Evidence map›Paper›PMID 32755643›Full record

ArticleToxicology2020

A set of six Gene expression biomarkers and their thresholds identify rat liver tumorigens in short-term assays.

Robert W Lewis, Thomas Hill, J Christopher Corton

Open access · greenAbstract read
In one paragraph

Article in Toxicology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.4field-weighted citation impact, top 31% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 11 citations in OpenAlex.

  1. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Robert W LewisCenter for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, United States. Electronic address: lewis.robert@epa.gov.
Thomas HillCenter for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, United States; Oak Ridge Institute for Science and Education (ORISE) fellow Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC, United States. Electronic address: thomas.Hill@fda.hhs.gov.
J Christopher CortonCenter for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, United States. Electronic address: corton.chris@epa.gov.
Environmental Protection Agency · US

Funding

Intramural EPA EPA999999
6 · The paper itself

Abstract

Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7-113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.

Indexed as

AnimalsBiological AssayBiomarkers, TumorCarcinogensGene ExpressionLiver NeoplasmsMaleRats, Sprague-DawleyBiomarkers, TumorCarcinogensAdverse outcome pathwayAryl hydrocarbon receptorConstitutive activated receptorCytotoxicityEstrogen receptorGenotoxicityKey eventsLiver cancerMolecular initiating eventsPeroxisome proliferator-activated receptor αThresholdsToxPiTranscript profiling

Identifiers

PMID32755643
PMCPMC10439517
OpenAlexW3046706720

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.