Conflicting Views on Chemical Carcinogenesis Arising from the Design and Evaluation of Rodent Carcinogenicity Studies

Conflicting views have been expressed frequently on assessments of human cancer risk of environmental agents based on animal carcinogenicity data; this is primarily because of uncertainties associated with extrapolations of toxicologic findings from studies in experimental animals to human circumstances. Underlying these uncertainties are issues related to how experiments are designed, how rigorously hypotheses are tested, and to what extent assertions extend beyond actual findings. National and international health agencies regard carcinogenicity findings in well-conducted experimental animal studies as evidence of potential carcinogenic risk to humans. Controversies arise when both positive and negative carcinogenicity data exist for a specific agent or when incomplete mechanistic data suggest a possible species difference in response. Issues of experimental design and evaluation that might contribute to disparate results are addressed in this article. To serve as reliable sources of data for the evaluation of the carcinogenic potential of environmental agents, experimental studies must include a) animal models that are sensitive to the end points under investigation; b) detailed characterization of the agent and the administered doses; c) challenging doses and durations of exposure (at least 2 years for rats and mice); d) sufficient numbers of animals per dose group to be capable of detecting a true effect; e) multiple dose groups to allow characterization of dose–response relationships, f) complete and peer-reviewed histopathologic evaluations; and g) pairwise comparisons and analyses of trends based on survival-adjusted tumor incidence. Pharmacokinetic models and mechanistic hypotheses may provide insights into the biological behavior of the agent; however, they must be adequately tested before being used to evaluate human cancer risk

Mutational analysis of Polycomb genes in solid tumours identifies <i>PHC3</i> amplification as a possible cancer-driving genetic alteration.

Background: Polycomb group genes (PcGs) are epigenetic effectors implicated in most cancer hallmarks. The mutational status of all PcGs has never been systematically assessed in solid tumours. Methods: We conducted a multi-step analysis on publically available databases and patient samples to identify somatic aberrations of PcGs. Results: Data from more than 1000 cancer patients show for the first time that the PcG member PHC3 is amplified in three epithelial neoplasms (rate: 8–35%). This aberration predicts poorer prognosis in lung and uterine carcinomas (Po0.01). Gene amplification correlates with mRNA overexpression (Po0.01), suggesting a functional role of this aberration. Conclusion: PHC3 amplification may emerge as a biomarker and potential therapeutic target in a relevant fraction of epithelial tumours

Perspective from a Younger Generation -- The Astro-Spectroscopy of Gisbert Winnewisser

Gisbert Winnewisser's astronomical career was practically coextensive with the whole development of molecular radio astronomy. Here I would like to pick out a few of his many contributions, which I, personally, find particularly interesting and put them in the context of newer results.Comment: 14 pages. (Co)authored by members of the MPIfR (Sub)millimeter Astronomy Group. To appear in the Proceedings of the 4th Cologne-Bonn-Zermatt-Symposium "The Dense Interstellar Medium in Galaxies" eds. S. Pfalzner, C. Kramer, C. Straubmeier, & A. Heithausen (Springer: Berlin

Azimuthal anisotropy and correlations at large transverse momenta in p+pp+p and Au+Au collisions at sNN\sqrt{s_{_{NN}}}= 200 GeV

Results on high transverse momentum charged particle emission with respect to the reaction plane are presented for Au+Au collisions at $\sqrt{s_{_{NN}}}$= 200 GeV. Two- and four-particle correlations results are presented as well as a comparison of azimuthal correlations in Au+Au collisions to those in $p+p$ at the same energy. Elliptic anisotropy, $v_2$, is found to reach its maximum at $p_t \sim 3$ GeV/c, then decrease slowly and remain significant up to $p_t\approx 7$ -- 10 GeV/c. Stronger suppression is found in the back-to-back high-$p_t$ particle correlations for particles emitted out-of-plane compared to those emitted in-plane. The centrality dependence of $v_2$ at intermediate $p_t$ is compared to simple models based on jet quenching.Comment: 4 figures. Published version as PRL 93, 252301 (2004

Azimuthal anisotropy in Au+Au collisions at sqrtsNN = 200 GeV

The results from the STAR Collaboration on directed flow (v_1), elliptic flow (v_2), and the fourth harmonic (v_4) in the anisotropic azimuthal distribution of particles from Au+Au collisions at sqrtsNN = 200 GeV are summarized and compared with results from other experiments and theoretical models. Results for identified particles are presented and fit with a Blast Wave model. Different anisotropic flow analysis methods are compared and nonflow effects are extracted from the data. For v_2, scaling with the number of constituent quarks and parton coalescence is discussed. For v_4, scaling with v_2^2 and quark coalescence is discussed.Comment: 26 pages. As accepted by Phys. Rev. C. Text rearranged, figures modified, but data the same. However, in Fig. 35 the hydro calculations are corrected in this version. The data tables are available at http://www.star.bnl.gov/central/publications/ by searching for "flow" and then this pape

Rapidity and Centrality Dependence of Proton and Anti-proton Production from Au+Au Collisions at sqrt(sNN) = 130GeV

We report on the rapidity and centrality dependence of proton and anti-proton transverse mass distributions from Au+Au collisions at sqrt(sNN) = 130GeV as measured by the STAR experiment at RHIC. Our results are from the rapidity and transverse momentum range of |y|<0.5 and 0.35 <p_t<1.00GeV/c. For both protons and anti-protons, transverse mass distributions become more convex from peripheral to central collisions demonstrating characteristics of collective expansion. The measured rapidity distributions and the mean transverse momenta versus rapidity are flat within |y|<0.5. Comparisons of our data with results from model calculations indicate that in order to obtain a consistent picture of the proton(anti-proton) yields and transverse mass distributions the possibility of pre-hadronic collective expansion may have to be taken into account.Comment: 4 pages, 3 figures, 1 table, submitted to PR

A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model

<p>Abstract</p> <p>Background</p> <p>Bioactivity profiling using high-throughput <it>in vitro </it>assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also reduce the need for animal testing. Several public efforts are aimed at discovering patterns or classifiers in high-dimensional bioactivity space that predict tissue, organ or whole animal toxicological endpoints. Supervised machine learning is a powerful approach to discover combinatorial relationships in complex <it>in vitro/in vivo </it>datasets. We present a novel model to simulate complex chemical-toxicology data sets and use this model to evaluate the relative performance of different machine learning (ML) methods.</p> <p>Results</p> <p>The classification performance of Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM) in the presence and absence of filter-based feature selection was analyzed using K-way cross-validation testing and independent validation on simulated <it>in vitro </it>assay data sets with varying levels of model complexity, number of irrelevant features and measurement noise. While the prediction accuracy of all ML methods decreased as non-causal (irrelevant) features were added, some ML methods performed better than others. In the limit of using a large number of features, ANN and SVM were always in the top performing set of methods while RPART and KNN (k = 5) were always in the poorest performing set. The addition of measurement noise and irrelevant features decreased the classification accuracy of all ML methods, with LDA suffering the greatest performance degradation. LDA performance is especially sensitive to the use of feature selection. Filter-based feature selection generally improved performance, most strikingly for LDA.</p> <p>Conclusion</p> <p>We have developed a novel simulation model to evaluate machine learning methods for the analysis of data sets in which in vitro bioassay data is being used to predict in vivo chemical toxicology. From our analysis, we can recommend that several ML methods, most notably SVM and ANN, are good candidates for use in real world applications in this area.</p