Histological and histochemical evaluation of human oral mucosa constructs developed by tissue engineering

Reconstruction of large oral mucosa defects is often challenging, since the shortage of healthy oral mucosa to replace the excised tissues is very common. In this context, tissue engineering techniques may provide a source of autologous tissues available for transplant in these patients. In this work, we developed a new model of artificial oral mucosa generated by tissue engineering using a fibrin-agarose scaffold. For that purpose, we generated primary cultures of human oral mucosa fibroblasts and keratinocytes from small biopsies of normal oral mucosa using enzymatic treatments. Then we determined the viability of the cultured cells by electron probe quantitative X-ray microanalysis, and we demonstrated that most of the cells in the primary cultures were alive and had high K/Na ratios. Once cell viability was determined, we used the cultured fibroblasts and keratinocytes to develop an artificial oral mucosa construct by using a fibrin-agarose extracellular matrix and a sequential culture technique using porous culture inserts. Histological analysis of the artificial tissues showed high similarities with normal oral mucosa controls. The epithelium of the oral substitutes had several layers, with desmosomes and apical microvilli and microplicae. Both the controls and the oral mucosa substitutes showed high suprabasal expression of cytokeratin 13 and low expression of cytokeratin 10. All these results suggest that our model of oral mucosa using fibrin-agarose scaffolds show several similarities with native human oral mucosa.

Test of four colon cancer risk-scores in formalin fixed paraffin embedded microarray gene expression data.

BACKGROUND: Prognosis prediction for resected primary colon cancer is based on the T-stage Node Metastasis (TNM) staging system. We investigated if four well-documented gene expression risk scores can improve patient stratification. METHODS: Microarray-based versions of risk-scores were applied to a large independent cohort of 688 stage II/III tumors from the PETACC-3 trial. Prognostic value for relapse-free survival (RFS), survival after relapse (SAR), and overall survival (OS) was assessed by regression analysis. To assess improvement over a reference, prognostic model was assessed with the area under curve (AUC) of receiver operating characteristic (ROC) curves. All statistical tests were two-sided, except the AUC increase. RESULTS: All four risk scores (RSs) showed a statistically significant association (single-test, P < .0167) with OS or RFS in univariate models, but with HRs below 1.38 per interquartile range. Three scores were predictors of shorter RFS, one of shorter SAR. Each RS could only marginally improve an RFS or OS model with the known factors T-stage, N-stage, and microsatellite instability (MSI) status (AUC gains < 0.025 units). The pairwise interscore discordance was never high (maximal Spearman correlation = 0.563) A combined score showed a trend to higher prognostic value and higher AUC increase for OS (HR = 1.74, 95% confidence interval [CI] = 1.44 to 2.10, P < .001, AUC from 0.6918 to 0.7321) and RFS (HR = 1.56, 95% CI = 1.33 to 1.84, P < .001, AUC from 0.6723 to 0.6945) than any single score. CONCLUSIONS: The four tested gene expression-based risk scores provide prognostic information but contribute only marginally to improving models based on established risk factors. A combination of the risk scores might provide more robust information. Predictors of RFS and SAR might need to be different.

Integrative molecular bioinformatics study of human adrenocortical tumors : microRNA, tissue-specific target prediction, and pathway analysis.

MicroRNAs (miRs) are involved in the pathogenesis of several neoplasms; however, there are no data on their expression patterns and possible roles in adrenocortical tumors. Our objective was to study adrenocortical tumors by an integrative bioinformatics analysis involving miR and transcriptomics profiling, pathway analysis, and a novel, tissue-specific miR target prediction approach. Thirty-six tissue samples including normal adrenocortical tissues, benign adenomas, and adrenocortical carcinomas (ACC) were studied by simultaneous miR and mRNA profiling. A novel data-processing software was used to identify all predicted miR-mRNA interactions retrieved from PicTar, TargetScan, and miRBase. Tissue-specific target prediction was achieved by filtering out mRNAs with undetectable expression and searching for mRNA targets with inverse expression alterations as their regulatory miRs. Target sets and significant microarray data were subjected to Ingenuity Pathway Analysis. Six miRs with significantly different expression were found. miR-184 and miR-503 showed significantly higher, whereas miR-511 and miR-214 showed significantly lower expression in ACCs than in other groups. Expression of miR-210 was significantly lower in cortisol-secreting adenomas than in ACCs. By calculating the difference between dCT(miR-511) and dCT(miR-503) (delta cycle threshold), ACCs could be distinguished from benign adenomas with high sensitivity and specificity. Pathway analysis revealed the possible involvement of G2/M checkpoint damage in ACC pathogenesis. To our knowledge, this is the first report describing miR expression patterns and pathway analysis in sporadic adrenocortical tumors. miR biomarkers may be helpful for the diagnosis of adrenocortical malignancy. This tissue-specific target prediction approach may be used in other tumors too.

Tissue-based immune monitoring I: tumor core needle biopsies allow in-depth interrogation of the tumor microenvironment.

We sought to assess the feasibility and reproducibility of performing tissue-based immune characterization of the tumor microenvironment using CT-compatible needle biopsy material. Three independent biopsies were obtained intraoperatively from one metastatic epithelial ovarian cancer lesion of 7 consecutive patients undergoing surgical cytoreduction using a 16-gauge core biopsy needle. Core specimens were snap-frozen and subjected to immunohistochemistry (IHC) against human CD3, CD4, CD8, and FoxP3. A portion of the cores was used to isolate RNA for 1) real-time quantitative (q)PCR for CD3, CD4, CD8, FoxP3, IL-10 and TGF-beta, 2) multiplexed PCR-based T cell receptor (TCR) CDR3 Vβ region spectratyping, and 3) gene expression profiling. Pearson's correlations were examined for immunohistochemistry and PCR gene expression, as well as for gene expression array data obtained from different tumor biopsies. Needle biopsy yielded sufficient tissue for all assays in all patients. IHC was highly reproducible and informative. Significant correlations were seen between the frequency of CD3+, CD8+ and FoxP3+ T cells by IHC with CD3ε, CD8A, and FoxP3 gene expression, respectively, by qPCR (r=0.61, 0.86, and 0.89; all p< 0.05). CDR3 spectratyping was feasible and highly reproducible in each tumor, and indicated a restricted repertoire for specific TCR Vβ chains in tumor-infiltrating T cells. Microarray gene expression revealed strong correlation between different biopsies collected from the same tumor. Our results demonstrate a feasible and reproducible method of immune monitoring using CT-compatible needle biopsies from tumor tissue, thereby paving the way for sophisticated translational studies during tumor biological therapy.

A conceptual and practical overview of cDNA microarray technology: implications for basic and clinical sciences

cDNA microarray is an innovative technology that facilitates the analysis of the expression of thousands of genes simultaneously. The utilization of this methodology, which is rapidly evolving, requires a combination of expertise from the biological, mathematical and statistical sciences. In this review, we attempt to provide an overview of the principles of cDNA microarray technology, the practical concerns of the analytical processing of the data obtained, the correlation of this methodology with other data analysis methods such as immunohistochemistry in tissue microarrays, and the cDNA microarray application in distinct areas of the basic and clinical sciences.

Impact of (pro)renin receptor deficiency in adipose tissue using a genetically engineered mouse model

La stimulation du récepteur de la rénine/prorénine [(P) RR], un membre récemment découvert du système rénine-angiotensine (SRA), augmente l'activité du SRA et des voies de signalisation angiotensine II-indépendante. Pour étudier l'impact potentiel du (P)RR dans le développement de l`obésité, nous avons émis l'hypothèse que les souris déficientes en (P)RR uniquement dans le tissus adipeux (KO) auront une diminution du poids corporel en ciblant le métabolisme du tissu adipeux, l'activité locomoteur et/ou la prise alimentaire. Ainsi, des souris KO ont été générées en utilisant la technologie Cre/Lox. Le gain de poids et la prise alimentaire ont été évalués hebdomadairement dans les mâles et femelles KO et de type sauvage (WT) pendant 4 semaines alors qu’ils étaient maintenu sur une diète normal. De plus, un groupe de femelles a été placé pour 6 semaines sur une diète riche en gras et en glucides (HF/HC). La composition corporelle et l'activité ambulatoire ont été évaluées par l’EchoMRI et à l’aide de cages Physioscan, respectivement. Les tissus adipeux ont été prélevés et pesés. De plus, les gras péri-gonadaux ont été utilisés pour le microarray. Finalement, le niveaux d'expression d'ARNm du (P)RR ont été évalués. Comme le gène du (P)RR est situé sur le chromosome X, les mâles étaient des KOs complets et les femelles étaient des KOs partielles. Les souris KO avaient un poids corporel significativement plus petit par rapport à WT, les différences étant plus prononcées chez les mâles. De plus, les femelles KOs étaient résistantes à l'obésité lorsqu'elles ont été placées sur la diète HF/HC et donc elles avaient significativement moins de masse grasse par rapport aux WTs. L’analyse histologique des gras péri-gonadaux des KOs nous ont dévoilés qu’il avait une réduction du nombre d'adipocytes mais de plus grande taille. Bien qu'il n'y ait eu aucun changement dans la consommation alimentaire, une augmentation de près de 3 fois de l'activité ambulatoire a été détectée chez les mâles. De plus, nous avons observé que leurs tibias étaient de longueur réduite ce qui suggère fortement l'affection de leur développement. Les gras péri-gonadaux des souris KO avaient une expression réduite de l`ABLIM2 (Actin binding LIM protein family, member 2) qui est associé avec le diabète de type II chez l'humain. Ainsi, les données recueillies suggèrent fortement que le (P)RR est impliquée dans la régulation du poids corporelle.

“Unmixing” Tissue Gene Expression Signatures from Tumor Biopsies

Emergent molecular measurement methods, such as DNA microarray, qRTPCR, and many others, offer tremendous promise for the personalized treatment of cancer. These technologies measure the amount of specific proteins, RNA, DNA or other molecular targets from tumor specimens with the goal of “fingerprinting” individual cancers. Tumor specimens are heterogeneous; an individual specimen typically contains unknown amounts of multiple tissues types. Thus, the measured molecular concentrations result from an unknown mixture of tissue types, and must be normalized to account for the composition of the mixture. For example, a breast tumor biopsy may contain normal, dysplastic and cancerous epithelial cells, as well as stromal components (fatty and connective tissue) and blood and lymphatic vessels. Our diagnostic interest focuses solely on the dysplastic and cancerous epithelial cells. The remaining tissue components serve to “contaminate” the signal of interest. The proportion of each of the tissue components changes as a function of patient characteristics (e.g., age), and varies spatially across the tumor region. Because each of the tissue components produces a different molecular signature, and the amount of each tissue type is specimen dependent, we must estimate the tissue composition of the specimen, and adjust the molecular signal for this composition. Using the idea of a chemical mass balance, we consider the total measured concentrations to be a weighted sum of the individual tissue signatures, where weights are determined by the relative amounts of the different tissue types. We develop a compositional source apportionment model to estimate the relative amounts of tissue components in a tumor specimen. We then use these estimates to infer the tissuespecific concentrations of key molecular targets for sub-typing individual tumors. We anticipate these specific measurements will greatly improve our ability to discriminate between different classes of tumors, and allow more precise matching of each patient to the appropriate treatment

Early Changes in Gene Expression Induced by Tobacco Smoke: Evidence for the Importance of Estrogen within Lung Tissue

Lung cancer is the leading cause of cancer deaths in the United States, surpassing breast cancer as the primary cause of cancer-related mortality in women. The goal of the present study was to identify early molecular changes in the lung induced by exposure to tobacco smoke and thus identify potential targets for chemoprevention. Female A/J mice were exposed to either tobacco smoke or HEPA-filtered air via a whole-body exposure chamber (6 h/d, 5 d/wk for 3, 8, and 20 weeks). Gene expression profiles of lung tissue from control and smoke-exposed animals were established using a 15K cDNA microarray. Cytochrome P450 1b1, a phase I enzyme involved in both the metabolism of xenobiotics and the 4-hydroxylation of 17 beta-estradiol (E(2)), was modulated to the greatest extent following smoke exposure. A panel of 10 genes were found to be differentially expressed in control and smoke-exposed lung tissues at 3, 8, and 20 weeks (P < 0.001). The interaction network of these differentially expressed genes revealed new pathways modulated by short-term smoke exposure, including estrogen metabolism. In addition, E(2) was detected within murine lung tissue by gas chromatography-coupled mass spectrometry and immunohistochemistry. Identification of the early molecular events that contribute to lung tumor formation is anticipated to lead to the development of promising targeted chemopreventive therapies. In conclusion, the presence of E2 within lung tissue when combined with the modulation of cytochrome P450 1b1 and other estrogen metabolism genes by tobacco smoke provides novel insight into a possible role for estrogens in lung cancer. Cancer Prev Res; 3(6); 707-17. (C) 2010 AACR.

Identification of genes associated with local aggressiveness and metastatic behavior in soft tissue tumors

Soft tissue tumors represent a group of neoplasia with different histologic and biological presentations varying from benign, locally confined to very aggressive and metastatic tumors. The molecular mechanisms responsible for such differences are still unknown. The understanding of these molecular alterations mechanism will be critical to discriminate patients who need systemic treatment from those that can be treated only locally and could also guide the development of new drugs` against this tumors. Using 102 tumor samples representing a large spectrum of these tumors, we performed expression profiling and defined differentially expression genes that are likely to be involved in tumors that are locally aggressive and in tumors with metastatic potential. We described a set of 12 genes (SNRPD3, MEGF9, SPTAN-1, AFAP1L2, ENDOD1, SERPIN5, ZWINTAS, TOP2A, UBE2C, ABCF1, MCM2, and ARL6IP5) showing opposite expression when these two conditions were compared. These genes are mainly related to cell-cell and cell-extracellular matrix interactions and cell proliferation and might represent helpful tools for a more precise classification and diagnosis as well as potential drug targets.

Conserved tissue expression signatures of intronic noncoding RNAs transcribed from human and mouse loci

It has been postulated that noncoding RNAs (ncRNAs) are involved in the posttranscriptional control of gene expression, and may have contributed to the emergence of the complex attributes observed in mammalians. We show here that the complement of ncRNAs expressed from intronic regions of the human and mouse genomes comprises at least 78,147 and 39,660 transcriptional units, respectively. To identify conserved intronic sequences expressed in both humans and mice, we used custom-designed human cDNA microarrays to separately interrogate RNA from mouse and human liver, kidney, and prostate tissues. An overlapping tissue expression signature was detected for both species, comprising 198 transcripts; among these, 22 RNAs map to intronic regions with evidence of evolutionary conservation in humans and mice. Transcription of selected human-mouse intronic ncRNAs was confirmed using strand-specific RT-PCR. Altogether, these results support an evolutionarily conserved role of intronic ncRNAs in human and mouse, which are likely to be involved in the fine tuning of gene expression regulation in different mammalian tissues. (C) 2008 Elsevier Inc. All rights reserved.

A tecnologia de microarray no estudo do câncer de cabeça e pescoço

(Microarray technology in study of head neck cancer). The microarray technology is a tool for global analysis of gene expression that allows investigating hundreds or thousands of genes in a sample using a hybridization reaction. This technology is based on hybridization between labeled targets derived from biological samples and an array of many DNA probes immobilized on a solid matrix, representing the genes of interest. The simultaneous study of hundreds of genes became the microarray technique a very important tool of global analysis, with applications in several areas, including the study of the development of cancer. Head and neck squamous cell carcinoma (HNSCC) is the fifth most common cancer worldwide, with a global annual incidence of 780,000 new cases. Large-scale studies involving microarrays have identified specific gene expression signatures associated with expression changes in HNSCC samples compared to normal tissue, as well as genes involved in clinical outcome and metastasis. However, the considerable heterogeneity among these studies occurs due to experimental design, number of samples, disease sites and stage, choice of microarray platform and results validation. Thus, there is much to be validated, before the technique has clinical utility. In relation to head and neck neoplasia, the large-scale gene analysis is very important, since the clinical and histopathological methods currently used appear to be insufficient to predict clinical progression and response to treatment. Thus, this approach could result in more effective diagnostic and prognostic and most appropriate therapy for this neoplasia.

GREB1 tissue expression is associated with organ-confined prostate cancer

Objective: By reason of its heterogeneous behavior, it is difficult to determine the prognosis of many prostate cancer cases. Patients with the same clinicopathologic conditions may present varying clinical findings and rates of progression. We determined the role of new genes as potential molecular markers for prostate cancer prognosis. Materials and methods: We performed a microarray analysis of two pools of patients with prostate cancer divided according to their clinicopathologic characteristics. After that, we validated these results by testing the genes with most different expressions between the two pools using the quantitative real time polymerase chain reaction method. We analyzed gene expression in 33 patients with localized prostate cancer according to prostate specific antigen (PSA), pathologic stage, Gleason score, and biochemical recurrence. For statistical analysis we used the Mann-Whitney Test. Results: The microarray analysis revealed that 4,147 genes presented a different expression between the two pools. Among them, 3 genes, TMEFF2, GREB1, and THIL,, were at least 13-times overexpressed, and 1 gene, IGH3, which was at least 5times under-expressed in pool 1 (good prognosis) compared with pool 2 (bad prognosis), were selected for analysis. After the validation tests, GREB1 was significantly more overexpressed among patients with stage T2 compared with T3 (P = 0.020). The expressions of other 3 genes did not present significant differences according to the clinicopatholoOcal variables. Conclusions: Tissue expression of GREB1 is associated with organ-confined prostate cancer and may constitute a gene associated with a favorable prognosis. (C) 2012 Elsevier Inc. All rights reserved.

Genome mapping and expression analyses of human intronic noncoding RNAs reveal tissue-specific patterns and enrichment in genes related to regulation of transcription

Abstract Background RNAs transcribed from intronic regions of genes are involved in a number of processes related to post-transcriptional control of gene expression. However, the complement of human genes in which introns are transcribed, and the number of intronic transcriptional units and their tissue expression patterns are not known. Results A survey of mRNA and EST public databases revealed more than 55,000 totally intronic noncoding (TIN) RNAs transcribed from the introns of 74% of all unique RefSeq genes. Guided by this information, we designed an oligoarray platform containing sense and antisense probes for each of 7,135 randomly selected TIN transcripts plus the corresponding protein-coding genes. We identified exonic and intronic tissue-specific expression signatures for human liver, prostate and kidney. The most highly expressed antisense TIN RNAs were transcribed from introns of protein-coding genes significantly enriched (p = 0.002 to 0.022) in the 'Regulation of transcription' Gene Ontology category. RNA polymerase II inhibition resulted in increased expression of a fraction of intronic RNAs in cell cultures, suggesting that other RNA polymerases may be involved in their biosynthesis. Members of a subset of intronic and protein-coding signatures transcribed from the same genomic loci have correlated expression patterns, suggesting that intronic RNAs regulate the abundance or the pattern of exon usage in protein-coding messages. Conclusion We have identified diverse intronic RNA expression patterns, pointing to distinct regulatory roles. This gene-oriented approach, using a combined intron-exon oligoarray, should permit further comparative analysis of intronic transcription under various physiological and pathological conditions, thus advancing current knowledge about the biological functions of these noncoding RNAs.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

Diagnostic and prognostic implications of the PAX8-PPARγ translocation in thyroid carcinomas-a TMA-based study of 226 cases

AIMS Follicular thyroid carcinoma (FTC) has been a diagnostic challenge for decades. The PAX8-PPARγ rearrangement has been detected in FTC and classic papillary thyroid carcinomas (PTCs). The aims of this study were to assess the presence of PAX8-PPARγ by using tissue microarrays in a large cohort of different thyroid neoplasms, and to assess its diagnostic and prognostic implications. METHODS AND RESULTS Fluorescence in-situ hybridization (FISH) analysis for PAX8-PPARγ was performed on 226 thyroid tumours, comprising FTCs (n = 59), PTCs (n = 126), poorly differentiated thyroid carcinomas (PDs; n = 34), follicular thyroid adenomas (FTAs; n = 5), and follicular tumours of unknown malignant potential (FTUMPs; n = 2). PAX8-PPARγ was detected in 12% of FTCs, 1% of PTCs, 7% of PDs, and in both cases of FTUMP. There was no correlation between the extent of capsular or vascular invasion and PAX8-PPARγ, or between lymph node or haematogenous metastasis and PAX8-PPARγ. Overall survival (OS), tumour-specific survival (TSS) and relapse-free-survival (RFS) were not influenced by PAX8-PPARγ. CONCLUSIONS In this study, we demonstrate for the first time the presence of PAX8-PPARγ in PDs and FTUMPs, whereas in FTCs and PTCs the prevalence of PAX8-PPARγ is lower than previously reported. PAX8-PPARγ did not correlate with invasiveness or affect prognosis in any tumour type.