Year: 2015
Type: article
Abstract: The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.
Source: Nature Biotechnology
DOI: 10.1038/nbt.3299
Authors: Federica Eduati, Lara M Mangravite, Tao Wang, Hao Tang, J Christopher Bare, Ruili Huang, Thea Norman, Mike Kellen, Michael P Menden, Jichen Yang, Xiaowei Zhan, Rui Zhong, Guanghua Xiao, Menghang Xia, Nour Abdo, Oksana Kosyk, Stephen Friend, Allen Dearry, Anton Simeonov, Raymond R Tice, Ivan Rusyn, Fred A Wright, Gustavo Stolovitzky, Yang Xie, Julio Saez-Rodriguez
Institutions: European Bioinformatics Institute, Sage Bionetworks, The University of Texas Southwestern Medical Center, Harold C. Simmons Comprehensive Cancer Center, National Institutes of Health, National Center for Advancing Translational Sciences, University of Michigan, University of North Carolina at Chapel Hill, Jordan University of Science and Technology, National Institute of Environmental Health Sciences, North Carolina State University, IBM (United States), IBM Research - Thomas J. Watson Research Center
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $12,690
Awards: G1255431339, G1376247601, Elucidating Risks: From Exposure and Mechanism to Outcome, Design and Analysis of Sequencing-based Studies for Complex Human Traits, Genetic Etiology of Cancer Drug Response, G3875785102, Predicting Adjuvant Chemotherapy Response in Lung Cancer, G43106003, G4690859862, Integrative Analysis to Identify Therapeutic Targets for Lung Cancer, G7533153406
OpenAlex ID: W2195098119