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Hyperpolarised magnetic resonance imaging (HP

The Gleason grading system that was initiated by a surgeon, created by a pathologist and developed by a statistician predated serum PSA testing, systematic 18-gauge needle biopsy protocols and immunohistochemistry. It has undergone a series of modifications, initially by Veterans Administration Cooperative Urological Research Group and later by the International Society of Urological Pathologists following consensus meetings in 2005 and 2014. This review focusses on selected areas of practical difficulty such as borderline grades, cores with different Gleason scores, reporting of percentage pattern 4 and minor high-grade patterns, intraductal carcinoma and the new grading system for prostate cancer. We offer a pragmatic guide to grading prostate carcinoma and explain how precision of grading becomes less important if the pathologist uses judgment to determine the Gleason score most suitable for that patient, communicates this data effectively in the report and helps clinicians interpret the information correctly.

Background There has been recent interest in nonproton MRI including hyperpolarized carbon-13 (13 C) imaging. Prostate cancer has been shown to have a higher tissue sodium concentration (TSC) than normal tissue. Sodium (23 Na) and 13 C nuclei have a frequency difference of only 1.66 MHz at 3T, potentially enabling 23 Na imaging with a 13 C-tuned coil and maximizing the metabolic information obtained from a single study. Purpose To compare TSC measurements from a 13 C-tuned endorectal coil to those quantified with a dedicated 23 Na-tuned coil. Study type Prospective. Population Eight patients with biopsy-proven, intermediate/high risk prostate cancer imaged prior to prostatectomy. Sequence 3T MRI with separate dual-tuned 1 H/23 Na and 1 H/13 C endorectal receive coils to quantify TSC. Assessment Regions-of-interest for TSC quantification were defined for normal peripheral zone (PZ), normal transition zone (TZ), and tumor, with reference to histopathology maps. Statistical tests Two-sided Wilcoxon rank sum with additional measures of correlation, coefficient of variation, and Bland-Altman plots to assess for between-test differences. Results Mean TSC for normal PZ and TZ were 39.2 and 33.9 mM, respectively, with the 23 Na coil and 40.1 and 36.3 mM, respectively, with the 13 C coil (P = 0.22 and P = 0.11 for the intercoil comparison, respectively). For tumor tissue, there was no statistical difference between the overall mean tumor TSC measured with the 23 Na coil (41.8 mM) and with the 13 C coil (46.6 mM; P = 0.38). Bland-Altman plots showed good repeatability for tumor TSC measurements between coils, with a reproducibility coefficient of 9 mM; the coefficient of variation between the coils was 12%. The Pearson correlation coefficient for TSC between coils for all measurements was r = 0.71 (r2 = 0.51), indicating a strong positive linear relationship. The mean TSC within PZ tumors was significantly higher compared with normal PZ for both the 23 Na coil (45.4 mM; P = 0.02) and the 13 C coil (49.4 mM; P = 0.002). Data conclusion We demonstrated the feasibility of using a carbon-tuned coil to quantify TSC, enabling dual metabolic information from a single coil. This approach could make the acquisition of both 23 Na-MRI and 13 C-MRI feasible in a single clinical imaging session. Level of evidence 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:90-97.

There are unexplained geographical variations in the incidence of kidney cancer with the high rates reported in Baltic countries, as well as eastern and central Europe. Having access to a large and well-annotated collection of “tumor/non-tumor” pairs of kidney cancer patients from the Czech Republic, Romania, Serbia, UK, and Russia, we aimed to analyze the morphology of non-neoplastic renal tissue in nephrectomy specimens. By applying digital pathology, we performed a microscopic examination of 1012 frozen non-neoplastic kidney tissues from patients with renal cell carcinoma. Four components of renal parenchyma were evaluated and scored for the intensity of interstitial inflammation and fibrosis, tubular atrophy, glomerulosclerosis, and arterial wall thickening, globally called chronic renal parenchymal changes. Moderate or severe changes were observed in 54 (5.3%) of patients with predominance of occurrence in Romania (OR = 2.67, CI 1.07–6.67) and Serbia (OR = 4.37, CI 1.20–15.96) in reference to those from Russia. Further adjustment for comorbidities, tumor characteristics, and stage did not change risk estimates. In multinomial regression model, relative probability of non-glomerular changes was 5.22 times higher for Romania and Serbia compared to Russia. Our findings show that the frequency of chronic renal parenchymal changes, with the predominance of chronic interstitial nephritis pattern, in kidney cancer patients varies by country, significantly more frequent in countries located in central and southeastern Europe where the incidence of kidney cancer has been reported to be moderate to high. The observed association between these pathological features and living in certain geographic areas requires a larger population-based study to confirm this association on a large scale. Supplementary Information The online version contains supplementary material available at 10.1007/s00428-020-02986-3.

Funder: University of Cambridge, Objectives: To assess the predictive value and correlation to pathological progression of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) scoring system in the follow-up of prostate cancer (PCa) patients on active surveillance (AS). Methods: A total of 295 men enrolled on an AS programme between 2011 and 2018 were included. Baseline multiparametric magnetic resonance imaging (mpMRI) was performed at AS entry to guide biopsy. The follow-up mpMRI studies were prospectively reported by two sub-specialist uroradiologists with 10 years and 13 years of experience. PRECISE scores were dichotomized at the cut-off value of 4, and the sensitivity, specificity, positive predictive value and negative predictive value were calculated. Diagnostic performance was further quantified by using area under the receiver operating curve (AUC) which was based on the results of targeted MRI-US fusion biopsy. Univariate analysis using Cox regression was performed to assess which baseline clinical and mpMRI parameters were related to disease progression on AS. Results: Progression rate of the cohort was 13.9% (41/295) over a median follow-up of 52 months. With a cut-off value of category ≥ 4, the PRECISE scoring system showed sensitivity, specificity, PPV and NPV for predicting progression on AS of 0.76, 0.89, 0.52 and 0.96, respectively. The AUC was 0.82 (95% CI = 0.74–0.90). Prostate-specific antigen density (PSA-D), Likert lesion score and index lesion size were the only significant baseline predictors of progression (each p < 0.05). Conclusion: The PRECISE scoring system showed good overall performance, and the high NPV may help limit the number of follow-up biopsies required in patients on AS. Key Points: • PRECISE scores 1–3 have high NPV which could reduce the need for re-biopsy during active surveillance. • PRECISE scores 4–5 have moderate PPV and should trigger either close monitoring or re-biopsy. • Three baseline predictors (PSA density, lesion size and Likert score) have a significant impact on the progression-free survival (PFS) time.

Inherited renal cell carcinoma (RCC) is associated with multiple familial cancer syndromes but most individuals with features of non-syndromic inherited RCC do not harbor variants in the most commonly tested renal cancer predisposition genes (CPGs). We investigated whether undiagnosed cases might harbor mutations in CPGs that are not routinely tested for by testing 118 individuals with features suggestive of inherited RCC (family history of RCC, two or more primary RCC aged

Summary The prostate gland produces prostatic fluid, high in zinc and citrate and essential for the maintenance of spermatozoa. Prostate cancer is a common condition with limited treatment efficacy in castration-resistant metastatic disease, including with immune checkpoint inhibitors. Using single-cell RNA-sequencing to perform an unbiased assessment of the cellular landscape of human prostate, we identify a subset of tumor-enriched androgen receptor-negative luminal epithelial cells with increased expression of cancer-associated genes. We also find a variety of innate and adaptive immune cells in normal prostate that were transcriptionally perturbed in prostate cancer. An exception is a prostate-specific, zinc transporter-expressing macrophage population (MAC-MT) that contributes to tissue zinc accumulation in homeostasis but shows enhanced inflammatory gene expression in tumors, including T cell-recruiting chemokines. Remarkably, enrichment of the MAC-MT signature in cancer biopsies is associated with improved disease-free survival, suggesting beneficial antitumor functions., Graphical abstract, Highlights • An immune cell atlas of healthy human prostate and prostate cancer • Low androgen receptor-expressing luminal epithelial cell present in prostate cancer • Metallothionein-expressing macrophage subset (MAC-MT) regulates prostate zinc • MAC-MT gene signature in prostate cancer associated with better outcomes, Tuong et al. generated a single-cell transcriptomic map of the human prostate immune landscape in health and show how this is perturbed in cancer. They identify a prostate-specific macrophage population that helps maintain tissue zinc and is associated with better outcomes in cancer.

Renal cell carcinomas (RCC) are characterized by their heterogenous clinical outcomes, and due to their indeterminate behavior and the absence of routine biomarkers, it is difficult to identify patients who are at high-risk for relapse after curative nephrectomy. To identify genomic biomarkers for clear cell RCC (ccRCC) risk-stratification we interrogated somatic mutation status of 12 RCC-relevant genes using next-generation sequencing (NGS) in tumor-normal pairs from 943 patients with matched follow up data from the Cancer Genomics of the Kidney (CAGEKID) study. We examined associations between genomically-defined patient groups, explained below, and disease-free as well as RCC-specific survival independently in two cohorts of patients (N=469 for cohort 1; 474 for cohort 2). We used the Kaplan-Meier method with log-rank tests to compare survival functions, and Cox proportional hazards models to stratify for patient stage and age to estimate association of each group with survival. RCC-specific survival was assessed with a competing-risks method to include deaths from other causes. Within these cohorts, 76.4% of patients harbored somatic mutations in VHL, the most common driver gene in ccRCC. The most commonly mutated genes within VHL-mutated tumors were PBRM1 (39.7%), SETD2 (19%), BAP1 (14.3%), and KDM5C (8.3%). Less frequently mutated genes included ATM, COL11A1, DMD, TP53, and TRRAP (~3-5%).Among VHL-driven tumors, we identified a new genomic classifier on the basis of the number of mutations in additional RCC driver genes in the panel examined. Patients were classified based on the presence of mutations only in VHL (VHL+0), those with mutations in VHL and one other driver gene (VHL+1), two other driver genes (VHL+2), and 3 or more other driver genes (VHL≥3). We observed within both cohorts that both the risk of disease recurrence as well as RCC-specific death were associated with an increased number of mutations within this classification. When stratified for patient stage and age, the hazard-ratio for 5-year disease-free survival for VHL≥3 patients was 6.69 (p=0.000212), 4.31 for VHL+2 (p=0.000862), and 2.43 for VHL+1 (p=0.035662), compared to patients with only mutations in VHL. These observations were replicated in the second patient cohort, with hazards ratios of 4.55, 2.49, and 1.40, for VHL≥3, VHL+2, and VHL+1 classified patients respectively, indicating that risk of disease recurrence increases with the number of driver mutations. Notably, tumor mutational burden (TMB) was not significantly different between the aforementioned groups, demonstrating that our classifier is independent of TMB. We created a model based on a set of 12 RCC-relevant genes, which can predict risk of relapse for the ~80% of patients with ccRCC that are VHL-driven. This classification can be defined based on a small panel of genes, making it easily applicable to the clinic, in the context of tumor or liquid biopsy analysis. Citation Format: Kate I. Glennon, Naveen S. Vasudev, Ghislaine Scelo, Michelle Wilson, Louis Letourneau, Robert Eveleigh, Nazanin Nourbehesht, Madeleine Arseneault, Antoine Paccard, Lars Egevad, Juris Viksna, Edgars Celms, Sharon M. Jackson, Behnoush Abedi-Ardekani, Anne Y. Warren, Peter J. Selby, Sebastian Trainor, Michael Kimuli, Naeem Soomro, Adebanji Adeyoju, Poulam Patel, Magdalena B. Wozniak, Ivana Holcatova, Antonin Brisuda, Vladimir Janout, Estelle Chanudet, David Zaridze, Anush Moukeria, Oxana Shangina, Lenka Foretova, Marie Navratilova, Dana Mates, Viorel Jinga, Ljiljana Bogdanovic, Bozidar Kovacevic, Anne Cambon-Thomsen, Guillaume Bourque, Alvis Brazma, Jörg Tost, Paul Brennan, Mark Lathrop, Yasser Riazalhosseini, Rosamonde E. Banks. Genomic classification to refine prognosis in clear cell renal cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr LB113.

PURPOSE: To retrospectively determine the prevalence and diagnostic performance of the capsular enhancement sign (CES) on multiparametric (mp) MRI for the detection of prostate cancer (PCa) extracapsular extension (ECE). METHODS: This retrospective study included patients who underwent mpMRI prior to radical prostatectomy. CES was defined as an area of asymmetrical early hyperenhancement on DCE-MRI adjacent to a peripheral zone tumour, matched or exceeded the tumour circumferential diameter, and with persistent enhancement. Two uro-radiologists evaluated the presence of CES on mpMRI, independently and in consensus, with interobserver agreement calculated using bias and prevalence-adjusted kappa (PABAK). CES performance for predicting ECE was assessed using sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: The study included 146 patients, with 91/146 (62%) having ECE on surgical pathology. Following initial review, Reader 1 identified 12/146 (8%) CES-positive cases, while Reader 2 reported 14/146 (10%) CES-positive cases, with 15/146 (10%) lesions determined as demonstrating the CES sign on consensus reading. PABAK for CES between the two readers was high at 0.90. All consensus determined CES-positive lesions represented pathological stage ≥ T3a disease, with the overall prevalence of CES among tumours with confirmed ECE being 15/91 (17%). The sign showed high specificity (100%) and PPV (100%) for ECE detection, but with low sensitivity, NPV, and accuracy at 16.5%, 41.3%, and 47.4%, respectively. CONCLUSIONS: CES was demonstrated to be a rare but highly specific ECE predictor on mpMRI that may improve local staging in the patients in whom it is demonstrated.

During the course of a lifetime normal human cells accumulate mutations. Here, using multiple samples from the same individuals we compared the mutational landscape in 29 anatomical structures from soma and the germline. Two ubiquitous mutational signatures, SBS1 and SBS5/40, accounted for the majority of acquired mutations in most cell types but their absolute and relative contributions varied substantially. SBS18, potentially reflecting oxidative damage, and several additional signatures attributed to exogenous and endogenous exposures contributed mutations to subsets of cell types. The mutation rate was lowest in spermatogonia, the stem cell from which sperm are generated and from which most genetic variation in the human population is thought to originate. This was due to low rates of ubiquitous mutation processes and may be partially attributable to a low cell division rate of basal spermatogonia. The results provide important insights into how mutational processes affect the soma and germline.

Over the course of an individual’s lifetime, normal human cells accumulate mutations1. Here we compare the mutational landscape in 29 cell types from the soma and germline using multiple samples from the same individuals. Two ubiquitous mutational signatures, SBS1 and SBS5/40, accounted for the majority of acquired mutations in most cell types, but their absolute and relative contributions varied substantially. SBS18, which potentially reflects oxidative damage2, and several additional signatures attributed to exogenous and endogenous exposures contributed mutations to subsets of cell types. The rate of mutation was lowest in spermatogonia, the stem cells from which sperm are generated and from which most genetic variation in the human population is thought to originate. This was due to low rates of ubiquitous mutational processes and may be partially attributable to a low rate of cell division in basal spermatogonia. These results highlight similarities and differences in the maintenance of the germline and soma. The authors report the mutational landscape of 29 cell types from microdissected biopsies from 19 organs and explore the mechanisms underlying mutation rates in normal tissues.

Tumor cells may share some patterns of gene expression with their cell of origin, providing clues into the differentiation state and origin of cancer. Here, we study the differentiation state and cellular origin of 1300 childhood and adult kidney tumors. Using single cell mRNA reference maps of normal tissues, we quantify reference “cellular signals” in each tumor. Quantifying global differentiation, we find that childhood tumors exhibit fetal cellular signals, replacing the presumption of “fetalness” with a quantitative measure of immaturity. By contrast, in adult cancers our assessment refutes the suggestion of dedifferentiation towards a fetal state in most cases. We find an intimate connection between developmental mesenchymal populations and childhood renal tumors. We demonstrate the diagnostic potential of our approach with a case study of a cryptic renal tumor. Our findings provide a cellular definition of human renal tumors through an approach that is broadly applicable to human cancer., Transcriptomic analysis may provide information about the differentiation state and cell of origin of a cancer. Here, the authors assess mRNA signals in 1300 childhood and adult renal tumors and report a fetal origin of childhood tumors and no dedifferentiation of adult tumors.

OBJECTIVE. The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cancer. MATERIALS AND METHODS. MRI examinations from five institutions were included in this study and were evaluated by nine readers. In the first round, readers evaluated mpMRI studies using the Prostate Imaging Reporting and Data System version 2. After 4 weeks, images were again presented to readers along with the AI-based detection system output. Readers accepted or rejected lesions within four AI-generated attention map boxes. Additional lesions outside of boxes were excluded from detection and categorization. The performances of readers using the mpMRI-only and AI-assisted approaches were compared. RESULTS. The study population included 152 case patients and 84 control patients with 274 pathologically proven cancer lesions. The lesion-based AUC was 74.9% for MRI and 77.5% for AI with no significant difference (p = 0.095). The sensitivity for overall detection of cancer lesions was higher for AI than for mpMRI but did not reach statistical significance (57.4% vs 53.6%, p = 0.073). However, for transition zone lesions, sensitivity was higher for AI than for MRI (61.8% vs 50.8%, p = 0.001). Reading time was longer for AI than for MRI (4.66 vs 4.03 minutes, p < 0.001). There was moderate interreader agreement for AI and MRI with no significant difference (58.7% vs 58.5%, p = 0.966). CONCLUSION. Overall sensitivity was only minimally improved by use of the AI system. Significant improvement was achieved, however, in the detection of transition zone lesions with use of the AI system at the cost of a mean of 40 seconds of additional reading time.

PURPOSE Spatial heterogeneity of tumors is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood because it relies on the accurate coregistration of medical images and tissue biopsies. Tumor molds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumor heterogeneity data. METHODS We have developed an open-source computational framework to automatically produce patient-specific 3-dimensional–printed molds that can be used in the clinical setting. Our approach achieves accurate coregistration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging, and pathology workflows. RESULTS We applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalized molds for 6 patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.96 for tumor regions and between 0.70 and 0.76 for healthy kidneys. The framework required minimal manual intervention, producing the final mold design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes. CONCLUSION Our work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumor heterogeneity on multiple spatial scales.

PURPOSE: To assess the added value of dynamic contrast-enhanced (DCE) in prostate MR in clinical practice. METHODS: Two hundred sixty-four patients underwent prostate MRI, with T2 and DWI sequences initially interpreted, prior to full multiparametric magnetic resonance imaging (mpMRI) interpretation using a Likert 1-5 scale. A prospective opinion was given on likely benefit of contrast prior to review of the DCE sequence, and retrospectively following full mpMRI review. The final histology result following targeted and/or systematic biopsy of the prostate was used for outcome purposes. RESULTS: Biparametric magnetic resonance imaging (bpMRI) and mpMRI were assigned the same score in 86% of cases; when dichotomising to a negative or positive MRI (Likert score ≥ 3), concordance increased to 92.8%. At Likert score ≥ 3 bpMRI detected 89.9% of all cancers and 93.5% clinically significant prostate cancers (csPCa) and mpMRI 90.7% and 94.6%, respectively. mpMRI had fewer false positives than bpMRI (11.4% vs 18.9%) and a lower Likert 3 rate (8.3% vs 17%), conferring higher specificity (74% vs 67%), but similar sensitivity (95% versus 94%) and ROC-AUC (90% vs 89%). At a positive MRI threshold of Likert ≥ 4, mpMRI had a higher sensitivity than bpMRI (89% versus 80%) and detected more csPCa (89.2% versus 79.6%). DCE was prospectively considered of potential benefit in 27.3%, but readers would only recall 11% of patients for DCE sequences, mainly to assess score 3 peripheral zone lesions. Following full mpMRI review, DCE was considered helpful in 28.4% of cases; in 23/75 (30.6%) of these cases this only became apparent after reviewing the sequence, reasons included increased confidence, presence of "safety-net" lesions or inflammatory lesions. CONCLUSION: BpMRI has equivalent cancer detection rates to mpMRI; however, mpMRI had fewer Likert 3 call rates and increased specificity and was subjectively considered of benefit by readers in 28.4% of cases. KEY POINTS: • bpMRI has similar cancer detection rates to the full mpMRI protocol at a positive MRI threshold of Likert 3. • mpMRI had fewer intermediate category 3 calls (8.3%) than bpMRI (17%) and fewer false positives than bpMRI (11.4% vs 18.9%), conferring higher specificity (74% vs 67%). • Readers considered DCE beneficial in 28.4% of cases, but in a relatively high number (30.6%) this only became apparent after reviewing the sequence.

Background There are unexplained geographical variations in the incidence of kidney cancer with the high rates reported in Baltic countries, as well as eastern and central Europe. Analysis of non-neoplastic tissues is a way to better understand the carcinogenesis. Methods Having access to a rich, well-annotated collection of “tumor/non-tumor” pairs of kidney cancer patients from Czech Republic, Romania, Serbia, United Kingdom, and Russia for studying genomics of kidney cancer, we aimed to analyze morphology of non-neoplastic renal tissue. By applying digital pathology, we performed microscopic examination of 1012 frozen non-neoplastic kidney tissues from patients with renal cell carcinoma. Renal parenchyma was evaluated and scored for the interstitial inflammation and fibrosis, tubular atrophy, glomerulosclerosis and arterial wall thickening, globally called chronic renal parenchymal changes. Results Moderate or severe changes was observed in 54 (5.3%) of patients with predominance of occurrence in Romania (OR = 2.67, CI 1.07–6.67) and Serbia (OR = 4.37, CI 1.20-15.96) in reference to those from Russia. Further adjustment for comorbidities, tumor characterstics and stage did not change risk estimates. In multinomial regression model, relative probability of non-glomerular changes were 5.22 times higher for Romania and Serbia compered to Russia. Conclusion Our findings show that the frequency of chronic renal parenchymal changes in kidney cancer patients varies by country, significantly more frequent in countries located in central and southeastern Europe where the incidence of kidney cancer has been reported to be high. We suggest that these parenchymal changes, possibly linked to environmental exposures, may be relevant to renal carcinogenesis in these countries.

Clear cell renal cell carcinoma (ccRCC) contains cancer stem‐like cells (CSCs) that express CD133 (ccRCC‐CD133+). CSCs are rarely in cell cycle and, as nonproliferating cells, resist most chemotherapeutic agents. Previously, we reported that tumor necrosis factor receptor‐2 (TNFR2) signaling promotes the cell cycle entry of ccRCC‐CD133+CSCs, rendering them susceptible to cell‐cycle‐dependent chemotherapeutics. Here, we describe a TNFR2‐activated signaling pathway in ccRCC‐CD133+CSCs that is required for cell survival. Wild‐type (wt)TNF or R2TNF but not R1TNF (TNF muteins that selectively bind to TNFR2 and TNFR1) induces phosphorylation of signal transducer and activator of transcription 3 (STAT3) on serine727 but not tyrosine705, resulting in pSTAT3Ser727 translocation to and colocalization with TNFR2 in mitochondria. R2TNF signaling activates a kinase cascade involving the phosphorylation of VEGFR2, PI‐3K, Akt, and mTORC. Inhibition of any of the kinases or siRNA knockdown of TNFR2 or STAT3 promotes cell death associated with mitochondrial morphological changes, cytochrome c release, generation of reactive oxygen species, and TUNEL+cells expressing phosphorylated mixed lineage kinase‐like (MLKL). Pretreatment with necrostatin‐1 is more protective than z‐VAD.fmk, suggesting that most death is necroptotic and TNFR2 signaling promotes cell survival by preventing mitochondrial‐mediated necroptosis. These data suggest that a TNFR2 selective agonist may offer a potential therapeutic strategy for ccRCC.

Objectives: To report the prospective multicentre clinical evaluation of a first-in-man disposable device, Cambridge Prostate Biopsy Device, to undertake local anaesthetic outpatient transperineal prostate biopsies. Material and methods: Disposable single-use Cambridge Prostate Biopsy devices were manufactured based on a previous prototype. The lead site developed a user training course and disseminated the method to other sites. The Cambridge Prostate Biopsy Device (CamPROBE) was offered as an alternative to transrectal ultrasound guided biopsy to men due for a biopsy as part of their clinical management. Data on safety (infections and device performance), clinical utility, patient reported experience, biopsy quality and cancer detection were collected. Procedure time and local anaesthetic use was recorded in the lead site. The study was funded by a United Kingdom National Institute for Health Research (NIHR) i4i product development award. Results: A total of 40 patients were recruited (median age 69 y) across six sites; five sites were new to the procedure. Overall, 19/40 were first prostate biopsies and 21/40 repeat procedures. Both image-targeted and systematic biopsy cores taken. There were no infections, device deficiencies or safety issues reported. The procedure was well tolerated with excellent patient-reported perception and low pain scores (median of 3, scale 0–10). Histopathology quality was good and the overall cancer diagnosis rate (first diagnostic procedures) was 68% (13/19) and for significant cancers (⩾ histological Grade Group 2), 47% (9/19). In the lead centre (most experienced), median procedure time was 25 minutes, and median local anaesthetic use 11 ml ( n=17). Conclusions: Data from this device evaluation study demonstrate that the United Kingdom-developed Cambridge Prostate Biopsy Device/method for transperineal biopsies is safe, transferable and maintains high diagnostic yields. The procedure is well tolerated by patients, suited to the local anaesthetic outpatient setting and could directly replace transrectal ultrasound guided biopsy. Level of evidence: Level III

MC3 Working Group: Rehan Akbani21, Pavana Anur22, Matthew H. Bailey1,2,3, Alex Buchanan9, Kami Chiotti9, Kyle Covington12,23, Allison Creason9, Li Ding1,2,3,20, Kyle Ellrott9, Yu Fan21, Steven Foltz1,2, Gad Getz8,14,15,16, Walker Hale12, David Haussler24,25, Julian M. Hess8,26, Carolyn M. Hutter27, Cyriac Kandoth28, Katayoon Kasaian29,30, Melpomeni Kasapi27, Dave Larson1 , Ignaty Leshchiner8, John Letaw31, Singer Ma32, Michael D. McLellan1,3,20, Yifei Men32, Gordon B. Mills33,34, Beifang Niu35, Myron Peto22, Amie Radenbaugh24, Sheila M. Reynolds36, Gordon Saksena8, Heidi Sofia27, Chip Stewart8, Adam J. Struck31, Joshua M. Stuart24,37, Wenyi Wang21, John N. Weinstein38, David A. Wheeler12,13, Christopher K. Wong24,39, Liu Xi12 & Kai Ye40,41 21Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 22Molecular and Medical Genetics, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 23Castle Biosciences Inc, Friendswood, TX 77546, USA. 24UC Santa Cruz Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 25Howard Hughes Medical Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 26Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02114, USA. 27National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20894, USA. 28Marie-Josée and Henry R. Kravis Center for Molecular Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA. 29Ontario Institute for Cancer Research, Toronto, ON M5G 0A3, Canada. 30Canada’s Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada. 31Computational Biology Program, School of Medicine, Oregon Health and Science University, Portland, OR 97239, USA. 32DNAnexus Inc, Mountain View, CA 94040, USA. 33Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA. 34Precision Oncology, OHSU Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA. 35Computer Network Information Center, Chinese Academy of Sciences, Beijing, China. 36Institute for Systems Biology, Seattle, WA 98109, USA. 37Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 38Department of Bioinformatics and Computational Biology and Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. 39Biomolecular Engineering Department, University of California Santa Cruz, Santa Cruz, CA 95064, USA. 40School of Elect, PCAWG novel somatic mutation calling methods working group: Matthew H. Bailey1,2,3, Beifang Niu35, Matthias Bieg42,43, Paul C. Boutros6,44,45,46, Ivo Buchhalter43,47,48, Adam P. Butler49, Ken Chen50, Zechen Chong51, Li Ding1,2,3,20, Oliver Drechsel52,53, Lewis Jonathan Dursi6,7, Roland Eils47,48,54,55, Kyle Ellrott9, Shadrielle M. G. Espiritu6, Yu Fan21, Robert S. Fulton1,3,20, Shengjie Gao56, Josep L. l. Gelpi57,58, Mark B. Gerstein5,18,19, Gad Getz8,14,15,16, Santiago Gonzalez59,60, Ivo G. Gut52,61, Faraz Hach62,63, Michael C. Heinold47,48, Julian M. Hess8,26, Jonathan Hinton49, Taobo Hu64, Vincent Huang6, Yi Huang65,66, Barbara Hutter43,67,68, David R. Jones49, Jongsun Jung69, Natalie Jäger47, Hyung-Lae Kim70, Kortine Kleinheinz47,48, Sushant Kumar5,19, Yogesh Kumar64, Christopher M. Lalansingh6, Ignaty Leshchiner8, Ivica Letunic71, Dimitri Livitz8, Eric Z. Ma64, Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Michael D. McLellan1,3,20, Andrew Menzies49, Ana Milovanovic57, Morten Muhlig Nielsen73, Stephan Ossowski52,53,74, Nagarajan Paramasivam43,47, Jakob Skou Pedersen73,75, Marc D. Perry76,77, Montserrat Puiggròs57, Keiran M. Raine49, Esther Rheinbay8,14,72, Romina Royo57, S. Cenk Sahinalp62,78,79, Gordon Saksena8, Iman Sarrafi62,78, Matthias Schlesner47,80, Jared T. Simpson6,17, Lucy Stebbings49, Chip Stewart8, Miranda D. Stobbe52,61, Jon W. Teague49, Grace Tiao8, David Torrents57,81, Jeremiah A. Wala8,14,82, Jiayin Wang1,40,66, Wenyi Wang21, Sebastian M. Waszak60, Joachim Weischenfeldt60,83,84, Michael C. Wendl1,10,11, Johannes Werner47,85, Zhenggang Wu64, Hong Xue64, Sergei Yakneen60, Takafumi N. Yamaguchi6, Kai Ye40,41, Venkata D. Yellapantula20,86, Christina K. Yung76 & Junjun Zhang76, PCAWG Consortium: Lauri A. Aaltonen87, Federico Abascal49, Adam Abeshouse88, Hiroyuki Aburatani89, David J. Adams49, Nishant Agrawal90, Keun Soo Ahn91, Sung-Min Ahn92, Hiroshi Aikata93, Rehan Akbani21, Kadir C. Akdemir50, Hikmat Al-Ahmadie88, Sultan T. Al-Sedairy94, Fatima Al-Shahrour95, Malik Alawi96,97, Monique Albert98, Kenneth Aldape99,100, Ludmil B. Alexandrov49,101,102, Adrian Ally30, Kathryn Alsop103, Eva G. Alvarez104,105,106, Fernanda Amary107, Samirkumar B. Amin108,109,110, Brice Aminou76, Ole Ammerpohl111,112, Matthew J. Anderson113, Yeng Ang114, Davide Antonello115, Pavana Anur22, Samuel Aparicio116, Elizabeth L. Appelbaum1,117, Yasuhito Arai118, Axel Aretz119, Koji Arihiro93, Shun-ichi Ariizumi120, Joshua Armenia121, Laurent Arnould122, Sylvia Asa123,124, Yassen Assenov125, Gurnit Atwal6,126,127, Sietse Aukema112,128, J. Todd Auman129, Miriam R. Aure130, Philip Awadalla6,126, Marta Aymerich131, Gary D. Bader126, Adrian Baez-Ortega132, Matthew H. Bailey1,2,3, Peter J. Bailey133, Miruna Balasundaram30, Saianand Balu134, Pratiti Bandopadhayay8,135,136, Rosamonde E. Banks137, Stefano Barbi138, Andrew P. Barbour139,140, Jonathan Barenboim6, Jill Barnholtz-Sloan141,142, Hugh Barr143, Elisabet Barrera59, John Bartlett98,144, Javier Bartolome57, Claudio Bassi115, Oliver F. Bathe145,146, Daniel Baumhoer147, Prashant Bavi148, Stephen B. Baylin149,150, Wojciech Bazant59, Duncan Beardsmore151, Timothy A. Beck152,153, Sam Behjati49, Andreas Behren154, Beifang Niu35, Cindy Bell155, Sergi Beltran52,61, Christopher Benz156, Andrew Berchuck157, Anke K. Bergmann158, Erik N. Bergstrom101,102, Benjamin P. Berman159,160,161, Daniel M. Berney162, Stephan H. Bernhart163,164,165, Rameen Beroukhim8,14,82, Mario Berrios166, Samantha Bersani167, Johanna Bertl73,168, Miguel Betancourt169, Vinayak Bhandari6,44, Shriram G. Bhosle49, Andrew V. Biankin133,170,171,172, Matthias Bieg42,43, Darell Bigner173, Hans Binder163,164, Ewan Birney59, Michael Birrer72, Nidhan K. Biswas174, Bodil Bjerkehagen147,175, Tom Bodenheimer134, Lori Boice176, Giada Bonizzato177, Johann S. De Bono178, Arnoud Boot179,180, Moiz S. Bootwalla166, Ake Borg181, Arndt Borkhardt182, Keith A. Boroevich183,184, Ivan Borozan6, Christoph Borst185, Marcus Bosenberg186, Mattia Bosio52,53,57, Jacqueline Boultwood187, Guillaume Bourque188,189, Paul C. Boutros6,44,45,46, G. Steven Bova190, David T. Bowen49,191, Reanne Bowlby30, David D. L. Bowtell103, Sandrine Boyault192, Rich Boyce59, Jeffrey Boyd193, Alvis Brazma59, Paul Brennan194, Daniel S. Brewer195,196, Arie B. Brinkman197, Robert G. Bristow44,198,199,200,201, Russell R. Broaddus99, Jane E. Brock202, Malcolm Brock203, Annegien Broeks204, Angela N. Brooks8,24,37,82, Denise Brooks30, Benedikt Brors67,205,206, Søren Brunak207,208, Timothy J. C. Bruxner113,209, Alicia L. Bruzos104,105,106, Alex Buchanan9, Ivo Buchhalter43,47,48, Christiane Buchholz210, Susan Bullman8,82, Hazel Burke211, Birgit Burkhardt212, Kathleen H. Burns213,214, John Busanovich8,215, Carlos D. Bustamante216,217, Adam P. Butler49, Atul J. Butte218, Niall J. Byrne76, Anne-Lise Børresen-Dale130,219, Samantha J. Caesar-Johnson220, Andy Cafferkey59, Declan Cahill221, Claudia Calabrese59,60, Carlos Caldas222,223, Fabien Calvo224, Niedzica Camacho178, Peter J. Campbell49,225, Elias Campo226,227, Cinzia Cantù177, Shaolong Cao21, Thomas E. Carey228, Joana Carlevaro-Fita229,230,231, Rebecca Carlsen30, Ivana Cataldo167,177, Mario Cazzola232, Jonathan Cebon154, Robert Cerfolio233, Dianne E. Chadwick234, Dimple Chakravarty235, Don Chalmers236, Calvin Wing Yiu Chan47,237, Kin Chan238, Michelle Chan-Seng-Yue148, Vishal S. Chandan239, David K. Chang133,170, Stephen J. Chanock240, Lorraine A. Chantrill170,241, Aurélien Chateigner76,242, Nilanjan Chatterjee149,243, Kazuaki Chayama93, Hsiao-Wei Chen114,121, Jieming Chen218, Ken Chen50, Yiwen Chen21, Zhaohong Chen244, Andrew D. Cherniack8,82, Jeremy Chien245, Yoke-Eng Chiew246,247, Suet-Feung Chin222,223, Juok Cho8, Sunghoon Cho248, Jung Kyoon Choi249, Wan Choi250, Christine Chomienne251, Zechen Chong51, Su Pin Choo252, Angela Chou170,246, Angelika N. Christ113, Elizabeth L. Christie103, Eric Chuah30, Carrie Cibulskis8, Kristian Cibulskis8, Sara Cingarlini253, Peter Clapham49, Alexander Claviez254, Sean Cleary148,255, Nicole Cloonan256, Marek Cmero257,258,259, Colin C. Collins62, Ashton A. Connor255,260, Susanna L. Cooke133, Colin S. Cooper178,196,261, Leslie Cope149, Vincenzo Corbo138,177, Matthew G. Cordes1,262, Stephen M. Cordner263, Isidro Cortés-Ciriano264,265,266, Kyle Covington12,23, Prue A. Cowin267, Brian Craft24, David Craft8,268, Chad J. Creighton269, Yupeng Cun270, Erin Curley271, Ioana Cutcutache179,180, Karolina Czajka272, Bogdan Czerniak99,273, Rebecca A. Dagg274, Ludmila Danilova149, Maria Vittoria Davi275, Natalie R. Davidson276,277,278,279,280, Helen Davies49,281,282, Ian J. Davis283, Brandi N. Davis-Dusenbery284, Kevin J. Dawson49, Francisco M. De La Vega216,217,285, Ricardo De Paoli-Iseppi211, Timothy Defreitas8, Angelo P. Dei Tos286, Olivier Delaneau287,288,289, John A. Demchok220, Jonas Demeulemeester290,291, German M. Demidov52,53,74, Deniz Demircioğlu292,293, Nening M. Dennis221, Robert E. Denroche148, Stefan C. Dentro49,290,294, Nikita Desai76, Vikram Deshpande72, Amit G. Deshwar295, Christine Desmedt296,297, Jordi Deu-Pons298,299, Noreen Dhalla30, Neesha C. Dhani300, Priyanka Dhingra301,302, Rajiv Dhir303, Anthony DiBiase304, Klev Diamanti305, Li Ding1,2,3,20, Shuai Ding306, Huy Q. Dinh159, Luc Dirix307, HarshaVardhan Doddapaneni12, Nilgun Donmez62,78, Michelle T. Dow244, Ronny Drapkin308, Oliver Drechsel52,53, Ruben M. Drews223, Serge Serge49, Tim Dudderidge150,221, Ana Dueso-Barroso57, Andrew J. Dunford8, Michael Dunn309, Lewis Jonathan Dursi6,7, Fraser R. Duthie133,310, Ken Dutton-Regester311, Jenna Eagles272, Douglas F. Easton312,313, Stuart Edmonds314, Paul A. Edwards223,315, Sandra E. Edwards178, Rosalind A. Eeles178,221, Anna Ehinger316, Juergen Eils54,55, Roland Eils47,48,54,55, Adel El-Naggar99,273, Matthew Eldridge223, Kyle Ellrott9, Serap Erkek60, Georgia Escaramis53,317,318, Shadrielle M. G. Espiritu6, Xavier Estivill53,319, Dariush Etemadmoghadam103, Jorunn E. Eyfjord320, Bishoy M. Faltas280, Daiming Fan321, Yu Fan21, William C. Faquin72, Claudiu Farcas244, Matteo Fassan322, Aquila Fatima323, Francesco Favero324, Nodirjon Fayzullaev76, Ina Felau220, Sian Fereday103, Martin L. Ferguson325, Vincent Ferretti76,326, Lars Feuerbach205, Matthew A. Field327, J. Lynn Fink57,113, Gaetano Finocchiaro328, Cyril Fisher221, Matthew W. Fittall290, Anna Fitzgerald329, Rebecca C. Fitzgerald282, Adrienne M. Flanagan330, Neil E. Fleshner331, Paul Flicek59, John A. Foekens332, Kwun M. Fong333, Nuno A. Fonseca59,334, Christopher S. Foster335,336, Natalie S. Fox6, Michael Fraser6, Scott Frazer8, Milana Frenkel-Morgenstern337, William Friedman338, Joan Frigola298, Catrina C. Fronick1,262, Akihiro Fujimoto184, Masashi Fujita184, Masashi Fukayama339, Lucinda A. Fulton1 , Robert S. Fulton1,3,20, Mayuko Furuta184, P. Andrew Futreal340, Anja Füllgrabe59, Stacey B. Gabriel8, Steven Gallinger148,255,260, Carlo Gambacorti-Passerini341, Jianjiong Gao121, Shengjie Gao56, Levi Garraway82, Øystein Garred342, Erik Garrison49, Dale W. Garsed103, Nils Gehlenborg8,343, Josep L. l. Gelpi57,58, Joshy George110, Daniela S. Gerhard344, Clarissa Gerhauser345, Jeffrey E. Gershenwald346,347, Mark B. Gerstein5,18,19, Moritz Gerstung59,60, Gad Getz8,14,15,16, Mohammed Ghori49, Ronald Ghossein348, Nasra H. Giama349, Richard A. Gibbs12, Anthony J. Gill170,350, Pelvender Gill351, Dilip D. Giri348, Dominik Glodzik49, Vincent J. Gnanapragasam352,353, Maria Elisabeth Goebler354, Mary J. Goldman24, Carmen Gomez355, Santiago Gonzalez59,60, Abel Gonzalez-Perez298,299,356, Dmitry A. Gordenin357, James Gossage358, Kunihito Gotoh359, Ramaswamy Govindan3, Dorthe Grabau360, Janet S. Graham133,361, Robert C. Grant148,260, Anthony R. Green315, Eric Green27, Liliana Greger59, Nicola Grehan282, Sonia Grimaldi177, Sean M. Grimmond362, Robert L. Grossman363, Adam Grundhoff97,364, Gunes Gundem88, Qianyun Guo75, Manaswi Gupta8, Shailja Gupta365, Ivo G. Gut52,61, Marta Gut52,61, Jonathan Göke292,366, Gavin Ha8, Andrea Haake111, David Haan37, Siegfried Haas185, Kerstin Haase290, James E. Haber367, Nina Habermann60, Faraz Hach62,63, Syed Haider6, Natsuko Hama118, Freddie C. Hamdy351, Anne Hamilton267, Mark P. Hamilton368, Leng Han369, George B. Hanna370, Martin Hansmann371, Nicholas J. Haradhvala8,72, Olivier Harismendy102,372, Ivon Harliwong113, Arif O. Harmanci5,373, Eoghan Harrington374, Takanori Hasegawa375, David Haussler24,25, Steve Hawkins223, Shinya Hayami376, Shuto Hayashi375, D. Neil Hayes134,377,378, Stephen J. Hayes379,380, Nicholas K. Hayward211,311, Steven Hazell221, Yao He381, Allison P. Heath382, Simon C. Heath52,61, David Hedley300, Apurva M. Hegde38, David I. Heiman8, Michael C. Heinold47,48, Zachary Heins88, Lawrence E. Heisler152, Eva Hellstrom-Lindberg383, Mohamed Helmy384, Seong Gu Heo385, Austin J. Hepperla134, José María Heredia-Genestar386, Carl Herrmann47,48,387, Peter Hersey211, Julian M. Hess8,26, Holmfridur Hilmarsdottir320, Jonathan Hinton49, Satoshi Hirano388, Nobuyoshi Hiraoka389, Katherine A. Hoadley134,390, Asger Hobolth75,168, Ermin Hodzic78, Jessica I. Hoell182, Steve Hoffmann163,164,165,391, Oliver Hofmann392, Andrea Holbrook166, Aliaksei Z. Holik53, Michael A. Hollingsworth393, Oliver Holmes209,311, Robert A. Holt30, Chen Hong205,237, Eun Pyo Hong385, Jongwhi H. Hong394, Gerrit K. Hooijer395, Henrik Hornshøj73, Fumie Hosoda118, Yong Hou56,396, Volker Hovestadt397, William Howat352, Alan P. Hoyle134, Ralph H. Hruban149, Jianhong Hu12, Taobo Hu64, Xing Hua240, Kuan-lin Huang1,398, Mei Huang176, Mi Ni Huang179,180, Vincent Huang6, Yi Huang65,66, Wolfgang Huber60, Thomas J. Hudson272,399, Michael Hummel400, Jillian A. Hung246,247, David Huntsman401, Ted R. Hupp402, Jason Huse88, Matthew R. Huska403, Barbara Hutter43,67,68, Carolyn M. Hutter27, Daniel Hübschmann48,54,404,405,406, Christine A. Iacobuzio-Donahue348, Charles David Imbusch205, Marcin Imielinski407,408, Seiya Imoto375, William B. Isaacs409, Keren Isaev6,44, Shumpei Ishikawa410, Murat Iskar397, S. M. Ashiqul Islam244, Michael Ittmann411,412,413, Sinisa Ivkovic284, Jose M. G. Izarzugaza414, Jocelyne Jacquemier415, Valerie Jakrot211, Nigel B. Jamieson133,172,416, Gun Ho Jang148, Se Jin Jang417, Joy C. Jayaseelan12, Reyka Jayasinghe1 , Stuart R. Jefferys134, Karine Jegalian418, Jennifer L. Jennings419, Seung-Hyup Jeon250, Lara Jerman60,420, Yuan Ji421,422, Wei Jiao6, Peter A. Johansson311, Amber L. Johns170, Jeremy Johns272, Rory Johnson230,423, Todd A. Johnson183, Clemency Jolly290, Yann Joly424, Jon G. Jonasson320, Corbin D. Jones425, David R. Jones49, David T. W. Jones426,427, Nic Jones428, Steven J. M. Jones30, Jos Jonkers204, Young Seok Ju49,249, Hartmut Juhl429, Jongsun Jung69, Malene Juul73, Randi Istrup Juul73, Sissel Juul374, Natalie Jäger47, Rolf Kabbe47, Andre Kahles276,277,278,279,430, Abdullah Kahraman431,432,433, Vera B. Kaiser434, Hojabr Kakavand211, Sangeetha Kalimuthu148, Christof von Kalle405, Koo Jeong Kang91, Katalin Karaszi351, Beth Karlan435, Rosa Karlić436, Dennis Karsch437, Katayoon Kasaian29,30, Karin S. Kassahn113,438, Hitoshi Katai439, Mamoru Kato440, Hiroto Katoh410, Yoshiiku Kawakami93, Jonathan D. Kay117, Stephen H. Kazakoff209,311, Marat D. Kazanov441,442,443, Maria Keays59, Electron Kebebew444,445, Richard F. Kefford446, Manolis Kellis8,447, James G. Kench170,350,448, Catherine J. Kennedy246,247, Jules N. A. Kerssemakers47, David Khoo273, Vincent Khoo221, Narong Khuntikeo115,449, Ekta Khurana301,302,450,451, Helena Kilpinen117, Hark Kyun Kim452, Hyung-Lae Kim70, Hyung-Yong Kim415, Hyunghwan Kim250, Jaegil Kim8, Jihoon Kim453, Jong K. Kim454, Youngwook Kim455,456, Tari A. King457,458,459, Wolfram Klapper128, Kortine Kleinheinz47,48, Leszek J. Klimczak460, Stian Knappskog49,461, Michael Kneba437, Bartha M. Knoppers424, Youngil Koh462,463, Jan Komorowski305,464, Daisuke Komura410, Mitsuhiro Komura375, Gu Kong415, Marcel Kool426,465, Jan O. Korbel59,60, Viktoriya Korchina12, Andrey Korshunov465, Michael Koscher465, Roelof Koster466, Zsofia Kote-Jarai178, Antonios Koures244, Milena Kovacevic284, Barbara Kremeyer49, Helene Kretzmer164,165, Markus Kreuz467, Savitri Krishnamurthy99,468, Dieter Kube469, Kiran Kumar8, Pardeep Kumar221, Sushant Kumar5,19, Yogesh Kumar64, Ritika Kundra114,121, Kirsten Kübler8,14,72, Ralf Küppers470, Jesper Lagergren383,471, Phillip H. Lai166, Peter W. Laird472, Sunil R. Lakhani473, Christopher M. Lalansingh6, Emilie Lalonde6, Fabien C. Lamaze6, Adam Lambert351, Eric Lander8, Pablo Landgraf474,475, Luca Landoni115, Anita Langerød130, Andrés Lanzós230,231,423, Denis Larsimont476, Erik Larsson477, Mark Lathrop189, Loretta M. S. Lau478, Chris Lawerenz55, Rita T. Lawlor177, Michael S. Lawrence8,72,183, Alexander J. Lazar99,108, Xuan Le479, Darlene Lee30, Donghoon Lee5, Eunjung Alice Lee480, Hee Jin Lee417, Jake June-Koo Lee264,266, Jeong-Yeon Lee481, Juhee Lee482, Ming Ta Michael Lee340, Henry Lee-Six49, Kjong-Van Lehmann276,277,278,279,430, Hans Lehrach483, Dido Lenze400, Conrad R. Leonard209,311, Daniel A. Leongamornlert49,178, Ignaty Leshchiner8, Louis Letourneau484, Ivica Letunic71, Douglas A. Levine88,485, Lora Lewis12, Tim Ley486, Chang Li56,396, Constance H. Li6,44, Haiyan Irene Li30, Jun Li21, Lin Li56, Shantao Li5, Siliang Li56,396, Xiaobo Li56,396, Xiaotong Li5, Xinyue Li56, Yilong Li49, Han Liang21, Sheng-Ben Liang234, Peter Lichter68,397, Pei Lin8, Ziao Lin8,487, W. M. Linehan488, Ole Christian Lingjærde489, Dongbing Liu56,396, Eric Minwei Liu88,301,302, Fei-Fei Liu201,490, Fenglin Liu381,491, Jia Liu492, Xingmin Liu56,396, Julie Livingstone6, Dimitri Livitz8, Naomi Livni221, Lucas Lochovsky5,19,110, Markus Loeffler467, Georgina V. Long211, Armando Lopez-Guillermo493, Shaoke Lou5,19, David N. Louis72, Laurence B. Lovat117, Yiling Lu38, Yong-Jie Lu162,494, Youyong Lu495,496,497, Claudio Luchini167, Ilinca Lungu144,148, Xuemei Luo152, Hayley J. Luxton117, Andy G. Lynch223,315,498, Lisa Lype36, Cristina López111,112, Carlos López-Otín499, Eric Z. Ma64, Yussanne Ma30, Gaetan MacGrogan500, Shona MacRae501, Geoff Macintyre223, Tobias Madsen73, Kazuhiro Maejima184, Andrea Mafficini177, Dennis T. Maglinte166,502, Arindam Maitra174, Partha P. Majumder174, Luca Malcovati232, Salem Malikic62,78, Giuseppe Malleo115, Graham J. Mann211,246,503, Luisa Mantovani-Löffler504, Kathleen Marchal505,506, Giovanni Marchegiani115, Elaine R. Mardis1,193,507, Adam A. Margolin31, Maximillian G. Marin37, Florian Markowetz223,315, Julia Markowski403, Jeffrey Marks508, Tomas Marques-Bonet61,81,386,509, Marco A. Marra30, Luke Marsden351, John W. M. Martens332, Sancha Martin49,510, Jose I. Martin-Subero81,511, Iñigo Martincorena49, Alexander Martinez-Fundichely301,302,451 Yosef E. Maruvka8,26,72, R. Jay Mashl1,2, Charlie E. Massie223, Thomas J. Matthew37, Lucy Matthews178, Erik Mayer221,512, Simon Mayes513, Michael Mayo30, Faridah Mbabaali272, Karen McCune514, Ultan McDermott49, Patrick D. McGillivray19, Michael D. McLellan1,3,20, John D. McPherson148,272,515, John R. McPherson179,180, Treasa A. McPherson260, Samuel R. Meier8, Alice Meng516, Shaowu Meng134, Andrew Menzies49, Neil D. Merrett115,517, Sue Merson178, Matthew Meyerson8,14,82, William U. Meyerson4,5, Piotr A. Mieczkowski518, George L. Mihaiescu76, Sanja Mijalkovic284, Ana Mijalkovic Mijalkovic-Lazic284, Tom Mikkelsen519, Michele Milella253, Linda Mileshkin103, Christopher A. Miller1 , David K. Miller113,170, Jessica K. Miller272, Gordon B. Mills33,34, Ana Milovanovic57, Sarah Minner520, Marco Miotto115, Gisela Mir Arnau267, Lisa Mirabello240, Chris Mitchell103, Thomas J. Mitchell49,315,352, Satoru Miyano375, Naoki Miyoshi375, Shinichi Mizuno521, Fruzsina Molnár-Gábor522, Malcolm J. Moore300, Richard A. Moore30, Sandro Morganella49, Quaid D. Morris127,490, Carl Morrison523,524, Lisle E. Mose134, Catherine D. Moser349, Ferran Muiños298,299, Loris Mularoni298,299, Andrew J. Mungall30, Karen Mungall30, Elizabeth A. Musgrove133, Ville Mustonen525,526,527, David Mutch528, Francesc Muyas52,53,74, Donna M. Muzny12, Alfonso Muñoz59, Jerome Myers529, Ola Myklebost461, Peter Möller530, Genta Nagae89, Adnan M. Nagrial170, Hardeep K. Nahal-Bose76, Hitoshi Nakagama531, Hidewaki Nakagawa184, Hiromi Nakamura118, Toru Nakamura388, Kaoru Nakano184, Tannistha Nandi532, Jyoti Nangalia49, Mia Nastic284, Arcadi Navarro61,81,386, Fabio C. P. Navarro19, David E. Neal223,352, Gerd Nettekoven533, Felicity Newell209,311, Steven J. Newhouse59, Yulia Newton37, Alvin Wei Tian Ng534, Anthony Ng535, Jonathan Nicholson49, David Nicol221, Yongzhan Nie321,536, G. Petur Nielsen72, Morten Muhlig Nielsen73, Serena Nik-Zainal49,281,282,537, Michael S. Noble8, Katia Nones209,311, Paul A. Northcott538, Faiyaz Notta148,539, Brian D. O’Connor76,540, Peter O’Donnell541, Maria O’Donovan282, Sarah O’Meara49, Brian Patrick O’Neill542, J. Robert O’Neill543, David Ocana59, Angelica Ochoa88, Layla Oesper544, Christopher Ogden221, Hideki Ohdan93, Kazuhiro Ohi375, Lucila Ohno-Machado244, Karin A. Oien523,545, Akinyemi I. Ojesina546,547,548, Hidenori Ojima549, Takuji Okusaka550, Larsson Omberg551, Choon Kiat Ong552, Stephan Ossowski52,53,74, German Ott553, B. F. Francis Ouellette76,554, Christine P’ng6, Marta Paczkowska6, Salvatore Paiella115, Chawalit Pairojkul523, Marina Pajic170, Qiang Pan-Hammarström56,555, Elli Papaemmanuil49, Irene Papatheodorou59, Nagarajan Paramasivam43,47, Ji Wan Park385, Joong-Won Park556, Keunchil Park557,558, Kiejung Park559, Peter J. Park264,266, Joel S. Parker518, Simon L. Parsons124, Harvey Pass560, Danielle Pasternack272, Alessandro Pastore276, Ann-Marie Patch209,311, Iris Pauporté251, Antonio Pea115, John V. Pearson209,311, Chandra Sekhar Pedamallu8,14,82, Jakob Skou Pedersen73,75, Paolo Pederzoli115, Martin Peifer270, Nathan A. Pennell561, Charles M. Perou129,518, Marc D. Perry76,77, Gloria M. Petersen562, Myron Peto22, Nicholas Petrelli563, Robert Petryszak59, Stefan M. Pfister426,465,564, Mark Phillips424, Oriol Pich298,299, Hilda A. Pickett478, Todd D. Pihl565, Nischalan Pillay566, Sarah Pinder567, Mark Pinese170, Andreia V. Pinho568, Esa Pitkänen60, Xavier Pivot569, Elena Piñeiro-Yáñez95, Laura Planko533, Christoph Plass345, Paz Polak8,14,15, Tirso Pons570, Irinel Popescu571, Olga Potapova572, Aparna Prasad52, Shaun R. Preston573, Manuel Prinz47, Antonia L. Pritchard311, Stephenie D. Prokopec6, Elena Provenzano574, Xose S. Puente499, Sonia Puig176, Montserrat Puiggròs57, Sergio Pulido-Tamayo505,506, Gulietta M. Pupo246, Colin A. Purdie575, Michael C. Quinn209,311, Raquel Rabionet52,53,576, Janet S. Rader577, Bernhard Radlwimmer397, Petar Radovic284, Benjamin Raeder60, Keiran M. Raine49, Manasa Ramakrishna49, Kamna Ramakrishnan49, Suresh Ramalingam578, Benjamin J. Raphael579, W. Kimryn Rathmell580, Tobias Rausch60, Guido Reifenberger475, Jüri Reimand6,44, Jorge Reis-Filho348, Victor Reuter348, Iker Reyes-Salazar298, Matthew A. Reyna579, Sheila M. Reynolds36, Esther Rheinbay8,14,72, Yasser Riazalhosseini189, Andrea L. Richardson323, Julia Richter111,128, Matthew Ringel581, Markus Ringnér181, Yasushi Rino582, Karsten Rippe405, Jeffrey Roach583, Lewis R. Roberts349, Nicola D. Roberts49, Steven A. Roberts584, A. Gordon Robertson30, Alan J. Robertson113, Javier Bartolomé Rodriguez57, Bernardo Rodriguez-Martin104,105,106, F. Germán Rodríguez-González83,332, Michael H. A. Roehrl44,123,148,234,585,586, Marius Rohde587, Hirofumi Rokutan440, Gilles Romieu588, Ilse Rooman170, Tom Roques262, Daniel Rosebrock8, Mara Rosenberg8,72, Philip C. Rosenstiel589, Andreas Rosenwald590, Edward W. Rowe221,591, Romina Royo57, Steven G. Rozen179,180,592, Yulia Rubanova17,127, Mark A. Rubin423,593,594,595,596, Carlota Rubio-Perez298,299,597, Vasilisa A. Rudneva60, Borislav C. Rusev177, Andrea Ruzzenente598, Gunnar Rätsch276,277,278,279,280,430, Radhakrishnan Sabarinathan298,299,599, Veronica Y. Sabelnykova6, Sara Sadeghi30, S. Cenk Sahinalp62,78,79, Natalie Saini357, Mihoko Saito-Adachi440, Gordon Saksena8, Adriana Salcedo6, Roberto Salgado600, Leonidas Salichos5,19, Richard Sallari8, Charles Saller601, Roberto Salvia115, Michelle Sam272, Jaswinder S. Samra115,602, Francisco Sanchez-Vega114,121, Chris Sander276,603,604, Grant Sanders134, Rajiv Sarin605, Iman Sarrafi62,78, Aya Sasaki-Oku184, Torill Sauer489, Guido Sauter520, Robyn P. M. Saw211, Maria Scardoni167, Christopher J. Scarlett170,606, Aldo Scarpa177, Ghislaine Scelo194, Dirk Schadendorf68,607, Jacqueline E. Schein30, Markus B. Schilhabel589, Matthias Schlesner47,80, Thorsten Schlomm84,608, Heather K. Schmidt1 , Sarah-Jane Schramm246, Stefan Schreiber609, Nikolaus Schultz121, Steven E. Schumacher8,323, Roland F. Schwarz59,403,405,610, Richard A. Scolyer211,448,602, David Scott428, Ralph Scully611, Raja Seethala612, Ayellet V. Segre8,613, Iris Selander260, Colin A. Semple434, Yasin Senbabaoglu276, Subhajit Sengupta614, Elisabetta Sereni115, Stefano Serra585, Dennis C. Sgroi72, Mark Shackleton103, Nimish C. Shah352, Sagedeh Shahabi234, Catherine A. Shang329, Ping Shang211, Ofer Shapira8,323, Troy Shelton271, Ciyue Shen603,604, Hui Shen615, Rebecca Shepherd49, Ruian Shi490, Yan Shi134, Yu-Jia Shiah6, Tatsuhiro Shibata118,616, Juliann Shih8,82, Eigo Shimizu375, Kiyo Shimizu617, Seung Jun Shin618, Yuichi Shiraishi375, Tal Shmaya285, Ilya Shmulevich36, Solomon I. Shorser6, Charles Short59, Raunak Shrestha62, Suyash S. Shringarpure217, Craig Shriver619, Shimin Shuai6,126, Nikos Sidiropoulos83, Reiner Siebert112,620, Anieta M. Sieuwerts332, Lina Sieverling205,237, Sabina Signoretti202,621, Katarzyna O. Sikora177, Michele Simbolo138, Ronald Simon520, Janae V. Simons134, Jared T. Simpson6,17, Peter T. Simpson473, Samuel Singer115,458, Nasa Sinnott-Armstrong8,217, Payal Sipahimalani30, Tara J. Skelly390, Marcel Smid332, Jaclyn Smith622, Karen Smith-McCune514, Nicholas D. Socci276, Heidi J. Sofia27, Matthew G. Soloway134, Lei Song240, Anil K. Sood623,624,625, Sharmila Sothi626, Christos Sotiriou244, Cameron M. Soulette37, Paul N. Span627, Paul T. Spellman22, Nicola Sperandio177, Andrew J. Spillane211, Oliver Spiro8, Jonathan Spring628, Johan Staaf181, Peter F. Stadler163,164,165, Peter Staib629, Stefan G. Stark277,279,618,630, Lucy Stebbings49, Ólafur Andri Stefánsson631, Oliver Stegle59,60,632, Lincoln D. Stein6,126, Alasdair Stenhouse633, Chip Stewart8, Stephan Stilgenbauer634, Miranda D. Stobbe52,61, Michael R. Stratton49, Jonathan R. Stretch211, Adam J. Struck31, Joshua M. Stuart24,37, Henk G. Stunnenberg396,635, Hong Su56,396, Xiaoping Su99, Ren X. Sun6, Stephanie Sungalee60, Hana Susak52,53, Akihiro Suzuki89,636, Fred Sweep637, Monika Szczepanowski128, Holger Sültmann67,638, Takashi Yugawa617, Angela Tam30, David Tamborero298,299, Benita Kiat Tee Tan639, Donghui Tan518, Patrick Tan180,532,592,640, Hiroko Tanaka375, Hirokazu Taniguchi616, Tomas J. Tanskanen641, Maxime Tarabichi49,290, Roy Tarnuzzer220, Patrick Tarpey642, Morgan L. Taschuk152, Kenji Tatsuno89, Simon Tavaré223,643, Darrin F. Taylor113, Amaro Taylor-Weiner8, Jon W. Teague49, Bin Tean Teh180,592,640,644,645, Varsha Tembe246, Javier Temes104,105, Kevin Thai76, Sarah P. Thayer393, Nina Thiessen30, Gilles Thomas646, Sarah Thomas221, Alan Thompson221, Alastair M. Thompson633, John F. Thompson211, R. Houston Thompson647, Heather Thorne103, Leigh B. Thorne176, Adrian Thorogood424, Grace Tiao8, Nebojsa Tijanic284, Lee E. Timms272, Roberto Tirabosco648, Marta Tojo106, Stefania Tommasi649, Christopher W. Toon170, Umut H. Toprak48,650, David Torrents57,81, Giampaolo Tortora651,652, Jörg Tost653, Yasushi Totoki118, David Townend654, Nadia Traficante103, Isabelle Treilleux655,656, Jean-Rémi Trotta61, Lorenz H. P. Trümper469, Ming Tsao124,539, Tatsuhiko Tsunoda183,657,658,659, Jose M. C. Tubio104,105,106, Olga Tucker660, Richard Turkington661, Daniel J. Turner513, Andrew Tutt323, Masaki Ueno376, Naoto T. Ueno662, Christopher Umbricht151,213,663, Husen M. Umer305,664, Timothy J. Underwood665, Lara Urban59,60, Tomoko Urushidate616, Tetsuo Ushiku339, Liis Uusküla-Reimand666,667, Alfonso Valencia57,81, David J. Van Den Berg166, Steven Van Laere307, Peter Van Loo290,291, Erwin G. Van Meir668, Gert G. Van den Eynden307, Theodorus Van der Kwast123, Naveen Vasudev137, Miguel Vazquez57,669, Ravikiran Vedururu267, Umadevi Veluvolu518, Shankar Vembu490,670, Lieven P. C. Verbeke506,671, Peter Vermeulen307, Clare Verrill351,672, Alain Viari177, David Vicente57, Caterina Vicentini177, K. Vijay Raghavan365, Juris Viksna673, Ricardo E. Vilain674, Izar Villasante57, Anne Vincent-Salomon635, Tapio Visakorpi190, Douglas Voet8, Paresh Vyas311,351, Ignacio Vázquez-García49,86,675,676, Nick M. Waddell209, Nicola Waddell209,311, Claes Wadelius677, Lina Wadi6, Rabea Wagener111,112, Jeremiah A. Wala8,14,82, Jian Wang56, Jiayin Wang1,40,66, Linghua Wang12, Qi Wang465, Wenyi Wang21, Yumeng Wang21, Zhining Wang220, Paul M. Waring523, Hans-Jörg Warnatz483, Jonathan Warrell5,19, Anne Y. Warren352,678, Sebastian M. Waszak60, David C. Wedge49,294,679, Dieter Weichenhan345, Paul Weinberger680, John N. Weinstein38, Joachim Weischenfeldt60,83,84, Daniel J. Weisenberger166, Ian Welch681, Michael C. Wendl1,10,11, Johannes Werner47,85, Justin P. Whalley61,682, David A. Wheeler12,13, Hayley C. Whitaker117, Dennis Wigle683, Matthew D. Wilkerson518, Ashley Williams244, James S. Wilmott211, Gavin W. Wilson6,148, Julie M. Wilson148, Richard K. Wilson1,684, Boris Winterhoff685, Jeffrey A. Wintersinger17,127,384, Maciej Wiznerowicz686,687, Stephan Wolf688, Bernice H. Wong689, Tina Wong1,30, Winghing Wong690, Youngchoon Woo250, Scott Wood209,311, Bradly G. Wouters44, Adam J. Wright6, Derek W. Wright133,691, Mark H. Wright217, Chin-Lee Wu72, Dai-Ying Wu285, Guanming Wu692, Jianmin Wu170, Kui Wu56,396, Yang Wu179,180, Zhenggang Wu64, Liu Xi12, Tian Xia693, Qian Xiang76, Xiao Xiao66, Rui Xing497, Heng Xiong56,396, Qinying Xu209,311, Yanxun Xu694, Hong Xue64, Shinichi Yachida118,695, Sergei Yakneen60, Rui Yamaguchi375, Takafumi N. Yamaguchi6, Masakazu Yamamoto120, Shogo Yamamoto89, Hiroki Yamaue376, Fan Yang490, Huanming Yang56, Jean Y. Yang696, Liming Yang220, Lixing Yang697, Shanlin Yang306, Tsun-Po Yang270, Yang Yang369, Xiaotong Yao408,698, Marie-Laure Yaspo483, Lucy Yates49, Christina Yau156, Chen Ye56,396, Kai Ye40,41, Venkata D. Yellapantula20,86, Christopher J. Yoon249, Sung-Soo Yoon463, Fouad Yousif6, Jun Yu699, Kaixian Yu700, Willie Yu701, Yingyan Yu702, Ke Yuan223,510,703, Yuan Yuan21, Denis Yuen6, Takashi Yugawa617, Christina K. Yung76, Olga Zaikova704, Jorge Zamora49,104,105,106, Marc Zapatka397, Jean C. Zenklusen220, Thorsten Zenz67, Nikolajs Zeps705,706, Cheng-Zhong Zhang8,707, Fan Zhang381, Hailei Zhang8, Hongwei Zhang494, Hongxin Zhang121, Jiashan Zhang220, Jing Zhang5, Junjun Zhang76, Xiuqing Zhang56, Xuanping Zhang66,369, Yan Zhang5,708,709, Zemin Zhang381,710, Zhongming Zhao711, Liangtao Zheng381, Xiuqing Zheng381, Wanding Zhou615, Yong Zhou56, Bin Zhu240, Hongtu Zhu700,712, Jingchun Zhu24, Shida Zhu56,396, Lihua Zou713, Xueqing Zou49, Anna deFazio246,247,714, Nicholas van As221, Carolien H. M. van Deurzen715, Marc J. van de Vijver523, L. van’t Veer716 & Christian von Mering433,717, The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

OBJECTIVE To assess early outcomes since the introduction of an active surveillance (AS) protocol incorporating multiparametric magnetic resonance imaging (mpMRI)-guided baseline biopsies and image-based surveillance. PATIENTS AND METHODS A new AS protocol mandating image-guided baseline biopsies, annual mpMRI and 3-monthly prostate-specific antigen (PSA) testing, but which retained protocol re-biopsies, was tested. Pathological progression, treatment conversion and triggers for non-protocol biopsy were recorded prospectively. RESULTS Data from 157 men enrolled in the AS protocol (median age 64 years, PSA 6.8 ng/mL, follow-up 39 months) were interrogated. A total of 12 men (7.6%) left the AS programme by choice. Of the 145 men who remained, 104 had re-biopsies either triggered by a rise in PSA level, change in mpMRI findings or by protocol. Overall, 23 men (15.9%) experienced disease progression; pathological changes were observed in 20 men and changes in imaging results were observed in three men. Of these 23 men, 17 switched to treatment, giving a conversion rate of 11.7% (

Introduction The aim of this study was to explore the impact of increasing proportions of high risk referrals on surgical margin outcomes of a surgeon’s learning curve in robotic prostatectomy. Methods All patients in this study underwent robot assisted radical prostatectomy (RARP) performed by three different consultant urological surgeons. Data collected included preoperative clinical stage, Gleason score and prostate specific antigen levels, which were used to risk stratify patients according to National Institute for Health and Care Excellence criteria. Oncological clearance was assessed by overall and stage specific positive margin status. Comparisons were made between each surgeon for the first and second 50 consecutive cases. Results For the three surgeons, there was a progressive increase in the proportion of high risk cases referred accompanied by a corresponding decline in low risk disease (pConclusions Our series demonstrates an upward trend in the risk profile of men referred for robotic prostatectomy over a nine-year period. Despite this, there was minimal impact on pathological and surgical outcomes among our surgeons, who were at the initial stages of their RARP learning curve. Our results suggest that there is no requirement for an active case selection bias against patients with high risk disease for surgeons newly embarking on their RARP learning experience.

Purpose To compare biparametric MRI (bpMRI) with multiparametric MRI (mpMRI) staging accuracy in assessing extracapsular extension (ECE) and seminal vesicle invasion (SVI). Method Biopsy-naive patients undergoing 3 T-MRI before radical prostatectomy for clinically significant prostate cancer were included in this single-centre retrospective study. Two uroradiologists separately evaluated bpMRI and mpMRI for presence of ECE and SVI using a 5-point Likert scale (1: ECE/SVI highly unlikely, 5: ECE/SVI highly likely). Results 110 men of median age 63 years and PSA 8.5 ng/mL were included. ECE and SVI was confirmed histologically in 71/110 (64.5 %) and 18/110 (16.4 %) patients, respectively. Sensitivity and specificity of bpMRI versus mpMRI for predicting ECE was 59.1 % and 87.2 % versus 66.2 % and 84.6 %, respectively. For SVI detection, the sensitivity and specificity for bpMRI versus mpMRI was 66.7 % and 92.4 % versus 83.3 % and 97.8 %, respectively. At an optimal cut-off Likert score ≥3 for ECE prediction, mpMRI area under the receiver operating curve (AUC) was 0.80 (95 % confidence interval (CI) 0.72–0.87) versus 0.78 (95 % CI 0.69–0.86) for bpMRI (p = 0.52) and for SVI, mpMRI AUC was 0.91 (95 % CI 0.84–0.96) versus 0.86 (95 % CI 0.78–0.92) for bpMRI (p = 0.02), respectively. Inter-reader agreement for both ECE and SVI prediction was substantial, with a marginally higher k-value for mpMRI (k range, 0.67–0.75) than bpMRI (k range, 0.65–0.69). Conclusions Diagnostic performance of bpMRI and mpMRI was comparable for detection of ECE, however, mpMRI with contrast was superior for SVI detection and improved the inter-reader agreement.

Purpose To evaluate sub-differentiation of PI-RADS-3 prostate lesions using pre-defined T2- and diffusion-weighted (DWI) MRI criteria, to aid the biopsy decision process. Methods 143 patients with PIRADS-3 index lesions on MRI underwent targeted transperineal-MR/US fusion biopsy. Radiologists with 2 and 7-years experience performed blinded retrospective second-reads using set criteria and assigned biopsy recommendations. Inter-reader agreement, Gleason score (GS), positive (PPV) predictive values (±95% confidence intervals) were calculated and compared by Fisher’s exact test with Bonferroni-Hom correction. Results 43% (61/143) patients had GS 6–10 and 21% (30/143) GS ≥ 3 + 4 cancer. For peripheral zone lesions, significant differences in any cancer detection were found for shape (0.26 ± 0.13 geographical vs. 0.69 ± 0.23 rounded; p = 0.0055) and ADC (mild 0.21 ± 0.12 vs marked 0.81 ± 0.19; p = 0.0001). For transition zone, significantly increased cancer detection was shown for location (anterior 0.63 ± 0.15 vs. mid/posterior 0.31 ± 0.14; p = 0.0048), border (pseudo-capsule 0.32 ± 0.14 vs. ill-defined 0.61 ± 0.15; p = 0.0092), and ADC (mild 0.35 ± 0.12 vs marked restriction 0.68 ± 0.17; p = 0.0057). Biopsy recommendations had 62% inter-reader agreement (89/143). Experienced reader PPVs were significantly higher for any cancer with “biopsy-recommended” 0.61 ± 0.11 vs. “no biopsy” 0.21 ± 0.10 (p = 0.0001), and for GS 7–10 cancers: 0.32 ± 0.10 vs. 0.08 ± 0.07, respectively (p = 0.0003). Conclusion Identification of certain objective imaging criteria as well as a subjective biopsy recommendation from an experienced radiologist can help to increase the predictive value of equivocal prostate lesions and inform the decision making process of whether or not to biopsy.

We have previously reported that the negative signaling regulator Similar Expression to FGF (hSef) is downregulated in prostate cancer and its loss is associated with clinical metastasis. Here, we explored the mechanistic basis of this finding. We first confirmed our clinical observation by testing hSef manipulation in an in vivo metastasis model. hSef stable expressing cells (PC3M‐hSef) or empty vector controls (PC3M‐EV) were injected subcutaneously into the lateral thoracic walls of NOD‐SCID gamma mice and lungs were harvested at autopsy. In this model, 6/7 PC3M‐EV xenografts had definitive lung micro‐metastasis whilst only 1/6 PC3M‐hSef xenografts exhibited metastasis recapitulating the clinical scenario (p = 0.03). Gene expression studies revealed key perturbations in genes involved in cell motility and epithelial to mesenchymal transition (EMT) along with alterations in cognate signaling pathways. These results were validated in an EMT specific PCR array whereby hSef over‐expression and silencing reciprocally altered E‐Cadherin expression (p =, What's new? The loss of negative signaling regulators may be implicated in prostate cancer metastasis, but the underlying mechanisms remain unclear. Here, by using a combination of xenograft, gene expression microarray, phosphokinase array and quantitative PCR techniques, the authors provide first evidence that the negative regulator hSef plays a key role in regulating epithelial to mesenchymal transition (EMT) in prostate cancer, which in turn results in changes in the metastatic ability of tumor cells. The results support the notion that the expression levels of hSef and other negative signaling regulators may be key biomarkers for the identification of triggers for metastasis.

A childhood tumor—from the beginning Many adult cancers arise from clonal expansions of mutant cells in normal tissue. These premalignant expansions are defined by somatic mutations shared by the cancers. Whether pediatric cancers originate in a similar way is unknown. Coorens et al. studied Wilms tumor, a childhood kidney cancer. Phylogenetic analyses revealed large clones of mutant cells in histologically and functionally normal kidney tissue long before tumor development. Thus, like adult tumors, Wilms tumor appears to arise from a premalignant tissue bed. Science , this issue p. 1247

Purpose: Inherited pathogenic variants in genes encoding the metabolic enzymes succinate dehydrogenase (SDH) and fumarate hydratase predispose to tumor development through accumulation of oncometabolites (succinate and fumarate, respectively; ref. 1). Noninvasive in vivo detection of tumor succinate by proton magnetic resonance spectroscopy (1H-MRS) has been reported in SDH-deficient tumors, but the potential utility of this approach in the management of patients with hereditary leiomyomatosis and renal cell cancer syndrome or Reed syndrome is unknown. Experimental Design: Magnetic resonance spectroscopy (1H-MRS) was performed on three cases and correlated with germline genetic results and tumor IHC when available. Results: Here, we have demonstrated a proof of principle that 1H-MRS can provide a noninvasive diagnosis of hereditary leiomyomatosis and renal cell cancer syndrome or Reed syndrome through detection of fumarate accumulation in vivo. Conclusions: This study demonstrates that in vivo detection of fumarate could be employed as a functional biomarker.

Cell-free tumour-derived DNA (ctDNA) allows non-invasive monitoring of cancers but its utility in renal cell cancer (RCC) has not been established. Here, untargeted and targeted sequencing methods, applied to two independent cohorts of renal tumour patients (n=90), were used to determine ctDNA content in plasma and urine. Our data revealed lower plasma ctDNA levels in RCC relative to other cancers, with untargeted detection of ∼33%. A sensitive personalised approach, applied to plasma and urine from select patients improved detection to ∼50%, including in patients with early-stage and even benign lesions.A machine-learning based model predicted detection, potentially offering a means of triaging samples for personalised analysis. In addition, with limited data we observed that plasma, and for the first time, urine ctDNA may better represent tumour heterogeneity than tissue biopsy. Furthermore, longitudinal sampling of >200 plasma samples revealed that ctDNA can track disease course. Additional datasets will be required to validate these findings.Overall, our data highlight RCC as a ctDNA-low malignancy, but indicate potential clinical utility provided improvement in detection approaches.One sentence summaryComplementary sequencing methods show that cell-free tumour DNA levels are low in renal cancer though, via various strategies, may still be informative.

AIM: To evaluate the effect of pre-biopsy magnetic resonance imaging (MRI) on cancer diagnostic times, and to report MRI-directed pathology outcomes. MATERIALS AND METHODS: In total, 1483 patients were referred with prostate cancer suspicion during a 30-month period. Upfront MRI was performed in 745 patients: 332 MRIs in the 15 months prior to dedicated scanning slots (group 1), and 413 in the 15 months post-introduction (group 2). A further 88 patients had initial MRI following clinical assessment. Biopsy via the transrectal (TR) or transperineal (TP) approach was performed, with MRI/ultrasound fusion for MRI targets. Clinically significant cancer (csPCa) was defined as Gleason ≥3+4. Negative MRIs were defined as Likert 1-2. Per-case clinical decisions were taken to biopsy or not. RESULTS: 44.4% of patients avoided biopsy. 484/833 (58.1%) MRIs were negative; 37.4% of these patients had biopsy with a negative predictive value (NPV) of 92.8% for Gleason ≥3+4 and 98.3% for ≥4+3. Overall prostate cancer prevalence was 34.3% (24.6% csPCa). In 323 MRI-positive cases, any cancer was present in 78.9% (csPCa 60.4%). Of the 1483 patients, 1232 (83.1%) completed all diagnostic tests within 28 days. Upfront MRI patients met this standard in 621/833 (74.5%), improving from 66.9% to 81.1% with reserved slots (group 2) with a reduced diagnostic time from median 25.5 to 20.9 days. Biopsy scheduling delayed the pathway in 69.7%, with MRI responsible in 22.3%, reducing to 10.3% in group 2. TP biopsies met the 28-day standard in significantly less cases (29.7%), compared to TR (67.4%, p

Castrate‐resistant prostate cancer (CRPC) is poorly characterized and heterogeneous and while the androgen receptor (AR) is of singular importance, other factors such as c‐Myc and the E2F family also play a role in later stage disease. HES6 is a transcription co‐factor associated with stem cell characteristics in neural tissue. Here we show that HES6 is up‐regulated in aggressive human prostate cancer and drives castration‐resistant tumour growth in the absence of ligand binding by enhancing the transcriptional activity of the AR, which is preferentially directed to a regulatory network enriched for transcription factors such as E2F1. In the clinical setting, we have uncovered a HES6‐associated signature that predicts poor outcome in prostate cancer, which can be pharmacologically targeted by inhibition of PLK1 with restoration of sensitivity to castration. We have therefore shown for the first time the critical role of HES6 in the development of CRPC and identified its potential in patient‐specific therapeutic strategies.

Clear cell renal cell carcinoma (ccRCC) contains cancer stem‐like cells (CSCs) that express CD133 (ccRCC‐CD133+). CSCs are rarely in cell cycle and, as nonproliferating cells, resist most chemotherapeutic agents. Previously, we reported that tumor necrosis factor receptor‐2 (TNFR2) signaling promotes the cell cycle entry of ccRCC‐CD133+CSCs, rendering them susceptible to cell‐cycle‐dependent chemotherapeutics. Here, we describe a TNFR2‐activated signaling pathway in ccRCC‐CD133+CSCs that is required for cell survival. Wild‐type (wt)TNF or R2TNF but not R1TNF (TNF muteins that selectively bind to TNFR2 and TNFR1) induces phosphorylation of signal transducer and activator of transcription 3 (STAT3) on serine727 but not tyrosine705, resulting in pSTAT3Ser727 translocation to and colocalization with TNFR2 in mitochondria. R2TNF signaling activates a kinase cascade involving the phosphorylation of VEGFR2, PI‐3K, Akt, and mTORC. Inhibition of any of the kinases or siRNA knockdown of TNFR2 or STAT3 promotes cell death associated with mitochondrial morphological changes, cytochrome c release, generation of reactive oxygen species, and TUNEL+cells expressing phosphorylated mixed lineage kinase‐like (MLKL). Pretreatment with necrostatin‐1 is more protective than z‐VAD.fmk, suggesting that most death is necroptotic and TNFR2 signaling promotes cell survival by preventing mitochondrial‐mediated necroptosis. These data suggest that a TNFR2 selective agonist may offer a potential therapeutic strategy for ccRCC.

Objectives To assess cancer detection rates of different target-dependent transperineal magnetic resonance (MR)/ultrasonography (US) fusion-guided biopsy templates with reduced number of systematic cores. Patients and methods Single-centre outcome of transperineal MR/US fusion-guided biopsies of 487 men with a single target MR imaging (MRI) lesion, prospectively collected between 2012 and 2016. All men underwent transperineal targeted biopsy (TB) with two cores, followed by 18-24 systematic sector biopsies (SB) using the Ginsburg protocol. Gleason score ≥7 prostate cancer detection rates for two-core TB, four-core extended TB (eTB), 10- to 20-core saturation TB (sTB) including cores from sectors adjacent to the target, and 14 core ipsilateral TB (iTB) were compared to combined TB+SB. Results Cancer was detected in 345 men and Gleason score 7-10 cancer in 211 men. TB alone detected 67%, eTB 76%, sTB 91% and iTB 91% of these Gleason score 7-10 cancers. In the subgroup of 33 men (7% of cohort) with an anterior >0.5 mL highly suspicious MRI lesion and a prostate volume ≤45 mL, four-core eTB detected 31 of 32 cancers (97%) and all 26 Gleason score 7-10 cancers. Conclusion sTB detected Gleason score 7-10 cancer in 25% more of the men than a two-core TB approach, and in almost as many men (91%) as the 20-26-core combined TB+SB, while needing only 10-20 cores. A four-core extended TB may suffice for large, highly suspicious anterior lesions in small or slightly enlarged prostates.

PURPOSESpatial heterogeneity of tumours is a major challenge in precision oncology. The relationship between molecular and imaging heterogeneity is still poorly understood, as it relies on the accurate co-registration of medical images and tissue biopsies. tumour moulds can guide the localization of biopsies, but their creation is time consuming, technologically challenging, and difficult to interface with routine clinical practice. These hurdles have so far hindered the progress in the area of multiscale integration of tumour heterogeneity data.METHODSWe have developed an open source computational framework to automatically produce patient-specific 3D-printed moulds that can be used in the clinical setting. Our approach achieves accurate co-registration of sampling location between tissue and imaging, and integrates seamlessly with clinical, imaging and pathology workflows.RESULTSWe applied our framework to patients with renal cancer undergoing radical nephrectomy. We created personalised moulds for five patients, obtaining Dice similarity coefficients between imaging and tissue sections ranging from 0.86 to 0.93 for tumour regions, and between 0.70 and 0.76 for healthy kidney. The framework required minimal manual intervention, producing the final mould design in just minutes, while automatically taking into account clinical considerations such as a preference for specific cutting planes.CONCLUSIONOur work provides a robust and automated interface between imaging and tissue samples, enabling the development of clinical studies to probe tumour heterogeneity on multiple spatial scales.

Transcriptional networks are critical for the establishment of tissue-specific cellular states in health and disease, including cancer. Yet, the transcriptional circuits that control carcinogenesis remain poorly understood. Here we report that Kruppel like factor 6 (KLF6), a transcription factor of the zinc finger family, regulates lipid homeostasis in clear cell renal cell carcinoma (ccRCC). We show that KLF6 supports the expression of lipid metabolism genes and promotes the expression of PDGFB, which activates mTOR signalling and the downstream lipid metabolism regulators SREBF1 and SREBF2. KLF6 expression is driven by a robust super enhancer that integrates signals from multiple pathways, including the ccRCC-initiating VHL-HIF2A pathway. These results suggest an underlying mechanism for high mTOR activity in ccRCC cells. More generally, the link between super enhancer-driven transcriptional networks and essential metabolic pathways may provide clues to the mechanisms that maintain the stability of cell identity-defining transcriptional programmes in cancer., Super enhancers are frequently involved in the dysregulation of gene expression in cancer. Here, in kidney cancer, a super enhancer is shown to drive the expression of KLF6, which alters the expression of lipid metabolism genes and promotes tumorigenesis.

There has been recent interest in nonproton MRI including hyperpolarized carbon-13 (To compare TSC measurements from aProspective.Eight patients with biopsy-proven, intermediate/high risk prostate cancer imaged prior to prostatectomy.3T MRI with separate dual-tunedRegions-of-interest for TSC quantification were defined for normal peripheral zone (PZ), normal transition zone (TZ), and tumor, with reference to histopathology maps.Two-sided Wilcoxon rank sum with additional measures of correlation, coefficient of variation, and Bland-Altman plots to assess for between-test differences.Mean TSC for normal PZ and TZ were 39.2 and 33.9 mM, respectively, with theWe demonstrated the feasibility of using a carbon-tuned coil to quantify TSC, enabling dual metabolic information from a single coil. This approach could make the acquisition of both2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:90-97.

Due to increased sensitivity, the expression of circulating nucleotides is rapidly gaining popularity in cancer diagnosis. Whole blood mRNA has been used in studies on a number of cancers, most notably two separate studies that used whole blood mRNA to define non-overlapping signatures of prostate cancer that has become castration independent. Prostate cancer is known to rely on androgens for initial growth, and there is increasing evidence on the importance of the androgen axis in advanced disease. Using whole blood mRNA samples from patients with prostate cancer, we have identified the four-gene panel ofFAM129A,MME,KRT7andSOD2in circulating mRNA that are differentially expressed in a discovery cohort of metastatic samples. Validation of these genes at the mRNA and protein level was undertaken in additional cohorts defined by risk of relapse following surgery and hormone status. All the four genes were downregulated at the mRNA level in the circulation and in primary tissue, but this was not always reflected in tissue protein expression.MMEdemonstrated significant differences in the hormone cohorts, whereasFAM129Ais downregulated at the mRNA level but is raised at the protein level in tumours. Using published ChIP-seq data, we have demonstrated that this may be due to AR binding at theFAM129AandMMEloci in multiple cell lines. These data suggest that whole blood mRNA of androgen-regulated genes has the potential to be used for diagnosis and monitoring of prostate cancer.

Aims: The treatment of prostate cancer has evolved markedly over the last 40 years, including radiotherapy, notably with escalated dose and targeting. However, the optimal treatment for localised disease has not been established in comparative randomised trials. The aim of this article is to describe the history ofprostate radiotherapy trials, including their quality assurance processes, and to compare these with the ProtecT trial.Materials and methods: The UK ProtecT randomised trial compares external beam conformal radiotherapy, surgery and active monitoring for clinically localized prostate cancer and will report on the primary outcome (disease-specific mortality) in 2016 following recruitment between 1999 and 2009. The embedded quality assurance programme consists of on-site machine dosimetry at the nine trial centres, a retrospective review of outlining and adherence to dose constraints based on the trial protocol in 54 participants (randomly selected, around 10% of the total randomised to radiotherapy, n ¼ 545). These quality assurance processes and results were compared with prostate radiotherapy trials of a comparable era.Results: There has been an increasingly sophisticated quality assurance programme in UK prostate radiotherapy trials over the last 15 years, reflecting dose escalation and treatment complexity. In ProtecT, machine dosimetry results were comparable between trial centres and with the UK RT01 trial. The outliningreview showed that most deviations were clinically acceptable, although three (1.4%) may have been of clinical significance and were related to outlining of theprostate. Seminal vesicle outlining varied, possibly due to several prostate trials running concurrently with different protocols. Adherence to dose constraints inProtecT was considered acceptable, with 80% of randomised participants having two or less deviations and planning target volume coverage was excellent.Conclusion: The ProtecT trial quality assurance results were satisfactory and comparable with trials of its era. Future trials should aim to standardise treatment protocols and quality assurance programmes where possible to reduce complexities for centres involved in multiple trials. 2016 Published by Elsevier Ltd on behalf of The Royal College of Radiologists.

Background Prostate biopsy supported by transperineal image fusion has recently been developed as a new method to the improve accuracy of prostate cancer detection. Objective To describe the Ginsburg protocol for transperineal prostate biopsy supported by multiparametric magnetic resonance imaging (mpMRI) and transrectal ultrasound (TRUS) image fusion, provide learning points for its application, and report biopsy results. The article is supplemented by a Surgery in Motion video. Design, setting, and participants This single-centre retrospective outcome study included 534 patients from March 2012 to October 2015. A total of 107 had no previous prostate biopsy, 295 had benign TRUS-guided biopsies, and 159 were on active surveillance for low-risk cancer. Surgical procedure A Likert scale reported mpMRI for suspicion of cancer from 1 (no suspicion) to 5 (cancer highly likely). Transperineal biopsies were obtained under general anaesthesia using BiopSee fusion software (Medcom, Darmstadt, Germany). All patients had systematic biopsies, two cores from each of 12 anatomic sectors. Likert 3–5 lesions were targeted with a further two cores per lesion. Outcome measurements and statistical analysis Any cancer and Gleason score 7–10 cancer on biopsy were noted. Descriptive statistics and positive predictive values (PPVs) and negative predictive values (NPVs) were calculated. Results and limitations The detection rate of Gleason score 7–10 cancer was similar across clinical groups. Likert scale 3–5 MRI lesions were reported in 378 (71%) of the patients. Cancer was detected in 249 (66%) and Gleason score 7–10 cancer was noted in 157 (42%) of these patients. PPV for detecting 7–10 cancer was 0.15 for Likert score 3, 0.43 for score 4, and 0.63 for score 5. NPV of Likert 1–2 findings was 0.87 for Gleason score 7–10 and 0.97 for Gleason score ≥4+3=7 cancer. Limitations include lack of data on complications. Conclusions Transperineal prostate biopsy supported by MRI/TRUS image fusion using the Ginsburg protocol yielded high detection rates of Gleason score 7–10 cancer. Because the NPV for excluding Gleason score 7–10 cancer was very high, prostate biopsies may not be needed for all men with elevated prostate-specific antigen values and nonsuspicious mpMRI. Patient summary We present our technique to sample (biopsy) the prostate by the transperineal route (the area between the scrotum and the anus) to detect prostate cancer using a fusion of magnetic resonance and ultrasound images to guide the sampling.

Thomas Whitington and colleagues present a detailed characterization of gene regulatory mechanisms underlying prostate cancer susceptibility. Their analysis highlights key mechanisms altered by disease-associated SNPs, including widespread disruption of ternary complexes involving AR, FOXA1 and HOXB13. Molecular characterization of genome-wide association study (GWAS) loci can uncover key genes and biological mechanisms underpinning complex traits and diseases. Here we present deep, high-throughput characterization of gene regulatory mechanisms underlying prostate cancer risk loci. Our methodology integrates data from 295 prostate cancer chromatin immunoprecipitation and sequencing experiments with genotype and gene expression data from 602 prostate tumor samples. The analysis identifies new gene regulatory mechanisms affected by risk locus SNPs, including widespread disruption of ternary androgen receptor (AR)-FOXA1 and AR-HOXB13 complexes and competitive binding mechanisms. We identify 57 expression quantitative trait loci at 35 risk loci, which we validate through analysis of allele-specific expression. We further validate predicted regulatory SNPs and target genes in prostate cancer cell line models. Finally, our integrated analysis can be accessed through an interactive visualization tool. This analysis elucidates how genome sequence variation affects disease predisposition via gene regulatory mechanisms and identifies relevant genes for downstream biomarker and drug development.

Objective:To compare the performance of Likert and Prostate Imaging–Reporting and Data System (PI-RADS) multiparametric (mp) MRI scoring systems for detecting clinically significant prostate cancer (csPCa).Methods:199 biopsy-naïve males undergoing prostate mpMRI were prospectively scored with Likert and PI-RADS systems by four experienced radiologists. A binary cut-off (threshold score ≥3) was used to analyze histological results by three groups: negative, insignificant disease (Gleason 3 + 3; iPCa), and csPCa (Gleason ≥3 +4). Lesion-level results and prostate zonal location were also compared.Results:129/199 (64.8%) males underwent biopsy, 96 with Likert or PI-RADS score ≥3, and 21 with negative MRI. A further 12 patients were biopsied during follow-up (mean 507 days). Prostate cancer was diagnosed in 87/199 (43.7%) patients, 65 with (33.6%) csPCa. 30/92 (32.6%) patients with negative MRI were biopsied, with an NPV of 83.3% for cancer and 86.7% for csPCa. Likert and PI-RADS score differences were observed in 92 patients (46.2%), but only for 16 patients (8%) at threshold score ≥3. Likert scoring had higher specificity than PI-RADS (0.77 vs 0.66), higher area under the curve (0.92 vs 0.87, p = 0.002) and higher PPV (0.66 vs 0.58); NPV and sensitivity were the same. Likert had more five score results (58%) compared to PI-RADS (36%), but with similar csCPa detection (81.0 and 80.6% respectively). Likert demonstrated lower proportion of false positive in the predominately AFMS-involving lesions.Conclusion:Likert and PI-RADS systems both demonstrate high cancer detection rates. Likert scoring had a higher AUC with moderately higher specificity and lower positive call rate and could potentially help to reduce the number of unnecessary biopsies performed.Advances in knowledge:This paper illustrates that the Likert scoring system has potential to help urologists reduce the number of prostate biopsies performed.

Cutaneous metastasis from renal cell carcinoma is rare, typically involves the head and neck, and occurs inlatestage disease, usually in the context of previously diagnosed and treated primary tumour, and after lymphaticspread. This patients’ initial presentation of clear cell renal cell carcinoma was a cutaneous lesion to the ipsilateral abdominal wall. The primary renal tumour was subsequently demonstrated on CT; this also showed no apparent lymph nodeinvolvement despite cutaneous metastatic disease. The patient underwent radical laparoscopic nephrectomy and biological therapy, but disease progression continued, and he passed away within 6 months of diagnosis.

This work was funded by the Bob Champion Cancer Trust, The Masonic Charitable Foundation successor to The Grand Charity, The King Family, and The University of East Anglia. We acknowledge support from Movember, from Prostate Cancer UK, Callum Barton, and from The Andy Ripley Memorial Fund. We would like to acknowledge the support of the National Institute for Health Research which funds the Cambridge Bio-medical Research Centre, Cambridge UK. Background : A critical problem in the clinical management of prostate cancer is that it is highly heterogeneous. Accurate prediction of individual cancer behaviour is therefore not achievable at the time of diagnosis leading to substantial overtreatment. It remains an enigma that, in contrast to breast cancer, unsupervised analyses of global expression profiles have not currently defined robust categories of prostate cancer with distinct clinical outcomes. Objective: To devise a novel classification framework for human prostate cancer based on unsupervised mathematical approaches. Design, setting, and participants: Our analyses are based on the hypothesis that previous attempts to classify prostate cancer have been unsuccessful because individual samples of prostate cancer frequently have heterogeneous compositions. To address this issue, we applied an unsupervised Bayesian procedure called Latent Process Decomposition to four independent prostate cancer transcriptome datasets obtained using samples from prostatectomy patients and containing between 78 and 182 participants. Outcome measurements and statistical analysis: Biochemical failure was assessed using log-rank analysis and Cox regression analysis. Results and limitations: Application of Latent Process Decomposition identified a common process in all four independent datasets examined. Cancers assigned to this process (designated DESNT cancers) are characterized by low expression of a core set of 45 genes, many encoding proteins involved in the cytoskeleton machinery, ion transport, and cell adhesion. For the three datasets with linked prostate-specific antigen failure data following prostatectomy, patients with DESNT cancer exhibited poor outcome relative to other patients (p = 2.65 × 10−5, p = 4.28 × 10−5, and p = 2.98 × 10−8). When these three datasets were combined the independent predictive value of DESNT membership was p = 1.61 × 10−7 compared with p = 1.00 × 10−5 for Gleason sum. A limitation of the study is that only prediction of prostate-specific antigen failure was examined. Conclusions: Our results demonstrate the existence of a novel poor prognosis category of human prostate cancer and will assist in the targeting of therapy, helping avoid treatment-associated morbidity in men with indolent disease. Patient Summary: Prostate cancer, unlike breast cancer, does not have a robust classification framework. We propose that this failure has occurred because prostate cancer samples selected for analysis frequently have heterozygous compositions (individual samples are made up of many different parts that each have different characteristics). Applying a mathematical approach that can overcome this problem we identify a novel poor prognosis category of human prostate cancer called DESNT. Publisher PDF

Purpose. Splenogonadal fusion (SGF) is a benign congenital anomaly that occurs predominantly in males and presents clinically as a testicular lump which is com-prised of normal splenic tissue. There are two types of SGF: Continuous and discon-tinuous. In continuous SGF, a fibrous band of splenic tissue is found between the spleen and testis and can be easily recognized on imaging studies. Conversely, diagnosis of discon-tinuous SGF is far from straightforward: If misdiagnosed, it leads to unnecessary orchiecto-my in about a third of reported cases. We report a case of discontinuous SGF which was diagnosed postoperatively in a pa-tient with left testicular mass. We also provide a comprehensive review of imaging features and differential diagnosis of discontinuous SGF.

To investigate the repeatability of diffusion-weighted imaging parameter including ADC-derived histogram values in prostate cancer.10 patients with prostate cancer were prospectively recruited to a retest cohort. 3 T diffusion-weighted MRI of the prostate was acquired consecutively with patient getting off the scanner between studies. Prostatectomy-histopathology defined tumour regions-of-interest were outlined on ADC maps and diffusion-weighted metrics including histograms were calculated. The coefficient of reproducibility (CoR) and Bland-Altman plots were used to assess repeatability.1010

Contains fulltext : 199964.pdf (Publisher’s version ) (Open Access) For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 /= 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p

Pediatric and adult kidney tumors differUnderstanding tumor origins and the similarities and differences between organ-specific cancers is important for determining treatment options. Younget al.generated more than 72,000 single-cell transcriptomes from healthy and cancerous human kidneys. From these data, they determined that Wilms tumor, a pediatric kidney cancer, originates from aberrant fetal cells, whereas adult kidney cancers are likely derived from a specific subtype of proximal convoluted tubular cell.Science, this issue p.594