Examining Perceptions of Online Harassment among Constables in England and Wales

The ubiquity of the Internet and computer technology has enabled individuals to engage in bullying, threats, and harassing communications online. Limited research has found that local line officers may not view these offenses as serious compared to real world crimes despite their negative physical and emotional impact on victims. The perceptions of officers can produce poor interactions with victims during calls for service, particularly victim blaming, which can reduce citizens’ confidence in police agencies generally. However, local law enforcement agencies are increasingly mandated to respond to these cases, calling to question how their views may impact the community. This study examined the attitudinal and demographic factors associated with the negative views of online harassment and bullying within a sample of 1,348 constables from 34 local agencies across England and Wales. The study found that constables with negative views toward cybercrimes and worked in agencies with inconsistent messaging related to online crimes were more likely to view online harassment as less serious and believe that these offenses could be avoided by victims. The implications of this study for local police staff and command are discussed in detail

Repression of human papillomavirus oncogene expression under hypoxia is mediated by PI3K/mTORC2/AKT signaling

Oncogenic HPV types are major human carcinogens. Under hypoxia, HPV-positive cancer cells can repress the viral E6/E7 oncogenes and induce a reversible growth arrest. This response could contribute to therapy resistance, immune evasion, and tumor recurrence upon reoxygenation. Here, we uncover evidence that HPV oncogene repression is mediated by hypoxia-induced activation of canonical PI3K/mTORC2/AKT signaling. AKT-dependent downregulation of E6/E7 is only observed under hypoxia and occurs, at least in part, at the transcriptional level. Quantitative proteome analyses identify additional factors as candidates to be involved in AKT-dependent E6/E7 repression and/or hypoxic PI3K/mTORC2/AKT activation. These results connect PI3K/mTORC2/AKT signaling with HPV oncogene regulation, providing new mechanistic insights into the cross talk between oncogenic HPVs and their host cells.Hypoxia is linked to therapeutic resistance and poor clinical prognosis for many tumor entities, including human papillomavirus (HPV)-positive cancers. Notably, HPV-positive cancer cells can induce a dormant state under hypoxia, characterized by a reversible growth arrest and strong repression of viral E6/E7 oncogene expression, which could contribute to therapy resistance, immune evasion and tumor recurrence. The present work aimed to gain mechanistic insights into the pathway(s) underlying HPV oncogene repression under hypoxia. We show that E6/E7 downregulation is mediated by hypoxia-induced stimulation of AKT signaling. Ablating AKT function in hypoxic HPV-positive cancer cells by using chemical inhibitors efficiently counteracts E6/E7 repression. Isoform-specific activation or downregulation of AKT1 and AKT2 reveals that both AKT isoforms contribute to hypoxic E6/E7 repression and act in a functionally redundant manner. Hypoxic AKT activation and consecutive E6/E7 repression is dependent on the activities of the canonical upstream AKT regulators phosphoinositide 3-kinase (PI3K) and mechanistic target of rapamycin (mTOR) complex 2 (mTORC2). Hypoxic downregulation of E6/E7 occurs, at least in part, at the transcriptional level. Modulation of E6/E7 expression by the PI3K/mTORC2/AKT cascade is hypoxia specific and not observed in normoxic HPV-positive cancer cells. Quantitative proteome analyses identify additional factors as candidates to be involved in hypoxia-induced activation of the PI3K/mTORC2/AKT signaling cascade and in the AKT-dependent repression of the E6/E7 oncogenes under hypoxia. Collectively, these data uncover a functional key role of the PI3K/mTORC2/AKT signaling cascade for viral oncogene repression in hypoxic HPV-positive cancer cells and provide new insights into the poorly understood cross talk between oncogenic HPVs and their host cells under hypoxia

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

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

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

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

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. RESULTS: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. CONCLUSIONS: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies

Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation.

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition of progenitor and stem-cell-like features. Here, we provide novel stemness indices for assessing the degree of oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic and epigenetic feature sets derived from non-transformed pluripotent stem cells and their differentiated progeny. Using OCLR, we were able to identify previously undiscovered biological mechanisms associated with the dedifferentiated oncogenic state. Analyses of the tumor microenvironment revealed unanticipated correlation of cancer stemness with immune checkpoint expression and infiltrating immune cells. We found that the dedifferentiated oncogenic phenotype was generally most prominent in metastatic tumors. Application of our stemness indices to single-cell data revealed patterns of intra-tumor molecular heterogeneity. Finally, the indices allowed for the identification of novel targets and possible targeted therapies aimed at tumor differentiation