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Tumour-infiltrating lymphocytes (TIL), known to be of prognostic value in various solid tumours, have been in the focus of research in the last years. TIL are often quantified via IMMUNOSCORE ® (IS), a scoring system based on TIL cell densities. Recent studies were able to replicate these findings for muscle-invasive bladder cancer (MIBC), however data regarding non-muscle-invasive bladder cancer (NMIBC) are scarce. This study aimed to evaluate the value of a modified Immunoscore (mIS) as a predictive marker for NMIBC prognosis using tissue-micro-arrays (TMAs). We analysed two TMAs containing 316 samples from 158 patients with NMIBC, stained for CD3, CD8, CD45RO and FOXP3. Stained TIL were captured by digital pathology, cumulated, averaged, and reported as density (stained cells per mm²). The mIS was then constructed based on density of all four immune-cell types. Clinical, pathological and follow-up data were collected retrospectively. Univariable and multivariable cox regression analysis was performed to assess the potential value of mIS as a predictor for progression free survival (PFS) and recurrence-free-survival (RFS). Patients within “European Organisation for Research and Treatment of Cancer” (EORTC) risk groups were further substratified in high mIS and low mIS subgroups. Finally log-rank test was used to compare the different survival curves. The median age in our cohort was 68 years (Interquartile Range (IQR): 60 - 76), and 117 (74%) patients were male. A total of 26 patients (16.5%) were classified as EORTC low risk, 45 (28.5%) as intermediate risk and 87 (55.1%) as high risk. Patients in the EORTC high risk group with low mIS showed a shorter PFS in comparison to high mIS (HR 2.9, CI 0.79 – 11.0, p=0.082). In contrast, no predictive potential regarding PFS was observed in intermediate or low risk groups. Furthermore, mIS was not able to predict RFS in any EORTC risk group. mIS could be utilized to predict prognosis more accurately in high-risk patients with NMIBC by identifying those with higher or lower risk of progression. Therefore, mIS could be used to allocate these highrisk patients to more streamlined follow-up or more aggressive treatment strategies.

Background We compared six commonly used logistic regression methods for accommodating missing risk factor data from multiple heterogeneous cohorts, in which some cohorts do not collect some risk factors at all, and developed an online risk prediction tool that accommodates missing risk factors from the end-user. Methods Ten North American and European cohorts from the Prostate Biopsy Collaborative Group (PBCG) were used for fitting a risk prediction tool for clinically significant prostate cancer, defined as Gleason grade group ≥ 2 on standard TRUS prostate biopsy. One large European PBCG cohort was withheld for external validation, where calibration-in-the-large (CIL), calibration curves, and area-underneath-the-receiver-operating characteristic curve (AUC) were evaluated. Ten-fold leave-one-cohort-internal validation further validated the optimal missing data approach. Results Among 12,703 biopsies from 10 training cohorts, 3,597 (28%) had clinically significant prostate cancer, compared to 1,757 of 5,540 (32%) in the external validation cohort. In external validation, the available cases method that pooled individual patient data containing all risk factors input by an end-user had best CIL, under-predicting risks as percentages by 2.9% on average, and obtained an AUC of 75.7%. Imputation had the worst CIL (-13.3%). The available cases method was further validated as optimal in internal cross-validation and thus used for development of an online risk tool. For end-users of the risk tool, two risk factors were mandatory: serum prostate-specific antigen (PSA) and age, and ten were optional: digital rectal exam, prostate volume, prior negative biopsy, 5-alpha-reductase-inhibitor use, prior PSA screen, African ancestry, Hispanic ethnicity, first-degree prostate-, breast-, and second-degree prostate-cancer family history. Conclusion Developers of clinical risk prediction tools should optimize use of available data and sources even in the presence of high amounts of missing data and offer options for users with missing risk factors.

Introduction and objectives In several urogenital cancers, organ-preserving surgery represents the preferred treatment approach, but in patients with testicular germ cell tumors (tGCTs), radical orchiectomy represents the standard of care. This study aimed to summarize published case series assessing oncological and functional outcomes after testis-sparing surgery (TSS) in patients with tGCTs. Materials and methods A systematic literature review and individual patient data meta-analysis were conducted of published cases with tGCT treated with TSS. Results Of 2,333 reports, we included 32 reports providing data on 285 patients, including 306 testicles treated with TSS. Adjacent germ cell neoplasia in situ (GCNIS) was described in 43%. Hypogonadism and infertility after TSS were diagnosed in 27% and 18%. In patients undergoing adjuvant testicular radiotherapy, hypogonadism was diagnosed in 40%. Patients treated with adjuvant testicular radiotherapy after TSS exhibited a significantly lower incidence of local recurrence (2% vs. 50%, p Conclusion The current data questions the benefits of TSS in tGCT patients. If at all, TSS should only be offered to well-informed patients with a singular testicle, excellent compliance, a singular tumor less than 2 cm located at the lower pole of the testicle, and normal preoperative endocrine function. Unless patients plan to father a child within a short time frame, adjuvant testicular radiotherapy should be recommended after TSS. Radical orchiectomy remains the standard of care, but future studies may support the use of TSS in selected men.

Urothelial carcinoma (UC) is a common disease causing significant morbidity and mortality as well as considerable costs for health systems. Extensive aberrant methylation of DNA is broadly documented in early UC, contributing to genetic instability, altered gene expression and tumor progression. However the triggers initiating aberrant methylation are unknown. Recently we discovered that several genes encoding key enzymes of methyl group and polyamine metabolism, including Ornithine Decarboxylase 1 (ODC1), are affected by DNA methylation in early stage UC. In this study, we investigated the hypothesis that these epigenetic alterations act in a feed-forward fashion to promote aberrant DNA methylation in UC. We demonstrate that siRNA-mediated knockdown of ODC1 expression elicits genome-wide LINE-1 demethylation, induction of LINE-1 transcripts and double-strand DNA breaks and decreases viability in primary cultured uroepithelial cells. Similarly, following siRNA-mediated knockdown of ODC1, UC cells undergo double-strand DNA breaks and apoptosis. Collectively, our findings provide evidence that ODC1 gene hypermethylation could be a starting point for the onset of genome-wide epigenetic aberrations in urothelial carcinogenesis. Furthermore, LINE-1 induction enabled by ODC1 interference provides a new experimental model to study mechanisms and consequences of LINE-1 activation in the etiology and progression of UC as well as presumably other cancers.

Background Non-invasive liquid biopsies could complement current pathological nomograms for risk stratification of prostate cancer patients. Development and testing of potential liquid biopsy markers is time, resource, and cost-intensive. For most protein targets, no antibodies or ELISAs for efficient clinical cohort pre-evaluation are currently available. We reasoned that mass spectrometry-based prescreening would enable the cost-effective and rational preselection of candidates for subsequent clinical-grade ELISA development. Methods Using Mass Spectrometry-GUided Immunoassay DEvelopment (MS-GUIDE), we screened 48 literature-derived biomarker candidates for their potential utility in risk stratification scoring of prostate cancer patients. Parallel reaction monitoring was used to evaluate these 48 potential protein markers in a highly multiplexed fashion in a medium-sized patient cohort of 78 patients with ground-truth prostatectomy and clinical follow-up information. Clinical-grade ELISAs were then developed for two of these candidate proteins and used for significance testing in a larger, independent patient cohort of 263 patients. Results Machine learning-based analysis of the parallel reaction monitoring data of the liquid biopsies prequalified fibronectin and vitronectin as candidate biomarkers. We evaluated their predictive value for prostate cancer biochemical recurrence scoring in an independent validation cohort of 263 prostate cancer patients using clinical-grade ELISAs. The results of our prostate cancer risk stratification test were statistically significantly 10% better than results of the current gold standards PSA alone, PSA plus prostatectomy biopsy Gleason score, or the National Comprehensive Cancer Network score in prediction of recurrence. Conclusion Using MS-GUIDE we identified fibronectin and vitronectin as candidate biomarkers for prostate cancer risk stratification., Clinical Proteomics, 19

Localized prostate cancer exhibits multiple genomic alterations and heterogeneity at the proteomic level. Single-cell technologies capture important cell-to-cell variability responsible for heterogeneity in biomarker expression that may be overlooked when molecular alterations are based on bulk tissue samples. This study aims to identify prognostic biomarkers and describe the heterogeneity of prostate cancer and the associated microenvironment by simultaneously quantifying 36 proteins using single-cell mass cytometry analysis of over 1.6 million cells from 58 men with localized prostate cancer. We perform this task, using a high-dimensional clustering pipeline named Franken to describe subpopulations of immune, stromal, and prostate cells, including changes occurring in tumor tissues and high-grade disease that provide insights into the coordinated progression of prostate cancer. Our results further indicate that men with localized disease already harbor rare subpopulations that typically occur in castration-resistant and metastatic disease., Cell Reports Medicine, 3 (4), ISSN:2666-3791

Background Online clinical risk prediction tools built on data from multiple cohorts are increasingly being utilized for contemporary doctor-patient decision-making and validation. This report outlines a comprehensive data science strategy for building such tools with application to the Prostate Biopsy Collaborative Group prostate cancer risk prediction tool. Methods We created models for high-grade prostate cancer risk using six established risk factors. The data comprised 8492 prostate biopsies collected from ten institutions, 2 in Europe and 8 across North America. We calculated area under the receiver operating characteristic curve (AUC) for discrimination, the Hosmer-Lemeshow test statistic (HLS) for calibration and the clinical net benefit at risk threshold 15%. We implemented several internal cross-validation schemes to assess the influence of modeling method and individual cohort on validation performance. Results High-grade disease prevalence ranged from 18% in Zurich (1863 biopsies) to 39% in UT Health San Antonio (899 biopsies). Visualization revealed outliers in terms of risk factors, including San Juan VA (51% abnormal digital rectal exam), Durham VA (63% African American), and Zurich (2.8% family history). Exclusion of any cohort did not significantly affect the AUC or HLS, nor did the choice of prediction model (pooled, random-effects, meta-analysis). Excluding the lowest-prevalence Zurich cohort from training sets did not statistically significantly change the validation metrics for any of the individual cohorts, except for Sunnybrook, where the effect on the AUC was minimal. Therefore the final multivariable logistic model was built by pooling the data from all cohorts using logistic regression. Higher prostate-specific antigen and age, abnormal digital rectal exam, African ancestry and a family history of prostate cancer increased risk of high-grade prostate cancer, while a history of a prior negative prostate biopsy decreased risk (all p-values

To evaluate the association of shock-wave lithotripsy (SWL) for kidney stones and hypertension or diabetes.Patients with urolithiasis treated by SWL were retrospectively identified. To assess whether shock-wave application to the kidney is associated with long-term adverse effects, patients after SWL for kidney stones were selected as the main group of interest. Patients treated with shock waves for distal ureter stones only were chosen as a comparison group. A questionnaire was sent to all patients to assess the prevalence of hypertension and diabetes. The Swiss Health Survey (SHS) dataset was used as an additional comparison group.After a median follow-up of 13.7 years, the odds ratio (OR) to report hypertension [OR 1.30 (95% CI 1.10-1.95)] or diabetes [OR 1.54 (95% CI 1.21-1.97)] was significantly higher in patients treated with SWL compared to the SHS dataset. In comparison with the kidney group, participants in the SHS had a significantly lower OR to report hypertension at follow-up [OR 0.79 (95% CI 0.65-0.95)], while the OR to report hypertension [1.16 (95% CI 0.79-1.70)] was not significantly different in the distal ureter group. For diabetes, a significantly lower [OR 0.60 (95% CI 0.46-0.78)] in the SHS group and a non-significantly lower [OR 0.68 (95% CI 0.38-1.22)] in the ureter group was noted compared to the kidney group.Compared to the SHS data set SWL was in general associated with hypertension and diabetes. However, no clear difference between patients after SWL to the kidney compared to SWL to the distal ureter was seen and thus the data do not support a causal relationship.

ISSN:0022-3417 ISSN:1096-9896

BACKGROUND: Prostate cancer prediction tools provide quantitative guidance for doctor-patient decision-making regarding biopsy. The widely used online Prostate Cancer Prevention Trial Risk Calculator (PCPTRC) utilized data from the 1990s based on six-core biopsies and outdated grading systems. OBJECTIVE: We prospectively gathered data from men undergoing prostate biopsy in multiple diverse North American and European institutions participating in the Prostate Biopsy Collaborative Group (PBCG) in order to build a state-of-the-art risk prediction tool. DESIGN, SETTING, AND PARTICIPANTS: We obtained data from 15 611 men undergoing 16 369 prostate biopsies during 2006–2017 at eight North American institutions for model-building and three European institutions for validation. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used multinomial logistic regression to estimate the risks of high-grade prostate cancer (Gleason score ≥7) on biopsy based on clinical characteristics, including age, prostate-specific antigen, digital rectal exam, African ancestry, first-degree family history, and prior negative biopsy. We compared the PBCG model to the PCPTRC using internal cross-validation and external validation on the European cohorts. RESULTS AND LIMITATIONS: Cross-validation on the North American cohorts (5992 biopsies) yielded the PBCG model area under the receiver operating characteristic curve (AUC) as 75.5% (95% confidence interval: 74.2–76.8), a small improvement over the AUC of 72.3% (70.9–73.7) for the PCPTRC (p < 0.0001). However, calibration and clinical net benefit were far superior for the PBCG model. Using a risk threshold of 10%, clinical use of the PBCG model would lead to the equivalent of 25 fewer biopsies per 1000 patients without missing any high-grade cancers. Results were similar on external validation on 10 377 European biopsies. CONCLUSIONS: The PBCG model should be used in place of the PCPTRC for prediction of prostate biopsy outcome. PATIENT SUMMARY: A contemporary risk tool for outcomes on prostate biopsy based on the routine clinical risk factors is now available for informed decision-making.