Targeting the Blood-brain Barrier with a Non-canonical Iron-mimicry Mechanism

Treatment of central nervous system (CNS) diseases is limited by the blood-brain barrier (BBB), a selective vascular interface restricting passage of most molecules from blood into brain. Specific transport systems have evolved allowing circulating polar molecules to cross the BBB and gain access to the brain parenchyma. However, to date, few ligands exploiting such systems have proven clinically viable in the setting of CNS diseases. We reasoned that combinatorial phage-display screenings in vivo would yield peptides capable of crossing the BBB and allow for the development of ligand-directed targeting strategies of the brain. Here we show the identification of a peptide mediating systemic targeting to the normal brain and to an orthotopic human glioma model. We demonstrate that this peptide functionally mimics iron through an allosteric mechanism and that a non-canonical association of (i) transferrin, (ii) the iron-mimic ligand motif, and (iii) transferrin receptor mediates binding and transport of particles across the BBB. We also show that in orthotopic human glioma xenografts, a combination of transferrin receptor over-expression plus extended vascular permeability and ligand retention result in remarkable brain tumor targeting. Moreover, such tumor targeting attributes enables Herpes simplex virus thymidine kinase-mediated gene therapy of intracranial tumors for molecular genetic imaging and suicide gene delivery with ganciclovir. Finally, we expand our data by analyzing a large panel of primary CNS tumors through comprehensive tissue microarrays. Together, our approach and results provide a translational avenue for the detection and treatment of brain tumors.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

Comparison of OG1RF and an isogenic fsrB deletion mutant by transcriptional analysis: the Fsr system of Enterococcus faecalis is more than the activator of gelatinase and serine protease.

The FsrABC system of Enterococcus faecalis controls the expression of gelatinase and a serine protease via a quorum-sensing mechanism, and recent studies suggest that the Fsr system may also regulate other genes important for virulence. To investigate the possibility that Fsr influences the expression of additional genes, we used transcriptional profiling, with microarrays based on the E. faecalis strain V583 sequence, to compare the E. faecalis strain OG1RF with its isogenic mutant, TX5266, an fsrB deletion mutant. We found that the presence of an intact fsrB influences expression of numerous genes throughout the growth phases tested, namely, late log to early stationary phase. In addition, the Fsr regulon is independent of the activity of the proteases, GelE and SprE, whose expression was confirmed to be activated at all three time points tested. While expression of some genes (i.e., ef1097 and ef0750 to -757, encoding hypothetical proteins) was activated in late log phase in OG1RF versus the fsrB deletion mutant, expression of ef1617 to -1634 (eut-pdu orthologues) was highly repressed by the presence of an intact Fsr at entry into stationary phase. This is the first time that Fsr has been characterized as a negative regulator. The newly recognized Fsr-regulated targets include other factors, besides gelatinase, described as important for biofilms (BopD), and genes predicted to encode the surface proteins EF0750 to -0757 and EF1097, along with proteins implicated in several metabolic pathways, indicating that the FsrABC system may be an important regulator in strain OG1RF, with both positive and negative effects.

LDOC1, A NOVEL BIOMARKER OF PROGNOSIS IN CHRONIC LYMPHOCYTIC LEUKEMIA

In chronic lymphocytic leukemia (CLL), one of the best predictors of outcome is the somatic mutation status of the immunoglobulin heavy chain variable region (IGHV) genes. Patients whose CLL cells have unmutated IGHV genes have a median survival of 8 years; those with mutated IGHV genes have a median survival of 25 years. To identify new prognostic biomarkers and molecular targets for therapy in untreated CLL patients, we reanalyzed the raw data from four published gene expression profiling microarray studies. Of 88 candidate biomarkers associated with IGHV somatic mutation status, we identified LDOC1 (Leucine Zipper, Down-regulated in Cancer 1), as one of the most significantly differentially expressed genes that distinguished mutated from unmutated CLL cases. LDOC1 is a putative transcription factor of unknown function in B-cell development and CLL pathophysiology. Using a highly sensitive quantitative RT-PCR (QRT-PCR) assay, we confirmed that LDOC1 mRNA was dramatically down-regulated in mutated compared to unmutated CLL cases. Expression of LDOC1 mRNA was also vii strongly associated with other markers of poor prognosis, including ZAP70 protein and cytogenetic abnormalities of poor prognosis (deletions of chromosomes 6q21, 11q23, and 17p13.1, and trisomy 12). CLL cases positive for LDOC1 mRNA had significantly shorter overall survival than negative cases. Moreover, in a multivariate model, LDOC1 mRNA expression predicted overall survival better than IGHV mutation status or ZAP70 protein, among the best markers of prognosis in CLL. We also discovered LDOC1S, a new LDOC1 splice variant. Using isoform-specific QRT-PCR assays that we developed, we found that both isoforms were expressed in normal B cells (naïve > memory), unmutated CLL cells, and in B-cell non-Hodgkin lymphomas with unmutated IGHV genes. To investigate pathways in which LDOC1 is involved, we knocked down LDOC1 in HeLa cells and performed global gene expression profiling. GFI1 (Growth Factor-Independent 1) emerged as a significantly up-regulated gene in both HeLa cells and CLL cells that expressed high levels of LDOC1. GFI1 oncoprotein is implicated in hematopoietic stem cell maintenance, lymphocyte development, and lymphomagenesis. Our findings indicate that LDOC1 mRNA is an excellent biomarker of overall survival in CLL, and may contribute to B-cell differentiation and malignant transformation.

Towards the identification of a tumor suppressor gene on chromosome 3p12: Physical mapping and candidate gene screening

Renal cell carcinoma (RCC) is the most common malignant tumor of the kidney. Characterization of RCC tumors indicates that the most frequent genetic event associated with the initiation of tumor formation involves a loss of heterozygosity or cytogenetic aberration on the short arm of human chromosome 3. A tumor suppressor locus Nonpapillary Renal Carcinoma-1 (NRC-1, OMIM ID 604442) has been previously mapped to a 5–7 cM region on chromosome 3p12 and shown to induce rapid tumor cell death in vivo, as demonstrated by functional complementation experiments. ^ To identify the gene that accounts for the tumor suppressor activities of NRC-1, fine-scale physical mapping was conducted with a novel real-time quantitative PCR based method developed in this study. As a result, NRC-1 was mapped within a 4.6-Mb region defined by two unique sequences within UniGene clusters Hs.41407 and Hs.371835 (78,545Kb–83,172Kb in the NCBI build 31 physical map). The involvement of a putative tumor suppressor gene Robo1/Dutt1 was excluded as a candidate for NRC-1. Furthermore, a transcript map containing eleven candidate genes was established for the 4.6-Mb region. Analyses of gene expression patterns with real-time quantitative RT-PCR assays showed that one of the eleven candidate genes in the interval (TSGc28) is down-regulated in 15 out of 20 tumor samples compared with matched normal samples. Three exons of this gene have been identified by RACE experiments, although additional exon(s) seem to exist. Further gene characterization and functional studies are required to confirm the gene as a true tumor suppressor gene. ^ To study the cellular functions of NRC-1, gene expression profiles of three tumor suppressive microcell hybrids, each containing a functional copy of NRC-1, were compared with those of the corresponding parental tumor cell lines using 16K oligonucleotide microarrays. Differentially expressed genes were identified. Analyses based on the Gene Ontology showed that introduction of NRC-1 into tumor cell lines activates genes in multiple cellular pathways, including cell cycle, signal transduction, cytokines and stress response. NRC-1 is likely to induce cell growth arrest indirectly through WEE1. ^

Involvement of histone H3 methylation in Tup1-Ssn6 repression

The Tup1-Ssn6 complex regulates the expression of diverse classes of genes in Saccharomyces cerevisiae including those regulated by mating type, DNA damage, glucose, and anaerobic stress. The complex is recruited to target genes by sequence-specific repressor proteins. Once recruited to particular promoters, it is not completely clear how it functions to block transcription. Repression probably occurs through interactions with both the basal transcriptional machinery and components of chromatin. Tup1 interactions with chromatin are strongly influenced by acetylation of histories H3 and H4. Tup1 binds to underacetylated histone tails and requires multiple histone deacetylases (HDACs) for its repressive functions. Like acetylation, histone methylation is involved in regulation of gene expression. The possible role of histone methylation in Tup1 repression is not known. Here we examined possible roles of histone methyltransferases in Tup1-Ssn6 functions. We found that like other genes, Tup1-Ssn6 target genes exhibit increases in the levels of histone H3 lysine 4 methylation upon activation. However, deletion of individual or multiple histone methyltransferases (HMTs) and other SET-domain containing genes has no apparent effect on Tup1-Ssn6 mediated repression of a number of well-defined targets. Interestingly, we discovered that Ssn6 interacts with Set2. Since deletion of SET2 does not affect Tup1-Ssn6 repression, Ssn6 may utilize Set2 in other contexts to regulate gene repression. In order examine if the two components of the Tup1-Ssn6 complex have independent functions in the cell, we identified genes differentially expressed in tup1Δ and ssn6Δ mutants using DNA microarrays. Our data indicate that ∼4% of genes in the cell are regulated by Ssn6 independently of Tup1. In addition, expression of genes regulated by Tup1-Ssn6 seems to be differently affected by deletion of Ssn6 and deletion of Tup1, which indicates that these components might have separate functions. Our data shed new light on the classical view of Tup1-Ssn6 functions, and indicate that Ssn6 might have repressive functions as well. ^

Integrating sequence information in microarray data analysis by free energy modeling

Microarray technology is a high-throughput method for genotyping and gene expression profiling. Limited sensitivity and specificity are one of the essential problems for this technology. Most of existing methods of microarray data analysis have an apparent limitation for they merely deal with the numerical part of microarray data and have made little use of gene sequence information. Because it's the gene sequences that precisely define the physical objects being measured by a microarray, it is natural to make the gene sequences an essential part of the data analysis. This dissertation focused on the development of free energy models to integrate sequence information in microarray data analysis. The models were used to characterize the mechanism of hybridization on microarrays and enhance sensitivity and specificity of microarray measurements. ^ Cross-hybridization is a major obstacle factor for the sensitivity and specificity of microarray measurements. In this dissertation, we evaluated the scope of cross-hybridization problem on short-oligo microarrays. The results showed that cross hybridization on arrays is mostly caused by oligo fragments with a run of 10 to 16 nucleotides complementary to the probes. Furthermore, a free-energy based model was proposed to quantify the amount of cross-hybridization signal on each probe. This model treats cross-hybridization as an integral effect of the interactions between a probe and various off-target oligo fragments. Using public spike-in datasets, the model showed high accuracy in predicting the cross-hybridization signals on those probes whose intended targets are absent in the sample. ^ Several prospective models were proposed to improve Positional Dependent Nearest-Neighbor (PDNN) model for better quantification of gene expression and cross-hybridization. ^ The problem addressed in this dissertation is fundamental to the microarray technology. We expect that this study will help us to understand the detailed mechanism that determines sensitivity and specificity on the microarrays. Consequently, this research will have a wide impact on how microarrays are designed and how the data are interpreted. ^

INTEGRATIVE BIOMARKER IDENTIFICATION AND CLASSIFICATION USING HIGH THROUGHPUT ASSAYS

It is well accepted that tumorigenesis is a multi-step procedure involving aberrant functioning of genes regulating cell proliferation, differentiation, apoptosis, genome stability, angiogenesis and motility. To obtain a full understanding of tumorigenesis, it is necessary to collect information on all aspects of cell activity. Recent advances in high throughput technologies allow biologists to generate massive amounts of data, more than might have been imagined decades ago. These advances have made it possible to launch comprehensive projects such as (TCGA) and (ICGC) which systematically characterize the molecular fingerprints of cancer cells using gene expression, methylation, copy number, microRNA and SNP microarrays as well as next generation sequencing assays interrogating somatic mutation, insertion, deletion, translocation and structural rearrangements. Given the massive amount of data, a major challenge is to integrate information from multiple sources and formulate testable hypotheses. This thesis focuses on developing methodologies for integrative analyses of genomic assays profiled on the same set of samples. We have developed several novel methods for integrative biomarker identification and cancer classification. We introduce a regression-based approach to identify biomarkers predictive to therapy response or survival by integrating multiple assays including gene expression, methylation and copy number data through penalized regression. To identify key cancer-specific genes accounting for multiple mechanisms of regulation, we have developed the integIRTy software that provides robust and reliable inferences about gene alteration by automatically adjusting for sample heterogeneity as well as technical artifacts using Item Response Theory. To cope with the increasing need for accurate cancer diagnosis and individualized therapy, we have developed a robust and powerful algorithm called SIBER to systematically identify bimodally expressed genes using next generation RNAseq data. We have shown that prediction models built from these bimodal genes have the same accuracy as models built from all genes. Further, prediction models with dichotomized gene expression measurements based on their bimodal shapes still perform well. The effectiveness of outcome prediction using discretized signals paves the road for more accurate and interpretable cancer classification by integrating signals from multiple sources.