Violence on campus: Practical recommendations for legal educators

Recent rampage killings compel greater attention to anger and violence on the college campus. In each of these tragic mass murders, vengeful individuals sought to address perceived grievances against faculty and/or other employees of the university. In each of these situations, numerous clues of impending violence were evident. Sadly, however, in each of these cases the schools failed to take preventive actions. While prediction of violent behavior will never be an exact science, universities must begin to enact violence prevention strategies. Maintaining an attitude that \u27this couldn\u27t happen here\u27 hampers the necessary education of faculty, staff, and security personnel. Our purpose in this paper is to provide guidelines for dealing with angry students. Additionally, we will point out characteristics of potentially violent students and suggest some violence prevention measures. Although we will touch on security issues, our goal is to help law educators prevent students from erupting violently rather than to stop a mass murder already in progress

Improved indel detection in DNA and RNA via realignment with ABRA2

Motivation: Genomic variant detection from next-generation sequencing has become established as an extremely important component of research and clinical diagnoses in both cancer and Mendelian disorders. Insertions and deletions (indels) are a common source of variation and can frequently impact functionality, thus making their detection vitally important. While substantial effort has gone into detecting indels from DNA, there is still opportunity for improvement. Further, detection of indels from RNA-Seq data has largely been an afterthought and offers another critical area for variant detection. Results: We present here ABRA2, a redesign of the original ABRA implementation that offers support for realignment of both RNA and DNA short reads. The process results in improved accuracy and scalability including support for human whole genomes. Results demonstrate substantial improvement in indel detection for a variety of data types, including those that were not previously supported by ABRA. Further, ABRA2 results in broad improvements to variant calling accuracy across a wide range of post-processing workflows including whole genomes, targeted exomes and transcriptome sequencing

Genetic determinants of the molecular portraits of epithelial cancers

The ability to characterize and predict tumor phenotypes is crucial to precision medicine. In this study, we present an integrative computational approach using a genome-wide association analysis and an Elastic Net prediction method to analyze the relationship between DNA copy number alterations and an archive of gene expression signatures. Across breast cancers, we are able to quantitatively predict many gene signatures levels within individual tumors with high accuracy based upon DNA copy number features alone, including proliferation status and Estrogen-signaling pathway activity. We can also predict many other key phenotypes, including intrinsic molecular subtypes, estrogen receptor status, and TP53 mutation. This approach is also applied to TCGA Pan-Cancer, which identify repeatedly predictable signatures across tumor types including immune features in lung squamous and basal-like breast cancers. These Elastic Net DNA predictors could also be called from DNA-based gene panels, thus facilitating their use as biomarkers to guide therapeutic decision making

Amplification of SOX4 promotes PI3K/Akt signaling in human breast cancer

Purpose: The PI3K/Akt signaling axis contributes to the dysregulation of many dominant features in breast cancer including cell proliferation, survival, metabolism, motility, and genomic instability. While multiple studies have demonstrated that basal-like or triple-negative breast tumors have uniformly high PI3K/Akt activity, genomic alterations that mediate dysregulation of this pathway in this subset of highly aggressive breast tumors remain to be determined. Methods: In this study, we present an integrated genomic analysis based on the use of a PI3K gene expression signature as a framework to analyze orthogonal genomic data from human breast tumors, including RNA expression, DNA copy number alterations, and protein expression. In combination with data from a genome-wide RNA-mediated interference screen in human breast cancer cell lines, we identified essential genetic drivers of PI3K/Akt signaling. Results: Our in silico analyses identified SOX4 amplification as a novel modulator of PI3K/Akt signaling in breast cancers and in vitro studies confirmed its role in regulating Akt phosphorylation. Conclusions: Taken together, these data establish a role for SOX4-mediated PI3K/Akt signaling in breast cancer and suggest that SOX4 may represent a novel therapeutic target and/or biomarker for current PI3K family therapies

A pan-cancer analysis of the frequency of DNA alterations across cell cycle activity levels

Pan-cancer genomic analyses based on the magnitude of pathway activity are currently lacking. Focusing on the cell cycle, we examined the DNA mutations and chromosome arm-level aneuploidy within tumours with low, intermediate and high cell-cycle activity in 9515 pan-cancer patients with 32 different tumour types. Boxplots showed that cell-cycle activity varied broadly across and within all cancers. TP53 and PIK3CA mutations were common in all cell cycle score (CCS) tertiles but with increasing frequency as cell-cycle activity levels increased (P < 0.001). Mutations in BRAF and gains in 16p were less frequent in CCS High tumours (P < 0.001). In Kaplan–Meier analysis, patients whose tumours were CCS Low had a longer Progression Free Interval (PFI) relative to Intermediate or High (P < 0.001) and this significance remained in multivariable analysis (CCS Intermediate: HR = 1.37; 95% CI 1.17–1.60, CCS High: 1.54; 1.29–1.84, CCS Low = Ref). These results demonstrate that whilst similar DNA alterations can be found at all cell-cycle activity levels, some notable exceptions exist. Moreover, independent prognostic information can be derived on a pan-cancer level from a simple measure of cell-cycle activity

Assembly-based inference of B-cell receptor repertoires from short read RNA sequencing data with V'DJer

Motivation: B-cell receptor (BCR) repertoire profiling is an important tool for understanding the biology of diverse immunologic processes. Current methods for analyzing adaptive immune receptor repertoires depend upon PCR amplification of VDJ rearrangements followed by long read amplicon sequencing spanning the VDJ junctions. While this approach has proven to be effective, it is frequently not feasible due to cost or limited sample material. Additionally, there are many existing datasets where short-read RNA sequencing data are available but PCR amplified BCR data are not. Results: We present here V'DJer, an assembly-based method that reconstructs adaptive immune receptor repertoires from short-read RNA sequencing data. This method captures expressed BCR loci from a standard RNA-seq assay. We applied this method to 473 Melanoma samples from The Cancer Genome Atlas and demonstrate V'DJer's ability to accurately reconstruct BCR repertoires from short read mRNA-seq data

Virus expression detection reveals RNA-sequencing contamination in TCGA

Background: Contamination of reagents and cross contamination across samples is a long-recognized issue in molecular biology laboratories. While often innocuous, contamination can lead to inaccurate results. Cantalupo et al., for example, found HeLa-derived human papillomavirus 18 (H-HPV18) in several of The Cancer Genome Atlas (TCGA) RNA-sequencing samples. This work motivated us to assess a greater number of samples and determine the origin of possible contaminations using viral sequences. To detect viruses with high specificity, we developed the publicly available workflow, VirDetect, that detects virus and laboratory vector sequences in RNA-seq samples. We applied VirDetect to 9143 RNA-seq samples sequenced at one TCGA sequencing center (28/33 cancer types) over 5 years. Results: We confirmed that H-HPV18 was present in many samples and determined that viral transcripts from H-HPV18 significantly co-occurred with those from xenotropic mouse leukemia virus-related virus (XMRV). Using laboratory metadata and viral transcription, we determined that the likely contaminant was a pool of cell lines known as the "common reference", which was sequenced alongside TCGA RNA-seq samples as a control to monitor quality across technology transitions (i.e. microarray to GAII to HiSeq), and to link RNA-seq to previous generation microarrays that standardly used the "common reference". One of the cell lines in the pool was a laboratory isolate of MCF-7, which we discovered was infected with XMRV; another constituent of the pool was likely HeLa cells. Conclusions: Altogether, this indicates a multi-step contamination process. First, MCF-7 was infected with an XMRV. Second, this infected cell line was added to a pool of cell lines, which contained HeLa. Finally, RNA from this pool of cell lines contaminated several TCGA tumor samples most-likely during library construction. Thus, these human tumors with H-HPV or XMRV reads were likely not infected with H-HPV 18 or XMRV