DNA copy number variation associated with anti-tumour necrosis factor drug response and paradoxical psoriasiform reactions in patients with moderate-to-severe psoriasis

Biological drugs targeting tumour necrosis factor are effective for psoriasis. However, 30–50% of patients do not respond to these drugs and may even develop paradoxical psoriasiform reactions. This study search-ed for DNA copy number variations that could predict anti-tumour necrotic factor drug response or the ap-pearance of anti-tumour necrotic factor induced pso-riasiform reactions. Peripheral blood samples were collected from 70 patients with anti-tumour necrotic factor drug-treated moderate-to-severe plaque pso-riasis. Samples were analysed with an Illumina 450K methylation microarray. Copy number variations were obtained from raw methylation data using conumee and Chip Analysis Methylation Pipeline (ChAMP) R packa-ges. One copy number variation was found, harbouring one gene (CPM) that was significantly associated with adalimumab response (Bonferroni-adjusted p-value < 0.05). Moreover, one copy number variation was identified harbouring 3 genes (ARNT2, LOC101929586 and MIR5572) related to the development of paradoxical psoriasiform reactions. In conclusion, this study has identified DNA copy number variations that could be good candidate markers to predict response to ada-limumab and the development of anti-tumour necrotic factor paradoxical psoriasiform reactions.This study was supported by Instituto de Salud Carlos III PI 13/01598 and the Ministry of Science and Innovation and the European Regional Development’s funds (FEDER). Conflicts of interest. FA-S has been a consultant or investigator in clinical trials sponsored by the following pharmaceutical companies: Abbott, Alter, Chemo, Farmalíder, Ferrer, GlaxoSmithKline, Gilead, Janssen-Cilag, Kern, Normon, Novartis, Servier, Teva, and Zambon. ED has potential conflicts of interest (advisory board member, consultant, grants, research support, participation in clinical trials, honoraria for speaking, and research support) with the following pharmaceutical companies: AbbVie (Abbott), Amgen, Janssen-Cilag, Leo Pharma, Novartis, Pfizer, MSD, Lilly and Celgene. ML-V has potential conflicts of interest as she has participated in clinical trials or as consultant with Abbvie (Abbott), Galderma, Janssen-Cilag, Leo Pharma, Pfizer, Novarties, Lilly, Almirall and Celgene. MCO-B has potential conflicts of interest (honoraria for speaking and research support) with Janssen-Cilag and Leo Pharma. The other authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. AS-T has served as a consultant and/or paid speaker for and/or participated in clinical trials sponsored by companies that manufacture drugs used for the treatment of psoriasis, including AbbVie, Celgene, Janssen-Cilag, LEO Pharma, Lilly, Novartis and Pfizer. RB-E has served as a consultant and/or paid speaker for and/or participated in clinical trials sponsored by companies that manufacture drugs used for the treatment of psoriasis, including AbbVie, Celgene, Janssen-Cilag, LEO Pharma, Lilly, Novartis and Pfizer

Comparison of machine learning algorithms for clinical event prediction (risk of coronary heart disease)

The aim of this study is to compare the utility of several supervised machine learning (ML) algorithms for predicting clinical events in terms of their internal validity and accuracy. The results, which were obtained using two statistical software platforms, were also compared. The data used in this research come from the open database of the Framingham Heart Study, which originated in 1948 in Framingham, Massachusetts as a prospective study of risk factors for cardiovascular disease. Through data mining processes, three data models were elaborated and a comparative methodological study between the different ML algorithms – decision tree, random forest, support vector machines, neural networks, and logistic regression – was carried out. The global selection criterium for choosing the right set of hyperparameters and the type of data manipulation was the area under a curve (AUC). The software tools used to analyze the data were R-Studio® and RapidMiner®. The Framingham study open database contains 4240 observations. The algorithm that yielded the greatest AUC when analyzing the data in R-Studio was neural network applied to a model that excluded all observations in which there was at least one missing value (AUC = 0.71); when analyzing the data in RapidMiner and applying the same model, the best algorithm was support vector machines (AUC = 0.75). ML algorithms can reinforce the diagnostic and prognostic capacity of traditional regression techniques. Differences between the applicability of those algorithms and the results obtained with them were a function of the software platforms used in the data analysis.2019/UEM113.526 JCR (2019) Q2, 32/109 Computer Science, Interdisciplinary Applications, 7/27 Medical Informatics1.140 SJR (2019) Q1, 115/1377 Computer Science Applications, 10/141 Health InformaticsNo data IDR 2019UE