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BackgroundA significant subset of prostate cancer (PC) patients with a castration-resistant form of the disease (CRPC) show primary resistance to androgen receptor (AR)-targeting drugs developed against CRPC. As one explanation could be the expression of constitutively active androgen receptor splice variants (AR-Vs), our current objectives were to study AR-Vs and other AR aberrations to better understand the emergence of CRPC.MethodsWe analysed specimens from different stages of prostate cancer by next-generation sequencing and immunohistochemistry.ResultsAR mutations and copy number variations were detected only in CRPC specimens. Genomic structural rearrangements of AR were observed in 5/30 metastatic CRPC patients, but they were not associated with expression of previously known AR-Vs. The predominant AR-Vs detected were AR-V3, AR-V7 and AR-V9, with the expression levels being significantly higher in CRPC cases compared to prostatectomy samples. Out of 25 CRPC metastases that expressed any AR variant, 17 cases harboured expression of all three of these AR-Vs. AR-V7 protein expression was highly heterogeneous and higher in CRPC compared to hormone-naïve tumours.ConclusionsAR-V3, AR-V7 and AR-V9 are co-expressed in CRPC metastases highlighting the fact that inhibiting AR function via regions common to all AR-Vs is likely to provide additional benefit to patients with CRPC.

GWAS have identified numerous SNPs associated with prostate cancer risk. One such SNP is rs10993994. It is located in the MSMB promoter, associates with MSMB encoded -microseminoprotein prostate secretion levels, and is associated with mRNA expression changes in MSMB and the adjacent gene NCOA4. In addition, our previous work showed a second SNP, rs7098889, is in LD with rs10993994 and associated with MSMB expression independent of rs10993994. Here, we generate a series of clones with single alleles removed by double guide RNA (gRNA) mediated CRISPR/Cas9 deletions, through which we demonstrate that each of these SNPs independently and greatly alters MSMB expression in an allele-specific manner. We further show that these SNPs have no substantial effect on the expression of NCOA4. These data demonstrate that a single SNP can have a large effect on gene expression and illustrate the importance of functional validation to deconvolute observed correlations. The method we have developed is generally applicable to test any SNP for which a relevant heterozygous cell line is available. Footnotes * We added results on RWPE-1 cell line and reformatted for a new journal.

Less than 500 participants have been included in randomized trials comparing hypothermia with regular care for out-of-hospital cardiac arrest patients, and many of these trials were small and at a high risk of bias. Consequently, the accrued data on this potentially beneficial intervention resembles that of a drug following small phase II trials. A large confirmatory trial is therefore warranted. The TTM2-trial is an international, multicenter, parallel group, investigator-initiated, randomized, superiority trial in which a target temperature of 33°C after cardiac arrest will be compared with a strategy to maintain normothermia and early treatment of fever (≥37.8°C). Participants will be randomized within 3 hours of return of spontaneous circulation with the intervention period lasting 40 hours in both groups. Sedation will be mandatory for all patients throughout the intervention period. The clinical team involved with direct patient care will not be blinded to allocation group due to the inherent difficulty in blinding the intervention. Prognosticators, outcome-assessors, the steering group, the trial coordinating team, and trial statistician will be blinded. The primary outcome will be all-cause mortality at 180 days after randomization. We estimate a 55% mortality in the control group. To detect an absolute risk reduction of 7.5% with an alpha of 0.05 and 90% power, 1900 participants will be enrolled. The main secondary neurological outcome will be poor functional outcome (modified Rankin Scale 4–6) at 180 days after arrest. The TTM2-trial will compare hypothermia to 33°C with normothermia and early treatment of fever (≥37.8°C) after out-of-hospital cardiac arrest.

Prostate cancer is a common malignancy in men and the worldwide burden of this disease is rising. Lifestyle modifications such as smoking cessation, exercise, and weight control offer opportunities to reduce the risk of developing prostate cancer. Early detection of prostate cancer by prostate-specific antigen (PSA) screening is controversial, but changes in the PSA threshold, frequency of screening, and the use of other biomarkers have the potential to minimise the overdiagnosis associated with PSA screening. Several new biomarkers for individuals with raised PSA concentrations or those diagnosed with prostate cancer are likely to identify individuals who can be spared aggressive treatment. Several pharmacological agents such as 5α-reductase inhibitors and aspirin could prevent development of prostate cancer. In this Review, we discuss the present evidence and research questions regarding prevention, early detection of prostate cancer, and management of men either at high risk of prostate cancer or diagnosed with low-grade prostate cancer.

Prostate cancer is one of the leading causes of death from malignant disease among men in the developed world. One strategy to decrease the risk of death from this disease is screening with prostate-specific antigen (PSA); however, the extent of benefit and harm with such screening is under continuous debate. In December, 1994, 20 000 men born between 1930 and 1944, randomly sampled from the population register, were randomised by computer in a 1:1 ratio to either a screening group invited for PSA testing every 2 years (n=10 000) or to a control group not invited (n=10 000). Men in the screening group were invited up to the upper age limit (median 69, range 67–71 years) and only men with raised PSA concentrations were offered additional tests such as digital rectal examination and prostate biopsies. The primary endpoint was prostate-cancer specific mortality, analysed according to the intention-to-screen principle. The study is ongoing, with men who have not reached the upper age limit invited for PSA testing. This is the first planned report on cumulative prostate-cancer incidence and mortality calculated up to Dec 31, 2008. This study is registered as an International Standard Randomised Controlled Trial ISRCTN54449243. In each group, 48 men were excluded from the analysis because of death or emigration before the randomisation date, or prevalent prostate cancer. In men randomised to screening, 7578 (76%) of 9952 attended at least once. During a median follow-up of 14 years, 1138 men in the screening group and 718 in the control group were diagnosed with prostate cancer, resulting in a cumulative prostate-cancer incidence of 12·7% in the screening group and 8·2% in the control group (hazard ratio 1·64; 95% CI 1·50–1·80; p<0·0001). The absolute cumulative risk reduction of death from prostate cancer at 14 years was 0·40% (95% CI 0·17–0·64), from 0·90% in the control group to 0·50% in the screening group. The rate ratio for death from prostate cancer was 0·56 (95% CI 0·39–0·82; p=0·002) in the screening compared with the control group. The rate ratio of death from prostate cancer for attendees compared with the control group was 0·44 (95% CI 0·28–0·68; p=0·0002). Overall, 293 (95% CI 177–799) men needed to be invited for screening and 12 to be diagnosed to prevent one prostate cancer death. This study shows that prostate cancer mortality was reduced almost by half over 14 years. However, the risk of over-diagnosis is substantial and the number needed to treat is at least as high as in breast-cancer screening programmes. The benefit of prostate-cancer screening compares favourably to other cancer screening programs. The Swedish Cancer Society, the Swedish Research Council, and the National Cancer Institute.

The European Randomized Study of Screening for Prostate Cancer continues to show a 21% reduction in prostate-cancer mortality in the screening group, after 11 years of follow-up. The number of cancers that would need to be detected to prevent one prostate-cancer death is 37. Screening does not affect all-cause mortality. Screening for prostate cancer has remained controversial, despite results showing a significant reduction in the rate of death from prostate cancer (relative reduction, 20%) among men offered screening for prostate-specific antigen (PSA). 1 The European Randomized Study of Screening for Prostate Cancer (ERSPC) is a multicenter trial initiated in 1991 in the Netherlands and in Belgium, with five more European countries (Sweden, Finland, Italy, Spain, and Switzerland) joining between 1994 and 1998. Recruitment was completed in these centers between 1995 and 2003. Later, France also joined, with enrollment in 2000–2005, but data from the French cohort were not included in the . . .

Purpose The majority of unconscious patients after cardiac arrest (CA) do not fulfill guideline criteria for a likely poor outcome, their prognosis is considered “indeterminate”. We compared brain injury markers in blood for prediction of good outcome and for identifying false positive predictions of poor outcome as recommended by guidelines. Methods Retrospective analysis of prospectively collected serum samples at 24, 48 and 72 h post arrest within the Target Temperature Management after out-of-hospital cardiac arrest (TTM)-trial. Clinically available markers neuron-specific enolase (NSE) and S100B, and novel markers neurofilament light chain (NFL), total tau, ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) and glial fibrillary acidic protein (GFAP) were analysed. Normal levels with a priori cutoffs specified by reference laboratories or defined from literature were used to predict good outcome (no to moderate disability, Cerebral Performance Category scale 1–2) at 6 months. Results Seven hundred and seventeen patients were included. Normal NFL, tau and GFAP had the highest sensitivities (97.2–98% of poor outcome patients had abnormal serum levels) and NPV (normal levels predicted good outcome in 87–95% of patients). Normal S100B and NSE predicted good outcome with NPV 76–82.2%. Normal NSE correctly identified 67/190 (35.3%) patients with good outcome among those classified as “indeterminate outcome” by guidelines. Five patients with single pathological prognostic findings despite normal biomarkers had good outcome. Conclusion Low levels of brain injury markers in blood are associated with good neurological outcome after CA. Incorporating biomarkers into neuroprognostication may help prevent premature withdrawal of life-sustaining therapy.

In this trial, investigators tested the effect of prostate-specific–antigen testing on the death rate from prostate cancer in more than 162,000 men between the ages of 55 and 69 years in seven European countries. A significant reduction in prostate-cancer mortality was found after a median follow-up of 9 years. Overdiagnosis and overtreatment were important limitations of the screening program. Measurement of serum prostate-specific antigen (PSA), a biomarker for prostate cancer, 1 is useful for the detection of early prostate cancer. 2 Nevertheless, the effect of PSA-based screening on prostate-cancer mortality remains unclear. 3 The European Randomized Study of Screening for Prostate Cancer (ERSPC) was initiated in the early 1990s to determine whether a reduction of 25% in prostate-cancer mortality could be achieved by PSA-based screening. 4 Preliminary data from this study have been published and can be accessed at www.erspc.org. Another randomized screening trial in the United States, the Prostate, Lung, Colon, and Ovarian (PLCO) Cancer Screening Trial, was initiated around the same . . .

Background Pre-hospital circumstances, cardiac arrest characteristics, comorbidities and clinical status on admission are strongly associated with outcome after out-of-hospital cardiac arrest (OHCA). Early prediction of outcome may inform prognosis, tailor therapy and help in interpreting the intervention effect in heterogenous clinical trials. This study aimed to create a model for early prediction of outcome by artificial neural networks (ANN) and use this model to investigate intervention effects on classes of illness severity in cardiac arrest patients treated with targeted temperature management (TTM). Methods Using the cohort of the TTM trial, we performed a post hoc analysis of 932 unconscious patients from 36 centres with OHCA of a presumed cardiac cause. The patient outcome was the functional outcome, including survival at 180 days follow-up using a dichotomised Cerebral Performance Category (CPC) scale with good functional outcome defined as CPC 1–2 and poor functional outcome defined as CPC 3–5. Outcome prediction and severity class assignment were performed using a supervised machine learning model based on ANN. Results The outcome was predicted with an area under the receiver operating characteristic curve (AUC) of 0.891 using 54 clinical variables available on admission to hospital, categorised as background, pre-hospital and admission data. Corresponding models using background, pre-hospital or admission variables separately had inferior prediction performance. When comparing the ANN model with a logistic regression-based model on the same cohort, the ANN model performed significantly better ( p  = 0.029). A simplified ANN model showed promising performance with an AUC above 0.852 when using three variables only: age, time to ROSC and first monitored rhythm. The ANN-stratified analyses showed similar intervention effect of TTM to 33 °C or 36 °C in predefined classes with different risk of a poor outcome. Conclusion A supervised machine learning model using ANN predicted neurological recovery, including survival excellently, and outperformed a conventional model based on logistic regression. Among the data available at the time of hospitalisation, factors related to the pre-hospital setting carried most information. ANN may be used to stratify a heterogenous trial population in risk classes and help determine intervention effects across subgroups.

Background Prognostication of neurological outcome in patients who remain comatose after cardiac arrest resuscitation is complex. Clinical variables, as well as biomarkers of brain injury, cardiac injury, and systemic inflammation, all yield some prognostic value. We hypothesised that cumulative information obtained during the first three days of intensive care could produce a reliable model for predicting neurological outcome following out-of-hospital cardiac arrest (OHCA) using artificial neural network (ANN) with and without biomarkers. Methods We performed a post hoc analysis of 932 patients from the Target Temperature Management trial. We focused on comatose patients at 24, 48, and 72 h post-cardiac arrest and excluded patients who were awake or deceased at these time points. 80% of the patients were allocated for model development (training set) and 20% for internal validation (test set). To investigate the prognostic potential of different levels of biomarkers (clinically available and research-grade), patients’ background information, and intensive care observation and treatment, we created three models for each time point: (1) clinical variables, (2) adding clinically accessible biomarkers, e.g., neuron-specific enolase (NSE) and (3) adding research-grade biomarkers, e.g., neurofilament light (NFL). Patient outcome was the dichotomised Cerebral Performance Category (CPC) at six months; a good outcome was defined as CPC 1–2 whilst a poor outcome was defined as CPC 3–5. The area under the receiver operating characteristic curve (AUROC) was calculated for all test sets. Results AUROC remained below 90% when using only clinical variables throughout the first three days in the ICU. Adding clinically accessible biomarkers such as NSE, AUROC increased from 82 to 94% ( p  < 0.01). The prognostic accuracy remained excellent from day 1 to day 3 with an AUROC at approximately 95% when adding research-grade biomarkers. The models which included NSE after 72 h and NFL on any of the three days had a low risk of false-positive predictions while retaining a low number of false-negative predictions. Conclusions In this exploratory study, ANNs provided good to excellent prognostic accuracy in predicting neurological outcome in comatose patients post OHCA. The models which included NSE after 72 h and NFL on all days showed promising prognostic performance.