Accumulation of FOXP3+T-cells in the tumor microenvironment is associated with an epithelial-mesenchymal-transition-type tumor budding phenotype and is an independent prognostic factor in surgically resected pancreatic ductal adenocarcinoma.

Here we explore the role of the interplay between host immune response and epithelial-mesenchymal-transition (EMT)-Type tumor-budding on the outcome of pancreatic adenocarcinoma (PDAC).CD4+, CD8+, and FOXP3+T-cells as well as iNOS+ (M1) and CD163+- macrophages (M2) were assessed on multipunch tissue-microarrays containing 120 well-characterized PDACs, precursor lesions (PanINs) and corresponding normal tissue. Counts were normalized for the percentage of tumor/spot and associated with the clinico-pathological features, including peritumoral (PTB) and intratumoral (ITB) EMT-Type tumor-budding and outcome.Increased FOXP3+T-cell-counts and CD163-macrophages and decreased CD8+T-cell-counts were observed in PDACs compared with normal tissues and PanINs (p < 0.0001). Increased peritumoral FOXP3+T-cell-counts correlated significantly with venous invasion, distant metastasis, R1-status, high-grade ITB, PTB and independently with reduced survival. Increased intratumoral FOXP3+T-cells correlated with lymphatic invasion, N1-stage, PTB and marginally with adverse outcome. High peritumoral CD163-counts correlated with venous invasion, PTB and ITB. High intratumoral CD163-counts correlated with higher T-stage and PTB.PDAC-microenvironment displays a tumor-favoring immune-cell composition especially in the immediate environment of the tumor-buds that promotes further growth and indicates a close interaction of the immune response with the EMT-process. Increased peritumoral FOXP3+T-cell density is identified as an independent adverse prognostic factor in PDAC. Patients with phenotypically aggressive PDACs may profit from targeted immunotherapy against FOXP3.

Expression of E-cadherin repressors SNAIL, ZEB1 and ZEB2 by tumour and stromal cells influences tumour-budding phenotype and suggests heterogeneity of stromal cells in pancreatic cancer.

BACKGROUND There is evidence that tumour-stroma interactions have a major role in the neoplastic progression of pancreatic ductal adenocarcinoma (PDAC). Tumour budding is thought to reflect the process of epithelial-mesenchymal transition (EMT); however, the relationship between tumour buds and EMT remains unclear. Here we characterize the tumour-budding- and stromal cells in PDAC at protein and mRNA levels concerning factors involved in EMT. METHODS mRNA in situ hybridisation and immunostaining for E-cadherin, β-catenin, SNAIL1, ZEB1, ZEB2, N-cadherin and TWIST1 were assessed in the main tumour, tumour buds and tumour stroma on multipunch tissue microarrays from 120 well-characterised PDACs and associated with the clinicopathological features, including peritumoural (PTB) and intratumoural (ITB) budding. RESULTS Tumour-budding cells showed increased levels of ZEB1 (P<0.0001) and ZEB2 (P=0.0119) and reduced E-cadherin and β-catenin (P<0.0001, each) compared with the main tumour. Loss of membranous β-catenin in the main tumour (P=0.0009) and tumour buds (P=0.0053), without nuclear translocation, as well as increased SNAIL1 in tumour and stromal cells (P=0.0002, each) correlated with high PTB. ZEB1 overexpression in the main tumour-budding and stromal cells was associated with high ITB (P=0.0084; 0.0250 and 0.0029, respectively) and high PTB (P=0.0005; 0.0392 and 0.0007, respectively). ZEB2 overexpression in stromal cells correlated with higher pT stage (P=0.03), lymphatic invasion (P=0.0172) and lymph node metastasis (P=0.0152). CONCLUSIONS In the tumour microenvironment of phenotypically aggressive PDAC, tumour-budding cells express EMT hallmarks at protein and mRNA levels underlining their EMT-type character and are surrounded by stromal cells expressing high levels of the E-cadherin repressors ZEB1, ZEB2 and SNAIL1, this being strongly associated with the tumour-budding phenotype. Moreover, our findings suggest the existence of subtypes of stromal cells in PDAC with phenotypical and functional heterogeneity.

Sterile-filtered saliva is a strong inducer of IL-6 and IL-8 in oral fibroblasts.

OBJECTIVES Saliva has been implicated to support oral wound healing, a process that requires a transient inflammatory reaction. However, definitive proof that saliva can provoke an inflammatory response remained elusive. MATERIALS AND METHODS We investigated the ability of freshly harvested and sterile-filtered saliva to cause an inflammatory response of oral fibroblasts and epithelial cells. The expression of cytokines and chemokines was assessed by microarray, RT-PCR, immunoassays, and Luminex technology. The involvement of signaling pathways was determined by Western blot analysis and pharmacologic inhibitors. RESULTS We report that sterile-filtered whole saliva was a potent inducer of IL-6 and IL-8 in fibroblasts from the gingiva, the palate, and the periodontal ligament, but not of oral epithelial cells. This strong inflammatory response requires nuclear factor-kappa B and mitogen-activated protein kinase signaling. The pro-inflammatory capacity is heat stable and has a molecular weight of <40 kDa. Genome-wide microarrays and Luminex technology further revealed that saliva substantially increased expression of other inflammatory genes and various chemokines. To preclude that the observed pro-inflammatory activity is the result of oral bacteria, sterile-filtered parotid saliva, collected under almost aseptic conditions, was used and also increased IL-6 and IL-8 expression in gingiva fibroblasts. The inflammatory response was, furthermore, independent of MYD88, an adapter protein of the Toll-like receptor signaling pathway. CONCLUSIONS We conclude that saliva can provoke a robust inflammatory response in oral fibroblasts involving the classical nuclear factor-kappa B and mitogen-activated protein kinase signaling pathway. CLINICAL RELEVANCE Since fibroblasts but not epithelial cells show a strong inflammatory response, saliva may support the innate immunity of defect sites exposing the oral connective tissue.

VE1 immunohistochemistry predicts BRAF V600E mutation status and clinical outcome in colorectal cancer

AIM VE1 is a monoclonal antibody detecting mutant BRAFV600E protein by immunohistochemistry. Here we aim to determine the inter-observer agreement and concordance of VE1 with mutational status, investigate heterogeneity in colorectal cancers and metastases and determine the prognostic effect of VE1 in colorectal cancer patients. METHODS Concordance of VE1 with mutational status and inter-observer agreement were tested on a pilot cohort of colorectal cancers (n = 34), melanomas (n = 23) and thyroid cancers (n = 8). Two prognostic cohorts were evaluated (n = 259, Cohort 1 and n = 226, Cohort 2) by multiple-punch tissue microarrays. VE1 staining on preoperative biopsies (n = 118 patients) was compared to expression in resections. Primary tumors and metastases from 13 patients were tested for VE1 heterogeneity using a tissue microarray generated from all available blocks (n = 100 blocks). RESULTS Inter-observer agreement was 100% (kappa = 1.0). Concordance between VE1 and V600E mutation was 98.5%. Cohort 1: VE1 positivity (seen in 13.5%) was associated with older age (p = 0.0175) and MLH1 deficiency (p < 0.0001). Cohort 2: VE1 positivity (seen in 12.8%) was associated with female gender (p = 0.0016), right-sided tumor location (p < 0.0001), higher tumor grade (p < 0.0001) and mismatch repair (MMR)-deficiency (p < 0.0001). In survival analysis, MMR status and postoperative therapy were identified as possible confounding factors. Adjusting for these features, VE1 was an unfavorable prognostic factor. Preoperative biopsy staining matched resections in all cases except one. No heterogeneity was found across any primary/metastatic tumor blocks. CONCLUSION VE1 is highly concordant for V600E and homogeneously expressed suggesting staining can be analysed on resection specimens, preoperative biopsies, metastatic lesions and tissue microarrays.

Clinical Significance of NOTCH1 and NOTCH2 Expression in Gastric Carcinomas: An Immunohistochemical Study.

BACKGROUND NOTCH signaling can exert oncogenic or tumor suppressive functions and can contribute to chemotherapy resistance in cancer. In this study, we aimed to clarify the clinicopathological significance and the prognostic and predictive value of NOTCH1 and NOTCH2 expression in gastric cancer (GC). METHODS NOTCH1 and NOTCH2 expression was determined immunohistochemically in 142 primarily resected GCs using tissue microarrays and in 84 pretherapeutic biopsies from patients treated by neoadjuvant chemotherapy. The results were correlated with survival, response to therapy, and clinico-pathological features. RESULTS Primarily resected patients with NOTCH1-negative tumors demonstrated worse survival. High NOTCH1 expression was associated with early-stage tumors and with significantly increased survival in this subgroup. Higher NOTCH2 expression was associated with early-stage and intestinal-type tumors and with better survival in the subgroup of intestinal-type tumors. In pretherapeutic biopsies, higher NOTCH1 and NOTCH2 expression was more frequent in non-responding patients, but these differences were statistically not significant. CONCLUSION Our findings suggested that, in particular, NOTCH1 expression indicated good prognosis in GC. The close relationship of high NOTCH1 and NOTCH2 expression with early tumor stages may indicate a tumor-suppressive role of NOTCH signaling in GC. The role of NOTCH1 and NOTCH2 in neoadjuvantly treated GC is limited.

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.

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.

Major cellular and physiological impacts of ocean acidification on a reef building coral

As atmospheric levels of CO2 increase, reef-building corals are under greater stress from both increased sea surface temperatures and declining sea water pH. To date, most studies have focused on either coral bleaching due to warming oceans or declining calcification due to decreasing oceanic carbonate ion concentrations. Here, through the use of physiology measurements and cDNA microarrays, we show that changes in pH and ocean chemistry consistent with two scenarios put forward by the Intergovernmental Panel on Climate Change (IPCC) drive major changes in gene expression, respiration, photosynthesis and symbiosis of the coral, Acropora millepora, before affects on biomineralisation are apparent at the phenotype level. Under high CO2 conditions corals at the phenotype level lost over half their Symbiodinium populations, and had a decrease in both photosynthesis and respiration. Changes in gene expression were consistent with metabolic suppression, an increase in oxidative stress, apoptosis and symbiont loss. Other expression patterns demonstrate upregulation of membrane transporters, as well as the regulation of genes involved in membrane cytoskeletal interactions and cytoskeletal remodeling. These widespread changes in gene expression emphasize the need to expand future studies of ocean acidification to include a wider spectrum of cellular processes, many of which may occur before impacts on calcification.

Identification of a biomarker panel for colorectal cancer diagnosis

Background Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955)

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies

Graph based study of allergen cross-reactivity of plant lipid transfer proteins (LTPs) using microarray in a multicenter study.

The study of cross-reactivity in allergy is key to both understanding. the allergic response of many patients and providing them with a rational treatment In the present study, protein microarrays and a co-sensitization graph approach were used in conjunction with an allergen microarray immunoassay. This enabled us to include a wide number of proteins and a large number of patients, and to study sensitization profiles among members of the LTP family. Fourteen LTPs from the most frequent plant food-induced allergies in the geographical area studied were printed into a microarray specifically designed for this research. 212 patients with fruit allergy and 117 food-tolerant pollen allergic subjects were recruited from seven regions of Spain with different pollen profiles, and their sera were tested with allergen microarray. This approach has proven itself to be a good tool to study cross-reactivity between members of LTP family, and could become a useful strategy to analyze other families of allergens.

Identification of a biomarker panel for colorectal cancer diagnosis

Background:Malignancies arising in the large bowel cause the second largest number of deaths from cancer in the Western World. Despite progresses made during the last decades, colorectal cancer remains one of the most frequent and deadly neoplasias in the western countries. Methods: A genomic study of human colorectal cancer has been carried out on a total of 31 tumoral samples, corresponding to different stages of the disease, and 33 non-tumoral samples. The study was carried out by hybridisation of the tumour samples against a reference pool of non-tumoral samples using Agilent Human 1A 60-mer oligo microarrays. The results obtained were validated by qRT-PCR. In the subsequent bioinformatics analysis, gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling were built. The consensus among all the induced models produced a hierarchy of dependences and, thus, of variables. Results: After an exhaustive process of pre-processing to ensure data quality--lost values imputation, probes quality, data smoothing and intraclass variability filtering--the final dataset comprised a total of 8, 104 probes. Next, a supervised classification approach and data analysis was carried out to obtain the most relevant genes. Two of them are directly involved in cancer progression and in particular in colorectal cancer. Finally, a supervised classifier was induced to classify new unseen samples. Conclusions: We have developed a tentative model for the diagnosis of colorectal cancer based on a biomarker panel. Our results indicate that the gene profile described herein can discriminate between non-cancerous and cancerous samples with 94.45% accuracy using different supervised classifiers (AUC values in the range of 0.997 and 0.955).