Identification of cancer driver gene mutations is crucial for advancing cancer therapeutics. Due to the overwhelming number of passenger mutations in the human tumor genome, it is difficult to pinpoint causative driver genes. Using transposon mutagenesis in mice many laboratories have conducted forward genetic screens and identified thousands of candidate driver genes that are highly relevant to human cancer. Unfortunately, this information is difficult to access and utilize because it is scattered across multiple publications using different mouse genome builds and strength metrics. To improve access to these findings and facilitate meta-analyses, we developed the Candidate Cancer Gene Database (CCGD, http://ccgd-starrlab.oit.umn.edu/). The CCGD is a manually curated database containing a unified description of all identified candidate driver genes and the genomic location of transposon common insertion sites (CISs) from all currently published transposon-based screens. To demonstrate relevance to human cancer, we performed a modified gene set enrichment analysis using KEGG pathways and show that human cancer pathways are highly enriched in the database. We also used hierarchical clustering to identify pathways enriched in blood cancers compared to solid cancers. The CCGD is a novel resource available to scientists interested in the identification of genetic drivers of cancer.

Acute liver failure (ALF), an often fatal condition characterized by massive hepatocyte necrosis, is frequently caused by drug poisoning, particularly with acetaminophen (N-acetyl-p-aminophenol/APAP). Hepatocyte necrosis is consecutive to glutathione (GSH) depletion and mitochondrial damage caused by reactive oxygen species (ROS) overproduction. Magnolol, one major phenolic constituent of Magnolia officinalis, have been known to exhibit potent antioxidative activity. In this study, the anti-hepatotoxic activity of magnolol on APAP-induced toxicity in the Sprague-Dawley rat liver was examined. After evaluating the changes of several biochemical parameters in serum, the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and lactate dehydrogenase (LDH) were elevated by APAP (500 mg/kg) intraperitoneal administration (8 and 24 h) and reduced by treatment with magnolol (0.5 h after APAP administration; 0.01, 0.1, and 1 mug/kg). Histological changes around the hepatic central vein, lipid peroxidation (thiobarbituric acid-reactive substance/TBARS), and GSH depletion in liver tissue induced by APAP were also recovered by magnolol treatment. The data show that oxidative stress followed by lipid peroxidation may play a very important role in the pathogenesis of APAP-induced hepatic injury; treatment with lipid-soluble antioxidant, magnolol, exerts anti-hepatotoxic activity. Our study points out the potential interest of magnolol in the treatment of toxic ALF.

In 2002, Ker-Chau Li introduced the liquid association measure to characterize three-way interactions between genes, and developed a computationally efficient estimator that can be used to screen gene expression microarray data for such interactions. That study, and others published since then, have established the biological validity of the method, and clearly demonstrated it to be a useful tool for the analysis of genomic data sets. To build on this work, we have sought a parametric family of multivariate distributions with the flexibility to model the full range of trivariate dependencies encompassed by liquid association. Such a model could situate liquid association within a formal inferential theory. In this article, we describe such a family of distributions, a trivariate, conditional normal model having Gaussian univariate marginal distributions, and in fact including the trivariate Gaussian family as a special case. Perhaps the most interesting feature of the distribution is that the parameterization naturally parses the three-way dependence structure into a number of distinct, interpretable components. One of these components is very closely aligned to liquid association, and is developed as a measure we call modified liquid association. We develop two methods for estimating this quantity, and propose statistical tests for the existence of this type of dependence. We evaluate these inferential methods in a set of simulations and illustrate their use in the analysis of publicly available experimental data.

Hirschsprung disease (HSCR) is a neurocristopathy characterized by absence of intramural ganglion cells along variable lengths of the gastrointestinal tract. The HSCR phenotype is highly variable with respect to gender, length of aganglionosis, familiality and the presence of additional anomalies. By molecular genetic analysis, a minimum of 11 neuro-developmental genes (RET, GDNF, NRTN, SOX10, EDNRB, EDN3, ECE1, ZFHX1B, PHOX2B, KIAA1279, TCF4) are known to harbor rare, high-penetrance mutations that confer a large risk to the bearer. In addition, two other genes (RET, NRG1) harbor common, low-penetrance polymorphisms that contribute only partially to risk and can act as genetic modifiers. To broaden this search, we examined whether a set of 67 proven and candidate HSCR genes harbored additional modifier alleles. In this pilot study, we utilized a custom-designed array CGH with ∼33,000 test probes at an average resolution of ∼185 bp to detect gene-sized or smaller copy number variants (CNVs) within these 67 genes in 18 heterogeneous HSCR patients. Using stringent criteria, we identified CNVs at three loci (MAPK10, ZFHX1B, SOX2) that are novel, involve regulatory and coding sequences of neuro-developmental genes, and show association with HSCR in combination with other congenital anomalies. Additional CNVs are observed under relaxed criteria. Our research suggests a role for CNVs in HSCR and, importantly, emphasizes the role of variation in regulatory sequences. A much larger study will be necessary both for replication and for identifying the full spectrum of small CNV effects.