, 6.0% and 22.5% for patients with low (60% of patients), intermediate (21% of patients), and high (19% of patients) GC scores, respectively (p<0.001). Conclusions These results indicate that genomic information from the primary tumor can identify patients with adverse pathology who are most at risk for metastasis and potentially lethal prostate cancer.

Papillary and invasive cancers of the urinary bladder appear to evolve and progress through distinct molecular pathways. Invasion in bladder cancer forebodes a graver prognosis, and these tumors are generally characterized by alterations in the p53 and retinoblastoma (RB) pathways that normally regulate the cell cycle by interacting with the Ras-mitogen activated protein kinase signal transduction pathway. Tumor angiogenesis further contributes to the neoplastic growth by providing a constant supply of oxygen and nutrients. Distinct epigenetic and genetic events characterize the interplay between the molecules involved in these pathways, thus affording their use as indicators of prognosis. Efforts are now underway to construct molecular panels comprising multiple markers that can serve as more robust predictors of outcome. While clinical trials for targeted chemotherapy for bladder cancer have commenced, novel genetic and pharmacologic agents that can target pathway-specific molecules are currently under development. The next generation of clinical management for urothelial carcinoma will witness the use of multimarker panels for prognostic prediction and combination therapy directed at novel molecular targets for treatment.

Despite elaborate characterization of the risk factors, bladder cancer is still a major epidemiological problem whose incidence continues to rise each year. Urothelial carcinoma is now recognized as a disease of alterations in several cellular processes. The more prevalent, less aggressive, recurrent, noninvasive tumors are characterized by constitutive activation of the Ras-MAPK pathway. The less common but more aggressive invasive tumors, which have a higher mortality rate, are characterized by alterations in the p53 and retinoblastoma pathways. Several diagnostic tests have attempted to identify these molecular alterations in tumor cells exfoliated in the urine, whereas prognostic tests have tried to identify aberrations so as to predict tumor behavior and identify therapeutic targets. The future of bladder cancer patient management will rely on the use of molecular tests to reliably diagnose the presence of disease, predict individual tumor behavior, and suggest potential targeted therapeutics.

Our enhanced recovery after surgery protocol expedites bowel function recovery and shortens hospital stay after RC and urinary diversion without an increase in the hospital readmission rates.

Long non-coding RNAs (ncRNAs) have been shown to regulate important biological processes that support normal cellular functions. Aberrant regulation of these essential functions can promote tumor development. In this review, we underscore the importance of the regulatory role played by this distinct class of ncRNAs in cancer-associated pathways that govern mechanisms such as cell growth, invasion, and metastasis. We also highlight the possibility of using these unique RNAs as diagnostic and prognostic biomarkers in malignancies.

We present WebGeSTer DB, the largest database of intrinsic transcription terminators (http://pallab.serc.iisc.ernet.in/gester). The database comprises of a million terminators identified in 1060 bacterial genome sequences and 798 plasmids. Users can obtain both graphic and tabular results on putative terminators based on default or user-defined parameters. The results are arranged in different tiers to facilitate retrieval, as per the specific requirements. An interactive map has been incorporated to visualize the distribution of terminators across the whole genome. Analysis of the results, both at the whole-genome level and with respect to terminators downstream of specific genes, offers insight into the prevalence of canonical and non-canonical terminators across different phyla. The data in the database reinforce the paradigm that intrinsic termination is a conserved and efficient regulatory mechanism in bacteria. Our database is freely accessible.

Purpose Use of molecular markers in urine, tissue or blood offers potential opportunities to improve understanding of bladder cancer biology which may help identify disease earlier, risk stratify patients, improve prediction of outcomes or help target therapy. Methods A review of the published literature was performed, without restriction of time. Results Despite the fast-growing literature about the topic and the approval of several urinary biomarkers for use in clinical practice, they have not reached the level of evidence for widespread utilization. Biomarkers could be used in different clinical scenarios, mainly to overcome the limitations of current diagnostic, predictive, and prognostic tools. They have been evaluated to detect bladder cancer in asymptomatic populations or those with hematuria and in surveillance of disease as adjuncts to cystoscopy. There is also a potential role as prognosticators of disease recurrence, progression and survival both in patients with non-invasive cancers and in those with advanced disease. Finally, they promise to be helpful in predicting the response to local and/or systemic chemotherapy and/or immunotherapy. Conclusions To date, due to the lack of high-quality prospective trials, the level of evidence provided by the current literature remains low and, therefore, the potential of biomarkers exceeds utilization in clinical practice.