Michael B. Mann

A multivariate analysis of 59 candidate genes in personality traits: the temperament and character inventory

Cloninger (Cloninger CR. Neurogenetic adaptive mechanisms in alcoholism. Science 1987: 236: 410–416) proposed three basic personality dimensions for temperament: novelty seeking, harm avoidance, and reward dependence. He suggested that novelty seeking primarily utilized dopamine pathways, harm avoidance utilized serotonin pathways, and reward dependence utilized norepinephrine pathways. Subsequently, one additional temperament dimension (persistence) and three character dimensions (cooperativeness, self‐directedness, and self‐transcendence) were added to form the temperament and character inventory (TCI). We have utilized a previously described multivariate analysis technique (Comings DE, Gade‐Andavolu R, Gonzalez N et al. Comparison of the role of dopamine, serotonin, and noradrenergic genes in ADHD, ODD and conduct disorder. Multivariate regression analysis of 20 genes. Clin Genet 2000: 57: 178–196; Comings DD, Gade‐Andavolu R, Gonzalez N et al. Multivariate analysis of associations of 42 genes in ADHD, ODD and conduct disorder. Clin Genet 2000: in press) to examine the relative role of 59 candidate genes in the seven TCI traits and test the hypothesis that specific personality traits were associated with specific genes. While there was some tendency for this to be true, a more important trend was the involvement of different ratios of functionally related groups of genes, and of different genotypes of the same genes, for different traits.

Transposon mutagenesis identifies genes and evolutionary forces driving gastrointestinal tract tumor progression

To provide a more comprehensive understanding of the genes and evolutionary forces driving colorectal cancer (CRC) progression, we performed Sleeping Beauty (SB) transposon mutagenesis screens in mice carrying sensitizing mutations in genes that act at different stages of tumor progression. This approach allowed us to identify a set of genes that appear to be highly relevant for CRC and to provide a better understanding of the evolutionary forces and systems properties of CRC. We also identified six genes driving malignant tumor progression and a new human CRC tumor-suppressor gene, ZNF292, that might also function in other types of cancer. Our comprehensive CRC data set provides a resource with which to develop new therapies for treating CRC.

Sleeping Beauty mutagenesis: exploiting forward genetic screens for cancer gene discovery

Sleeping Beauty (SB) is a powerful insertional mutagen used in somatic forward genetic screens to identify novel candidate cancer genes. In the past two years, SB has become widely adopted to model human pancreatic, hepatocellular, colorectal and neurological cancers to identify loci that participate in tumor initiation, progression and metastasis. Oncogenomic approaches have directly linked hundreds of genes identified by SB with human cancers, many with prognostic implications. These SB candidate cancer genes are aiding to prioritize punitive human cancer genes for follow-up studies and as possible biomarkers or therapeutic targets. This review highlights recent advances in SB cancer gene discovery, approaches to validate candidate cancer genes, and efforts to integrate SB data across all tumor types to prioritize drug development and tumor specificity.

Association between the phenylethanolamine N-methyltransferase gene and multiple sclerosis

Phenylethanolamine N-methyltransferase (PNMT), the terminal enzyme of the catecholamine biosynthesis pathway, catalyzes the conversion of norepinephrine (NE) to epinephrine (EPI). PNMT is a candidate gene for multiple sclerosis (MS) for two reasons. PNMT is known to map to a region identified in two genome screens for MS and it directly regulates the amounts of NE and EPI, both of which play a significant role in the modulation of the innate immune response. The frequencies of two promoter polymorphisms of the PNMT gene showed genetic association in a case-control study of 108 patients with MS and 774 ethnically and age-matched control subjects. In subjects with MS, significant differences in the frequency of the GG genotype at the G-387A marker and the AA genotype at the G-182A marker were observed. Additionally, when both markers were combined and evaluated, highly significant differences between the polymorphism distributions in patients with MS and control subjects were detected. The data suggest that these promoter polymorphisms of the PNMT gene, both independently and cumulatively, show association with MS.

Transposon mutagenesis identifies genes that cooperate with mutant Pten in breast cancer progression

Significance Triple-negative breast cancer (TNBC) is the most aggressive breast cancer subtype. Despite extensive cancer genome-sequencing efforts, there is still an incomplete understanding of the genetic networks driving TNBC. Here, we used Sleeping Beauty transposon mutagenesis to identify genes that cooperate with mutant Pten in the induction of TNBC. We identified 12 candidate TNBC trunk drivers and a larger number of progression genes. Subsequent functional validation studies identified eight human TNBC tumor suppressor genes, including the GATA-like transcriptional repressor TRPS1, which was shown to inhibit lung metastasis by deregulating the expression of multiple serpin and epithelial-to-mesenchymal transition (EMT) pathway genes. Our study provides a better understanding of the genetic forces driving TNBC and discovered genes with clinical importance in TNBC. Triple-negative breast cancer (TNBC) has the worst prognosis of any breast cancer subtype. To better understand the genetic forces driving TNBC, we performed a transposon mutagenesis screen in a phosphatase and tensin homolog (Pten) mutant mice and identified 12 candidate trunk drivers and a much larger number of progression genes. Validation studies identified eight TNBC tumor suppressor genes, including the GATA-like transcriptional repressor TRPS1. Down-regulation of TRPS1 in TNBC cells promoted epithelial-to-mesenchymal transition (EMT) by deregulating multiple EMT pathway genes, in addition to increasing the expression of SERPINE1 and SERPINB2 and the subsequent migration, invasion, and metastasis of tumor cells. Transposon mutagenesis has thus provided a better understanding of the genetic forces driving TNBC and discovered genes with potential clinical importance in TNBC.

Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq

A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline, Sleeping Beauty (SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumors major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.

Using targeted transgenic reporter mice to study promoter-specific p53 transcriptional activity

The p53 transcription factor modulates gene expression programs that induce cell cycle arrest, senescence, or apoptosis, thereby preventing tumorigenesis. However, the mechanisms by which these fates are selected are unclear. Our objective is to understand p53 target gene selection and, thus, enable its optimal manipulation for cancer therapy. We have generated targeted transgenic reporter mice in which EGFP expression is driven by p53 transcriptional activity at a response element from either the p21 or Puma promoter, which induces cell cycle arrest/senescence and apoptosis, respectively. We demonstrate that we could monitor p53 activity in vitro and in vivo and detect variations in p53 activity depending on the response element, tissue type, and stimulus, thereby validating our reporter system and illustrating its utility for preclinical drug studies. Our results also show that the sequence of the p53 response element itself is sufficient to strongly influence p53 target gene selection. Finally, we use our reporter system to provide evidence for p53 transcriptional activity during early embryogenesis, showing that p53 is active as early as embryonic day 3.5 and that p53 activity becomes restricted to embryonic tissue by embryonic day 6.5. The data from this study demonstrate that these reporter mice could serve as powerful tools to answer questions related to basic biology of the p53 pathway, as well as cancer therapy and drug discovery.

MelanomaDB: A Web Tool for Integrative Analysis of Melanoma Genomic Information to Identify Disease-Associated Molecular Pathways.

Despite on-going research, metastatic melanoma survival rates remain low and treatment options are limited. Researchers can now access a rapidly growing amount of molecular and clinical information about melanoma. This information is becoming difficult to assemble and interpret due to its dispersed nature, yet as it grows it becomes increasingly valuable for understanding melanoma. Integration of this information into a comprehensive resource to aid rational experimental design and patient stratification is needed. As an initial step in this direction, we have assembled a web-accessible melanoma database, MelanomaDB, which incorporates clinical and molecular data from publically available sources, which will be regularly updated as new information becomes available. This database allows complex links to be drawn between many different aspects of melanoma biology: genetic changes (e.g., mutations) in individual melanomas revealed by DNA sequencing, associations between gene expression and patient survival, data concerning drug targets, biomarkers, druggability, and clinical trials, as well as our own statistical analysis of relationships between molecular pathways and clinical parameters that have been produced using these data sets. The database is freely available at http://genesetdb.auckland.ac.nz/melanomadb/about.html. A subset of the information in the database can also be accessed through a freely available web application in the Illumina genomic cloud computing platform BaseSpace at http://www.biomatters.com/apps/melanoma-profiler-for-research. The MelanomaDB database illustrates dysregulation of specific signaling pathways across 310 exome-sequenced melanomas and in individual tumors and identifies the distribution of somatic variants in melanoma. We suggest that MelanomaDB can provide a context in which to interpret the tumor molecular profiles of individual melanoma patients relative to biological information and available drug therapies.

Transposon insertional mutagenesis models of cancer.

Transposon-based insertional mutagenesis in the mouse provides a powerful approach for identifying new cancer genes. Transposon insertions in cancer genes are selected during tumor development because of their positive effect on tumor growth, and the transposon insertion sites in tumors thus serve as tags for identifying new cancer genes. Direct comparisons of transposon-mutated genes in mouse tumors with mutated genes in human tumors can lend insight into the genes and signaling pathways that drive tumorigenesis. This is critical for prioritizing genes for further study, either for their efficacy as biomarkers or drug targets. In this article, we will introduce DNA transposon-based systems used for gene discovery in mice and discuss their application to identify candidate cancer genes in light of recently published tumor studies.

SBCDDB: Sleeping Beauty Cancer Driver Database for gene discovery in mouse models of human cancers

Abstract Large-scale oncogenomic studies have identified few frequently mutated cancer drivers and hundreds of infrequently mutated drivers. Defining the biological context for rare driving events is fundamentally important to increasing our understanding of the druggable pathways in cancer. Sleeping Beauty (SB) insertional mutagenesis is a powerful gene discovery tool used to model human cancers in mice. Our lab and others have published a number of studies that identify cancer drivers from these models using various statistical and computational approaches. Here, we have integrated SB data from primary tumor models into an analysis and reporting framework, the Sleeping Beauty Cancer Driver DataBase (SBCDDB, http://sbcddb.moffitt.org), which identifies drivers in individual tumors or tumor populations. Unique to this effort, the SBCDDB utilizes a single, scalable, statistical analysis method that enables data to be grouped by different biological properties. This allows for SB drivers to be evaluated (and re-evaluated) under different contexts. The SBCDDB provides visual representations highlighting the spatial attributes of transposon mutagenesis and couples this functionality with analysis of gene sets, enabling users to interrogate relationships between drivers. The SBCDDB is a powerful resource for comparative oncogenomic analyses with human cancer genomics datasets for driver prioritization.