Dataset Information

Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge.

ABSTRACT: Crowdsourcing has emerged as a framework to address methodological challenges in omics data analysis and assess the extent to which omics data are predictive of phenotypes of interest. The sbv IMPROVER Systems Toxicology Challenge was designed to leverage crowdsourcing to determine whether human blood gene expression levels are informative of current and past smoking. Participating teams were invited to use a training gene expression dataset to derive parsimonious models (up to 40 genes) that can accurately classify subjects into exposure groups: smokers, former smokers that quit for at least one year, and never-smokers. Teams were ranked based on two classification performance metrics evaluated on a blinded test dataset. The analytical approaches of the first- and third-ranked teams, that are presented in detail in this article, involved feature selection by moderated t-test or LASSO regression and linear discriminant analysis (LDA) and logistic regression classifiers, respectively. While the 12-gene signature of the top team allowed the classification of current smokers with 100% sensitivity at 93% specificity, discriminating former smokers from never-smokers was much more challenging (65% sensitivity at 57% specificity). Gene ontology molecular functions and KEGG pathways associated with current smoking included G protein-coupled receptor activity, signaling receptor activity, calcium ion binding, and the Neuroactive ligand-receptor interaction pathway. Selection of marker genes by either moderated t-test or multivariate LASSO regression followed by LDA or logistic regression, are robust approaches to classification with omics data, confirming in part findings of previous sbv IMPROVER challenges. While current smoking is accurately identified based on blood mRNA levels, smoking cessation for more than one year is accompanied by a "normalization" of the expression of certain mRNAs, making it difficult to distinguish former smokers from never-smokers.

SUBMITTER: Tarca AL

PROVIDER: S-EPMC5856124 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge.

Tarca Adi L AL Gong Xiaofeng X Romero Roberto R Yang Wenxin W Duan Zhongqu Z Yang Hao H Zhang Chengfang C Wang Peixuan P

Computational toxicology (Amsterdam, Netherlands) 20170718

Crowdsourcing has emerged as a framework to address methodological challenges in omics data analysis and assess the extent to which omics data are predictive of phenotypes of interest. The sbv IMPROVER Systems Toxicology Challenge was designed to leverage crowdsourcing to determine whether human blood gene expression levels are informative of current and past smoking. Participating teams were invited to use a training gene expression dataset to derive parsimonious models (up to 40 genes) that ca ...[more]

PMID: 29556588

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Project description:Cigarette smoking entails chronic exposure to a mixture of harmful chemicals that trigger molecular changes over time, and is known to increase the risk of developing diseases. Risk assessment in the context of 21st century toxicology relies on the elucidation of mechanisms of toxicity and the identification of exposure response markers, usually from high-throughput data, using advanced computational methodologies. The sbv IMPROVER Systems Toxicology computational challenge (Fall 2015-Spring 2016) aimed to evaluate whether robust and sparse (?40 genes) human (sub-challenge 1, SC1) and species-independent (sub-challenge 2, SC2) exposure response markers (so called gene signatures) could be extracted from human and mouse blood transcriptomics data of current (S), former (FS) and never (NS) smoke-exposed subjects as predictors of smoking and cessation status. Best-performing computational methods were identified by scoring anonymized participants' predictions. Worldwide participation resulted in 12 (SC1) and six (SC2) final submissions qualified for scoring. The results showed that blood gene expression data were informative to predict smoking exposure (i.e. discriminating smoker versus never or former smokers) status in human and across species with a high level of accuracy. By contrast, the prediction of cessation status (i.e. distinguishing FS from NS) remained challenging, as reflected by lower classification performances. Participants successfully developed inductive predictive models and extracted human and species-independent gene signatures, including genes with high consensus across teams. Post-challenge analyses highlighted "feature selection" as a key step in the process of building a classifier and confirmed the importance of testing a gene signature in independent cohorts to ensure the generalized applicability of a predictive model at a population-based level. In conclusion, the Systems Toxicology challenge demonstrated the feasibility of extracting a consistent blood-based smoke exposure response gene signature and further stressed the importance of independent and unbiased data and method evaluations to provide confidence in systems toxicology-based scientific conclusions.

Project description:MotivationInferring how humans respond to external cues such as drugs, chemicals, viruses or hormones is an essential question in biomedicine. Very often, however, this question cannot be addressed because it is not possible to perform experiments in humans. A reasonable alternative consists of generating responses in animal models and 'translating' those results to humans. The limitations of such translation, however, are far from clear, and systematic assessments of its actual potential are urgently needed. sbv IMPROVER (systems biology verification for Industrial Methodology for PROcess VErification in Research) was designed as a series of challenges to address translatability between humans and rodents. This collaborative crowd-sourcing initiative invited scientists from around the world to apply their own computational methodologies on a multilayer systems biology dataset composed of phosphoproteomics, transcriptomics and cytokine data derived from normal human and rat bronchial epithelial cells exposed in parallel to 52 different stimuli under identical conditions. Our aim was to understand the limits of species-to-species translatability at different levels of biological organization: signaling, transcriptional and release of secreted factors (such as cytokines). Participating teams submitted 49 different solutions across the sub-challenges, two-thirds of which were statistically significantly better than random. Additionally, similar computational methods were found to range widely in their performance within the same challenge, and no single method emerged as a clear winner across all sub-challenges. Finally, computational methods were able to effectively translate some specific stimuli and biological processes in the lung epithelial system, such as DNA synthesis, cytoskeleton and extracellular matrix, translation, immune/inflammation and growth factor/proliferation pathways, better than the expected response similarity between species.Contactpmeyerr@us.ibm.com or Julia.Hoeng@pmi.comSupplementary informationSupplementary data are available at Bioinformatics online.

Project description:Importance:Hospital networks formed around top-ranked cancer hospitals represent an opportunity to optimize complex cancer care in the community. Objective:To compare the short- and long-term survival after complex cancer treatment at top-ranked cancer hospitals and the affiliates of top-ranked hospitals. Design, Setting, and Participants:This cohort study was conducted using data from the unabridged version of the National Cancer Database. Included patients were individuals 18 years or older who underwent surgical treatment for esophageal, gastric, lung, pancreatic, colorectal, or bladder cancer diagnosed between January 1, 2012, and December 31, 2016. Patient outcomes after complex surgical procedures for cancer at top-ranked cancer hospitals (as ranked in top 50 by US News and World Report) were compared with outcomes at affiliates of top-ranked cancer hospitals (affiliation listed in American Hospitals Association survey and confirmed by search of internet presence). Data were analyzed from July through December 2019. Exposures:Undergoing complex cancer treatment at a top-ranked cancer hospital or an affiliated hospital. Main Outcomes and Measures:The association of affiliate status with short-term survival (ie, 90-day mortality) was compared using logistic regression, and the association of affiliate status with long-term survival was compared using time-to-event models, adjusting for patient demographic, payer, clinical, and treatment factors. Results:Among 119?834 patients who underwent surgical treatment for cancer, 79?981 patients (66.7%) were treated at top-ranked cancer hospitals (median [interquartile range] age, 66 [58-74] years; 40?910 [54.9%] men) and 39?853 patients (33.3%) were treated at affiliate hospitals (median [interquartile range] age, 69 [60-77] years; 19?004 [50.0%] men). In a pooled analysis of all cancer types, adjusted perioperative mortality within 90 days of surgical treatment was higher at affiliate hospitals compared with top-ranked hospitals (odds ratio, 1.67 [95% CI, 1.49-1.89]; P?<?.001). Adjusted long-term survival following cancer treatment at affiliate hospitals was only 77% that of top-ranked hospitals (time ratio, 0.77 [95% CI, 0.72-0.83]; P?<?.001). The survival advantage was not fully explained by differences in annual surgical volume, with both long- and short-term survival remaining superior at top-ranked hospitals even after models were adjusted for volume. Conclusions and Relevance:These findings suggest that short- and long-term survival after complex cancer treatment were superior at top-ranked hospitals compared with affiliates of top-ranked hospitals. Further study of cancer care within top-ranked cancer networks could reveal collaborative opportunities to improve survival across a broad contingent of the US population.

Dataset Information

Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge.

Publications

Human blood gene signature as a marker for smoking exposure: computational approaches of the top ranked teams in the sbv IMPROVER Systems Toxicology challenge.

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