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Incorporating population-level genetic variability within laboratory models in toxicology: From the individual to the population.

ABSTRACT: Humans respond to chemical exposures differently due to many factors, such as previous and concurrent stressors, age, sex, and genetic background. The vast majority of laboratory-based toxicology studies, however, have not considered the impact of population-level variability within dose-response relationships. The lack of data dealing with the influence of genetic diversity on the response to chemical exposure provides a difficult challenge for risk assessment as individuals within the population will display a wide-range of responses following toxicant challenge. Notably, the genetic background of individuals plays a major role in the variability seen in a population-level response to a drug or chemical and, thus, there is growing interest in including genetic diversity into laboratory-models. Here we outline several laboratory-based models that can be used to assay the influence of genetic variability on an individual's response to chemicals: 1) genetically-diverse cell lines, 2) human primary cells, 3) and genetically-diverse mouse panels. We also provide a succinct review for several seminal studies to highlight the capability, feasibility, and power of each of these models. This article is intended to highlight the need to include population-level genetic diversity into toxicological study designs via laboratory-based models with the goal to provide and supplement evidence in assessing the risk posed by chemicals to the human population. As such, incorporation of genetic variability will positively impact human-based risk assessment and provide empirical data to aid and influence decision-making processes in relation to chemical exposures.

SUBMITTER: Dornbos P

PROVIDER: S-EPMC5801153 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Incorporating population-level genetic variability within laboratory models in toxicology: From the individual to the population.

Dornbos Peter P LaPres John J JJ

Toxicology 20171221

Humans respond to chemical exposures differently due to many factors, such as previous and concurrent stressors, age, sex, and genetic background. The vast majority of laboratory-based toxicology studies, however, have not considered the impact of population-level variability within dose-response relationships. The lack of data dealing with the influence of genetic diversity on the response to chemical exposure provides a difficult challenge for risk assessment as individuals within the populati ...[more]

PMID: 29275117

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Project description:BackgroundBreast cancer risk models guide screening and chemoprevention decisions, but the extent and effect of variability among models, particularly at the individual level, is uncertain.ObjectiveTo quantify the accuracy and disagreement between commonly used risk models in categorizing individual women as average vs. high risk for developing invasive breast cancer.DesignComparison of three risk prediction models: Breast Cancer Risk Assessment Tool (BCRAT), Breast Cancer Surveillance Consortium (BCSC) model, and International Breast Intervention Study (IBIS) model.SubjectsWomen 40 to 74 years of age presenting for screening mammography at a multisite health system between 2011 and 2015, with 5-year follow-up for cancer outcome.Main measuresComparison of model discrimination and calibration at the population level and inter-model agreement for 5-year breast cancer risk at the individual level using two cutoffs (≥ 1.67% and ≥ 3.0%).Key resultsA total of 31,115 women were included. When using the ≥ 1.67% threshold, more than 21% of women were classified as high risk for developing breast cancer in the next 5 years by one model, but average risk by another model. When using the ≥ 3.0% threshold, more than 5% of women had disagreements in risk severity between models. Almost half of the women (46.6%) were classified as high risk by at least one of the three models (e.g., if all three models were applied) for the threshold of ≥ 1.67%, and 11.1% were classified as high risk for ≥ 3.0%. All three models had similar accuracy at the population level.ConclusionsBreast cancer risk estimates for individual women vary substantially, depending on which risk assessment model is used. The choice of cutoff used to define high risk can lead to adverse effects for screening, preventive care, and quality of life for misidentified individuals. Clinicians need to be aware of the high false-positive and false-negative rates and variation between models when talking with patients.

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Dataset Information

Incorporating population-level genetic variability within laboratory models in toxicology: From the individual to the population.

Publications

Incorporating population-level genetic variability within laboratory models in toxicology: From the individual to the population.

Similar Datasets

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