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A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations.

ABSTRACT: Although genome-wide association studies (GWAS) have identified many genetic variants that are strongly associated with lung cancer, these variants have low penetrance and serve as poor predictors of lung cancer in individuals. We sought to increase the predictive value of germline variants by considering their cumulative effects in the context of biologic pathways.For individuals in the Environment and Genetics in Lung Cancer Etiology study (1,815 cases/1,971 controls), we computed pathway-level susceptibility effects as the sum of relevant SNP variant alleles weighted by their log-additive effects from a separate lung cancer GWAS meta-analysis (7,766 cases/37,482 controls). Logistic regression models based on age, sex, smoking, genetic variants, and principal components of pathway effects and pathway-smoking interactions were trained and optimized in cross-validation and further tested on an independent dataset (556 cases/830 controls). We assessed prediction performance using area under the receiver operating characteristic curve (AUC).Compared with typical binomial prediction models that have epidemiologic predictors (AUC = 0.607) in addition to top GWAS variants (AUC = 0.617), our pathway-based smoking-interactive multinomial model significantly improved prediction performance in external validation (AUC = 0.656, P < 0.0001).Our biologically informed approach demonstrated a larger increase in AUC over nongenetic counterpart models relative to previous approaches that incorporate variants.This model is the first of its kind to evaluate lung cancer prediction using subtype-stratified genetic effects organized into pathways and interacted with smoking. We propose pathway-exposure interactions as a potentially powerful new contributor to risk inference. Cancer Epidemiol Biomarkers Prev; 25(8); 1208-15. ©2016 AACR.

SUBMITTER: Qian DC

PROVIDER: S-EPMC5492390 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations.

Qian David C DC Han Younghun Y Byun Jinyoung J Shin Hae Ri HR Hung Rayjean J RJ McLaughlin John R JR Landi Maria Teresa MT Seminara Daniela D Amos Christopher I CI

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20160524 8

<h4>Background</h4>Although genome-wide association studies (GWAS) have identified many genetic variants that are strongly associated with lung cancer, these variants have low penetrance and serve as poor predictors of lung cancer in individuals. We sought to increase the predictive value of germline variants by considering their cumulative effects in the context of biologic pathways.<h4>Methods</h4>For individuals in the Environment and Genetics in Lung Cancer Etiology study (1,815 cases/1,971 ...[more]

PMID: 27222311

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Project description:Multiple single biomarkers have been associated with poor outcomes in acute lung injury; however, no single biomarker has sufficient discriminating power to clearly indicate prognosis. Using both derivation and replication cohorts, we tested novel risk reclassification methods to determine whether measurement of multiple plasma biomarkers at the time of acute lung injury diagnosis would improve mortality prediction in acute lung injury.Analysis of plasma biomarker levels and prospectively collected clinical data from patients enrolled in two randomized controlled trials of ventilator therapy for acute lung injury.Intensive care units of university hospitals participating in the National Institutes of Health Acute Respiratory Distress Syndrome Network.Subjects enrolled in a trial of lower tidal volume ventilation (derivation cohort) and subjects enrolled in a trial of higher vs. lower positive end-expiratory pressure (replication cohort).None.The plasma biomarkers were intercellular adhesion molecule-1, von Willebrand factor, interleukin-8, soluble tumor necrosis factor receptor-1, and surfactant protein-D. In the derivation cohort (n = 547), adding data on these biomarkers to clinical predictors (Acute Physiology and Chronic Health Evaluation III score) at the time of study enrollment improved the accuracy of risk prediction, as reflected by a net reclassification improvement of 22% (95% confidence interval 13% to 32%; p < .001). In the replication cohort (n = 500), the net reclassification improvement was 17% (95% confidence interval 7% to 26%; p < .001). A reduced set of three biomarkers (interleukin-8, soluble tumor necrosis factor receptor-1, and surfactant protein-D) had nearly equivalent prognostic value in both cohorts.When combined with clinical data, plasma biomarkers measured at the onset of acute lung injury can improve the accuracy of risk prediction. Combining three or more biomarkers may be useful for selecting a high-risk acute lung injury population for enrollment in clinical trials of novel therapies.

Dataset Information

A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations.

Publications

A Novel Pathway-Based Approach Improves Lung Cancer Risk Prediction Using Germline Genetic Variations.

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