BlackOPs: Increasing confidence in variant detection through mappability filtering

Identifying variants using high-throughput sequen-cing data is currently a challenge because true biological variants can be indistinguishable from technical artifacts. One source of technical arti-fact results from incorrectly aligning experimen-tally observed sequences to their true genomic origin (‘mismapping’) and inferring differences in mismapped sequences to be true variants. We de-veloped BlackOPs, an open-source tool that simu-lates experimental RNA-seq and DNA whole exome sequences derived from the reference genome, aligns these sequences by custom parameters, detects variants and outputs a blacklist of positions and alleles caused by mismapping. Blacklist

SigFuge: Single gene clustering of RNA-seq reveals differential isoform usage among cancer samples

High-throughput sequencing technologies, including RNA-seq, have made it possible to move beyond gene expression analysis to study transcriptional events including alternative splicing and gene fusions. Furthermore, recent studies in cancer have suggested the importance of identifying transcriptionally altered loci as biomarkers for improved prognosis and therapy. While many statistical methods have been proposed for identifying novel transcriptional events with RNA-seq, nearly all rely on contrasting known classes of samples, such as tumor and normal. Few tools exist for the unsupervised discovery of such events without class labels. In this paper, we present SigFuge for identifying genomic loci exhibiting differential transcription patterns across many RNA-seq samples. SigFuge combines clustering with hypothesis testing to identify genes exhibiting alternative splicing, or differences in isoform expression. We apply SigFuge to RNA-seq cohorts of 177 lung and 279 head and neck squamous cell carcinoma samples from the Cancer Genome Atlas, and identify several cases of differential isoform usage including CDKN2A, a tumor suppressor gene known to be inactivated in a majority of lung squamous cell tumors. By not restricting attention to known sample stratifications, SigFuge offers a novel approach to unsupervised screening of genetic loci across RNA-seq cohorts. SigFuge is available as an R package through Bioconductor

ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

Summary: Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item-consensus and cluster-consensus plots. These new features provide users with detailed information that enable more specific decisions in unsupervised class discovery

The LKB1 Tumor Suppressor as a Biomarker in Mouse and Human Tissues

Germline mutations in the LKB1 gene (also known as STK11) cause the Peutz-Jeghers Syndrome, and somatic loss of LKB1 has emerged as causal event in a wide range of human malignancies, including melanoma, lung cancer, and cervical cancer. The LKB1 protein is a serine-threonine kinase that phosphorylates AMP-activated protein kinase (AMPK) and other downstream targets. Conditional knockout studies in mouse models have consistently shown that LKB1 loss promotes a highly-metastatic phenotype in diverse tissues, and human studies have demonstrated a strong association between LKB1 inactivation and tumor recurrence. Furthermore, LKB1 deficiency confers sensitivity to distinct classes of anticancer drugs. The ability to reliably identify LKB1-deficient tumors is thus likely to have important prognostic and predictive implications. Previous research studies have employed polyclonal antibodies with limited success, and there is no widely-employed immunohistochemical assay for LKB1. Here we report an assay based on a rabbit monoclonal antibody that can reliably detect endogenous LKB1 protein (and its absence) in mouse and human formalin-fixed, paraffin-embedded tissues. LKB1 protein levels determined through this assay correlated strongly with AMPK phosphorylation both in mouse and human tumors, and with mRNA levels in human tumors. Our studies fully validate this immunohistochemical assay for LKB1 in paraffin-embedded formalin tissue sections. This assay should be broadly useful for research studies employing mouse models and also for the development of human tissue-based assays for LKB1 in diverse clinical settings

Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

Identifying somatic mutations is critical for cancer genome characterization and for prioritizing patient treatment. DNA whole exome sequencing (DNA-WES) is currently the most popular technology; however, this yields low sensitivity in low purity tumors. RNA sequencing (RNA-seq) covers the expressed exome with depth proportional to expression. We hypothesized that integrating DNA-WES and RNA-seq would enable superior mutation detection versus DNA-WES alone. We developed a first-of-its-kind method, called UNCeqR, that detects somatic mutations by integrating patient-matched RNA-seq and DNA-WES. In simulation, the integrated DNA and RNA model outperformed the DNA-WES only model. Validation by patient-matched whole genome sequencing demonstrated superior performance of the integrated model over DNA-WES only models, including a published method and published mutation profiles. Genome-wide mutational analysis of breast and lung cancer cohorts (n = 871) revealed remarkable tumor genomics properties. Low purity tumors experienced the largest gains in mutation detection by integrating RNA-seq and DNA-WES. RNA provided greater mutation signal than DNA in expressed mutations. Compared to earlier studies on this cohort, UNCeqR increased mutation rates of driver and therapeutically targeted genes (e.g. PIK3CA, ERBB2 and FGFR2). In summary, integrating RNA-seq with DNA-WES increases mutation detection performance, especially for low purity tumors

Development of an invasively monitored porcine model of acetaminophen-induced acute liver failure

Background: The development of effective therapies for acute liver failure (ALF) is limited by our knowledge of the pathophysiology of this condition, and the lack of suitable large animal models of acetaminophen toxicity. Our aim was to develop a reproducible invasively-monitored porcine model of acetaminophen-induced ALF. Method: 35kg pigs were maintained under general anaesthesia and invasively monitored. Control pigs received a saline infusion, whereas ALF pigs received acetaminophen intravenously for 12 hours to maintain blood concentrations between 200-300 mg/l. Animals surviving 28 hours were euthanased. Results: Cytochrome p450 levels in phenobarbital pre-treated animals were significantly higher than non pre-treated animals (300 vs 100 pmol/mg protein). Control pigs (n=4) survived 28-hour anaesthesia without incident. Of nine pigs that received acetaminophen, four survived 20 hours and two survived 28 hours. Injured animals developed hypotension (mean arterial pressure; 40.8+/-5.9 vs 59+/-2.0 mmHg), increased cardiac output (7.26+/-1.86 vs 3.30+/-0.40 l/min) and decreased systemic vascular resistance (8.48+/-2.75 vs 16.2+/-1.76 mPa/s/m3). Dyspnoea developed as liver injury progressed and the increased pulmonary vascular resistance (636+/-95 vs 301+/-26.9 mPa/s/m3) observed may reflect the development of respiratory distress syndrome. Liver damage was confirmed by deterioration in pH (7.23+/-0.05 vs 7.45+/-0.02) and prothrombin time (36+/-2 vs 8.9+/-0.3 seconds) compared with controls. Factor V and VII levels were reduced to 9.3 and 15.5% of starting values in injured animals. A marked increase in serum AST (471.5+/-210 vs 42+/-8.14) coincided with a marked reduction in serum albumin (11.5+/-1.71 vs 25+/-1 g/dL) in injured animals. Animals displayed evidence of renal impairment; mean creatinine levels 280.2+/-36.5 vs 131.6+/-9.33 mumol/l. Liver histology revealed evidence of severe centrilobular necrosis with coagulative necrosis. Marked renal tubular necrosis was also seen. Methaemoglobin levels did not rise >5%. Intracranial hypertension was not seen (ICP monitoring), but there was biochemical evidence of encephalopathy by the reduction of Fischer's ratio from 5.6 +/- 1.1 to 0.45 +/- 0.06. Conclusion: We have developed a reproducible large animal model of acetaminophen-induced liver failure, which allows in-depth investigation of the pathophysiological basis of this condition. Furthermore, this represents an important large animal model for testing artificial liver support systems

The next steps in next-gen sequencing of cancer genomes

The necessary infrastructure to carry out genomics-driven oncology is now widely available and has resulted in the exponential increase in characterized cancer genomes. While a subset of genomic alterations is clinically actionable, the majority of somatic events remain classified as variants of unknown significance and will require functional characterization. A careful cataloging of the genomic alterations and their response to therapeutic intervention should allow the compilation of an “actionability atlas” and the creation of a genomic taxonomy stratified by tumor type and oncogenic pathway activation. The next phase of genomic medicine will therefore require talented bioinformaticians, genomic navigators, and multidisciplinary approaches to decode complex cancer genomes and guide potential therapy. Equally important will be the ethical and interpretable return of results to practicing oncologists. Finally, the integration of genomics into clinical trials is likely to speed the development of predictive biomarkers of response to targeted therapy as well as define pathways to acquired resistance