Toxicogenomics<i>In vitro</i>: Gene Expression Signatures for Differentiating Genotoxic Mechanisms
Bibliographic record
Abstract
Genotoxicity testing is a fundamental component of chemical assessment. It is used to estimate the ability of a chemical to damage DNA, which can ultimately lead to cancer or heritable mutations. Although the existing genotoxicity testing paradigm is sensitive, it lacks specificity, human relevance, and mechanistic insight into a chemical's mode of action. The use of predictive in vitro toxicogenomics using human cells to complement the current genotoxicity testing battery has been proposed as a means to address these shortcomings. In this chapter, we review the development and validation of predictive toxicogenomic signatures for genotoxicity using mammalian cells in culture. We also address the issue of suboptimal metabolic activation in many of the in vitro systems, which may lead to misinterpretation of the results. We emphasize the need for validated signatures to predict genotoxic outcomes that have been robustly tested across different cell culture systems, laboratories, gene expression platforms, and experimental designs. Our review of the literature suggests that this field has matured to a stage where it is ready for specific applications in human health risk assessment. However, the public release of validated predictive signatures and analytical methods is required for full implementation in routine risk assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".