SCRIB expression is deregulated in human prostate cancer, and its deficiency in mice promotes prostate neoplasia

Loss of cellular polarity is a hallmark of epithelial cancers, raising the possibility that regulators of polarity have a role in suppressing tumorigenesis. The Scribble complex is one of at least three interacting protein complexes that have a critical role in establishing and maintaining epithelial polarity. In human colorectal, breast, and endometrial cancers, expression of the Scribble complex member SCRIB is often mislocalized and deregulated. Here, we report that Scrib is indispensable for prostate homeostasis in mice. Scrib heterozygosity initiated prostate hyperplasia, while targeted biallelic Scrib loss predisposed mice to prostate intraepithelial neoplasia. Mechanistically, Scrib was shown to negatively regulate the MAPK cascade to suppress tumorigenesis. Further analysis revealed that prostate-specific loss of Scrib in mice combined with expression of an oncogenic Kras mutation promoted the progression of prostate cancer that recapitulated the human disease. The clinical significance of the work in mice was highlighted by our observation that SCRIB deregulation strongly correlated with poor survival in human prostate cancer. These data suggest that the polarity network could provide a new avenue for therapeutic intervention

Feasibility of a cognitive behavioural group intervention to reduce fear of falling and associated avoidance of activity in community-living older people: a process evaluation

BACKGROUND: Fear of falling and associated avoidance of activity are common among older people and may have negative consequences in terms of functional decline, quality of life and institutionalisation. We evaluated the effects of a cognitive behavioural group intervention to reduce fear of falling and associated avoidance of activity among older persons. This intervention showed favourable effects on fear of falling, avoidance of activity, daily activity, and several secondary outcomes. The aim of the present study is to assess the feasibility of this cognitive behavioural group intervention for participants and facilitators. METHODS: The intervention consisted of eight weekly group sessions lasting two hours each and a booster session after six months. Self-administered questionnaires, registration forms and interviews were used to collect data from participants (n = 168) and facilitators (n = 6) on the extent to which the intervention was performed according to protocol, participant attendance, participant adherence, and participants' and facilitators' opinion of the intervention. Quantitative data from the questionnaires and registration forms were analysed by means of descriptive statistics. Qualitative data were categorised based on matching contents of the answers. RESULTS: Facilitators reported no major protocol deviations. Twenty-six percent of the participants withdrew before the start of the programme. Of the persons who started the programme, 84% actually completed it. The participants reported their adherence as good, but facilitators had a less favourable opinion of this. The majority of participants still reported substantial benefits from the programme after six and twelve months of follow-up (71% and 61% respectively). Both participants and facilitators provided suggestions for improvement of the intervention. CONCLUSION: Results of this study show that the current cognitive behavioural group intervention is feasible for both participants and facilitators and fits in well with regular care. Minor refinement of the intervention, however, is warranted to further improve intervention effectiveness and efficiency. Based on these positive findings, we recommend implementing a refined version of this effective and feasible intervention in regular care. TRIAL REGISTRATION: ISRCTN43792817

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Оценка надежности высоконадежных систем с учетом ЗИП

Предложены приближенные верхние и нижние оценки коэффициента готовности высоконадежной восстанавливаемой системы со структурной избыточностью. Полученные расчетные соотношения могут использоваться для оценки надежности высоконадежных систем с учетом различных стратегий пополнения ЗИП

A feasibility study for NOn-Traditional providers to support the management of Elderly People with Anxiety and Depression: the NOTEPAD study Protocol

BACKGROUND: Anxiety and depression are common among older people, with up to 20% reporting such symptoms, and the prevalence increases with co-morbid chronic physical health problems. Access to treatment for anxiety and depression in this population is poor due to a combination of factors at the level of patient, practitioner and healthcare system. There is evidence to suggest that older people with anxiety and/or depression may benefit both from one-to-one interventions and group social or educational activities, which reduce loneliness, are participatory and offer some activity. Non-traditional providers (support workers) working within third-sector (voluntary) organisations are a valuable source of expertise within the community but are under-utilised by primary care practitioners. Such a resource could increase access to care, and be less stigmatising and more acceptable for older people. METHODS: The study is in three phases and this paper describes the protocol for phase III, which will evaluate the feasibility of recruiting general practices and patients into the study, and determine whether support workers can deliver the intervention to older people with sufficient fidelity and whether this approach is acceptable to patients, general practitioners and the third-sector providers. Phase III of the NOTEPAD study is a randomised controlled trial (RCT) that is individually randomised. It recruited participants from approximately six general practices in the UK. In total, 100 participants aged 65 years and over who score 10 or more on PHQ9 or GAD7 for anxiety or depression will be recruited and randomised to the intervention or usual general practice care. A mixed methods approach will be used and follow-up will be conducted 12 weeks post-randomisation. DISCUSSION: This study will inform the design and methods of a future full-scale RCT. TRIAL REGISTRATION: ISRCTN, ID: ISRCTN16318986 . Registered 10 November 2016. The ISRCTN registration is in line with the World Health Organization Trial Registration Data Set. The present paper represents the original version of the protocol. Any changes to the protocol will be communicated to ISRCTN

A Neural Network Model Combining [-2]proPSA, freePSA, Total PSA, Cathepsin D, and Thrombospondin-1 Showed Increased Accuracy in the Identification of Clinically Significant Prostate Cancer

Background: The Prostate Health Index (PHI) and Proclarix (PCLX) have been proposed as blood-based tests for prostate cancer (PCa). In this study, we evaluated the feasibility of an artificial neural network (ANN)-based approach to develop a combinatorial model including PHI and PCLX biomarkers to recognize clinically significant PCa (csPCa) at initial diagnosis. Methods: To this aim, we prospectively enrolled 344 men from two different centres. All patients underwent radical prostatectomy (RP). All men had a prostate-specific antigen (PSA) between 2 and 10 ng/mL. We used an artificial neural network to develop models that can identify csPCa efficiently. As inputs, the model uses [-2]proPSA, freePSA, total PSA, cathepsin D, thrombospondin, and age. Results: The output of the model is an estimate of the presence of a low or high Gleason score PCa defined at RP. After training on a dataset of up to 220 samples and optimization of the variables, the model achieved values as high as 78% for sensitivity and 62% for specificity for all-cancer detection compared with those of PHI and PCLX alone. For csPCa detection, the model showed 66% (95% CI 66–68%) for sensitivity and 68% (95% CI 66–68%) for specificity. These values were significantly different compared with those of PHI (p < 0.0001 and 0.0001, respectively) and PCLX (p = 0.0003 and 0.0006, respectively) alone. Conclusions: Our preliminary study suggests that combining PHI and PCLX biomarkers may help to estimate, with higher accuracy, the presence of csPCa at initial diagnosis, allowing a personalized treatment approach. Further studies training the model on larger datasets are strongly encouraged to support the efficiency of this approach