Bladder cancer is a highly prevalent disease and is associated with substantial morbidity, mortality and cost. Environmental or occupational exposures to carcinogens, especially tobacco, are the main risk factors for bladder cancer. Most bladder cancers are diagnosed after patients present with macroscopic haematuria, and cases are confirmed after transurethral resection of bladder tumour (TURBT), which also serves as the first stage of treatment. Bladder cancer develops via two distinct pathways, giving rise to non-muscle-invasive papillary tumours and non-papillary (solid) muscle-invasive tumours. The two subtypes have unique pathological features and different molecular characteristics. Indeed, The Cancer Genome Atlas project identified genetic drivers of muscle-invasive bladder cancer (MIBC) as well as subtypes of MIBC with distinct characteristics and therapeutic responses. For non-muscle-invasive bladder cancer (NMIBC), intravesical therapies (primarily Bacillus Calmette-Guérin (BCG)) with maintenance are the main treatments to prevent recurrence and progression after initial TURBT; additional therapies are needed for those who do not respond to BCG. For localized MIBC, optimizing care and reducing morbidity following cystectomy are important goals. In metastatic disease, advances in our genetic understanding of bladder cancer and in immunotherapy are being translated into new therapies.

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Clear cell renal cell carcinoma (ccRCC) is the predominant RCC subtype, but even within this classification, the natural history is heterogeneous and difficult to predict. A sophisticated understanding of the molecular features most discriminatory for the underlying tumor heterogeneity should be predicated on identifiable and biologically meaningful patterns of gene expression. Gene expression microarray data were analyzed using software that implements iterative unsupervised consensus clustering algorithms to identify the optimal molecular subclasses, without clinical or other classifying information. ConsensusCluster analysis identified two distinct subtypes of ccRCC within the training set, designated clear cell type A (ccA) and B (ccB). Based on the core tumors, or most well-defined arrays, in each subtype, logical analysis of data (LAD) defined a small, highly predictive gene set that could then be used to classify additional tumors individually. The subclasses were corroborated in a validation data set of 177 tumors and analyzed for clinical outcome. Based on individual tumor assignment, tumors designated ccA have markedly improved disease-specific survival compared to ccB (median survival of 8.6 vs 2.0 years, P = 0.002). Analyzed by both univariate and multivariate analysis, the classification schema was independently associated with survival. Using patterns of gene expression based on a defined gene set, ccRCC was classified into two robust subclasses based on inherent molecular features that ultimately correspond to marked differences in clinical outcome. This classification schema thus provides a molecular stratification applicable to individual tumors that has implications to influence treatment decisions, define biological mechanisms involved in ccRCC tumor progression, and direct future drug discovery.

Background Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting. Objective To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification. Design, setting, and participants A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina. Outcome measurements and statistical analysis Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence. Results and limitations The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms. Conclusions The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients. Patient summary We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes.

IMPORTANCEPatients diagnosed with localized prostate cancer have to decide among treatment strategies that may differ in their likelihood of adverse effects.OBJECTIVE To compare quality of life (QOL) after radical prostatectomy, external beam radiotherapy, and brachytherapy vs active surveillance. DESIGN, SETTING, AND PARTICIPANTSPopulation-based prospective cohort of 1141 men (57% participation among eligible men) with newly diagnosed prostate cancer were enrolled from January 2011 through June 2013 in collaboration with the North Carolina Central Cancer Registry. Median time from diagnosis to enrollment was 5 weeks, and all men were enrolled with written informed consent prior to treatment. Final follow-up date for current analysis was September 9, 2015.EXPOSURES Treatment with radical prostatectomy, external beam radiotherapy, brachytherapy, or active surveillance. MAIN OUTCOMES AND MEASURESQuality of life using the validated instrument Prostate Cancer Symptom Indices was assessed at baseline (pretreatment) and 3, 12, and 24 months after treatment. The instrument contains 4 domains-sexual dysfunction, urinary obstruction and irritation, urinary incontinence, and bowel problems-each scored from 0 (no dysfunction) to 100 (maximum dysfunction). Propensity-weighted mean domain scores were compared between each treatment group vs active surveillance at each time point. RESULTSOf 1141 enrolled men, 314 pursued active surveillance (27.5%), 469 radical prostatectomy (41.1%), 249 external beam radiotherapy (21.8%), and 109 brachytherapy (9.6%). After propensity weighting, median age was 66 to 67 years across groups, and 77% to 80% of participants were white. Across groups, propensity-weighted mean baseline scores were 41.8 to 46.4 for sexual dysfunction, 20.8 to 22.8 for urinary obstruction and irritation, 9.7 to 10.5 for urinary incontinence, and 5.7 to 6.1 for bowel problems. Compared with active surveillance, mean sexual dysfunction scores worsened by 3 months for patients who received radical prostatectomy (36.2 [95% CI, 30.4-42.0]), external beam radiotherapy (13.9 [95% CI, 6.7-21.2]), and brachytherapy (17.1 [95% CI, 7.8-26.6]). Compared with active surveillance at 3 months, worsened urinary incontinence was associated with radical prostatectomy (33.6 [95% CI,.2]); acute worsening of urinary obstruction and irritation with external beam radiotherapy (11.7 [95% CI,) and brachytherapy (20.5 [95% CI,.9]); and worsened bowel symptoms with external beam radiotherapy (4.9 [95% CI, 2.4-7.4]). By 24 months, mean scores between treatment groups vs active surveillance were not significantly different in most domains.CONCLUSIONS AND RELEVANCE In this cohort of men with localized prostate cancer, each treatment strategy was associated with distinct patterns of adverse effects over 2 years. These findings can be used to promote treatment decisions that incorporate individual preferences.

Purpose/Objectives To evaluate the feasibility and acceptability of a newly developed web-based, couple-oriented intervention called Prostate Cancer Education and Resources for Couples (PERC). Design Quantitative, qualitative, mixed-methods approach. Setting Oncology outpatient clinics at the University of North Carolina (UNC) Lineberger Comprehensive Cancer Center at UNC–Chapel Hill. Sample 26 patients with localized prostate cancer (PCa) and their partners. Methods Pre- and postpilot quantitative assessments and a postpilot qualitative interview were conducted. Main Research Variables General and PCa-specific symptoms, quality of life, psychosocial factors, PERC's ease of use, and web activities. Findings Improvement was shown in some PCa-specific and general symptoms (small effect sizes for patients and small-to-medium effect sizes for partners), overall quality of life, and physical and social domains of quality of life for patients (small effect sizes). Web activity data indicated high PERC use. Qualitative and quantitative analyses indicated that participants found PERC easy to use and understand, as well as engaging, of high quality, and relevant. Overall, participants were satisfied with PERC and reported that PERC improved their knowledge about symptom management and communication as a couple. Conclusions PERC was a feasible, acceptable method of reducing the side effects of PCa treatment–related symptoms and improving quality of life. Implications for Nursing PERC has the potential to reduce the negative impacts of symptoms and enhance quality of life for patients with localized PCa and their partners, particularly for those who live in rural areas and have limited access to post-treatment supportive care.

Background Patients with end-stage renal disease (ESRD) receiving dialysis have been reported to have increased risk of cancer. However, contemporary cancer burden estimates in this population are sparse and do not account for the high competing risk of death characteristic of dialysis patients. Study Design Retrospective cohort study. Setting & Participants US adult patients enrolled in Medicare's ESRD program who received in-center hemodialysis. Factors Demographic/clinical characteristics. Outcomes For overall and site-specific cancers identified using claims-based definitions, we calculated annual incidence rates (1996-2009). We estimated 5-year cumulative incidence since dialysis therapy initiation using competing-risk methods. Results We observed a constant rate of incident cancers for all sites combined, from 3,923 to 3,860 cases per 100,000 person-years (annual percentage change, 0.1; 95% CI, −0.4 to 0.6). Rates for some common site-specific cancers increased (ie, kidney/renal pelvis) and decreased (ie, colon/rectum, lung/bronchus, pancreas, and other sites). Of 482,510 incident hemodialysis patients, cancer was diagnosed in 37,128 within 5 years after dialysis therapy initiation. The 5-year cumulative incidence of any cancer was 9.48% (95% CI, 9.39%-9.57%) and was higher for certain subgroups: older age, males, nonwhites, non-Hispanics, nondiabetes primary ESRD cause, recent dialysis therapy initiation, and history of transplantation evaluation. Among blacks and whites, we observed 35,767 cases compared with 25,194 expected cases if the study population had experienced rates observed in the US general population (standardized incidence ratio [SIR], 1.42; 95% CI, 1.41-1.43). Risk was most elevated for cancers of the kidney/renal pelvis (SIR, 4.03; 95% CI, 3.88-4.19) and bladder (SIR, 1.57; 95% CI, 1.51-1.64). Limitations Claims-based cancer definitions have not been validated in the ESRD population. Information for cancer risk factors was not available in our data source. Conclusions These results suggest a high burden of cancer in the dialysis population compared to the US general population, with varying patterns of cancer incidence in subgroups.

The burden of readmissions after major cancer surgery is high, resulting in substantially poorer patient outcomes and higher costs. Risk of readmission was most strongly associated with length of stay and discharge destination. Travel distance also has an impact on patterns of readmission. Interventions targeted at higher risk individuals could potentially decrease the population burden of readmissions after major cancer surgery.