In-Vivo Visualization of Tumor Microvessel Density and Response to Anti-Angiogenic Treatment by High Resolution MRI in Mice

Purpose: Inhibition of angiogenesis has shown clinical success in patients with cancer. Thus, imaging approaches that allow for the identification of angiogenic tumors and the detection of response to anti-angiogenic treatment are of high clinical relevance. Experimental Design: We established an in vivo magnetic resonance imaging (MRI) approach that allows us to simultaneously image tumor microvessel density and tumor vessel size in a NSCLC model in mice. Results: Using microvessel density imaging we demonstrated an increase in microvessel density within 8 days after tumor implantation, while tumor vessel size decreased indicating a switch from macro- to microvessels during tumor growth. Moreover, we could monitor in vivo inhibition of angiogenesis induced by the angiogenesis inhibitor PTK787, resulting in a decrease of microvessel density and a slight increase in tumor vessel size. Conclusions: We present an in vivo imaging approach that allows us to monitor both tumor microvessel density and tumor vessel size in the tumor. Moreover, this approach enables us to assess, early-on, treatment effects on tumor microvessel density as well as on tumor vessel size. Thus, this imaging-based strategy of validating anti-angiogenic treatment effects ha

A close examination of double filtering with fold change and t test in microarray analysis

<p>Abstract</p> <p>Background</p> <p>Many researchers use the double filtering procedure with fold change and <it>t </it>test to identify differentially expressed genes, in the hope that the double filtering will provide extra confidence in the results. Due to its simplicity, the double filtering procedure has been popular with applied researchers despite the development of more sophisticated methods.</p> <p>Results</p> <p>This paper, for the first time to our knowledge, provides theoretical insight on the drawback of the double filtering procedure. We show that fold change assumes all genes to have a common variance while <it>t </it>statistic assumes gene-specific variances. The two statistics are based on contradicting assumptions. Under the assumption that gene variances arise from a mixture of a common variance and gene-specific variances, we develop the theoretically most powerful likelihood ratio test statistic. We further demonstrate that the posterior inference based on a Bayesian mixture model and the widely used significance analysis of microarrays (SAM) statistic are better approximations to the likelihood ratio test than the double filtering procedure.</p> <p>Conclusion</p> <p>We demonstrate through hypothesis testing theory, simulation studies and real data examples, that well constructed shrinkage testing methods, which can be united under the mixture gene variance assumption, can considerably outperform the double filtering procedure.</p

Evolution and spread of Venezuelan equine encephalitis complex alphavirus in the Americas.

Venezuelan equine encephalitis (VEE) complex alphaviruses are important re-emerging arboviruses that cause life-threatening disease in equids during epizootics as well as spillover human infections. We conducted a comprehensive analysis of VEE complex alphaviruses by sequencing the genomes of 94 strains and performing phylogenetic analyses of 130 isolates using complete open reading frames for the nonstructural and structural polyproteins. Our analyses confirmed purifying selection as a major mechanism influencing the evolution of these viruses as well as a confounding factor in molecular clock dating of ancestors. Times to most recent common ancestors (tMRCAs) could be robustly estimated only for the more recently diverged subtypes; the tMRCA of the ID/IAB/IC/II and IE clades of VEE virus (VEEV) were estimated at ca. 149–973 years ago. Evolution of the IE subtype has been characterized by a significant evolutionary shift from the rest of the VEEV complex, with an increase in structural protein substitutions that are unique to this group, possibly reflecting adaptation to its unique enzootic mosquito vector Culex (Melanoconion) taeniopus. Our inferred tree topologies suggest that VEEV is maintained primarily in situ, with only occasional spread to neighboring countries, probably reflecting the limited mobility of rodent hosts and mosquito vectors

Sensitivity of MRI Tumor Biomarkers to VEGFR Inhibitor Therapy in an Orthotopic Mouse Glioma Model

MRI biomarkers of tumor edema, vascular permeability, blood volume, and average vessel caliber are increasingly being employed to assess the efficacy of tumor therapies. However, the dependence of these biomarkers on a number of physiological factors can compromise their sensitivity and complicate the assessment of therapeutic efficacy. Here we examine the response of these MRI tumor biomarkers to cediranib, a potent vascular endothelial growth factor receptor (VEGFR) inhibitor, in an orthotopic mouse glioma model. A significant increase in the tumor volume and relative vessel caliber index (rVCI) and a slight decrease in the water apparent diffusion coefficient (ADC) were observed for both control and cediranib treated animals. This contrasts with a clinical study that observed a significant decrease in tumor rVCI, ADC and volume with cediranib therapy. While the lack of a difference between control and cediranib treated animals in these biomarker responses might suggest that cediranib has no therapeutic benefit, cediranib treated mice had a significantly increased survival. The increased survival benefit of cediranib treated animals is consistent with the significant decrease observed for cediranib treated animals in the relative cerebral blood volume (rCBV), relative microvascular blood volume (rMBV), transverse relaxation time (T2), blood vessel permeability (Ktrans), and extravascular-extracellular space (νe). The differential response of pre-clinical and clinical tumors to cediranib therapy, along with the lack of a positive response for some biomarkers, indicates the importance of evaluating the whole spectrum of different tumor biomarkers to properly assess the therapeutic response and identify and interpret the therapy-induced changes in the tumor physiology

Senescence and tumour clearance is triggered by p53 restoration in murine liver carcinomas

Although cancer arises from a combination of mutations in oncogenes and tumour suppressor genes, the extent to which tumour suppressor gene loss is required for maintaining established tumours is poorly understood. p53 is an important tumour suppressor that acts to restrict proliferation in response to DNA damage or deregulation of mitogenic oncogenes, by leading to the induction of various cell cycle checkpoints, apoptosis or cellular senescence(1,2). Consequently, p53 mutations increase cell proliferation and survival, and in some settings promote genomic instability and resistance to certain chemotherapies(3). To determine the consequences of reactivating the p53 pathway in tumours, we used RNA interference (RNAi) to conditionally regulate endogenous p53 expression in a mosaic mouse model of liver carcinoma(4,5). We show that even brief reactivation of endogenous p53 in p53-deficient tumours can produce complete tumour regressions. The primary response to p53 was not apoptosis, but instead involved the induction of a cellular senescence program that was associated with differentiation and the upregulation of inflammatory cytokines. This program, although producing only cell cycle arrest in vitro, also triggered an innate immune response that targeted the tumour cells in vivo, thereby contributing to tumour clearance. Our study indicates that p53 loss can be required for the maintenance of aggressive carcinomas, and illustrates how the cellular senescence program can act together with the innate immune system to potently limit tumour growth

Empirical Bayes models for multiple probe type microarrays at the probe level

<p>Abstract</p> <p>Background</p> <p>When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are adjusted towards a global estimate, producing more stable results compared to ordinary t-tests. However, for Affymetrix type data a clear dependency between variability and intensity-level generally exists, even for logged intensities, most clearly for data at the probe level but also for probe-set summarizes such as the MAS5 expression index. As a consequence, adjustment towards a global estimate results in an intensity-level dependent false positive rate.</p> <p>Results</p> <p>We propose two new methods for finding differentially expressed genes, Probe level Locally moderated Weighted median-t (PLW) and Locally Moderated Weighted-t (LMW). Both methods use an empirical Bayes model taking the dependency between variability and intensity-level into account. A global covariance matrix is also used allowing for differing variances between arrays as well as array-to-array correlations. PLW is specially designed for Affymetrix type arrays (or other multiple-probe arrays). Instead of making inference on probe-set summaries, comparisons are made separately for each perfect-match probe and are then summarized into one score for the probe-set.</p> <p>Conclusion</p> <p>The proposed methods are compared to 14 existing methods using five spike-in data sets. For RMA and GCRMA processed data, PLW has the most accurate ranking of regulated genes in four out of the five data sets, and LMW consistently performs better than all examined moderated t-tests when used on RMA, GCRMA, and MAS5 expression indexes.</p

Parallel multiplicity and error discovery rate (EDR) in microarray experiments

<p>Abstract</p> <p>Background</p> <p>In microarray gene expression profiling experiments, differentially expressed genes (DEGs) are detected from among tens of thousands of genes on an array using statistical tests. It is important to control the number of false positives or errors that are present in the resultant DEG list. To date, more than 20 different multiple test methods have been reported that compute overall Type I error rates in microarray experiments. However, these methods share the following dilemma: they have low power in cases where only a small number of DEGs exist among a large number of total genes on the array.</p> <p>Results</p> <p>This study contrasts parallel multiplicity of objectively related tests against the traditional simultaneousness of subjectively related tests and proposes a new assessment called the Error Discovery Rate (EDR) for evaluating multiple test comparisons in microarray experiments. Parallel multiple tests use only the negative genes that parallel the positive genes to control the error rate; while simultaneous multiple tests use the total unchanged gene number for error estimates. Here, we demonstrate that the EDR method exhibits improved performance over other methods in specificity and sensitivity in testing expression data sets with sequence digital expression confirmation, in examining simulation data, as well as for three experimental data sets that vary in the proportion of DEGs. The EDR method overcomes a common problem of previous multiple test procedures, namely that the Type I error rate detection power is low when the total gene number used is large but the DEG number is small.</p> <p>Conclusions</p> <p>Microarrays are extensively used to address many research questions. However, there is potential to improve the sensitivity and specificity of microarray data analysis by developing improved multiple test comparisons. This study proposes a new view of multiplicity in microarray experiments and the EDR provides an alternative multiple test method for Type I error control in microarray experiments.</p

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration