Comparison of multiple comparison methods for identifying differential gene expression in simulated and real papillary thyroid cancer microarray data

The difficulty of detecting differential gene expression in microarray data has existed for many years. Several correction procedures try to avoid the family-wise error rate in multiple comparison process, including the Bonferroni and Sidak single-step p-value adjustments, Holm's step-down correction method, and Benjamini and Hochberg's false discovery rate (FDR) correction procedure. Each multiple comparison technique has its advantages and weaknesses. We studied each multiple comparison method through numerical studies (simulations) and applied the methods to the real exploratory DNA microarray data, which detect of molecular signatures in papillary thyroid cancer (PTC) patients. According to our results of simulation studies, Benjamini and Hochberg step-up FDR controlling procedure is the best process among these multiple comparison methods and we discovered 1277 potential biomarkers among 54675 probe sets after applying the Benjamini and Hochberg's method to PTC microarray data.^

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.

THEORETICAL STUDIES ON THE METHODS OF RECONSTRUCTING PHYLOGENETIC TREES FROM DNA SEQUENCE DATA (MOLECULAR EVOLUTION, HOMINOID EVOLUTION, COMPUTER SIMULATION)

(1) A mathematical theory for computing the probabilities of various nucleotide configurations is developed, and the probability of obtaining the correct phylogenetic tree (model tree) from sequence data is evaluated for six phylogenetic tree-making methods (UPGMA, distance Wagner method, transformed distance method, Fitch-Margoliash's method, maximum parsimony method, and compatibility method). The number of nucleotides (m*) necessary to obtain the correct tree with a probability of 95% is estimated with special reference to the human, chimpanzee, and gorilla divergence. m* is at least 4,200, but the availability of outgroup species greatly reduces m* for all methods except UPGMA. m* increases if transitions occur more frequently than transversions as in the case of mitochondrial DNA. (2) A new tree-making method called the neighbor-joining method is proposed. This method is applicable either for distance data or character state data. Computer simulation has shown that the neighbor-joining method is generally better than UPGMA, Farris' method, Li's method, and modified Farris method on recovering the true topology when distance data are used. A related method, the simultaneous partitioning method, is also discussed. (3) The maximum likelihood (ML) method for phylogeny reconstruction under the assumption of both constant and varying evolutionary rates is studied, and a new algorithm for obtaining the ML tree is presented. This method gives a tree similar to that obtained by UPGMA when constant evolutionary rate is assumed, whereas it gives a tree similar to that obtained by the maximum parsimony tree and the neighbor-joining method when varying evolutionary rate is assumed. ^

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. ^

CHROMOSOMAL ORIGIN OF DM-DNA

Double minutes (dm) are small chromatin particles of 0.3 microns diameter found only in the metaphase cells of human and murine tumors. Dm are unique cytogenetic structures since their numbers per cell show wide variation. At cell division, dm are retained despite the lack of centromeres. In squash preparations, dm show clustering often in association with chromosomes. Human carcinoma cell line SW613-S18 was found to have large numbers of dm and biological characteristics favorable for mitotic synchronization and chromosome isolation experiments.^ S18 cells were synchronized to mitosis with metabolic and mitotic blocking compounds. Mitotic cells were lysed to release chromosomes and dm from the mitotic spindle and the resulting suspensions were fractionated to enrich for dm. The DNA in enriched fractions was characterized. The reassociation kinetics of dm-DNA driven with placental human DNA was similar to the reassociation curve of labeled placental DNA under similar conditions. In situ hybridization of dm-DNA to tumor and normal metaphase cells showed grain localization over the entire karyotype. Dm-DNA was shown by pulse chase DNA replication experiments to replicate during early and mid S-phase of the cell cycle, but not in late S-phase. In addition, BrdUrd incorporation studies showed that dm-DNA replicates only once during the S-phase. Premature chromosome condensation studies suggest the basis of numerical heterogeneity of dm is nondisjunction, not anomalous or unscheduled DNA replication.^ These data and previous cytochemical banding studies of dm in SW613-S18 indicate that dm-DNA is chromosomal in origin. No evidence of gene amplification was found in the DNA reassociation data. It is likely that dm-DNA represents the pale-staining G-band regions of the human karyotype in this cell line. ^

New algorithms for automated phylogenetic reconstruction using artificial intelligence and data mining techniques

Academic and industrial research in the late 90s have brought about an exponential explosion of DNA sequence data. Automated expert systems are being created to help biologists to extract patterns, trends and links from this ever-deepening ocean of information. Two such systems aimed on retrieving and subsequently utilizing phylogenetically relevant information have been developed in this dissertation, the major objective of which was to automate the often difficult and confusing phylogenetic reconstruction process. ^ Popular phylogenetic reconstruction methods, such as distance-based methods, attempt to find an optimal tree topology (that reflects the relationships among related sequences and their evolutionary history) by searching through the topology space. Various compromises between the fast (but incomplete) and exhaustive (but computationally prohibitive) search heuristics have been suggested. An intelligent compromise algorithm that relies on a flexible “beam” search principle from the Artificial Intelligence domain and uses the pre-computed local topology reliability information to adjust the beam search space continuously is described in the second chapter of this dissertation. ^ However, sometimes even a (virtually) complete distance-based method is inferior to the significantly more elaborate (and computationally expensive) maximum likelihood (ML) method. In fact, depending on the nature of the sequence data in question either method might prove to be superior. Therefore, it is difficult (even for an expert) to tell a priori which phylogenetic reconstruction method—distance-based, ML or maybe maximum parsimony (MP)—should be chosen for any particular data set. ^ A number of factors, often hidden, influence the performance of a method. For example, it is generally understood that for a phylogenetically “difficult” data set more sophisticated methods (e.g., ML) tend to be more effective and thus should be chosen. However, it is the interplay of many factors that one needs to consider in order to avoid choosing an inferior method (potentially a costly mistake, both in terms of computational expenses and in terms of reconstruction accuracy.) ^ Chapter III of this dissertation details a phylogenetic reconstruction expert system that selects a superior proper method automatically. It uses a classifier (a Decision Tree-inducing algorithm) to map a new data set to the proper phylogenetic reconstruction method. ^

Regulation and transcript analysis of the ceroperon responsible for quorum sensing in Rhodobacter sphaeroides 2.4.1

Rhodobacter sphaeroides 2.4.1 is a Gram negative facultative photoheterotrophic bacterium that has been shown to have an N-acyl homoserine lactone-based quorum sensing system called cer for c&barbelow;ommunity e&barbelow;scape r&barbelow;esponse. The cer ORFs are cerR, the transcriptional regulator, cerI, the autoinducer synthase and cerA , whose function is unknown. The autoinducer molecule, 7,8- cis-N-(tetradecenoyl) homoserine lactone, has been characterized. The objective of this study was to identify an environmental stimulus that influences the regulation of cerRAI and, to characterize transcription of the cer operon. ^ A cerR::lacZ transcriptional fusion was made and β-Galactosidase assays were performed in R. sphaeroides 2.4.1 strains, wild type, AP3 (CerI−) and AP4 (CerR−). The cerR::lacZ β-Galactosidase assays were used as an initial survey of the mode of regulation of the Cer system. A cerA::lacZ translational fusion was created and was used to show that cerA can be translated. The presence of 7,8-cis-N-(tetradecenoyl) homoserine lactone was detected from R. sphaeroides strains wild type and AP4 (CerR−) using a lasR::lacZ translational fusion autoinducer bioassay. The cerR::lacZ transcriptional fusion in R. sphaeroides 2.4.1 wild type was tested under different environmental stimuli, such as various carbon sources, oxygen tensions, light intensities and culture media to determine if they influence transcription of the cer ORFs. Although lacZ assay data implicated high light intensity at 100 W/m2 to stimulate cer transcription, quantitative Northern RNA data of the cerR transcript showed that low light intensity at 3 W/m2 is at least one environmental stimulus that induces cer transcription. This finding was supported by DNA microarray analysis. Northern analysis of the cerRAI transcript provided evidence that the cer ORFs are co-transcribed, and that the cer operon contains two additional genes. Bioinformatics was used to identify genes that may be regulated by the Cer system by identifying putative lux box homologue sequences in the presumed promoter region of these genes. Genes that were identified were fliQ, celB and calsymin, all implicated in interacting with plants. Primer extension was used to help localize cis-elements in the promoter region. The cerR::lacZ transcriptional fusion was monitored in a subset of different global DNA binding transcriptional regulator mutant strains of R. sphaeroides 2.4.1. Those regulators involved in maintaining an anaerobic photosynthetic lifestyle appeared to have an effect. Collectively, the data imply that R. sphaeroides 2.4.1 activates the Cer system when grown anaerobic photosynthetically at low light intensity, 3 W/m2, and it may be involved in an interaction with plants. ^

Regulation of two P450 isoforms: CYP2D6 and CYP4F11

Cytochromes P450 catalyze a monooxygenase reaction in which molecular oxygen is split and one oxygen atom is incorporated into the substrate. As a whole, P450 researchers have focused most of their attention on substrate metabolism and relatively little on how these enzymes are regulated. This study will focus on the regulation of two P450 isoforms known as, CYP2D6 and CYP4F11. ^ The human CYP2D gene locus contains two pseudogenes and one functional gene known as CYP2D6. This locus is highly polymorphic and produces several alternatively spliced transcripts from the pseudogene CYP2D7. My objective was to understand the role of SV5-in (splice variant 5), one of several alternative splice variants transcribed from the CYP2D7 pseudogene. My results indicate that SV5-in mRNA causes an increase in CYP2D6 protein levels and suggest that there is a role for SV5-in in regulation of CYP2D6 expression. ^ Second, CYP4F11 is a recently discovered and uncharacterized isoform, derived from the CYP4F subfamily. It metabolizes several clinically relevant drugs (i.e.—erythromycin and benzphetamine) and some endogenous inflammatory mediators (i.e.—LTB4). After evaluation of microarray data, I observed an increase in CYP4F11 mRNA levels from wild-type HCT116 cells compared to p53-null cells. Our objectives were to explore and understand this connection between p53 and CYP4F11. Microarray data were confirmed by Q-PCR, after which this effect was again observed at the protein level via Western blot and again at the promoter level via luciferase assay and chromatin immunoprecipitation. Our results indicate that p53 protein regulates expression of CYP4F11 mRNA and protein through CYP4F11 promoter binding (note that p53 binding to CYP4F11 DNA was not shown to be direct). These results signify a whole new level of regulation of drug metabolizing enzymes by p53. ^ An understanding of CYP4F11 regulation by p53 could help us understand another pathway leading to apoptosis or cell growth arrest. This can aid future drug studies and discover new drug metabolism pathways under the control of a tumor suppressor protein. An understanding of the CYP2D6 regulation pathway could illuminate the role of non-coding RNAs in the P450 field and potentially explain several inter-individual drug response variations observed in clinical medicine that are not yet completely explained by genotyping analysis. ^

Repression of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) but not its receptors during oral cancer progression.

BACKGROUND: TRAIL plays an important role in host immunosurveillance against tumor progression, as it induces apoptosis of tumor cells but not normal cells, and thus has great therapeutic potential for cancer treatment. TRAIL binds to two cell-death-inducing (DR4 and DR5) and two decoy (DcR1, and DcR2) receptors. Here, we compare the expression levels of TRAIL and its receptors in normal oral mucosa (NOM), oral premalignancies (OPM), and primary and metastatic oral squamous cell carcinomas (OSCC) in order to characterize the changes in their expression patterns during OSCC initiation and progression. METHODS: DNA microarray, immunoblotting and immunohistochemical analyses were used to examine the expression levels of TRAIL and its receptors in oral epithelial cell lines and in archival tissues of NOM, OPM, primary and metastatic OSCC. Apoptotic rates of tumor cells and tumor-infiltrating lymphocytes (TIL) in OSCC specimens were determined by cleaved caspase 3 immunohistochemistry. RESULTS: Normal oral epithelia constitutively expressed TRAIL, but expression was progressively lost in OPM and OSCC. Reduction in DcR2 expression levels was noted frequently in OPM and OSCC compared to respective patient-matched uninvolved oral mucosa. OSCC frequently expressed DR4, DR5 and DcR1 but less frequently DcR2. Expression levels of DR4, DR5 and DcR1 receptors were not significantly altered in OPM, primary OSCC and metastatic OSCC compared to patient-matched normal oral mucosa. Expression of proapoptotic TRAIL-receptors DR4 and DR5 in OSCC seemed to depend, at least in part, on whether or not these receptors were expressed in their parental oral epithelia. High DR5 expression in primary OSCC correlated significantly with larger tumor size. There was no significant association between TRAIL-R expression and OSSC histology grade, nodal status or apoptosis rates of tumor cells and TIL. CONCLUSION: Loss of TRAIL expression is an early event during oral carcinogenesis and may be involved in dysregulation of apoptosis and contribute to the molecular carcinogenesis of OSCC. Differential expressions of TRAIL receptors in OSCC do not appear to play a crucial role in their apoptotic rate or metastatic progression.

THE MECHANISM OF TUMORIGENESIS IN THE IMMORTALIZED HUMAN PANCREATIC CELL LINES: CELL CULTURE MODELS OF HUMAN PANCREATIC CANCER

The mechanism of tumorigenesis in the immortalized human pancreatic cell lines: cell culture models of human pancreatic cancer Pancreatic ductal adenocarcinoma (PDAC) is the most lethal cancer in the world. The most common genetic lesions identified in PDAC include activation of K-ras (90%) and Her2 (70%), loss of p16 (95%) and p14 (40%), inactivation p53 (50-75%) and Smad4 (55%). However, the role of these signature gene alterations in PDAC is still not well understood, especially, how these genetic lesions individually or in combination contribute mechanistically to human pancreatic oncogenesis is still elusive. Moreover, a cell culture transformation model with sequential accumulation of signature genetic alterations in human pancreatic ductal cells that resembles the multiple-step human pancreatic carcinogenesis is still not established. In the present study, through the stepwise introduction of the signature genetic alterations in PDAC into the HPV16-E6E7 immortalized human pancreatic duct epithelial (HPDE) cell line and the hTERT immortalized human pancreatic ductal HPNE cell line, we developed the novel experimental cell culture transformation models with the most frequent gene alterations in PDAC and further dissected the molecular mechanism of transformation. We demonstrated that the combination of activation of K-ras and Her2, inactivation of p16/p14 and Smad4, or K-ras mutation plus p16 inactivation, was sufficient for the tumorigenic transformation of HPDE or HPNE cells respectively. We found that these transformed cells exhibited enhanced cell proliferation, anchorage-independent growth in soft agar, and grew tumors with PDAC histopathological features in orthotopic mouse model. Molecular analysis showed that the activation of K-ras and Her2 downstream effector pathways –MAPK, RalA, FAK, together with upregulation of cyclins and c-myc were involved in the malignant transformation. We discovered that MDM2, BMP7 and Bmi-1 were overexpressed in the tumorigenic HPDE cells, and that Smad4 played important roles in regulation of BMP7 and Bmi-1 gene expression and the tumorigenic transformation of HPDE cells. IPA signaling pathway analysis of microarray data revealed that abnormal signaling pathways are involved in transformation. This study is the first complete transformation model of human pancreatic ductal cells with the most common gene alterations in PDAC. Altogether, these novel transformation models more closely recapitulate the human pancreatic carcinogenesis from the cell origin, gene lesion, and activation of specific signaling pathway and histopathological features.

Population genetics of VNTR loci and their applications in evolutionary studies

Variable number of tandem repeats (VNTR) are genetic loci at which short sequence motifs are found repeated different numbers of times among chromosomes. To explore the potential utility of VNTR loci in evolutionary studies, I have conducted a series of studies to address the following questions: (1) What are the population genetic properties of these loci? (2) What are the mutational mechanisms of repeat number change at these loci? (3) Can DNA profiles be used to measure the relatedness between a pair of individuals? (4) Can DNA fingerprint be used to measure the relatedness between populations in evolutionary studies? (5) Can microsatellite and short tandem repeat (STR) loci which mutate stepwisely be used in evolutionary analyses?^ A large number of VNTR loci typed in many populations were studied by means of statistical methods developed recently. The results of this work indicate that there is no significant departure from Hardy-Weinberg expectation (HWE) at VNTR loci in most of the human populations examined, and the departure from HWE in some VNTR loci are not solely caused by the presence of population sub-structure.^ A statistical procedure is developed to investigate the mutational mechanisms of VNTR loci by studying the allele frequency distributions of these loci. Comparisons of frequency distribution data on several hundreds VNTR loci with the predictions of two mutation models demonstrated that there are differences among VNTR loci grouped by repeat unit sizes.^ By extending the ITO method, I derived the distribution of the number of shared bands between individuals with any kinship relationship. A maximum likelihood estimation procedure is proposed to estimate the relatedness between individuals from the observed number of shared bands between them.^ It was believed that classical measures of genetic distance are not applicable to analysis of DNA fingerprints which reveal many minisatellite loci simultaneously in the genome, because the information regarding underlying alleles and loci is not available. I proposed a new measure of genetic distance based on band sharing between individuals that is applicable to DNA fingerprint data.^ To address the concern that microsatellite and STR loci may not be useful for evolutionary studies because of the convergent nature of their mutation mechanisms, by a theoretical study as well as by computer simulation, I conclude that the possible bias caused by the convergent mutations can be corrected, and a novel measure of genetic distance that makes the correction is suggested. In summary, I conclude that hypervariable VNTR loci are useful in evolutionary studies of closely related populations or species, especially in the study of human evolution and the history of geographic dispersal of Homo sapiens. (Abstract shortened by UMI.) ^

New aspects of estrogen action in the endometrium: Reciprocal interplay with retinoic acid pathway and a novel estrogen-regulated gene

The uterine endometrium is a major target for the estrogen. However, the molecular basis of estrogen action in the endometrium is largely unknown. I have used two approaches to study the effects of estrogen on the endometrium. One approach involved the study of the interaction between estrogen and retinoic acid (RA) pathways in the endometrium. I have demonstrated that estrogen administration to rodents and estrogen replacement therapy (ERT) in postmenopausal women selectively induced the endometrial expression of retinaldehyde dehydrogenase II (RALDH2), a critical enzyme of RA biosynthesis. RALDH2 was expressed exclusively in the stromal cells, especially in the stroma adjacent to the luminal and glandular epithelia. The induction of RALDH2 by estrogen required estrogen receptor and occurred via a direct increase in RALDH2 transcription. Among the three RA receptors, estrogen selectively induced the expression of RARα. In parallel, estrogen also increased the utilization of all-trans retinol (the substrate for RA biosynthesis) and the expression of two RA-regulated marker genes, cellular retinoic acid binding protein II (CRABP2) and tissue transglutaminase (tTG) in the endometrium. Thus estrogen coordinately upregulated both the production and signaling of RA in both the rodent and human endometrium. This coordinate upregulation of RA system appeared to play a role in counterbalancing the stimulatory effects of estrogen on the endometrium, since the depletion of endogenous RA in mice led to an increase in estrogen-stimulated stromal proliferation and endometrial Akt phosphorylation. In addition, I have also used a systematic approach (DNA microarray) to categorize genes and pathways affected by the ERT in the endometrium of postmenopausal women and identified a novel estrogen-regulated gene EIG121. EIG121 was exclusively expressed in the glandular epithelial cells of the endometrium and induced by estrogen in vivo and in cultured cell lines. Compared with the normal endometrium, EIG121 was highly overexpressed in type 1 endometrial cancer, but profoundly suppressed in type 2 endometrial tumors. Taken together, these studies suggested that estrogen regulates the expression of many genes of both the pro-proliferative and anti-proliferative pathways and the abnormality of these pathways may increase the risks for estrogen-dependent endometrial hyperplasia and endometrial cancer. ^

Metastatic lung cancer in a new mouse model inheriting p53 and latent K-ras mutations

Lung cancer is a devastating disease with very poor prognosis. The design of better treatments for patients would be greatly aided by mouse models that closely resemble the human disease. The most common type of human lung cancer is adenocarcinoma with frequent metastasis. Unfortunately, current models for this tumor are inadequate due to the absence of metastasis. Based on the molecular findings in human lung cancer and metastatic potential of osteosarcomas in mutant p53 mouse models, I hypothesized that mice with both K-ras and p53 missense mutations might develop metastatic lung adenocarcinomas. Therefore, I incorporated both K-rasLA1 and p53RI72HΔg alleles into mouse lung cells to establish a more faithful model for human lung adenocarcinoma and for translational and mechanistic studies. Mice with both mutations ( K-rasLA1/+ p53R172HΔg/+) developed advanced lung adenocarcinomas with similar histopathology to human tumors. These lung adenocarcinomas were highly aggressive and metastasized to multiple intrathoracic and extrathoracic sites in a pattern similar to that seen in lung cancer patients. This mouse model also showed gender differences in cancer related death and developed pleural mesotheliomas in 23.2% of them. In a preclinical study, the new drug Erlotinib (Tarceva) decreased the number and size of lung lesions in this model. These data demonstrate that this mouse model most closely mimics human metastatic lung adenocarcinoma and provides an invaluable system for translational studies. ^ To screen for important genes for metastasis, gene expression profiles of primary lung adenocarcinomas and metastases were analyzed. Microarray data showed that these two groups were segregated in gene expression and had 79 highly differentially expressed genes (more than 2.5 fold changes and p<0.001). Microarray data of Bub1b, Vimentin and CCAM1 were validated in tumors by quantitative real-time PCR (QPCR). Bub1b , a mitotic checkpoint gene, was overexpressed in metastases and this correlated with more chromosomal abnormalities in metastatic cells. Vimentin, a marker of epithelial-mesenchymal transition (EMT), was also highly expressed in metastases. Interestingly, Twist, a key EMT inducer, was also highly upregulated in metastases by QPCR, and this significantly correlated with the overexpression of Vimentin in the same tumors. These data suggest EMT occurs in lung adenocarcinomas and is a key mechanism for the development of metastasis in K-ras LA1/+ p53R172HΔg/+ mice. Thus, this mouse model provides a unique system to further probe the molecular basis of metastatic lung cancer.^

A mechanism of insulin-like growth factor binding protein 2-induced cell mobility in glioma

Insulin-like growth factor binding protein 2 (IGFBP2) is a protein known to be overexpressed in a majority of glioblastoma multiforme (GBM) tumors. While it is known the IGFBP2 is involved in promoting GBM tumor cell invasion, no mechanism exists for how the protein is involved in signal transduction pathways leading to enhanced cell invasion. ^ We follow up on preliminary microarray data on IGFBP2-overexpressing GBM cells and protein sequence analysis of IGFBP2 in generating the hypothesis that IGFBP2 interacts with integnn α5 in regulating cell mobility. Microarray data showing upregulation of integrin α5 by IGFBP2 is validated and evidence of protein-protein interaction between IGFBP2 and integrin α5 is found. The exact binding domain on IGFBP2 responsible for its interaction with integrin α5 is also determined, confirming our initial findings and reaffirming that the IGFBP2/integrin α5 interaction is specific. Disruption of this interaction resulted in attenuation of IGFBP2-enhanced cell mobility. Further, we found that cell mobility is only enhanced when IGFBP2 and integrin α5 are both overexpressed and able to interact with each other. ^ We also determined fibronectin to be a critical player in the activation of the IGFBP2/integrin α5 pathway. The activation of this pathway appears to be progressive and initiates once GBM cells have sufficiently established anchorage. ^