Profiling invasiveness in head and neck cancer: recent contributions of genomic and transcriptomic approaches.

High-throughput molecular profiling approaches have emerged as precious research tools in the field of head and neck translational oncology. Such approaches have identified and/or confirmed the role of several genes or pathways in the acquisition/maintenance of an invasive phenotype and the execution of cellular programs related to cell invasion. Recently published new-generation sequencing studies in head and neck squamous cell carcinoma (HNSCC) have unveiled prominent roles in carcinogenesis and cell invasion of mutations involving NOTCH1 and PI3K-patwhay components. Gene-expression profiling studies combined with systems biology approaches have allowed identifying and gaining further mechanistic understanding into pathways commonly enriched in invasive HNSCC. These pathways include antigen-presenting and leucocyte adhesion molecules, as well as genes involved in cell-extracellular matrix interactions. Here we review the major insights into invasiveness in head and neck cancer provided by high-throughput molecular profiling approaches.

Performance of gene-expression profiling test score variability to predict future clinical events in heart transplant recipients.

BACKGROUND A single non-invasive gene expression profiling (GEP) test (AlloMap®) is often used to discriminate if a heart transplant recipient is at a low risk of acute cellular rejection at time of testing. In a randomized trial, use of the test (a GEP score from 0-40) has been shown to be non-inferior to a routine endomyocardial biopsy for surveillance after heart transplantation in selected low-risk patients with respect to clinical outcomes. Recently, it was suggested that the within-patient variability of consecutive GEP scores may be used to independently predict future clinical events; however, future studies were recommended. Here we performed an analysis of an independent patient population to determine the prognostic utility of within-patient variability of GEP scores in predicting future clinical events. METHODS We defined the GEP score variability as the standard deviation of four GEP scores collected ≥315 days post-transplantation. Of the 737 patients from the Cardiac Allograft Rejection Gene Expression Observational (CARGO) II trial, 36 were assigned to the composite event group (death, re-transplantation or graft failure ≥315 days post-transplantation and within 3 years of the final GEP test) and 55 were assigned to the control group (non-event patients). In this case-controlled study, the performance of GEP score variability to predict future events was evaluated by the area under the receiver operator characteristics curve (AUC ROC). The negative predictive values (NPV) and positive predictive values (PPV) including 95 % confidence intervals (CI) of GEP score variability were calculated. RESULTS The estimated prevalence of events was 17 %. Events occurred at a median of 391 (inter-quartile range 376) days after the final GEP test. The GEP variability AUC ROC for the prediction of a composite event was 0.72 (95 % CI 0.6-0.8). The NPV for GEP score variability of 0.6 was 97 % (95 % CI 91.4-100.0); the PPV for GEP score variability of 1.5 was 35.4 % (95 % CI 13.5-75.8). CONCLUSION In heart transplant recipients, a GEP score variability may be used to predict the probability that a composite event will occur within 3 years after the last GEP score. TRIAL REGISTRATION Clinicaltrials.gov identifier NCT00761787.

Retinoic acid receptor beta and angiopoietin-like protein 1 are involved in the regulation of human androgen biosynthesis

Androgens are essential for sexual development and reproduction. However, androgen regulation in health and disease is poorly understood. We showed that human adrenocortical H295R cells grown under starvation conditions acquire a hyperandrogenic steroid profile with changes in steroid metabolizing enzymes HSD3B2 and CYP17A1 essential for androgen production. Here we studied the regulatory mechanisms underlying androgen production in starved H295R cells. Microarray expression profiling of normal versus starved H295R cells revealed fourteen differentially expressed genes; HSD3B2, HSD3B1, CYP21A2, RARB, ASS1, CFI, ASCL1 and ENC1 play a role in steroid and energy metabolism and ANGPTL1, PLK2, DUSP6, DUSP10 and FREM2 are involved in signal transduction. We discovered two new gene networks around RARB and ANGPTL1, and show how they regulate androgen biosynthesis. Transcription factor RARB stimulated the promoters of genes involved in androgen production (StAR, CYP17A1 and HSD3B2) and enhanced androstenedione production. For HSD3B2 regulation RARB worked in cooperation with Nur77. Secretory protein ANGPTL1 modulated CYP17A1 and DUSP6 expression by inducing ERK1/2 phosphorylation. By contrast, our studies revealed no evidence for hormones or cell cycle involvement in regulating androgen biosynthesis. In summary, these studies establish a firm role for RARB and ANGPTL1 in the regulation of androgen production in H295R cells.

Integrated mRNA and miRNA expression profiling in blood reveals candidate biomarkers associated with endurance exercise in the horse.

The adaptive response to extreme endurance exercise might involve transcriptional and translational regulation by microRNAs (miRNAs). Therefore, the objective of the present study was to perform an integrated analysis of the blood transcriptome and miRNome (using microarrays) in the horse before and after a 160 km endurance competition. A total of 2,453 differentially expressed genes and 167 differentially expressed microRNAs were identified when comparing pre- and post-ride samples. We used a hypergeometric test and its generalization to gain a better understanding of the biological functions regulated by the differentially expressed microRNA. In particular, 44 differentially expressed microRNAs putatively regulated a total of 351 depleted differentially expressed genes involved variously in glucose metabolism, fatty acid oxidation, mitochondrion biogenesis, and immune response pathways. In an independent validation set of animals, graphical Gaussian models confirmed that miR-21-5p, miR-181b-5p and miR-505-5p are candidate regulatory molecules for the adaptation to endurance exercise in the horse. To the best of our knowledge, the present study is the first to provide a comprehensive, integrated overview of the microRNA-mRNA co-regulation networks that may have a key role in controlling post-transcriptomic regulation during endurance exercise.

Computer vision profiling of neurite outgrowth dynamics reveals spatiotemporal modularity of Rho GTPase signaling

Rho guanosine triphosphatases (GTPases) control the cytoskeletal dynamics that power neurite outgrowth. This process consists of dynamic neurite initiation, elongation, retraction, and branching cycles that are likely to be regulated by specific spatiotemporal signaling networks, which cannot be resolved with static, steady-state assays. We present NeuriteTracker, a computer-vision approach to automatically segment and track neuronal morphodynamics in time-lapse datasets. Feature extraction then quantifies dynamic neurite outgrowth phenotypes. We identify a set of stereotypic neurite outgrowth morphodynamic behaviors in a cultured neuronal cell system. Systematic RNA interference perturbation of a Rho GTPase interactome consisting of 219 proteins reveals a limited set of morphodynamic phenotypes. As proof of concept, we show that loss of function of two distinct RhoA-specific GTPase-activating proteins (GAPs) leads to opposite neurite outgrowth phenotypes. Imaging of RhoA activation dynamics indicates that both GAPs regulate different spatiotemporal Rho GTPase pools, with distinct functions. Our results provide a starting point to dissect spatiotemporal Rho GTPase signaling networks that regulate neurite outgrowth.

Application of stellar photometry to the analysis of microarray images

Improvements in the analysis of microarray images are critical for accurately quantifying gene expression levels. The acquisition of accurate spot intensities directly influences the results and interpretation of statistical analyses. This dissertation discusses the implementation of a novel approach to the analysis of cDNA microarray images. We use a stellar photometric model, the Moffat function, to quantify microarray spots from nylon microarray images. The inherent flexibility of the Moffat shape model makes it ideal for quantifying microarray spots. We apply our novel approach to a Wilms' tumor microarray study and compare our results with a fixed-circle segmentation approach for spot quantification. Our results suggest that different spot feature extraction methods can have an impact on the ability of statistical methods to identify differentially expressed genes. We also used the Moffat function to simulate a series of microarray images under various experimental conditions. These simulations were used to validate the performance of various statistical methods for identifying differentially expressed genes. Our simulation results indicate that tests taking into account the dependency between mean spot intensity and variance estimation, such as the smoothened t-test, can better identify differentially expressed genes, especially when the number of replicates and mean fold change are low. The analysis of the simulations also showed that overall, a rank sum test (Mann-Whitney) performed well at identifying differentially expressed genes. Previous work has suggested the strengths of nonparametric approaches for identifying differentially expressed genes. We also show that multivariate approaches, such as hierarchical and k-means cluster analysis along with principal components analysis, are only effective at classifying samples when replicate numbers and mean fold change are high. Finally, we show how our stellar shape model approach can be extended to the analysis of 2D-gel images by adapting the Moffat function to take into account the elliptical nature of spots in such images. Our results indicate that stellar shape models offer a previously unexplored approach for the quantification of 2D-gel spots. ^

Tp53-dependent repression of cell cycle target genes: Global and mechanistic analysis

Most studies of p53 function have focused on genes transactivated by p53. It is less widely appreciated that p53 can repress target genes to affect a particular cellular response. There is evidence that repression is important for p53-induced apoptosis and cell cycle arrest. It is less clear if repression is important for other p53 functions. A comprehensive knowledge of the genes repressed by p53 and the cellular processes they affect is currently lacking. We used an expression profiling strategy to identify p53-responsive genes following adenoviral p53 gene transfer (Ad-p53) in PC3 prostate cancer cells. A total of 111 genes represented on the Affymetrix U133A microarray were repressed more than two fold (p ≤ 0.05) by p53. An objective assessment of array data quality was carried out using RT-PCR of 20 randomly selected genes. We estimate a confirmation rate of >95.5% for the complete data set. Functional over-representation analysis was used to identify cellular processes potentially affected by p53-mediated repression. Cell cycle regulatory genes exhibited significant enrichment (p ≤ 5E-28) within the repressed targets. Several of these genes are repressed in a p53-dependent manner following DNA damage, but preceding cell cycle arrest. These findings identify novel p53-repressed targets and indicate that p53-induced cell cycle arrest is a function of not only the transactivation of cell cycle inhibitors (e.g., p21), but also the repression of targets that act at each phase of the cell cycle. The mechanism of repression of this set of p53 targets was investigated. Most of the repressed genes identified here do not harbor consensus p53 DNA binding sites but do contain binding sites for E2F transcription factors. We demonstrate a role for E2F/RB repressor complexes in our system. Importantly, p53 is found at the promoter of CDC25A. CDC25A protein is rapidly degraded in response to DNA damage. Our group has demonstrated for the first time that CDC25A is also repressed at the transcript level by p53. This work has important implications for understanding the DNA damage cell cycle checkpoint response and the link between E2F/RB complexes and p53 in the repression of target genes. ^

From protein microarray to biology: The discovery of epigenetic regulatory molecules

Chromatin, composed of repeating nucleosome units, is the genetic polymer of life. To aid in DNA compaction and organized storage, the double helix wraps around a core complex of histone proteins to form the nucleosome, and is therefore no longer freely accessible to cellular proteins for the processes of transcription, replication and DNA repair. Over the course of evolution, DNA-based applications have developed routes to access DNA bound up in chromatin, and further, have actually utilized the chromatin structure to create another level of complexity and information storage. The histone molecules that DNA surrounds have free-floating tails that extend out of the nucleosome. These tails are post-translationally modified to create docking sites for the proteins involved in transcription, replication and repair, thus providing one prominent way that specific genomic sequences are accessed and manipulated. Adding another degree of information storage, histone tail-modifications paint the genome in precise manners to influence a state of transcriptional activity or repression, to generate euchromatin, containing gene-dense regions, or heterochromatin, containing repeat sequences and low-density gene regions. The work presented here is the study of histone tail modifications, how they are written and how they are read, divided into two projects. Both begin with protein microarray experiments where we discover the protein domains that can bind modified histone tails, and how multiple tail modifications can influence this binding. Project one then looks deeper into the enzymes that lay down the tail modifications. Specifically, we studied histone-tail arginine methylation by PRMT6. We found that methylation of a specific histone residue by PRMT6, arginine 2 of H3, can antagonize the binding of protein domains to the H3 tail and therefore affect transcription of genes regulated by the H3-tail binding proteins. Project two focuses on a protein we identified to bind modified histone tails, PHF20, and was an endeavor to discover the biological role of this protein. Thus, in total, we are looking at a complete process: (1) histone tail modification by an enzyme (here, PRMT6), (2) how this and other modifications are bound by conserved protein domains, and (3) by using PHF20 as an example, the functional outcome of binding through investigating the biological role of a chromatin reader. ^

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.^

Statistical Methods for Differential Expressions of Genes Detected in Multiple-Condition Experiment of Microarray

Most studies of differential gene-expressions have been conducted between two given conditions. The two-condition experimental (TCE) approach is simple in that all genes detected display a common differential expression pattern responsive to a common two-condition difference. Therefore, the genes that are differentially expressed under the other conditions other than the given two conditions are undetectable with the TCE approach. In order to address the problem, we propose a new approach called multiple-condition experiment (MCE) without replication and develop corresponding statistical methods including inference of pairs of conditions for genes, new t-statistics, and a generalized multiple-testing method for any multiple-testing procedure via a control parameter C. We applied these statistical methods to analyze our real MCE data from breast cancer cell lines and found that 85 percent of gene-expression variations were caused by genotypic effects and genotype-ANAX1 overexpression interactions, which agrees well with our expected results. We also applied our methods to the adenoma dataset of Notterman et al. and identified 93 differentially expressed genes that could not be found in TCE. The MCE approach is a conceptual breakthrough in many aspects: (a) many conditions of interests can be conducted simultaneously; (b) study of association between differential expressions of genes and conditions becomes easy; (c) it can provide more precise information for molecular classification and diagnosis of tumors; (d) it can save lot of experimental resources and time for investigators.^