TP53, ATRX alterations, and low tumor mutation load feature IDH-wildtype giant cell glioblastoma despite exceptional ultra-mutated tumors

Background: Giant cell glioblastoma (gcGBM) is a rare morphological variant of IDH-wildtype (IDHwt) GBM that occurs in young adults and have a slightly better prognosis than "classic" IDHwt GBM. Methods: We studied 36 GBMs, 14 with a histopathological diagnosis of gcGBM and 22 with a giant cell component. We analyzed the genetic profile of the most frequently mutated genes in gliomas and assessed the tumor mutation load (TML) by gene-targeted next-generation sequencing. We validated our findings using The Cancer Genome Atlas (TCGA) data. Results: p53 was altered by gene mutation or protein overexpression in all cases, while driver IDH1, IDH2, BRAF, or H3F3A mutations were infrequent or absent. Compared to IDHwt GBMs, gcGBMs had a significant higher frequency of TP53, ATRX, RB1, and NF1 mutations, while lower frequency of EGFR amplification, CDKN2A deletion, and TERT promoter mutation. Almost all tumors had low TML values. The high TML observed in only 2 tumors was consistent with POLE and MSH2 mutations. In the histopathological review of TCGA IDHwt, TP53-mutant tumors identified giant cells in 37% of the cases. Considering our series and that of the TCGA, patients with TP53-mutant gcGBMs had better overall survival than those with TP53wt GBMs (log-rank test, P < .002). Conclusions: gcGBMs have molecular features that contrast to "classic" IDHwt GBMs: unusually frequent ATRX mutations and few EGFR amplifications and CDKN2A deletions, especially in tumors with a high number of giant cells. TML is frequently low, although exceptional high TML suggests a potential for immune checkpoint therapy in some cases, which may be relevant for personalized medicine

Probabilistic reframing for cost-sensitive regression

© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Knowledge Discovery from Data (TKDD), VOL. 8, ISS. 4, (October 2014) http://doi.acm.org/10.1145/2641758Common-day applications of predictive models usually involve the full use of the available contextual information. When the operating context changes, one may fine-tune the by-default (incontextual) prediction or may even abstain from predicting a value (a reject). Global reframing solutions, where the same function is applied to adapt the estimated outputs to a new cost context, are possible solutions here. An alternative approach, which has not been studied in a comprehensive way for regression in the knowledge discovery and data mining literature, is the use of a local (e.g., probabilistic) reframing approach, where decisions are made according to the estimated output and a reliability, confidence, or probability estimation. In this article, we advocate for a simple two-parameter (mean and variance) approach, working with a normal conditional probability density. Given the conditional mean produced by any regression technique, we develop lightweight “enrichment” methods that produce good estimates of the conditional variance, which are used by the probabilistic (local) reframing methods. We apply these methods to some very common families of costsensitive problems, such as optimal predictions in (auction) bids, asymmetric loss scenarios, and rejection rules.This work was supported by the MEC/MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, and TIN 2013-45732-C4-1-P and GVA projects PROMETEO/2008/051 and PROMETEO2011/052. Finally, part of this work was motivated by the REFRAME project (http://www.reframe-d2k.org) granted by the European Coordinated Research on Long-term Challenges in Information and Communication Sciences & Technologies ERA-Net (CHIST-ERA) and funded by Ministerio de Economia y Competitividad in Spain (PCIN-2013-037).Hernández Orallo, J. (2014). Probabilistic reframing for cost-sensitive regression. ACM Transactions on Knowledge Discovery from Data. 8(4):1-55. https://doi.org/10.1145/2641758S15584G. Bansal, A. Sinha, and H. Zhao. 2008. Tuning data mining methods for cost-sensitive regression: A study in loan charge-off forecasting. Journal of Management Information System 25, 3 (Dec. 2008), 315--336.A. P. Basu and N. Ebrahimi. 1992. Bayesian approach to life testing and reliability estimation using asymmetric loss function. Journal of Statistical Planning and Inference 29, 1--2 (1992), 21--31.A. Bella, C. Ferri, J. Hern&#225;ndez-Orallo, and M. J. Ram&#237;rez-Quintana. 2010. Quantification via probability estimators. In Proceedings of the 2010 IEEE International Conference on Data Mining. IEEE, 737--742.A. Bella, C. Ferri, J. Hern&#225;ndez-Orallo, and M. J. Ram&#237;rez-Quintana. 2013. Aggregative quantification for regression. Data Mining and Knowledge Discovery (2013), 1--44.A. Bella, C. Ferri, J. Hern&#225;ndez-Orallo, and M. J. Ram&#237;rez-Quintana. 2009. Calibration of machine learning models. In Handbook of Research on Machine Learning Applications. IGI Global, 128--146.A. Bella, C. Ferri, J. Hern&#225;ndez-Orallo, and M. J. Ram&#237;rez-Quintana. 2011. Using negotiable features for prescription problems. Computing 91, 2 (2011), 135--168.J. Bi and K. P. Bennett. 2003. Regression error characteristic curves. In Proceedings of the 20th International Conference on Machine Learning (ICML&#8217;03).Z. Bosni&#263; and I. Kononenko. 2008. Comparison of approaches for estimating reliability of individual regression predictions. Data &#38; Knowledge Engineering 67, 3 (2008), 504--516.Z. Bosni&#263; and I. Kononenko. 2009. An overview of advances in reliability estimation of individual predictions in machine learning. Intelligent Data Analysis 13, 2 (2009), 385--401.L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth.P. F. Christoffersen and F. X. Diebold. 1996. Further results on forecasting and model selection under asymmetric loss. Journal of Applied Econometrics 11, 5 (1996), 561--571.P. F. Christoffersen and F. X. Diebold. 1997. Optimal prediction under asymmetric loss. Econometric Theory 13 (1997), 808--817.I. Cohen and M. Goldszmidt. 2004. Properties and benefits of calibrated classifiers. Knowledge Discovery in Databases: PKDD 2004 (2004), 125--136.S. Crone. 2002. Training artificial neural networks for time series prediction using asymmetric cost functions. In Proceedings of the 9th International Conference on Neural Information Processing.J. Dem&#353;ar. 2006. Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research 7 (2006), 1--30.M. Dumas, L. Aldred, G. Governatori, and A. H. M. Ter Hofstede. 2005. Probabilistic automated bidding in multiple auctions. Electronic Commerce Research 5, 1 (2005), 25--49.C. Elkan. 2001. The foundations of cost-sensitive learning. In Proceedings of the 17th International Conference on Artificial Intelligence (&#8217;01), Bernhard Nebel (Ed.). San Francisco, CA, 973--978.G. Elliott and A. Timmermann. 2004. Optimal forecast combinations under general loss functions and forecast error distributions. Journal of Econometrics 122, 1 (2004), 47--79.T. Fawcett. 2006a. An introduction to ROC analysis. Pattern Recognition Letters 27, 8 (2006), 861--874.T. Fawcett. 2006b. ROC graphs with instance-varying costs. Pattern Recognition Letters 27, 8 (2006), 882--891.C. Ferri, P. Flach, and J. Hern&#225;ndez-Orallo. 2002. Learning decision trees using the area under the ROC curve. In Proceedings of the International Conference on Machine Learning. 139--146.C. Ferri, P. Flach, and J. Hern&#225;ndez-Orallo. 2003. Improving the AUC of probabilistic estimation trees. In Proceedings of the 14th European Conference on Machine Learning (ECML&#8217;03). Springer, 121--132.C. Ferri and J. Hern&#225;ndez-Orallo. 2004. Cautious classifiers. In ROC Analysis in Artificial Intelligence, 1st International Workshop, ROCAI-2004, Valencia, Spain, August 22, 2004, J. Hern&#225;ndez-Orallo, C. Ferri, N. Lachiche, and P. A. Flach (Eds.). 27--36.P. Flach. 2012. Machine Learning: The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press.G. Forman. 2008. Quantifying counts and costs via classification. Data Mining and Knowledge Discovery 17, 2 (2008), 164--206.S. Garc&#237;a and F. Herrera. 2008. An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons. The Journal of Machine Learning Research 9, 2677--2694 (2008), 66.R. Ghani. 2005. Price prediction and insurance for online auctions. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD&#8217;05). ACM, New York, NY, 411--418.C. W. J. Granger. 1969. Prediction with a generalized cost of error function. Operational Research (1969), 199--207.C. W. J. Granger. 1999. Outline of forecast theory using generalized cost functions. Spanish Economic Review 1, 2 (1999), 161--173.P. Hall, J. Racine, and Q. Li. 2004. Cross-validation and the estimation of conditional probability densities. Journal of the American Statistical Association 99, 468 (2004), 1015--1026.P. Hall, R. C. L. Wolff, and Q. Yao. 1999. Methods for estimating a conditional distribution function. Journal of the American Statistical Association (1999), 154--163.T. J. Hastie, R. J. Tibshirani, and J. H. Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer.J. Hern&#225;ndez-Orallo. 2013. ROC curves for regression. Pattern Recognition 46, 12 (2013), 3395--3411.J. Hern&#225;ndez-Orallo, P. Flach, and C. Ferri. 2012. A unified view of performance metrics: Translating threshold choice into expected classification loss. Journal of Machine Learning Research 13 (2012), 2813--2869.J. Hern&#225;ndez-Orallo, P. Flach, and C. Ferri. 2013. ROC curves in cost space. Machine Learning 93, 1 (2013), 71--91.J. N. Hwang, S. R. Lay, and A. Lippman. 1994. Nonparametric multivariate density estimation: A comparative study. IEEE Transactions on Signal Processing 42, 10 (1994), 2795--2810.R. J. Hyndman, D. M. Bashtannyk, and G. K. Grunwald. 1996. Estimating and visualizing conditional densities. Journal of Computational and Graphical Statistics (1996), 315--336.N. Japkowicz and M. Shah. 2011. Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press.M. Jino, B. T. de Abreu, and others. 2010. Machine learning methods and asymmetric cost function to estimate execution effort of software testing. In Proceedings of the 2010 3rd International Conference on Software Testing, Verification and Validation (ICST&#8217;10). IEEE, 275--284.B. Kitts and B. Leblanc. 2004. Optimal bidding on keyword auctions. Electronic Markets 14, 3 (2004), 186--201.N. Lachiche and P. Flach. 2003. Improving accuracy and cost of two-class and multi-class probabilistic classifiers using ROC curves. In Proceedings of the International Conference on Machine Learning, Vol. 20-1. 416.H. Papadopoulos. 2008. Inductive conformal prediction: Theory and application to neural networks. Tools in Artificial Intelligence 18 (2008), 315--330.H. Papadopoulos, K. Proedrou, V. Vovk, and A. Gammerman. 2002. Inductive confidence machines for regression. In Machine Learning: ECML 2002, Tapio Elomaa, Heikki Mannila, and Hannu Toivonen (Eds.). Lecture Notes in Computer Science, Vol. 2430. Springer, Berlin, 185--194.H. Papadopoulos, V. Vovk, and A. Gammerman. 2011. Regression conformal prediction with nearest neighbours. Journal of Artificial Intelligence Research 40, 1 (2011), 815--840.T. Pietraszek. 2007. On the use of ROC analysis for the optimization of abstaining classifiers. Machine Learning 68, 2 (2007), 137--169.J. C. Platt. 1999. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In Advances in Large Margin Classifiers. MIT Press, Boston, 61--74.F. Provost and P. Domingos. 2003. Tree induction for probability-based ranking. Machine Learning 52, 3 (2003), 199--215.R Team and others. 2012. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.R. Ribeiro. 2011. Utility-based Regression. PhD thesis, Department of Computer Science, Faculty of Sciences, University of Porto.M. Rosenblatt. 1969. Conditional probability density and regression estimators. Multivariate Analysis II 25 (1969), 31.S. Rosset, C. Perlich, and B. Zadrozny. 2007. Ranking-based evaluation of regression models. Knowledge and Information Systems 12, 3 (2007), 331--353.R. E. Schapire, P. Stone, D. McAllester, M. L. Littman, and J. A. Csirik. 2002. Modeling auction price uncertainty using boosting-based conditional density estimation. In Proceedings of the International Conference on Machine Learning. 546--553.G. Shafer and V. Vovk. 2008. A tutorial on conformal prediction. Journal of Machine Learning Research 9 (2008), 371--421.J. A. Swets, R. M. Dawes, and J. Monahan. 2000. Better decisions through science. Scientific American 283, 4 (Oct. 2000), 82--87.R. D. Thompson and A. P. Basu. 1996. Asymmetric loss functions for estimating system reliability. In Bayesian Analysis in Statistics and Econometrics. John Wiley &#38; Sons, 471--482.L. Torgo. 2005. Regression error characteristic surfaces. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. ACM, 697--702.L. Torgo. 2010. Data Mining with R. Chapman and Hall/CRC Press.L. Torgo and R. Ribeiro. 2007. Utility-based regression. Knowledge Discovery in Databases: PKDD 2007. 597--604.L. Torgo and R. Ribeiro. 2009. Precision and recall for regression. In Discovery Science. Springer, 332--346.P. Turney. 2000. Types of cost in inductive concept learning. Canada National Research Council Publications Archive.L. Wasserman. 2006. All of Nonparametric Statistics. Springer-Verlag, New York.M. P. Wellman, D. M. Reeves, K. M. Lochner, and Y. Vorobeychik. 2004. Price prediction in a trading agent competition. Journal of Artificial Intelligence Research 21 (2004), 19--36.K. Yu and M. C. Jones. 2004. Likelihood-based local linear estimation of the conditional variance function. Journal of the American Statistical Association 99, 465 (2004), 139--144.B. Zadrozny and C. Elkan. 2002. Transforming classifier scores into accurate multiclass probability estimates. In Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 694--699.A. Zellner. 1986. Bayesian estimation and prediction using asymmetric loss functions. Journal of the American Statistical Association (1986), 446--451.H. Zhao, A. P. Sinha, and G. Bansal. 2011. An extended tuning method for cost-sensitive regression and forecasting. Decision Support Systems

Systemic Effects Induced by Hyperoxia in a Preclinical Model of Intra-abdominal Sepsis

Supplemental oxygen is a supportive treatment in patients with sepsis to balance tissue oxygen delivery and demand in the tissues. However, hyperoxia may induce some pathological effects. We sought to assess organ damage associated with hyperoxia and its correlation with the production of reactive oxygen species (ROS) in a preclinical model of intra-abdominal sepsis. For this purpose, sepsis was induced in male, Sprague-Dawley rats by cecal ligation and puncture (CLP). We randomly assigned experimental animals to three groups: control (healthy animals), septic (CLP), and sham-septic (surgical intervention without CLP). At 18 h after CLP, septic (n = 39), sham-septic (n = 16), and healthy (n = 24) animals were placed within a sealed Plexiglas cage and randomly distributed into four groups for continuous treatment with 21%, 40%, 60%, or 100% oxygen for 24 h. At the end of the experimental period, we evaluated serum levels of cytokines, organ damage biomarkers, histological examination of brain and lung tissue, and ROS production in each surviving animal. We found that high oxygen concentrations increased IL-6 and biomarkers of organ damage levels in septic animals, although no relevant histopathological lung or brain damage was observed. Healthy rats had an increase in IL-6 and aspartate aminotransferase at high oxygen concentration. IL-6 levels, but not ROS levels, are correlated with markers of organ damage. In our study, the use of high oxygen concentrations in a clinically relevant model of intra-abdominal sepsis was associated with enhanced inflammation and organ damage. These findings were unrelated to ROS release into circulation. Hyperoxia could exacerbate sepsis-induced inflammation, and it could be by itself detrimental. Our study highlights the need of developing safer thresholds for oxygen therapy

Development of Full-Length cDNAs from Chinese Cabbage (Brassica rapa Subsp. pekinensis) and Identification of Marker Genes for Defence Response

Arabidopsis belongs to the Brassicaceae family and plays an important role as a model plant for which researchers have developed fine-tuned genome resources. Genome sequencing projects have been initiated for other members of the Brassicaceae family. Among these projects, research on Chinese cabbage (Brassica rapa subsp. pekinensis) started early because of strong interest in this species. Here, we report the development of a library of Chinese cabbage full-length cDNA clones, the RIKEN BRC B. rapa full-length cDNA (BBRAF) resource, to accelerate research on Brassica species. We sequenced 10 000 BBRAF clones and confirmed 5476 independent clones. Most of these cDNAs showed high homology to Arabidopsis genes, but we also obtained more than 200 cDNA clones that lacked any sequence homology to Arabidopsis genes. We also successfully identified several possible candidate marker genes for plant defence responses from our analysis of the expression of the Brassica counterparts of Arabidopsis marker genes in response to salicylic acid and jasmonic acid. We compared gene expression of these markers in several Chinese cabbage cultivars. Our BBRAF cDNA resource will be publicly available from the RIKEN Bioresource Center and will help researchers to transfer Arabidopsis-related knowledge to Brassica crops

Anti-tumour necrosis factor discontinuation in inflammatory bowel disease patients in remission: study protocol of a prospective, multicentre, randomized clinical trial

Background: Patients with inflammatory bowel disease who achieve remission with anti-tumour necrosis factor (anti-TNF) drugs may have treatment withdrawn due to safety concerns and cost considerations, but there is a lack of prospective, controlled data investigating this strategy. The primary study aim is to compare the rates of clinical remission at 1?year in patients who discontinue anti-TNF treatment versus those who continue treatment. Methods: This is an ongoing, prospective, double-blind, multicentre, randomized, placebo-controlled study in patients with Crohn?s disease or ulcerative colitis who have achieved clinical remission for ?6?months with an anti-TNF treatment and an immunosuppressant. Patients are being randomized 1:1 to discontinue anti-TNF therapy or continue therapy. Randomization stratifies patients by the type of inflammatory bowel disease and drug (infliximab versus adalimumab) at study inclusion. The primary endpoint of the study is sustained clinical remission at 1?year. Other endpoints include endoscopic and radiological activity, patient-reported outcomes (quality of life, work productivity), safety and predictive factors for relapse. The required sample size is 194 patients. In addition to the main analysis (discontinuation versus continuation), subanalyses will include stratification by type of inflammatory bowel disease, phenotype and previous treatment. Biological samples will be obtained to identify factors predictive of relapse after treatment withdrawal. Results: Enrolment began in 2016, and the study is expected to end in 2020. Conclusions: This study will contribute prospective, controlled data on outcomes and predictors of relapse in patients with inflammatory bowel disease after withdrawal of anti-TNF agents following achievement of clinical remission. Clinical trial reference number: EudraCT 2015-001410-1