Dendritic cells (DCs) are major antigen-presenting cells that play a key role in initiating and regulating innate and adaptive immune responses. DCs are critical mediators of tolerance and immunity. The functional properties of DCs decline with age. The purpose of this study was to define the age-associated molecular changes in DCs by gene array analysis using Affymatrix GeneChips. The expression levels of a total of 260 genes (1.8%) were significantly different (144 down-regulated and 116 upregulated) in monocyte-derived DCs (MoDCs) from aged compared to young human donors. Of the 260 differentially expressed genes, 24% were down-regulated by more than 3-fold, suggesting that a large reduction in expression occurred for a notable number of genes in the aged. Our results suggest that the genes involved in immune response to pathogens, cell migration and T cell priming display significant age-related changes. Furthermore, downregulated genes involved in cell cycle arrest and DNA replication may play a critical role in aging-associated genetic instability. These changes in gene expression provide molecular based evidence for age-associated functional abnormalities in human DCs that may be responsible for the defects in adaptive immunity observed in the elderly.
A randomized clinical experiment to compare two types of endotracheal tubes utilized a block design where each of the six participating anesthesiologists performed tube insertions for an equal number of patients for each type of tube. Five anesthesiologists intubated at least three patients with each tube type, but one anesthesiologist intubated only one patient per tube type. Overall, one type of tube outperformed the other on all three effectiveness measures. However, analysis of the data using an interaction model gave conflicting and misleading results, making the tube with the better performance appear to perform worse. This surprising result was caused by the undue influence of the data for the anesthesiologist who intubated only two patients. We therefore urge caution in interpreting results from interaction models with designs containing small blocks.
This review postulates the role of transforming growth factor-beta (TGF-?) and insulin-like growth factor (IGF-I/IGF-II) signaling in stromal cells during prostate carcinogenesis and progression. It is known that stromal cells have a reciprocal relationship to the adjacent epithelial cells in the maintenance of structural and functional integrity of the prostate. An interaction between TGF-? and IGF signaling occupies a central part in this stromal-epithelial interaction. An increase in TGF-? and IGF signaling will set off the imbalance of this relationship and will lead to cancer development. A continuous input from TGF-? and IGF in the tumor microenvironment will result in cancer progression. Understanding of these events can help prevention, diagnosis, and therapy of prostate cancer.
Associating microRNAs (miRNAs) with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna) based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers. We further prioritized cancer-related miRNAs at the network level with the random-walk algorithm, achieving a relatively higher performance than previous miRNA disease networks. Finally, we examined the top 5 candidate miRNAs for each kind of cancer and found that 71% of them are confirmed experimentally. miCancerna would be an alternative resource for the cancer-related miRNA identification.
Lin and Wang have introduced a quadratic version of the logrank test, appropriate for situations in which the underlying survival distributions may cross. In this note, we generalize the Lin-Wang procedure to incorporate weights and investigate the performance of Lin and Wang's test and weighted versions in various scenarios. We find that weighting does increase statistical power in certain situations; however, none of the procedures was dominant under every scenario.
It is difficult to construct a control group for trials of adjuvant therapy (Rx) of prostate cancer after radical prostatectomy (RP) due to ethical issues and patient acceptance. We utilized 8 curve-fitting models to estimate the time to 60%, 65%, … 95% chance of progression free survival (PFS) based on the data derived from Kattan post-RP nomogram. The 8 models were systematically applied to a training set of 153 post-RP cases without adjuvant Rx to develop 8 subsets of cases (reference case sets) whose observed PFS times were most accurately predicted by each model. To prepare a virtual control group for a single-arm adjuvant Rx trial, we first select the optimal model for the trial cases based on the minimum weighted Euclidean distance between the trial case set and the reference case set in terms of clinical features, and then compare the virtual PFS times calculated by the optimum model with the observed PFSs of the trial cases by the logrank test. The method was validated using an independent dataset of 155 post-RP patients without adjuvant Rx. We then applied the method to patients on a Phase II trial of adjuvant chemo-hormonal Rx post RP, which indicated that the adjuvant Rx is highly effective in prolonging PFS after RP in patients at high risk for prostate cancer recurrence. The method can accurately generate control groups for single-arm, post-RP adjuvant Rx trials for prostate cancer, facilitating development of new therapeutic strategies.
The wide application of multi-walled carbon nanotubes (MWCNT) has raised serious concerns about their safety on human health and the environment. However, the potential harmful effects of MWCNT remain unclear and contradictory. To clarify the potentially toxic effects of MWCNT and to elucidate the associated underlying mechanisms, the effects of MWCNT on human lung adenocarcinoma A549 cells were examined at both the cellular and the protein level. Cytotoxicity and genotoxicity were examined, followed by a proteomic analysis (2-DE coupled with LC-MS/MS) of the cellular response to MWCNT. Our results demonstrate that MWCNT induces cytotoxicity in A549 cells only at relatively high concentrations and longer exposure time. Within a relatively low dosage range (30 µg/ml) and short time period (24 h), MWCNT treatment does not induce significant cytotoxicity, cell cycle changes, apoptosis, or DNA damage. However, at these low doses and times, MWCNT treatment causes significant changes in protein expression. A total of 106 proteins show altered expression at various time points and dosages, and of these, 52 proteins were further identified by MS. Identified proteins are involved in several cellular processes including proliferation, stress, and cellular skeleton organization. In particular, MWCNT treatment causes increases in actin expression. This increase has the potential to contribute to increased migration capacity and may be mediated by reactive oxygen species (ROS).
Survival analysis focuses on modeling and predicting the time to an event of interest. Many statistical models have been proposed for survival analysis. They often impose strong assumptions on hazard functions, which describe how the risk of an event changes over time depending on covariates associated with each individual. In particular, the prevalent proportional hazards model assumes that covariates are multiplicatively related to the hazard. Here we propose a nonparametric model for survival analysis that does not explicitly assume particular forms of hazard functions. Our nonparametric model utilizes an ensemble of regression trees to determine how the hazard function varies according to the associated covariates. The ensemble model is trained using a gradient boosting method to optimize a smoothed approximation of the concordance index, which is one of the most widely used metrics in survival model performance evaluation. We implemented our model in a software package called GBMCI (gradient boosting machine for concordance index) and benchmarked the performance of our model against other popular survival models with a large-scale breast cancer prognosis dataset. Our experiment shows that GBMCI consistently outperforms other methods based on a number of covariate settings. GBMCI is implemented in R and is freely available online.
In case-control profiling studies, increasing the sample size does not always improve statistical power because the variance may also be increased if samples are highly heterogeneous. For instance, tumor samples used for gene expression assay are often heterogeneous in terms of tissue composition or mechanism of progression, or both; however, such variation is rarely taken into account in expression profiles analysis. We use a prostate cancer prognosis study as an example to demonstrate that solely recruiting more patient samples may not increase power for biomarker detection at all. In response to the heterogeneity due to mixed tissue, we developed a sample selection strategy termed Stepwise Enrichment by which samples are systematically culled based on tumor content and analyzed with t-test to determine an optimal threshold for tissue percentage. The selected tissue-percentage threshold identified the most significant data by balancing the sample size and the sample homogeneity; therefore, the power is substantially increased for identifying the prognostic biomarkers in prostate tumor epithelium cells as well as in prostate stroma cells. This strategy can be generally applied to profiling studies where the level of sample heterogeneity can be measured or estimated.
Chlamydia trachomatis is the most common sexually transmitted bacterial pathogen in the World and there is a need for a vaccine. To enhance the immunogenicity of a vaccine formulated with the Chlamydia muridarum (Cm) mouse pneumonitis recombinant major outer membrane protein (MOMP), we used combinations of Pam2CSK4 + CpG-1826 and Montanide ISA 720 VG + CpG-1826 as adjuvants. Neisseria gonorrhoeae recombinant porin B (Ng-PorB) was used as the antigen control with the same adjuvants. Female BALB/c mice were immunized twice in the nares (i.n.) or in the colon (cl.) and were boosted twice by the intramuscular plus subcutaneous (i.m. + s.c.) routes. Based on the IgG2a/IgG1 ratio in sera, mice immunized with MOMP + Pam2CSK4 + CpG-1826 showed a strong Th2 response while animals vaccinated with MOMP + Montanide ISA 720 VG + CpG-1826 had a Th1 response. Both groups of mice also developed robust Cm-specific T cell proliferation and high levels of IFN-?. Four weeks after the last immunization, the mice were challenged i.n. with 10(4) inclusion-forming units (IFU) of Cm. Using changes in body weight and number of IFU recovered from the lungs at 10 days post-challenge mice immunized i.n. + i.m./s.c. with MOMP + Pam2CSK4 + CpG-1826 were better protected than other groups. In conclusion, MOMP adjuvanted with Pam2CSK4 + CpG-1826, elicits strong humoral and cellular immune responses and induces significant protection against Chlamydia.
BACKGROUND: It has been suggested that the etiology of cerebral microbleeds (CMBs) differs according to their location in the brain, with lobar microbleeds being caused by cerebral amyloid angiopathy and deep or infratentorial microbleeds resulting from hypertension and atherosclerosis. We hypothesized that there were associations between cerebral arterial branches, cardiovascular risk factors, and the occurrence of CMBs. We examined these relationships in the current study. METHODS: Three hundred ninety-three patients with CMBs were analyzed in this study. The CMBs were listed according to the various arterial territories, and these were assessed for their relationship with cardiovascular risk factors, markers of small vessel disease, and their presence and location using multiple logistic regression. RESULTS: Systolic blood pressure had a significant association with CMBs in the territory of the posterior cerebral artery and the deep and infratentorial locations. The presence of lacunar infarcts, hemorrhage, and white matter changes were associated with CMBs in nearly all arterial territories. CONCLUSIONS: Hypertension increases the risk of microbleeds in the territory of the posterior cerebral artery and the deep and infratentorial locations. Cerebral amyloid angiopathy may be responsible for the microbleeds in the lobar area of brain.
In prostate cancer, race/ethnicity is the highest risk factor after adjusting for age. African Americans have more aggressive tumors at every clinical stage of the disease, resulting in poorer prognosis and increased mortality. A major barrier to identifying crucial gene activity differences is heterogeneity, including tissue composition variation intrinsic to the histology of prostate cancer. We hypothesized that differences in gene expression in specific tissue types would reveal mechanisms involved in the racial disparities of prostate cancer. We examined 17 pairs of arrays for AAs and Caucasians that were formed by closely matching the samples based on the known tissue type composition of the tumors. Using pair-wise t-test we found significantly altered gene expression between AAs and CAs. Independently, we performed multiple linear regression analyses to associate gene expression with race considering variation in percent tumor and stroma tissue. The majority of differentially expressed genes were associated with tumor-adjacent stroma rather than tumor tissue. Extracellular matrix, integrin family and signaling mediators of the epithelial-to-mesenchymal transition (EMT) pathways were all downregulated in stroma of AAs. Using MetaCore (GeneGo) analysis, we observed that 35% of significant (p < 10(-3)) pathways identified EMT and 25% identified immune response pathways especially for interleukins-2, -4, -5, -6, -7, -10, -13, -15 and -22 as the major changes. Our studies reveal that altered immune and EMT processes in tumor-adjacent stroma may be responsible for the aggressive nature of prostate cancer in AAs.
Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, particularly in developing countries. Despite the achievements in clinical therapeutics, the HCC mortality rate remains high. A number of artificial microRNA (amiRNA)-based HCC gene therapy studies have demonstrated significant inhibition of invasion and induction of apoptosis of HCC cancer cells, indicating that this type of therapy may be a promising alternative to current therapeutics. Since the structure of the amiRNA precursor in the specific intracellular environment is critical for the processing to mature amiRNA, a precursor structure that may be efficiently processed is desired. In this study, we constructed amiRNAs targeting firefly luciferase with the precursor structures of six HCC-abundant microRNAs: miR-18a, miR-21, miR-192, miR-221, miR-222 and miR-224, and evaluated the processing efficiency of these amiRNAs in the HCC cell lines Hep3B and HepG2 using a luciferase reporter system. The results demonstrated that these amiRNA precursors are capable of being expressed in HCC cells, with the miR-221 precursor-based amiRNA exhibiting the most efficient inhibition on firefly luciferase at the levels of mRNA and protein activity. This finding provides a basis for constructing HCC-targeting amiRNAs with potent processing efficiency using the precursor structure of miR-221.
Members of the early growth response (EGR) family of transcription factors play diverse functions in response to many cellular stimuli, including growth, stress, and inflammation. Egr3 has gone relatively unstudied, but here through use of the SPECS (Strategic Partners for the Evaluation of Predictive Signatures of Prostate Cancer) Affymetrix whole genome gene expression database we report that Egr3 mRNA is significantly over-expressed in prostate cancer compared to normal prostate tissue (5-fold). The Human Protein Atlas (http://www.proteinatlas.org), a database of tissue microarrays labeled with antibodies against over 11,000 human proteins, was utilized to quantify Egr3 protein expression in normal prostate and prostate cancer patients. In agreement with the SPECS data, we found that Egr3 protein is significantly increased in prostate cancer. The SPECS database has the benefit of extensive clinical follow up for the prostate cancer patients. Analysis of Egr3 mRNA expression in relation to the relapse status reveals that Egr3 mRNA expression is increased in tumor cells of non-relapsed samples (n?=?63) compared to normal prostate cells, but is significantly lower in relapsed samples (n?=?38) compared to non-relapse. The observations were confirmed using an independent data set. A list of genes correlating with this unique expression pattern was determined. These Egr3-correlated genes were enriched with Egr binding sites in their promoters. The gene list contains inflammatory genes such as IL-6, IL-8, IL1? and COX-2, which have extensive connections to prostate cancer.
Aneuploidy with chromosome instability is a cancer hallmark. We studied chromosome 7 (Chr7) copy number variation (CNV) in gliomas and in primary cultures derived from them. We found tumor heterogeneity with cells having Chr7-CNV commonly occurs in gliomas, with a higher percentage of cells in high-grade gliomas carrying more than 2 copies of Chr7, as compared to low-grade gliomas. Interestingly, all Chr7-aneuploid cell types in the parental culture of established glioma cell lines reappeared in single-cell-derived subcultures. We then characterized the biology of three syngeneic glioma cultures dominated by different Chr7-aneuploid cell types. We found phenotypic divergence for cells following Chr7 mis-segregation, which benefited overall tumor growth in vitro and in vivo. Mathematical modeling suggested the involvement of chromosome instability and interactions among cell subpopulations in restoring the optimal equilibrium of tumor cell types. Both our experimental data and mathematical modeling demonstrated that the complexity of tumor heterogeneity could be enhanced by the existence of chromosomes with structural abnormality, in addition to their mis-segregations. Overall, our findings show, for the first time, the involvement of chromosome instability in maintaining tumor heterogeneity, which underlies the enhanced growth, persistence and treatment resistance of cancers.
More than one million prostate biopsies are performed in the United States every year. A failure to find cancer is not definitive in a significant percentage of patients due to the presence of equivocal structures or continuing clinical suspicion. We have identified gene expression changes in stroma that can detect tumor nearby. We compared gene expression profiles of 13 biopsies containing stroma near tumor and 15 biopsies from volunteers without prostate cancer. About 3,800 significant expression changes were found and thereafter filtered using independent expression profiles to eliminate possible age-related genes and genes expressed at detectable levels in tumor cells. A stroma-specific classifier for nearby tumor was constructed on the basis of 114 candidate genes and tested on 364 independent samples including 243 tumor-bearing samples and 121 nontumor samples (normal biopsies, normal autopsies, remote stroma, as well as stroma within a few millimeters of tumor). The classifier predicted the tumor status of patients using tumor-free samples with an average accuracy of 97% (sensitivity = 98% and specificity = 88%) whereas classifiers trained with sets of 100 randomly generated genes had no diagnostic value. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for categorizing the presence of tumor in patients when a prostate sample is derived from near the tumor but does not contain any recognizable tumor.
The peroxiredoxins (PRDXs) are emerging as regulators of antioxidant defense, apoptosis, and therapy resistance in cancer. Because their significance in prostate cancer (PCa) is unclear, we investigated their expression and clinical associations in PCa.
Tissue samples from many diseases have been used for gene expression profiling studies, but these samples often vary widely in the cell types they contain. Such variation could confound efforts to correlate expression with clinical parameters. In principle, the proportion of each major tissue component can be estimated from the profiling data and used to triage samples before studying correlations with disease parameters. Four large gene expression microarray data sets from prostate cancer, whose tissue components were estimated by pathologists, were used to test the performance of multivariate linear regression models for in silico prediction of major tissue components. Ten-fold cross-validation within each data set yielded average differences between the pathologists predictions and the in silico predictions of 8% to 14% for the tumor component and 13% to 17% for the stroma component. Across independent data sets that used similar platforms and fresh frozen samples, the average differences were 11% to 12% for tumor and 12% to 17% for stroma. When the models were applied to 219 arrays of "tumor-enriched" samples in the literature, almost one quarter were predicted to have 30% or less tumor cells. Furthermore, there was a 10.5% difference in the average predicted tumor content between 37 recurrent and 42 nonrecurrent cancer patients. As a result, genes that correlated with tissue percentage generally also correlated with recurrence. If such a correlation is not desired, then some samples might be removed to rebalance the data set or tissue percentages might be incorporated into the prediction algorithm. A web service, "CellPred," has been designed for the in silico prediction of sample tissue components based on expression data.
Most current microarray oligonucleotide probe design strategies are based on probe design factors (PDFs), which include probe hybridization free energy (PHFE), probe minimum folding energy (PMFE), dimer score, hairpin score, homology score and complexity score. The impact of these PDFs on probe performance was evaluated using four sets of microarray comparative genome hybridization (aCGH) data, which included two array manufacturing methods and the genomes of two species. Since most of the hybridizing DNA is equimolar in CGH data, such data are ideal for testing the general hybridization properties of almost all candidate oligonucleotides. In all our data sets, PDFs related to probe secondary structure (PMFE, hairpin score and dimer score) are the most significant factors linearly correlated with probe hybridization intensities. PHFE, homology and complexity score are correlating significantly with probe specificities, but in a non-linear fashion. We developed a new PDF, pseudo probe binding energy (PPBE), by iteratively fitting dinucleotide positional weights and dinucleotide stacking energies until the average residue sum of squares for the model was minimized. PPBE showed a better correlation with probe sensitivity and a better specificity than all other PDFs, although training data are required to construct a PPBE model prior to designing new oligonucleotide probes. The physical properties that are measured by PPBE are as yet unknown but include a platform-dependent component. A practical way to use these PDFs for probe design is to set cutoff thresholds to filter out bad quality probes. Programs and correlation parameters from this study are freely available to facilitate the design of DNA microarray oligonucleotide probes.
Harrells c-index or concordance C has been widely used as a measure of separation of two survival distributions. In the absence of censored data, the c-index estimates the Mann-Whitney parameter Pr(X>Y), which has been repeatedly utilized in various statistical contexts. In the presence of randomly censored data, the c-index no longer estimates Pr(X>Y); rather, a parameter that involves the underlying censoring distributions. This is in contrast to Efrons maximum likelihood estimator of the Mann-Whitney parameter, which is recommended in the setting of random censorship.
The present study was undertaken to test the efficacy of immunization with the native major outer membrane protein (nMOMP) of Chlamydia trachomatis mouse pneumonitis (MoPn) serovar in combination with a novel immunostimulatory adjuvant consisting of CpG oligodeoxynucleotide (ODN) linked to the nontoxic B subunit of cholera toxin (CTB-CpG) to elicit a protective immune response to C. trachomatis. High levels of Chlamydia-specific IgG antibodies were detected in the sera from BALB/c mice immunized intramuscularly and subcutaneously (i.m.+s.c.) with the nMOMP/CTB-CpG vaccine or with nMOMP adjuvanted with a mixture of CT and CpG ODN (CT+CpG). Further, these immunization schemes gave rise to significant T-cell-mediated Chlamydia-specific immune responses. No Chlamydia-specific humoral or cell-mediated immune responses were detected in the control mice vaccinated with ovalbumin together with either CTB-CpG or CT+CpG. Following an intranasal challenge with C. trachomatis the groups of mice immunized with nMOMP plus CTB-CpG, CT+CpG or live C. trachomatis were found to be protected based on their change in body weight and lung weight as well as number of inclusion forming unit recovered from the lungs, as compared with control groups immunized with ovalbumin plus either adjuvants. Interestingly, IFN-gamma-producing CD4(+), but not CD8(+), T-cells showed a significant correlation with the outcomes of the challenge. In conclusion, nMOMP in combination with the novel adjuvant CTB-CpG elicited a significant antigen-specific antibody and cell-mediated immune responses as well as protection against a pulmonary challenge with C. trachomatis.
Classification and regression trees have long been used for cancer diagnosis and prognosis. Nevertheless, instability and variable selection bias, as well as overfitting, are well-known problems of tree-based methods. In this article, we investigate whether ensemble tree classifiers can ameliorate these difficulties, using data from two recent studies of radical prostatectomy in prostate cancer.
One of the major challenges in the development of prostate cancer prognostic biomarkers is the cellular heterogeneity in tissue samples. We developed an objective Cluster-Correlation (CC) analysis to identify gene expression changes in various cell types that are associated with progression. In the Cluster step, samples were clustered (unsupervised) based on the expression values of each gene through a mixture model combined with a multiple linear regression model in which cell-type percent data were used for decomposition. In the Correlation step, a Chi-square test was used to select potential prognostic genes. With CC analysis, we identified 324 significantly expressed genes (68 tumor and 256 stroma cell expressed genes) which were strongly associated with the observed biochemical relapse status. Significance Analysis of Microarray (SAM) was then utilized to develop a seven-gene classifier. The Classifier has been validated using two independent Data Sets. The overall prediction accuracy and sensitivity is 71% and 76%, respectively. The inclusion of the Gleason sum to the seven-gene classifier raised the prediction accuracy and sensitivity to 83% and 76% respectively based on independent testing. These results indicated that our prognostic model that includes cell type adjustments and using Gleason score and the seven-gene signature has some utility for predicting outcomes for prostate cancer for individual patients at the time of prognosis. The strategy could have applications for improving marker performance in other cancers and other diseases.
Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001). We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.
Related JoVE Video
Journal of Visualized Experiments
What is Visualize?
JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.
How does it work?
We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.
Video X seems to be unrelated to Abstract Y...
In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.